rafmacalaba/datause-encoder-data huggingface dataset:
WPS4112

# **Can Sub-Saharan Africa Leap into Global Network Trade?**

Uma Subramanian<sup>1</sup> and Matthias Matthijs

## **ABSTRACT**

**This paper examines opportunities for Sub-Saharan African countries to effectively participate in globalization, particularly given the increasing interest of China and India in Sub-Saharan Africa.** How could Sub-Saharan Africa fully engage and gain benefits from global network trade? Over the last 15 years Asia has become Africa’s fastest growing export market. Asian countries are much more open to trade than Europe or America. There seems to be no evidence to suggest that this trend will not continue in the near future. We acknowledge the numerous caveats in Asia’s growing interest in the African continent; not least the “resource curse” of exports that are heavily concentrated on oil, minerals and raw materials, as well as the fierce competition from Asia’s cheap manufactured exports. However, we believe that there is strong evidence to suggest a clear potential for South-South cooperation in trade and investment. Drawing on evidence from our extensive research into international value chains, **we identify five “critical factors” for effective participation in global network trade: price, speed-to-market, labor productivity, flexibility and product quality.** Underlying competitive performance of these critical factors are a country’s policies and institutions. Effective policies, efficient institutions and the necessary infrastructure will ensure the best outcome for trading countries. In order to improve the depth and sustainability of these five critical factors, it is critical that developing countries create a supportive policy and institutional framework from the outset.

## World Bank Policy Research Working Paper 4112, January 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 FIAS, World Bank Group.  Please contact <u>usubramanian@worldbank.org for any comments.</u>
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# **A. Appendix Figures and Tables**

Figure A.1: Distributions of Schooling and Mortality in the Model Economy.

## **(a)** Distributions of Schooling

## **(b)** Distributions of Mortality

<!-- Start of picture text --> 1 0.4<br>Low Income Low Income<br>0.9 Middle Income 0.35 Middle Income<br>High Income High Income<br>0.8<br>0.3<br>0.7<br>0.25<br>0.6<br>0.5 0.2<br>0.4<br>0.15<br>0.3<br>0.1<br>0.2<br>0.05<br>0.1<br>0 0<br>0 5 10 15 20 25 0 10 20 30 40 50 60 70 80 90 100<br>Schooling s Age a<br> years of schooling<br> s<br>a) (<br>Share of population with at least<br><!-- End of picture text --> Sources – United Nations (2019), I2D2 database.

Figure A.2: Distributions of Schooling by Gender and Income Level

**(a)** Low-Income Economies

## **(b)** Middle-Income Economies

<!-- Start of picture text --> LIC MIC<br>1 1<br>Male Male<br>0.9 Female 0.9 Female<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0 0<br>0 5 10 15 20 25 0 5 10 15 20 25<br>Schooling s Schooling s<br> years of schooling years of schooling<br> s s<br>Share of population with at least Share of population with at least<br><!-- End of picture text -->

**(c)** High-Income Economies

<!-- Start of picture text --> HIC<br>1<br>Male<br>0.9 Female<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 5 10 15 20 25<br>Schooling s<br> years of schooling<br> s<br>Share of population with at least<br><!-- End of picture text --> Sources – I2D2 database.
The microeconomic analyses have been conducted by analyzing consumption at an individual, more often household, level, by using survey data.

Macroeconomic analyses, on the other hand, have used macro-data on consumption and income, sometimes organized as a panel (of countries or regions within a country) data-set.

The main goal of this paper is therefore that of evaluating the degree of consumption insurance enjoyed by LAC countries, with respect to various reference areas (LAC countries themselves, OECD countries, etc...). This will be done essentially by estimating a parameter expressing the sensitivity of a country’s consumption growth to a measure of idiosyncratic shocks to income. Common econometric implementations of this test, used in recent contributions, will also be surveyed and compared to the original methodology followed in this work.

A subsequent, though still preliminary, step in the analysis will be to try and identify some macroeconomic factors which may be responsible for a lower degree of risk pooling (international openness, some measures of financial depths, credit availability, etc...).

Finally, an attempt will be done to estimate the potential welfare gains of non insured countries from risk-pooling with other countries in the area, or more developed countries in different areas, following the approach by van Wincoop (1994, 1999).

# **2. Empirical implications of risk sharing**

Since the pioniering work by Cochrane (1991) and Mace (1991), the hypothesis of market completeness (or of complete insurability of idiosyncratic risks) has been implemented by estimating an equation of the form: where the left hand side is the logarithmic difference of national per capita (non durable) consumption, Δ log( _cat_ ) is the logarithmic difference of per capita aggregate (over a reference set of countries) consumption, and _ISi_ is any variable capturing an idiosyncratic shock to country _i_ . Most often, as will also be done in the sequel, the variable _ISi_ will be replaced by a variable expressing idiosyncratic shocks to log differenced income, Δ log( _yitid_ ) , leading to the specification: 3
estimates presents a cost-effective option.<sup>23</sup> Furthermore, in contexts where relatively less intensive survey efforts can be spent on collecting such data (e.g., especially where receipts for such expenditures are strongly digitalized), these advantages appear even stronger. Given the increasingly popular digitalization of payment transactions around the world, these contexts may become much more available in the near future.

> 23 Dang _et al._ (2024) offer experimental evidence from Tanzania that suggests collecting data on either reduced or more aggregated food consumption categories could help significantly improve the imputation accuracy of poverty estimates.

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**<u>Table 9. First Stage Regression Result</u>**

||IV<br>Coef.|Std.Err.||
|---|---|---|---|
|KMTRL|-0.0011|(0.0004)|***|
|KMTAZARA|-0.0004|(0.0002)|*|
|lnELEV|0.028|(0.018)||
|lnSLOP|-0.067|(0.027)|**|
|lnZ|0.008|(0.005)|*|
|lnWT- lnWM|-0.009|(0.007)||
|lnWL- lnWM|-0.008|(0.004)|*|
|lnWE- lnWM|-0.011|(0.006)|**|
|lnWF- lnWM|0.001|(0.006)||
|lnWW- lnWM|0.021|(0.007)|***|
|lnWC- lnWM|0.006|(0.007)||
|lnY|0.008|(0.009)||
|lnAGE|0.057|(0.018)|***|
|EDU|-0.010|(0.009)||
|GLBL|0.023|(0.045)||
|FORN|0.001|(0.001)||
|Constant|-0.131|(0.166)||
|Obs.|343|||
|R squared|0.2602|||
|F-stat|0.750|||
|No. of dummy var|iables:|||
|Industry|4|||
|Region|16|||

The dependent variable is _RAIL_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively.

One might be concerned about robustness of the results showing the benefits from rail use. A different functional form is considered: The translog cost function is assumed. To avoid the endogeneity of firms’ rail use, the three-stage least squares (3SLS) regression is used. As discussed, if it was exogenous, the SUR model would be consistent and efficient. The Hausman test statistic is estimated at 92.54, rejecting the exogeneity hypothesis. The estimated coefficients are consistent with the above estimates ( **Table 10** ). The use of rail freight transportation has the advantage to save firm costs. The coefficient of _RAIL_ is estimated at -0.94, which is statistically significant. Therefore, rail transport is clearly costeffective for firm production.
costs increased by 13 percent due to greater spending on input materials to compensate for the growing shortages of electricity from 1999 onward, although this was partially offset by a 5 percent reduction in unit cost due to savings in the other inputs and small total factor productivity improvements.

In fact, we find an overall decrease in other non-electricity energy sources, suggesting that these primary energy sources are complementary inputs in producing the intermediate products that have been outsourced in response to electricity shortages. The overall effect of electricity shortages, which we proxy for with a measure of scarcity, was to increase production costs. From 1999 to 2004, firms’ costs rose by 8 percent, primarily due to input factor substitutions.

Grainger and Zhang (2019) go further by investigating the ultimate impact of unreliable power supply on manufacturing productivity, using firm survey data for Pakistan covering the period 2010 – 2011. They show that an additional average daily hour of unexpected power shortages decreases annual revenues by almost 10 percent, decreasing annual value-added at the firm level by approximately 20 percent, and increasing the labor share of output. Their findings underscore the importance of a reliable electricity supply to sustain firm productivity.

Mensah (2018) additionally explores the implications of unreliable power supply for employment. He finds that firms respond to the productivity losses arising from power outages by cutting jobs. To estimate the causal impact of electricity shortages on employment, Mensah (2018) employs household data from the Afrobarometer and Enterprise Surveys from more than 20 African countries. The results show that outages reduce the total factor productivity of African firms by more than 2.3 percent and reduce the probability that an individual is employed by between 35 and 41 percentage points. Significantly, the negative impact on employment is concentrated in high-skilled high-wage employment in non-agriculture sectors.

Finally, power outages also have negative impacts on development outcomes for households, which partially offset the benefits of electrification. Using household survey data from rural households for 2005 and 2010 in Bangladesh, Samad and Zhang (2017) find that a one hour increase in power outages per day is associated with a 0.3 percent reduction in annual income. Indeed, the income differential between electrified and non-electrified households disappears if the power outages last longer than 21 hours a day. The study also finds that the benefits of electrification increase over time, albeit at a decreasing rate, such that each additional year of being connected to the grid is associated with a 1 percent increase in annual income.

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For countries with multiple data waves, a subset of firms is re-interviewed over time, producing a panel component. This study exploits this panel component for 57 economies<sup>1</sup> surveyed at least twice between 2006 and 2019, amounting to approximately 14,000 unique firms. The panel data allows for the empirical estimations to include firm-level fixed effects, thereby accounting for time-invariant firm-specific omitted variables. However, there are some limitations of the panel subset of firms. First, typically less than 50 percent of firms surveyed in the first round are followed between the two waves, and thus the selection of firms may not be random and consists of survivor firms. Second, there are also high rates of attrition between the two waves.

An important feature of the ES data is the breadth of coverage in terms of topics. The surveys cover a wide range of topics, including access to finance, corruption, infrastructure, crime, competition, labor, the business environment, and performance. This enables the analyses to explore various channels of the effects, allowing for the possibility to build on studies using datasets based on registration lists (Huang et al., 2018). The data have been widely used by several studies to explore the private sector in developing economies (Paunov, 2016; Besley and Mueller, 2018; Chauvet and Ehrhar, 2018; Hjort and Poulsen, 2019).

Firm-level investment is the main outcome, captured by two types of variables. The first is a binary variable that takes a value of 1 if the firm purchased any fixed assets such as machinery, vehicles, equipment, land, or buildings were purchased (new or used) in the previous fiscal year. The second variable is the amount of expenditure on the fixed assets. These variables can be further broken down into type of investment – (a) land and buildings and (b) equipment and machinery. Summary statistics can be found in Table 1.

The main variable of interest is central government debt as a percentage of GDP at the country level. This data is obtained from the IMF Global Debt Database (Mbaye et al., 2018). The data is available for virtually the entire world. The integrity of the data has been checked through consultations with IMF country desks.

> 1 See Appendix A5 for the list of countries and years.

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calibrated from the LES demand using the Frisch parameter and household specific expenditure elasticities that have been estimated from household survey data. Rampulla, Semega and Vellutini (2006) provide details on each of these parameters, including production and trade elasticities. An important parameter for the analysis in this paper is the elasticity of substitution between domestic sales and imports of rice. This elasticity has been set to 2 for a first set of results, corresponding to an Armington’s function exponent of 0.5. That is, imported rice and domestic rice are only partial substitutes, as many Malians prefer the local variety over imports (as noted in USAID, 2008). For sensitivity analysis, we also present a second set of results under conditions of lower substitutability, with the Armington function elasticity set at 1.2.

Regarding the calibration of the dynamic model, emphasis has been put on identifying a realistic exogenous growth path of the economy in the medium run. The assumptions are shown in table 3. In particular, fiscal efficiency is assumed to improve moderately by 0.5 percent per year. As explained by Rampulla, Semega and Vellutini (2006), this reflects recent efforts by the Government to increase the tax base. In addition, population is assumed to grow at 2.2 percent, and the growth in population contributes to increase aggregate demand via the effects on subsistence consumption from the LES demand system. Labor supply is assumed exogenous, with a two percent annual growth. Total factor productivity growth was set to 1.5 percent per year, based on data from IMF (2006), and capital stock was assumed to depreciate by four percent per year. Finally, the grant from the rest of the world to the Government will continue to grow by five percent per year, whereas the annual growth of the Government current expenditures will stay at four percent.

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# **1.** **<mark>Introduction</mark>**

<mark>Do parents’ early childhood exposure to refugee shocks impact their children’s health outcomes? This paper addresses this question by looking retrospectively at the 1993-2001 refugee crisis in Northwestern Tanzania and studying the likelihood of stunting for children born to mothers who lived through the refugee crisis during their early childhood.</mark> <mark>Early childhood exposure to economic and political shocks is known to have persistent and long-lasting adverse consequences in adulthood and across generations (Alderman et al., 2006; Currie, 2009; Almond et al., 2005; Maluccio et al., 2009; Almond and Currie, 2011; Shah and Steinberg, 2013). In a recent paper, Akresh et al. (2018) provide evidence of the intergenerational impact of conflict exposure on children born to women who were directly exposed to the 1967-1970 Nigerian Civil War. They found that exposure of mothers to civil war had an adverse impact on child growth, survival, and education. In a similar study, Tafere (2016) found that children born to mothers who were exposed to the 1983-85 Ethiopian famine in their first 3 years of life have poor health status, lower schooling achievement and perform poorly in standardized tests.</mark><sup>3</sup> <mark>Cook et al. (2018) use representative survey data from the United States and found evidence of a multigenerational effect of in-utero exposure to the 1918 influenza pandemic on education, economic, and health outcomes of the children and grandchildren of those affected. Likewise, Caruso (2017) investigates the long-term and intergenerational effects of childhood exposure to natural disasters that occurred in Latin America 100 years ago. He finds that children who were in utero and young at the time of the natural disasters are less likely to accumulate human and physical capital in adulthood (they have worse health and fewer assets). Most importantly, he finds evidence of an intergenerational transmission of the shock, indicating that children of mothers who have been exposed to natural disasters achieved fewer years of education. Caruso and Miller (2015) find similar results for the 1970 Ancash earthquake in Peru. Using the 1993 and 2007 Peruvian census data, their study reveals that both males and females affected by the shock in utero respectively complete 0.5 and 0.8 less years of education in adulthood as compared to those not affected by the shock. In addition, evidence of an intergenerational transmission of the shock was found with children of exposed mothers at birth completing 0.4 less years of education. Comparable findings emerge from Northeast Tanzania where Caruso (2015) found that children of women afected by the 1993 flood in the Tanga region before the age of 18 present lower Height-for-Age Z scores (HAZ) as compared to unexposed adjacent cohorts.</mark> <mark>The question of whether refugee crises are beneficial or detrimental to the socio-economic well-being of host communities is a topic that has attracted substantial interest only in recent years and the evidence</mark>

> 3 <mark>Peabody Picture Vocabulary Test (PPVT).</mark>

2
|S/N|**Table A3 continu**<br>Data dimension|**ed**<br>Data category|Representative indicators|Categorical disaggregation|Coverage element 1 scoring guidelines*|
|---|---|---|---|---|---|
|20|Environmental<br>statistics|Pollution|(20.1) CO2 or other<br>greenhouse gas (GHG)<br>emissions;<br>(20.2) Emissions of air or<br>water pollutants.|(20.1,20.2) None|To receive full point: Must have all indicators. CO2 must<br>be specified.<br>To receive half point:Must have at least one indicator.|
|21||Built Environment|(21.1) Proportion of<br>households with access to<br>water;<br>(21.2) Proportion of<br>households with access to<br>sanitation;<br>(21.3) Housing quality<br>indicators.|(21.1) Water supply type;<br>(21.2) Sanitation facility type;<br>(21.3) Number of houses by<br>type; number of rooms;<br>Houses by construction<br>material; Houses by piping<br>type; other.|To receive full point: Must have (21.1) and (21.2); as well as<br>(21.3) with one disaggregation.<br>To receive half point: Must have either (21.1) and (21.2), or<br>(21.3) with at least one disaggregation.|

<mark>Source: Authors own compilation from ODIN 2018/19 Methodology Report.</mark> <u><mark>https://docs.google.com/document/d/1ubPL1l_3im9bjlCVZ6W2ICAy6UAiXl1hGeA1aXImkxI/edit</mark></u> <mark>*Scoring options for elements 2 to 10 are presented in Table A4.</mark> For the elements of data openness, scoring is calculated independent of the data coverage.

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**Figure 17. Correlation between change in tax rates and cyclical component of real GDP in Argentina (1990-2020)**

<!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>VAT CIT PIT Financial Export Duties<br>Transaction<br><!-- End of picture text --> _Source:_ Ministry of Economy, INDEC. _Notes:_ the cyclical components of GDP and tax rates have been estimated using the Hodrick-Prescott filter. A positive (negative) correlation indicates a countercyclical (procyclical) tax policy. Note: results are not statistically significant.

In summary, the analysis indicates that Argentina has pursued an acyclical tax policy, with traditional tax sources such as CIT, PIT, and VAT being less representative of overall tax revenue due to the dominance of unconventional tax sources. While correlations between traditional tax sources and real GDP are positive at both the aggregate and individual levels, they lack statistical significance, suggesting an absence of a clear relationship. Conversely, correlations between changes in tax rates for unconventional taxes like financial transactions and export duties and real GDP are negative, indicating an acyclical behavior. However, the study has limitations. It only covers the three traditional taxes, overlooking other significant sources of taxation, which is particularly relevant for countries like Argentina with a prevalence of non-traditional taxation. Additionally, using a single representative rate per tax may not accurately reflect the actual situation within each tax structure. Furthermore, it is important to note that correlations do not imply causality, and a more comprehensive analysis controlling for endogeneity issues would be necessary to establish causality. Nevertheless, previous studies (Vegh & Vuletin, 2012 and 2015; Ilzetzki & Vegh, 2008; Carneiro & Odawara, 2015) have demonstrated that the results hold even after addressing these issues using instrumental variables.<sup>19</sup>

> 19 These papers tend to use the following as instrumental variables: a weighted index for real GDP growth of trading partners, changes in price of exports and change of real returns on US Treasury Bills.