rafmacalaba/datause-encoder-data huggingface dataset:
3 in high-income economies, compared to 2 _._ 2 percent in middle-income economies and 1 _._ 5 percent in low-income economies.<sup>2</sup> After ten years, when the direct impact of the shock has worn off, the welfare impact is 1 _._ 2 percent in high-income countries, compared to 1 _._ 2 percent in middle-income countries and 0 _._ 7 percent in low-income countries.<sup>3</sup> Finally, at the beginning, the employment shock accounts for 84, 70 and 75 percent of the welfare cost going forward in high-income, middle-income and lowincome countries respectively (73 percent for the world). There is still, however, some enduring cost from lost experience, particularly in high-income countries. After 10 years, experience accounts for 52, 21 and 27 percent of the enduring cost in high-income, middle-income and low-income countries respectively (27 percent for the world).

Lastly, the policy literature suggests COVID-19 had a disproportionate impact on female workers.<sup>4</sup> To explore this issue, we repeat our quantitative exercise separately for men and women. We accomplish this through several steps.

First, we find lower returns to experience for women, especially in richer nations. While this may be surprising given the lower rates of discrimination in richer nations, returns to experience are lower in poorer nations, which constrains the degree to which returns for men and women can differ in absolute terms. In contrast, returns to education are higher for women, possibly because education is a stronger signal of productivity for employers when experience is discounted due to discrimination. Second, we use these returns along with data on the schooling and experience distributions for men and women to compute the impact of the shock on the income of men and women separately. We find that differences are small, peaking on impact at 2 _._ 5% in high and middle income countries, and at just 1 _._ 7% in low income countries and essentially disappearing after 10 years. The reason is simply that measured differences in returns, while leading to significant differences in average income across men and women, are not enough to substantially affect transition dynamics after the COVID-19 shock. Instead, the main factor behind differences in relative income across genders is the fact that the employment shock is simply more severe for women than for men.

> 2This is equivalent to a one-off hit of 63, 55 and 36 percent of income respectively. Alternatively, this is equivalent to $52 _,_ 628, $6 _,_ 541 and $887. Overall, this amounts to 59 percent of global GDP ($50 _._ 3 trillion). 3This is equivalent to a one-off hit of 29, 29 and 17 percent of income respectively. Alternatively, this is equivalent to $15 _,_ 478, $3 _,_ 478 and $419. Overall, this amounts to 29 percent of global GDP ($24 _._ 4 trillion).

> 4For example, the United Nations (2020), the World Bank (2020a) and Brookings (2020b) have highlighted how COVID-19 will permanently, not just temporarily, hurt women more than men.
The paper is organized as follows. In section 2, we develop a model of export choice and marketing costs. In section 3, we describe the Uganda household survey and we introduce our marketing costs hypothesis. In section 4, we explain the empirical strategy and discuss results. In Section 5, we provide robustness checks and sensitivity analysis. Section 6 concludes.

# **2 A Model of Marketing Costs and Exports**

We begin by laying out a simple model of export crop participation with marketing costs. Farmers, who are endowed with one unit of land, must decide whether to specialize in the production of food crops or export crops. Specialization in export crops delivers a physical output of _A_ per unit of land. Farmers are heterogeneous in land quality, ability in crop husbandry, fixed assets and labor endowment. To capture this exogenous heterogeneity across farmers, we let output _A_ follow a density function _f_ with support [0 _, ∞_ ) _._ In contrast, specialization in food crops has a homogeneous return of _R_ per unit of land.<sup>5</sup> The relative price of export crops to food crops is denoted by _p_ .

Food crops and export crops differ in their marketability. Food crops are used for own consumption or can be marketed at no cost. To simplify the analysis, we assume that there is a fringe of food producers that supply food crops at a constant marginal cost. These food producers are arbitrage food traders, landless individuals working on public land or farmers who cannot meet the fixed costs of export participation (in scale, know-how, etc.). This assumption allows us to pin down the price of food, which we thereby set to one.<sup>6</sup> Marketing export crops is costly. We assume that export activities are carried out by

> 5This is a simplifying assumption. If _R_ depends on household characteristics, all the results remain qualitatively unchanged but the model becomes less tractable.

> 6The assumptions of complete specialization in production and of the existence of a fringe of food production at constant marginal cost simplify the model. In a different technological environment, with decreasing returns for instance, some farmers will end up in an interior solution with positive production of both food and export crops. This just complicates the computation of the expected export output without adding further insights. On the other hand, in the absence of the fringe food production, the expansion of export cropping may cause food supply to decline and food price to increase. This will prevent some farmers at the margin from specializing in exports. While these dynamics seem interesting to explore, our qualitative conclusion from the model—that lower trading costs lead to more export participation and lower poverty—are not affected by them.

3
As a final point of context, it is worth noting that on May 18, 2009, towards the end of our training sessions, the Sri Lankan government formally declared the end of a 25-year civil war. GDP growth was 8.0 percent in 2010 and 8.3 percent in 2011, the highest growth level since Sri Lanka’s independence.<sup>10</sup> Growth was widespread through manufacturing and services. As such, our interventions took part in an environment in which the economy was growing and there may have been possibilities for firms to start-up and/or expand.

# **4. Theory: How might business training and grants affect who runs a business and the profitability of these businesses?**

Training and grants may have effects on both the intensive and extensive margins. That is, they may affect business outcomes conditional on operating a business, and also affect the likelihood that an individual of a given type operates a business. In the context of our project, the effects at the extensive margin are particularly likely in the sample of potential entrants. To clarify these selection effects, we sketch a simple model of occupational choice.<sup>11</sup> We start by assuming that individuals are endowed with wealth _A_ , and innate entrepreneurial ability, _θ_ . If they pay a start-up cost _x(θ)_ , which is declining in _θ_ , they are able to combine their single unit of labor, capital _k_ , and their ability to produce according to the common production function . The production function is assumed to be strictly increasing in both arguments, and strictly concave. We assume there is no borrowing, so individuals must finance the start-up cost out of their initial wealth. Individuals not starting an enterprise can earn _w_ as a wage worker or opt out of the labor market. In the latter case, we assume they obtain wageequivalent utility of _w_ from leisure and household production.<sup>12</sup> Wealth not invested in the business earns return _r_ , which reflects either the interest rate on savings or the opportunity cost of funds for household uses. We assume _r_ is low enough that individuals with sufficient wealth and ability find starting a business worthwhile. Let _e_ denote the entry choice, with _e=1_ if the individual starts a firm, and _e=0_ otherwise. The individual chooses _e_ and _k_ to maximize:

> 10http://www.statistics.gov.lk/national_accounts/Press%20Release/PRESS%20NOTE%20%202011%20Annual%20 <u>English.pdf</u> [accessed 23 May, 2012].

> 11 This model is related to that in Lloyd-Ellis and Bernhardt (2000), but one key difference is that we allow not just the start-up cost of operating an enterprise, but also the output produced conditional on opening, to depend on _θ._ 12 Since we are focused on the decision of whether or not to operate a business, we abstract from the decision of whether to be in wage work versus exit the labor force.

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# **Table 4a: Communes that rehabilitated less pre-project than RTP1 allocation: Double difference estimates of project** **<u>impact on rural road kilometer increments (total km)</u>**

|||**Total samp**|**le**||**Treatment sup**|**port**||**Common sup**|**port**|
|---|---|---|---|---|---|---|---|---|---|
||**DD**|**PS kernel**<br>**matched DD**|**PS weighted**<br>**DD**|**DD**|**PS kernel**<br>**matched DD**|<br>**PS weighted**<br>**DD**|**DD**|**PS kernel**<br>**matched DD**|**PS weighted**<br>**DD**|
|**Total km**|6.26|7.20|8.14|5.32|7.08|8.13|4.35|5.91|7.15|
||(2.63)**|(2.98)**|(3.36)**|(2.73)*|(2.94)**|(3.41)**|(2.26)*|(2.73)**|(3.08)**|
|**_Of which:_**||||||||||
|<br>**Built**|2.41|2.78|2.87|3.00|2.80|2.94|2.50|2.43|2.26|
||(1.37)**|(1.56)*|(1.32)**|(1.38)**|(1.62)*|(1.33)**|(1.24)**|(1.40)|(1.13)**|
|**Rehabilitated**|3.85|4.42|5.28|2.32|4.28|5.19|1.84|3.64|4.72|
||(1.91)**|(2.22)**|(2.54)**|(1.89)|(2.18)**|(2.59)**|(1.71)|(2.09)*|(2.48)*|
|**Allocated by RTP1**<br>(Project database)||<br>5.67|||<br>5.67|||<br>5.55||
|<br>**p-value for test**:||||||||||
|<br>rehab=allocated|0.35|0.57|0.87|0.09|0.52|0.85|0.03|0.34|0.70|
|total=allocated|0.82|0.61|0.46|0.89|0.63|0.48|0.56|0.93|0.63|

Note: (i) Standard errors clustered at the district level are given in parentheses; PS kernel matched standard errors are obtained by bootstrapping (100 repetitions). (ii) ** and * indicate significance levels of 5 and 10%; (iii) The total sample has 149 observations (96 comparison and 53 project communes); treatment support has 119 observations (66 comparison and 53 project communes); common support has 114 observations ( 66 comparison and 51 project communes). (iv) The sample of project communes for this table are those that rehabilitated less km pre-project than were allocated by RTP1.

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another, but unlike in other developing areas, Africa has only a few industrial clusters, such as textiles in East Africa. There is little evidence, although many African countries are currently experiencing rapid urbanization.

This study explored the relationship between firm location and transport connectivity. Using the recent firm registry database in Liberia, firms’ decisions on where to locate were examined with respect to various indicators that measure transport connectivity. The instrumental variable spatial autoregressive model was used to address the potential endogeneity that infrastructure placement causes spatial autocorrelation between neighboring locations. The results indicate significant agglomeration economies. It is likely that Monrovia continues to grow because that is where (formal) enterprises are located. Other cities will also grow when their intercity transport connectivity is improved.

It was also found that market accessibility is a main driver of firm agglomeration; it is important to connect firms to large markets. Other connectivity measures do not have significant coefficients. Traditional indicators such as road density and the share of good roads have no power to explain firms’ decisions on where to locate. These measurements may not be important to consider firm location and promote firm investment.

Finally, from a methodological point of view, the study found the importance of addressing spatial autocorrelation in the firm location literature. Spatial autocorrelation is always significant. There are externalities of firm location choice around neighboring areas.
One less known aspect of the COVID-19 pandemic, and the focus of this paper, is not only how large the shock has been but also how persistent, potentially affecting poverty and inequality in the medium term. We document that extensive work stoppages in the second quarter of 2020 persisted, to some degree, through 2020 and in most economies all the way through 2021. We confirm earlier findings that female workers and those with lower levels of education experienced more severe employment losses on account of the pandemic disproportionately impacting services sectors such as retail and hospitality and low-tech manufacturing sectors in which they tend to work. The uneven distribution of care responsibilities left women assuming the bulk of the increased needs of care for children following massive school closures. We also find women and those with lower levels of education have experienced smaller gains in employment through the second half of 2020 and 2021 following the initial shock.

While there is evidence of work restarting after initial job losses, one prominent feature of the recovery is the increased prevalence of self-employment relative to prepandemic patterns. It appears that the mass job displacement that was caused by the COVID-19 labor market shock was buffered partially by informal employment. Given that informal self-employment in developing economies tends to be concentrated in low-return activities, this may indicate that while employment improves, the new jobs may be of lower quality than those held prior to the pandemic. Similarly, participation in the agriculture sector in low-income countries (LICs) and lower middle-income countries (LMICs) also increased and acted as a buffer to absorb shocks. However, income losses remained quite widespread throughout the pandemic.

Beyond employment losses, the welfare impacts of the COVID-19 crisis have been much broader, especially when impacts remained largely unmitigated. Most economies implemented COVID-19 fiscal packages to counter the pandemic impacts on the economy and people, however there was wide variation in the scale of the response, with higher-income economies spending significantly more than lower-income economies on support to households and firms. A synthesis of available microsimulation studies shows that increases in poverty were only partly mitigated in poorer countries World Bank (2022b). While the phone surveys are an imperfect source of data to assess receipt of assistance, they point to benefits expanding in coverage over the course of the crisis, but not reaching many affected households, especially in LICs. Many income losses went unmitigated, as reflected in the strong correlation between income losses and high levels of food insecurity. Households adopted various coping strategies, including the use of savings and sales of assets, which could hurt their future productive capacity and ability to recover from the crisis, leading to higher future poverty and inequality, an economic version of “long COVID”.

2
in 2008, when the new baseline Gini is 0.9 Gini points lower than the one obtained from the full sample.

First, we replace survey means by private consumption from national accounts, a “re-anchoring” that has been often done in the literature except that it was typically done using GDP rather than private consumption. Such “re-anchoring” obviously leaves within-country inequality unchanged. The entire effect on global inequality comes from the change in the between component (and, in the case of the Gini index, indirectly from the change in the overlap component). The between component obviously changes because the country-means change. _A priori_ the direction in which we expect the global Gini to change is unclear. However, previous calculations have mostly shown that re-anchoring to GDP (with no other adjustments) tends in more recent years to lower global inequality (see Milanovic, 2005, p. 118, and Appendix Table 1). We find here the same result. As can be seen in Table 4 (row 2), Gini declines by approximately 1 point except (again) in 2008 when it goes down by 2 Gini points. Intuitively, the reason for the downward change is that survey underestimation is greater in poorer (population-weighted) countries. In other words, poor countries appear less poor when we replace surveys with private consumption from national accounts.

Second we assume that the distribution of the top 10% can be approximated by a Pareto distribution, which is similar to Atkinson (2007) approach.<sup>67</sup> This “proportional adjustment with Pareto tail” allows within-country inequality to increase, while between-national inequality remains the same (since means are unchanged), and the overlap term remains the same or increases. The overlap component is likely to increase because the “elongation” of the distributions will tend to increase the “mixing up” of incomes of people from poorer and richer countries (or in the extreme case, leave it unchanged).<sup>68</sup> Consequently, we would expect the overall Gini, compared to the one from row 2, to be greater. This is indeed the case (see row 3) although the increase is quite moderate: at most 1⁄2 Gini point.

> 67 Our approach differs from Atkinson’s in three respects: First, we use private consumption, whereas Atkinson uses GDP. Second, we keep survey means when they are greater than national accounts means, whereas Atkinson does not. Third, Atkinson assumes the Pareto distribution only for the top 5%.

> 68 The overlap component remains unchanged if the country-means are very far apart and the Pareto imputation only has a small effect. For example, if we have only Switzerland and Congo in the sample, the Pareto elongation of the top of the Congolese distribution may not yet place any Congolese fractiles within the range of the Swiss distribution (that is, all Swiss fractiles will still have higher incomes).

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# **Housing Finance and Inclusive Growth in Africa** **_Benchmarking, Determinants, and Effects_**<sup>**1**</sup>

Christian-Lambert Nguena<sup>2</sup> The University of Dschang Fulbert Tchana Tchana<sup>3</sup> Albert G. Zeufack<sup>4</sup> The World Bank The World Bank

**KEYWORDS** : housi ng fi na nce, Sub-Saharan Africa, inclusive growth, inequality and shared prosperity.

**JEL CLASSIFICATION** : G21, R1, O4.

> 1 The views expressed in this paper are not necessarily those of the World Bank Group. The authors thank Simon Walley for sharing Badev et al. (2014) database on housing finance and Constant Lonkeng Ngouana and Harold Ngalawa for comments and suggestions on an earlier version of the paper. All errors and omissions are the authors’ sole responsibility.

> 2 Lecturer/Research, The University of Dschang and African Association of Young Economists, nguena@aaye.org.

> 3 Senior Economist, The World Bank, ftchanatchana@worldbank.org.

> 4 Chief Economist, The Africa Region, The World Bank, azeufack@worldbank.org.
sample and 30% of single women without children are neither working nor looking for jobs. This compares with 8% of husbands and 13% of single men. Thus, wives, 92% of whom have working husbands, drive the female participation statistic and obscure the fact that a high percentage of women without spouses are in the labor force. In fact, the participation patterns of women without spouses or children are more similar to those of men with the same household structure (no spouse, no children) than they are to women who have differently structured households.

Sectoral allocation also divides more cleanly along household roles rather than biological lines, as shown by Table 4. Due to the large number of wives out of the labor force, we will only consider the economically active population (EAP).1<sup>0</sup> Over 13% of the working wives are in unpaid jobs, while less than 0.65% of all other groups are. In fact, all unmarried people, whether male or female, have approximately the same level of participation in the unpaid sector. This difference occurs because all unpaid wives have another source of labor income: the breadwinner (husband), but for those who are the primary earners in their households, unpaid employment does not seem to be an option, even if they are female.

Informal paid jobs are often cited as employing the most vulnerable workers, usually single mothers (Standing 1989). However, single mothers, who are the primary breadwinners of the family, have informal sector attachment patterns that are closer to men with similar economic responsibilities. While 34.9% of single mothers are selfemployed, 30% of men, who are also the primary breadwinners, are self-employed but only 24% of wives, those without primary income generating responsibilities, are in this

> 10 The EAP sample sizes are 18,766 wives, 7,066 single mothers, 1,383 unmarried women without children, 72,496 husbands, and 3,497 men without spouses.

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3 Such studies provide policymakers with supposedly objective criteria for expanding (or contracting) major training programs, and thus should lead to more efficient national training policies. On the other hand, in selecting to evaluate the external efficiency of one particular institution--usually the government funded institution, these studies have the disadvantage of tending to exaggerate the centrality of such programs in overall training provision. Several distortions may result from policies designed in response to these types of studies. First, governments and their public agencies may be inclined to devote too many resources to publicly fmanced national training programs, or to secondary, postsecondary, or vocational institutions, and too few resources to alternative, non-mainstream modes of training. Second, targeted individuals may be receiving too much, too little, or inappropriate kinds of training. Third, highly structured, publicly subsidized, training programs may be edging out relatively less costly traditional or informal training arrangements. Finally, at the very least, it is possible that significant opportunities for efficiently and flexibly meeting training needs in developing countries for a variety of skilled and semiskilled occupation.s are being overlooked by policymakers.

Reverse tracer studies differ from the typical follow-up study both in technique and in aim. Instead of following completers of a particular pre-employment training program into the job market, reverse tracing works backwards, so to speak, by focusing the analysis on samples of workers currently employed in particular occupations and tracing back the educational histories and training paths pursued by these workers. Specifically, reverse tracer studies permit the analyst to identify each major alternative training route pursued by workers in the same occupation<sup>3</sup> Metaphorically, the reverse tracer study begins with the intended destination and seeks to identify all the paths that individuals may take to arrive at that destination, and the frequency with which each of these paths are pursued. The follow-up study begins with 3 For a lively advocacy of the reverse tracer study approach, see Dougherty (1988).