Abstract

     This paper explores the determinants of credit risk for the Colombian economy, a small emerging economy in Latin American. Using a sample of 28 large banks over the 2009–2019 period and the dynamic data panel approach, we find that the macroeconomic environment’s deterioration affects the credit risk perception held by banks as measured through non-performing loans and loan loss provisions. On the other hand, a better political environment brought about by peace accords smoothed such an impact. Estimates indicate different reactions when distinguishing by loan type. Business credit depends heavily on unemployment, while consumer credit risk is more sensitive to the interest rate. In the case of mortgage loans, economic growth and the unemployment rate are the most critical variables to mitigate risk. These results shed light on the impact of the economic environment on credit lines with different features.

Introduction

     The current economic recession caused by the COVID-19 pandemic has drawn attention to the consequences it may have on the banking system. As a result, there is great interest in knowing which indicators can provide efficient signals about financial instability (Chau, Lin, and Lin 2020). Loan performance is an essential channel to analyze the effect that macroeconomic factors have on banking crises. Unfavorable economic conditions such as lower economic growth and high unemployment can increase loan defaults and bankruptcies, so the issue of the procyclicality of the financial system should attract the attention of both academics and policymakers because it can exacerbate the economic downturn (Castro 2013: Cecchetti and Kohler 2014; De Moraes and De Mendonça 2019).

     Credit risk is the main channel for relating economic shocks to bank balance sheets. It is defined as the risk that a loan will not be paid (partially or totally) to the lender and it is a problem that has received extensive theoretical research. The seminal paper of Altman and Saunders (1998) initially surveyed the approaches that existed on the subject. Credit risk analyses have changed from so-called banking expert systems analyses to more objective schemes. Until the late 1980s, bankers used microeconomic information on the borrower such as reputation, capital, ability to pay, and collaterals to make subjective judgments about credit risk. By the late 1990s, these models began to fail because they did not consider the economic environment in which banks operate. New approaches began to explore alternatives to explain credit risk based on the possible states of the economy. In particular, the study by Salas and Saurina (2002) pioneered this issue by combining micro and macroeconomic data to assess credit risk.

Conclusions

     The global economic crisis is paying attention to the problems it can bring to the financial system. In this sense, this study analyzed the effect of macroeconomic perspectives on credit risk for the Colombian case. Through a dynamic data panel approach for the period 2009–2019, it was found that the macroeconomic environment is the primary determinant of Colombia’s credit risk. Credit risk increases with declines in GDP growth, rising unemployment, and tightening monetary policy rates.

     In 2020 the Colombian economy entered a severe recession and, according to the results of this study, credit risk is likely to increase. In terms of economic policy, this means a threat to financial stability and the possibility of a credit crunch (Louzis, Vouldis, and Metaxas 2012), so different incentives for each type of loan are needed to recover the financial system. Government endorsements and mortgage subsidy policies can help mitigate financial market failures.

1- Introduction

2- Data

3- Methodology

4- Empirical results and discussion

5- Concluding remarks

References

Abstract

We investigate the spillover across real estate (REU), macroeconomic (MU) and financial uncertainties (FU) in the United States based on monthly data covering the period of July, 1970 to December, 2017. To estimate the propagation of uncertainties across the sectors, a time-varying parameter vector autoregression (TVP-VAR)-based connectedness procedure has been applied. In sum, we show that that since the 1970s, FU has been the main transmitter of shocks driving both, MU and REU, with MU dominating the REU. Our results support the need for better macroprudential policy decisions.

Introduction

Following the ‘Great Recession’ a burgeoning literature has aimed to develop time-varying measures of economic uncertainty (risk), and quantify its impact on the macroeconomy and financial markets (Gupta et al., 2018). In this regard, while studies like Jurado et al. (2015), Baker et al. (2016) and Rossi and Sekhposyan (2015) develop measures of macroeconomic and financial uncertainties, Nguyen Thanh et al. (2018) obtained timevarying estimates of uncertainty associated with the US real estate sector, considered as a leading indicator for US business cycles (Leamer, 2015). Against this backdrop, the objective of our paper is to utilize the connectedness approach of Diebold and Yılmaz (2009, 2012, 2014), but based on a fully-fledged time-varying parameter vector autoregression with heteroscedastic volatility (TVP-VAR), as suggested by Antonakakis and Gabauer (2017) and Korobilis and Yilmaz (2018), to analyse the spillovers across the measures of macroeconomic, financial and real estate uncertainties. The TVP-VAR framework improves the widely-used above-mentioned traditional methodology of spillovers analysis substantially, since we do not need to arbitrarily set the size of the rolling-window and hence, there is no loss of observations. In addition, the results are not sensitive to outliers as the approach is build on multivariate Kalman filters (Durbin and Koopman, 2012). Given the historical interconnectedness across the real, financial and housing sectors of the US economy (Emirmahmutoglu et al., 2016; Li et al., 2015), this analysis of time-variation in spillover of corresponding uncertainties is, understandably, of paramount importance to policy authorities. This is because, if indeed these measures are connected, then uncertainty of a particular sector can end up increasing even when the shock did not originate in that sector. In addition, the effects of the uncertainty shocks are likely to be prolonged via the feedbacks across the measures of sectoral uncertainties. In sum, interrelatedness is likely to deepen the well-established negative impacts of uncertainty shocks (Bloom, 2009) on the economy as a whole.

1. Introduction

2. Literature review

3. Proposed framework

4. Data and forecasting results

5. Conclusions

Acknowledgements

References

Abstract

Tactical sales forecasting is fundamental to production, transportation and personnel decisions at all levels of a supply chain. Traditional forecasting methods extrapolate historical sales information to predict future sales. As a result, these methods are not capable of anticipating macroeconomic changes in the business environment that often have a significant impact on the demand. To account for these macroeconomic changes, companies adjust either their statistical forecast manually or rely on an expert forecast. However, both approaches are notoriously biased and expensive. This paper investigates the use of leading macroeconomic indicators in the tactical sales forecasting process. A forecasting framework is established that automatically selects the relevant variables and predicts future sales. Next, the seasonal component is predicted by the seasonal naive method and the long-term trend using a LASSO regression method with macroeconomic indicators, while keeping the size of the indicator’s set as small as possible. Finally, the accuracy of the proposed framework is evaluated by quantifying the impact of each individual component. The carried out analysis has shown that the proposed framework achieves a reduction of 54.5% in mean absolute percentage error when compared to the naive forecasting method. Moreover, compared to the best performing conventional methods, a reduction of 25.6% is achieved in the tactical time window over three different real-life case studies from different geographical areas.

Introduction

Forecasting is one of the key aspects of operations management (Oliva & Watson, 2009). Sales forecasting plays a major role in the allocation of corporate resources (Stein, 1997), marketing (Crittenden, Gardiner, & Stam, 1993), and impacts decisions on production, transportation and personnel at all kinds of horizons in the supply chain (Hyndman & Athanasopoulos, 2014). Historically, forecasting research attempted to find the best model for the used data set (De Gooijer & Hyndman, 2006). With the rapid expansion of the internet, a lot of external data has become available. IBM estimates that in 2020 43 trillion GB of data will be created, which is 300 times the volume produced in 2008. This growth in data availability is causing a shift from finding the best model to finding the right data (causal method forecasting).

Traditional statistical forecasting methods only extrapolate historical trends and seasonal influences to predict future sales. As a consequence, these methods are not capable of anticipating macroeconomic changes in the business environment, which often significantly impact the demand. To account for these future changes, companies either adjust their statistical forecast manually or rely on expert forecasts. However, both approaches are notoriously biased, as humans are generally bad in making these adjustments, and are time consuming.

Financial crises occur out of prolonged and credit-fueled boom periods and, at times, they are initiated by relatively small shocks that can have large effects. Consistent with these empirical observations, this paper extends a standard macroeconomic model to include financial intermediation, long-term loans, and occasional financial crises. Within this framework, intermediaries raise their lending and leverage in good times, thereby building up financial fragility. Crises typically occur at the end of a prolonged boom, initiated by a moderate adverse shock that triggers a liquidation of existing investment, a contraction in lending, and ultimately a deep and persistent recession.

Introduction

The 2007-09 financial crisis revealed the need for macroeconomic models to incorporate connections between the financial sector and the macroeconomy that can amplify economic shocks and lead to occasional deep economic downturns. Over the past few years, rapid advances have been made to extend standard macroeconomic models and include financial intermediation to account for episodes of severe financial distress. At the same time, a quickly growing empirical literature has revealed several stylized facts about financial crises. Crises are rare events that are usually preceded by prolonged boom periods and a buildup of macro-financial imbalances. For example, in the run-up to crises, credit usually rises rapidly, and credit growth is a robust early-warning indicator of crises (e.g., Schularick and Taylor, 2012; see also Figure 13 in Appendix A.4.2). Financial crises are associated with severe recessions that are typically deeper than normal recessions, particularly if they are preceded by a buildup of credit (Jordà, Schularick, and Taylor, 2013). However, the ultimate triggers of crises can be relatively small. With respect to the 2007-09 financial crisis, Gorton and Ordoñez (2014) argue that losses from mortgage-backed securities − the relevant shock for the financial sector around that time − were actually quite modest (see also Ospina and Uhlig, 2018). These empirical facts about crises pose challenges to current macroeconomic models. Why does financial fragility build up in good times? What is the propagation mechanism that turns shocks that are not particularly large into severe macroeconomic events? In this paper, I develop a quantitative macroeconomic model that addresses these questions. In my model, crises are as frequent and severe as in the data, financial fragility endogenously builds up during booms when credit expands, and crises are usually initiated by a moderate adverse shock.

1- Introduction

2- Underlying causes of the GFC

3- The macroeconomic policy response

4- Problems with basic Keynesian theory

5- The Australian GFC experience

6- The Australian macroeconomic policy response

7- Lessons for Australian macroeconomic policy

References

Abstract

This paper proposes lessons for macroeconomic policy stemming from the monetary and fiscal responses to the 2008–09 Global Financial Crisis, with an emphasis on Australia’s experience. After canvassing hitherto underemphasised global factors leading up to the crisis, including China’s rise and its effect on global saving, external imbalances and world interest rates, the paper critically evaluates the G20’s macroeconomic policy response to the crisis and its public debt legacy, especially for advanced economies. It then revisits Australia’s GFC performance, highlighting its macroeconomic behaviour at the time and the effectiveness of the monetary and fiscal responses. Lastly, it summarises what the episode implies for any future macroeconomic policy response to a crisis.

Introduction

With the benefit of a decade’s hindsight, what lessons should we have learned about the macroeconomic policy response to the 2008–09 Global Financial Crisis? Deemed the most cataclysmic financial event since the Great Depression, Group of Twenty (G20) central banks and governments deployed a range of co-ordinated monetary and fiscal measures to counter the macroeconomic impact of the crisis. Although the GFC buffeted economies worldwide, it affected advanced economies (AEs) more severely than developing and emerging economies (EEs), with AEs subsequently enduring weak economic growth, record low interest rates, anaemic private investment, poor productivity, reduced working hours, sluggish wages growth and high public debt. Sparked by the collapse of Lehman Brothers bank in September 2008, the GFC sparked further bank failures in the US, Europe and the United Kingdom, shrank asset values, and created havoc in global debt, equity, derivative and foreign exchange markets, reflecting a major loss of investor confidence. Bank credit contracted, stock and commodity prices plummeted and real economies contracted worldwide over the following two years. Although robust banking and financial systems protected many economies including Australia, from the worst effects of the GFC, due to the widespread slump in private investment nearly all economies around the globe experienced a marked slowdown in GDP, reduced working hours and rise in unemployment. This paper’s main aim is to address the question posed at the outset. As background, Section 2 canvasses the underlying causes of the GFC, highlighting the global saving boost stemming from China’s rise, external imbalances between the major Asia-Pacific economies, and the low interest rates and easy credit that prevailed in many advanced economies in the years leading up to the crisis. Section 3 then critically evaluates the monetary and fiscal policy responses to the GFC co-ordinated through the G20 that resulted in historically high public debt levels and low interest rates, especially in AEs. Section 4 then recaps the Australian GFC experience, before Section 5 summarises the main lessons that should have been learned for monetary and fiscal policy.

1- Introduction

2- Empirical model and data

3- Empirical results

4- Factor model

5- Conclusion

References

Abstract

We study the forecasting power of financial variables for macroeconomic variables in 62 countries between 1980 and 2013. We find that financial variables such as credit growth, stock prices, and house prices have considerable predictive power for macroeconomic variables at the one- to four-quarter horizons. A forecasting model that includes financial variables outperforms the World Economic Outlook (WEO) forecasts in up to 85% of our sample countries at the four-quarter horizon. We also find that cross-country panel models produce more accurate out-of-sample forecasts than individual country models.

Introduction

The crisis of 2007−2009 caused widespread disruptions in the financial market, followed by a global economic downturn. These developments have led to an intense debate on macrofinancial linkages. The present paper contributes to this debate in the context of macroeconomic forecasts. Building our analysis on the extensive body of literature on forecasting, we examine the forecasting power of financial variables for macroeconomic variables in 62 countries between 1980 and 2013. We show that incorporating financial variables such as credit growth, stock prices, house prices, and bond yields in an otherwise simple model improves the accuracy of macroeconomic forecasts significantly. Our rationale for using financial variables to forecast macroeconomic variables is threefold. First, in the presence of financial market imperfections when the Modigliani-Miller theorem does not hold, changes in credit conditions are likely to result in changes in future macroeconomic conditions. In addition, by affecting the wealth of firms and households, changes in asset prices also affect their investment and consumption decisions. Second, the forward-looking nature of financial variables means that they incorporate information about the future of the economy that is not yet reflected in current macroeconomic outcomes. Finally, contemporaneous financial variables such as stock prices and interest rates can help to nowcast macroeconomic variables in countries where the latter are collected with considerable time lags.

The macroeconomic climate influences operations with regard to, e.g., raw material prices, financing, supply chain utilization and demand quotas. In order to adapt to the economic environment, decision-makers across the public and private sectors require accurate forecasts of the economic outlook. Existing predictive frameworks base their forecasts primarily on time series analysis, as well as the judgments of experts. As a consequence, current approaches are often biased and prone to error. In order to reduce forecast errors, this paper presents an innovative methodology that extends lag variables with unstructured data in the form of financial news: (1) we apply a variety of models from machine learning to word counts as a high-dimensional input. However, this approach suffers from low interpretability and overfitting, motivating the following remedies. (2) We follow the intuition that the economic climate is driven by general sentiments and suggest a projection of words onto latent semantic structures as a means of feature engineering. (3) We propose a semantic path model, together with estimation technique based on regularization, in order to yield full interpretability of the forecasts. We demonstrate the predictive performance of our approach by utilizing 80,813 ad hoc announcements in order to make long-term forecasts of up to 24 months ahead regarding key macroeconomic indicators. Back-testing reveals a considerable reduction in forecast errors.

Introduction

Macroeconomic developments, such as cyclic downturns or the economic circumstances associated with the U. S. subprime crisis, affect firm operations in multiple ways and represent direct challenges to management (e. g. Demyanyk & Hasan, 2010; Goudie & Meeks, 1982). Examples include changes in the price of goods and raw materials, as well as the impact on overall demand, supply chain utilization and even operational processes (Xu, Pinedo, & Xue, 2017). Therefore, firms are interested in foreseeing the future economic climate in order to manage operations accordingly and hedge potential risks. In this context, operational research (OR) has a long tradition of addressing such risks (Demyanyk & Hasan, 2010). Our discipline has thus contributed to anticipating a variety of developments at a macroeconomic level, including financial distress (Geng, Bose, & Chen, 2015), liquidity risks (Shaik, 2015), credit risks (Akkoç, 2012; Desai, Crook, & Overstreet, 1996), financial crises (Demyanyk & Hasan, 2010; Huang, Kou, & Peng, 2017), currency crises (Sevim, Oztekin, Bali, Gumus, & Guresen, 2014) and bankruptcy (Du Jardin, 2015; McKee & Lensberg, 2002; Sun & Shenoy, 2007), especially in the financial sector (Tam & Kiang, 1992) Future expectations regarding the macroeconomic environment play a critical role in the decision-making process for many organizations (Xu et al., 2017). Hence, decision-makers across all sectors must analyze the current economic environment and form accurate expectations about future economic trends in order to support the operational strategy of organization and long-term management. As a result, macroeconomic variables, and the accurate prediction thereof, form the basis for a wide array of OR models (e. g. Calabrese, Degl’Innocenti, & Osmetti, 2017; Fethi & Pasiouras, 2010; Gutiérrez & Lozano, 2012). The importance of accurate long-term forecasts for firm operations has driven the extensive amount of research conducted with respect to macroeconomic predictions. Specific examples from the OR domain include short-term predictions of asset-related values, including government bonds (Tay & Cao, 2001) and stock indices (e. g. Huang, Nakamori, & Wang, 2005; Kung & Yu, 2008; Oztekin, Kizilaslan, Freund, & Iseri, 2016). Further research focuses on forecasting macroeconomic indicators of single countries (e. g. Mahmoud, Motwani, & Rice, 1990) or the relationship between countries (Sermpinis, Theofilatos, Karathanasopoulos, Georgopoulos, & Dunis, 2013). Other works propose agent-based simulations to study the behavior of human forecasters (Bovi & Cerqueti, 2016). Previous efforts at forecasting macroeconomic indicators have made use of various input features and methodologies. Historic time series data is a staple input for macroeconomic forecasting, and has been applied to make both short- and long-term predictions (e. g. Jansen, Jin, & de Winter, 2016; Mahmoud et al., 1990; Sermpinis et al., 2013). A prevalent alternative is the subjective judgments of professional forecasters (Matsypura, Thompson, & Vasnev, 2018) such as those used by the European Central Bank.

Productivity

Monetary policy

Fiscal policy

Nominal wages as a macroeconomic instrument?

Conclusion

Notes

Introduction

IF YOU ask a macroeconomist how macroeconomic policy should change as a result of Brexit, the chances are they will reply by saying it is a daft question. Leaving the Single Market and Customs Union of the EU will have a negative effect on UK productivity and, therefore, inevitably living standards, that the conventional tools of monetary and fiscal policy can do nothing to prevent or reverse. In this chapter I want to suggest this is rather oldfashioned thinking. What the global financial crisis (GFC), austerity and now Brexit have shown us is that sharp negative shocks to the economy can have additional permanent effects if they are not offset quickly and strongly by macroeconomic policy. One reason the UK has had a poor productivity performance since the GFC is that macroeconomic policy tools have failed to do this. To the extent that Brexit itself will reduce growth in living standards, it is imperative that we do not compound this by repeating the macroeconomic policy mistakes of the last decade. This chapter looks at where we have gone wrong and how we can do better after Brexit.

Productivity

Unless we stay in both the Customs Union and Single Market, Brexit will make trade with the EU substantially more difficult. Brexiters talk about making new trade agreements with countries outside the EU, but in reality, we are likely to lose more agreements with those countries by leaving the EU than we are likely to gain. Even if trade with third countries did increase, gravity equations—empirical relationships that look at the extent of bilateral trade—tell us that trade with third countries will never compensate for the trade lost to the EU simply because the EU is on our doorstep. That is what the government’s own analysis tells us, and it reflects every serious academic study. Less trade with the EU means that the UK economy becomes less open. That reduces productivity (how much output you get from a given labour force) because you lose some of the gains of specialisation. There is also good evidence that less open economies (in terms of goods and migration) have slower productivity growth than more open economies. Productivity growth is the main determinant, in the medium and long run, of growth in living standards.

Several studies have investigated projectification and its effects at the firm level, but the macroeconomic implications of project work have scarcely been considered. This paper analyzes the macroeconomic effects of firm-level projectification. We study the interlinkages between different sectors by extending standard input-output modeling and analyze the static and dynamic effects of projectification. The results indicate that projectification can have positive macroeconomic implications for production/innovativeness, employment and income that differ across economic sectors, but projectification can also have negative impacts. As a major implication, the use of temporary forms of organizing cannot be recommended without reproach but depends on the economic sector and sectoral interdependencies.

Introduction

20 years ago, the term ‘projectification’ was introduced to describe an important change in organizational structures and processes. Using the case of the car manufacturer Renault, Midler (1995) portrayed how a quantitative increase in project work accompanied by qualitative changes in the functioning of new product development resulted in increased primacy of project work throughout the company. Ekstedt et al. (1999) and Powell (2001) placed projectification in a larger context to describe the more general transformation of firms from industrial to post- (or neo-) industrial organizations. This context includes related developments such as globalization, servitization, knowledgeization, and digitization (Lundin et al., 2015). Despite some criticism of the actual extent of projectification and its degree of novelty (Hodgson, 2004; Cicmil et al., 2009), the literature generally agrees on the positive aspects and the increasing prevalence of project work. Projects as temporary organizations contribute to making firms more flexible, innovative and capable of dealing with unique, risky and complex tasks (Hobday, 2000; Keegan and Turner, 2002; Whitley, 2006; Bakker, 2010; Hanisch and Wald, 2014; Lundin et al., 2015; Spanuth and Wald, 2017; Manning, 2017).

Introduction

Literature review

Estimation techniques

Results and discussion

Conclusions and recommendations

References

Abstract

This research paper investigates the effect of macroeconomic variables on the exchange rate USD/CYN using yearly time series data for China economy from 1980 to 2017. ARDL bounds test approach for cointegration is applied to test the long-run relation between the dependent and the independent variables. The results of long-run ARDL indicate that gross domestic product growth and trade openness have a positive effect on the exchange rate USD/CNY while interest and inflation rates have a negative effect on the exchange rate. Based on the results of this study, it is recommended that the policymakers of the Chinese government should implement vital monetary and fiscal policies to determine the less volatile and productive exchange rate for China to manage sustainable economic growth for a long time with its trading partners.

Introduction

Exchange rate is the value of one country’s currency into another country’s currency. Different economists argue that a flexible exchange rate is considered to be an important factor for any economy. Moffett et al. (2017) categorized exchange rate into four types. The fixed exchange rate is controlled by the government, which is using country’s reserves for a specific time period. Managed floating exchange rate is the rate based on the demand and supply of specific currencies under certain interaction of the government. Freely floating rate is the exchange rate that fully relies on the force of demand and supply at an open market, without any government interaction. The last type of exchange rate is pegged exchange rate – when home currency is pegged as compared to the currency of another country, and both countries are similar as compared to other countries’ currencies (Moffett et al., 2016; Piana 2001). Nowadays floating and pegged exchange rates are the most popular exchange rates. The floating exchange rate is adopted by the US, Europe, Japan for international trade and the currencies of these countries depend on how the currency trades in foreign exchange markets. Countries having international trade relation with other countries should maintain the stability of their currencies. To protect against the currency depreciation countries will choose to peg their currency for their exported goods and services.

Introduction

The 2008–2009 economic and financial sovereign debt crisis led to a substantial drop in both GDP and investment levels and growth rates. Moreover, it led to substantial changes in economic policy, namely budgetary policy. Under budgetary duress, the level of government indebtedness is deemed to have a negative impact on public investment in EMU member countries (see, for instance, Turrini 2004, for the cases in the 1980s and in the 1990s). In fact, the abovementioned changes took in several countries the form of reduced expenditure, including public investment, and increased taxation. It is expectable that these changes may well constitute a policy regime change with structural implications on previous estimations regarding the relevance of investment for long-term growth. Additionally, such policy changes, and especially in euro area countries following adjustment programs, notably after the 2008–2009 economic and financial crisis, came with an emphasis on structural reforms that concern public spending levels and structure, and more generally, the way the economy and markets operate. It becomes then important to test if macroeconomic efficiency changes effectively occurred, and in what direction. For instance, Afonso and Jalles (2015) argue that the relevance of fiscal components differs for private and public investment developments. Understanding and measuring linkages between public and private investment and economic growth is of crucial importance in both developed economies and emerging markets. Public investment is a part of public expenditure (typically accounted for in the General Government) and decisions are taken within the larger framework of public finance. At the same time, it constitutes an addition to public capital. The latter, together with private and human capital, labour and other inputs, is in several approaches considered as a production factor. Public investment may therefore be linked to growth prospects. However, and as it is well documented in the literature, as part of public expenditure, it may crowd other types of investment, namely private, so that in some circumstances the net impact of public investment on GDP may be negative (see, for instance, Dreger and Reimers 2016; Cavalcanti et al. 2014; IMF 2014). At the same time, note the importance of public investment in the fiscal surveillance mechanisms of the EU, where n° 3 of Article 126 of the Treaty of the European Union (TEU 2012) reads:

Introduction

Africa–China economic cooperation began since the Second World War era (Adewumi et al., 2006). However, it was until early 1990s that the cooperation grew stronger owing to China’s economic enlargement program, i.e. global vision and Africa growth program. Various initiatives have emerged, the latest being “China One Belt Road or Maritime silk Road initiatives”, connecting China via railway and shipping link with major markets in the Middle East, Central Asia and Africa. What is unique about China’s partnership in connection with African economies is that, it is mainly centered on economic motive with less interference politically. Since economics and politics cannot be separated (mutually exclusive), China at some point meddles in Africa political matters. However, Compared to the USA and EU, the margin of China’s involvement in political matters is considered minimal. Because of that, China has become an acceptable pattern in Africa economy. They have investment portfolios across the length and breadth of the region. Currently, China’s is presence in about 45 countries in Africa, engaging in different types of businesses. Most of these businesses are privately own. There are also significant number of state-owned enterprises (Gu, 2009). Statistically, more than 2,200 Chinese enterprises are operating in Africa, (most of them are private ownership) (UNCTAD, 2014). China is a significant player in global manufacturing sector. It owns its successes (in industrial development) to low wages, active skilled labor force and large domestic market (Pigato and Tang, 2015). The latter was the key to economic (industrial) sustenance because it allowed emerging and small and medium scale industries to grow effectively. Later on, they became strong partakers in the international trade market. China found itself in a favorable condition, an edge in development most developed and emerging economies were not exposed to the beginning of development. The economic space of China became a magnet in attracting various types of investment portfolios. Many manufacturing firms relocated to take advantage of low wage highly skilled labor force to hence profit and expedite growth. Today, China is among countries with the largest market footprint in the globe. As a significant global player, it faces huge gap in natural and human resources demand (to feed existing industries), which are beyond the carrying capacity of the domestic market, hence rest on China–Africa economic cooperation and other foreign partners to supply them (as a way to maintain the pace of growth). Africa as a continent has a huge deposit of natural resources capacity and youthful population. Statistically, it has over one-third of world resources capacity, making it a potential partner for China. Yet, compared to China, African industrialization program and growth are constraint by economic challenges, among them being technical, financial and infrastructural problems. However, these potential gaps or challenges faced by Africa’s economies are, conceivably, China’s greatest strength, hence the highlight of China–Africa economic cooperation. China–Africa economic cooperation is an official platform for establishing a gain again solution to diverse developmental problems facing China and Africa, with too much stringencies. Advantage-wise, Africa use this platform (give its high resource potentials) as a leverage to attract highly efficient technical and financial investment from China, to support infrastructural development and improve the standard of living (reduce poverty).

Abstract

In this paper we examine the macroeconomic information content of aggregate earnings from the labor market’s perspective. We use insights from the labor economics literature to characterize the information contained in aggregate GAAP earnings and its components that is relevant for predicting aggregate job creation and destruction, labor income, and unemployment. Our results suggest that not only does aggregate earnings news convey information about future labor market aggregates, but its information content is incremental to other macroeconomic variables at near-term horizons. Further, the source of this information stems primarily from two earnings components: aggregate core earnings and special items. Core earnings news signals persistent changes in economy-wide profitability that anticipate aggregate job creation and destruction up to four quarters ahead, while special items contain news only about job destruction at horizons of up to two quarters. Taken together, our results suggest that aggregate GAAP earnings contain useful information about future labor market conditions, with the nature of such information varying across earnings components.

Introduction

Prior research finds that aggregate GAAP earnings predict future GDP growth and attributes this finding to a “corporate profit” channel, whereby aggregate earnings predict the corporate profit component of future Gross Domestic Income (GDI), the income-based equivalent of GDP (e.g., Konchitchki and Patatoukas 2014a). Corporate profits, however, represent only a small portion of GDI (on average about 9% over the past 10 years). Thus, if aggregate earnings convey macroeconomic information primarily through corporate profits, the role of accounting information for gauging the health of the macroeconomy is limited. Such a view is inconsistent with recent evidence that aggregate earnings news conveys important information about future inflation and monetary policy (Gallo et al. 2016; Shivakumar and Urcan 2017). In this paper, we focus on another aspect of the macroeconomic information contained in GAAP earnings—the link between aggregate GAAP earnings and the labor market.