rafmacalaba/datause-encoder-data huggingface dataset:
**The gap of microfinance in OIC is not only demonstrated by its limited scope, but also by the lack of regulation in OIC countries compared to other developing countries.** In MENA region for example, only Egypt, Morocco, Syria, Tunisia, and Yemen have specific legislation for Microfinance institutions (Figure 4). Also, according to the CGAP financial access database (Figure 5), 68% of the covered 37 OIC countries have appointed agencies to promote access to finance in rural areas, while 57% of the countries have dedicated team or unit in charge of the task. The ratios are above the developing countries average, but below the low-income countries average.

**Figure 4. Microfinance regulation and supervision by region**

<!-- Start of picture text --> 100<br>90 20<br>7.7<br>7.8<br>80<br>70<br>30 35.4 Supervised only by other<br>60 42.7<br>regulator<br>50<br>Supervised only by a bank<br>40 regulator<br>30<br>Not regulated<br>50 47.7<br>20 37.9<br>10<br>0<br>MENA (%) Developing All (%)<br>Countries (%)<br><!-- End of picture text --> Source: Peace (2010), M Khaled, CGAP (2010)

**Figure 5. Who is responsible for rural finance promotion?**

<!-- Start of picture text --> 90%<br>79%<br>80%<br>68%<br>70% 62%<br>57%<br>60%<br>47% OIC countries<br>50%<br>36%<br>40% Developing countries<br>30% Low‐income countries<br>20%<br>10%<br>0%<br>Agency is responsible Dedicated team or unit<br><!-- End of picture text --> Source: CGAP(2010) 28
Table 5 presents the results from an ordered logit regression of perception of access to credit as an obstacle. The regression is based on the direct opinion-based question on the degree of obstacle access to finance represents to the current operations of the firm using a five point scale: no obstacle, minor obstacle, moderate obstacle, severe obstacle and very severe obstacle. This type of variable has often being used in the literature as a proxy for being credit constrained. The results of the regression show that the perception of the obstacle is positively and highly significantly correlated to our objective measure of credit constraint. The perception also shows a negative significant correlation with size and with age: smaller firms and younger firms tend to find access to credit to be more of a constraint to their operations than larger and older firms.

# **4.2 Which Sources of External Finance Do Firms Use and to What Extent?**

The data collected by Enterprise Surveys also provide information on the different types of external sources of financing used by firms as well as its relative intensity. The surveys provide information on sources of financing for both working capital and purchases of fixed assets.

The different sources of external finance for purchase of fixed assets are classified into four categories: equity finance and three options for debt finance: formal debt finance, including bank and non-banking financial institutions, trade finance, which includes credit from suppliers and/or customers, and the other category, which includes informal sources of credit such as moneylenders, friends and relatives, etc.<sup>3</sup> It is worth clarifying that equity finance is phrased in

> 3 In the ECA region, the other category also includes non-banking financial institutions as the questionnaire used in this region group together these two categories.

16
their consumption and production patterns for rice as well as for the other commodities after the change in their price.

Before providing the results, one more word of caution is required. As mentioned in the introduction, it remains an open question as to the higher prices paid by consumers translate into higher prices paid to producers. If one has doubts as to producers will really benefit in the short run from higher prices, one could consider estimates based on consumption impacts only as an upper bound for the impact on poverty, and estimates taking into account both consumer and producer impacts as a lower bound.

Key results from the simulations are provided in tables 8 to 10. Consider first table 8, which is based only on data on the consumption of food. At the time of the survey, the share of the population in poverty was 47.45 percent. If the price of rice increases by 25 percent, and if we look only at the impact on the consumer side, the headcount index would increase by about one and a half percentage point to 48.9 percent. The increase for millet and sorghum is smaller at less than one point, due in part to the fact that much of production is auto-consumed. For corn, the increase is minimal, and the same can be said for wheat. For all cereals combined, the increase is 2.5 percentage points. This is large, and it would mean that some 290,000 persons would fall into poverty). The other poverty measures (poverty gap and squared poverty gap) follow a similar pattern, but with smaller increases in absolute terms since these measures are also smaller to start with. For information, we provide also the impacts for those households who consume the various foods, but in the case of rice, millet and sorghum and wheat, since most households are consumers, this does not change much the overall estimates.

If we now look at the impact of changes in producer prices in table 9, the impacts are reversed. The beneficial impact of the increase in rice prices is however smaller than the negative impact on the consumption size since perhaps half of the total consumption of rice is imported. With a 25 percent increase in prices, and if we look only at the impact on the producer side, national poverty measures would be reduced by about half a point (but there would be a larger reduction in poverty among rice producers of about four points). In the case of millet and sorghum, if the price for producers were to increase by 25 percent, the headcount index of poverty would decrease nationally by only a third of a point. For corn, the impact is one tenth of a point. The combined impact of higher producer prices for rice, millet and sorghum, and corn is a reduction in the national estimate of the headcount index of slightly less than one point.
|**HS**||||Year|||
|---|---|---|---|---|---|---|
|<br>**code**<br>|**_Type of products_**|2001|2002|2003|2004|2005|
|**2 digits**|||||||
|63||22.9|23.4|26.1|19.9|19.7|
|||_(16.5)_|_(14.1)_|_(31.6)_|_(7.5)_|_(5.9)_|
|||82<br>|82<br>|82<br>|82<br>|82<br>|
|64||24.2|32.2|22.2|31.6|26.5|
||_FOOTWEAR_|_(14.3)_|_(24.0)_|_(18.0)_|_(23.6)_|_(10.0)_|
|||22|22|22|22|22|
|65|_HATS_|20.0|20.4|20.7|20.0|20.0|
|||_(0.0)_|_(2.0)_|_(3.3)_|_(0.0)_|_(0.0)_|
|66|_UMBRELLAS_|9<br>20.0|9<br>20.0|9<br>20.0|9<br>20.0|9<br>20.0|
|||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|67|_BIRD SKIN FEATHERS_|8<br>23.1|8<br>24.6|8<br>26.8|8<br>23.1|8<br>22.5|
||_,_|_(6.8)_|_(9.4)_|_(13.2)_|_(6.0)_|_(5.2)_|
|68|_STONE PLASTER_|76<br>14.9|76<br>14.9|76<br>14.9|76<br>14.9|76<br>14.9|
||_,_|_(1.3)_|_(1.3)_|_(1.3)_|_(1.3)_|_(1.3)_|
|||51<br>|53<br>|52<br>|52<br>|52<br>|
|69|_CERAMIC PRODCTS_|18.8|19.0|18.7|18.4|18.9|
||_U_|_(5.2)_|_(9.7)_|_(4.2)_|_(4.0)_|_(4.1)_|
|||134|134|134|134|140|
|70|_GLASS AND GLASSWARE_|14.4|14.4|14.4|14.4|14.2|
|||_(1.8)_|_(1.9)_|_(1.9)_|_(1.9)_|_(2.5)_|
|||63|63|63|63|63|
|71|_Natural or cultured pearls precious or_|19.7|19.7|19.7|19.7|19.7|
||_,_|_(2.5)_|_(2.5)_|_(2.5)_|_(2.5)_|_(2.5)_|
|||432|432|432|432|432|
|72|_STONE PLASTER_|5.0|5.0|5.0|5.0|5.0|
||_,_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|||296|298|297|297|305|
|73||14.2|14.1|14.2|14.2|14.0|
||_Products from ferrous metals_|_(2.6)_|_(2.8)_|_(2.7)_|_(2.7)_|_(3.1)_|
|||69|69|69|69|69|
|74||5.0|5.0|5.0|5.0|5.0|
||_Cuprum and products of cuprum_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|||18|18|18|18|18|
|75|_Nickel and products of nickel_|11.1|11.1|11.1|11.1|11.1|
|||_(5.0)_|_(5.0)_|_(5.0)_|_(5.0)_|_(5.0)_|
||_Aluminum and products of aluminum_|65|65|65|65|65|

30
Policy Research Working Paper 5483

# **Abstract**

This paper estimates the relationship between initial village inequality and subsequent household income growth for a large sample of households in rural China. Using a rich longitudinal survey spanning the years 1987–2002, and controlling for an array of household and village characteristics, the paper finds that households located in higher inequality villages experienced significantly lower income growth through the 1990s. However, local inequality’s predictive power and effects are significantly diminished by the end of the sample. The paper exploits several advantages of the householdlevel data to explore hypotheses that shed light on the channels by which inequality affects growth. Biases due to aggregation and heterogeneity of returns to ownresources, previously suggested as candidate explanations for the relationship, are both ruled out. Instead, the evidence points to unobserved village institutions at the time of economic reforms that were associated with household access to higher income activities as the source of the link between inequality and growth. The empirical analysis addresses a number of pertinent econometric issues including measurement error and attrition, but underscores others that are likely to be intractable for all investigations of the inequality-growth relationship.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to understand the relationship between inequality and economic performance. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jgiles@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
47 Table 5. Global averages of national price-transmission elasticities for 10 key agricultural products in 82 focus countries, 1985–2010

||Coeff. of corr.<br>between NRA<br>and int’l price|Short-run price-<br>transmission<br>elasticity|
|---|---|---|
|Rice|-0.85|0.49|
|Wheat|-0.48|0.55|
|Maize|-0.41|0.63|
|Soybean|-0.38|0.73|
|Sugar|-0.80|0.43|
|Cotton|-0.08|0.57|
|Milk|-0.74|0.71|
|Beef|-0.50|0.66|
|Pork|-0.43|0.51|
|Poultry|-0.70|0.68|
|**Unweighted average,**<br>**ten products**|**-0.54**|**0.56**|

Source: Anderson and Nelgen 2012a.

Note: Short-run price-transmission elasticity is the weighted average of national elasticities for the period 1985–2010, using the value of national production at undistorted prices as weights. The elasticities are based on partial adjustment according to a geometric distributed lag formulation (Nerlove 1972).
Policy Research Working Paper 7872

# **Abstract**

This paper analyzes the effects of a multilateral debt relief program on child health. The International Monetary Fund and the World Bank launched the Heavily Indebted Poor Countries Initiative in the late 1990s to reduce the debt burdens of poor countries, and explicitly linked the initiative to the aim of poverty reduction and social targets. As a result, debt-servicing costs have gone down by an average 1.8 percentage points of gross domestic product in Heavily Indebted Poor Countries. However, the social effects of debt relief are not well known. The paper employs micro data on infant mortality from 56 country-specific Demographic and Health Surveys to investigate the effects of the Heavily Indebted Poor Countries Initiative on child health. The retrospective fertility structure of the data allows for analysis using the within-mother variation in the probability of survival of babies before and after different stages of the initiative. The results suggest that after a debt-ridden country enters the program, which is conditional on reform and pro-development policies, and receives interim debt relief, the probability of infant mortality goes down by about 0.5 percentage point. This translates into about 3,000 fewer infant deaths in an average Heavily Indebted Poor Country. The findings are particularly strong for infants born to poor mothers and mothers living in rural areas, and are driven by access to vaccines early in life and during pregnancy. There are no child health effects from graduating from the program and receiving full debt relief.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at anna. welander@nek.lu.se.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Figure 2. Share of Hours per Activity, by Socioeconomic Status and Gender**

<!-- Start of picture text --> Non-UP<br>36% 22% 8% 7% 26%<br>Women<br>UP Women 17% 6% 19% 2% 57%<br>UP Men 9% 7% 31% 7% 46%<br>Own Livestock Rearing<br>Own Agriculture or Other Business<br>Paid Labor: Agriculture<br>Formal/Salaried Employment<br>Paid Labor: Other<br><!-- End of picture text --> _Notes._ The figure compares the share of time spent on different income-earning activities by gender (primary UP women vs. primary UP men) and across socioeconomic status (primary UP women vs. primary non-UP women). Results are derived using non-UP and UP control group survey data at follow-up in 2018. Other paid labor includes maid services, non-agricultural activities outside of the household, and other paid work.

For women, the highest earnings per hour are in formal/salaried work at USD 2.39 PPP per hour (USD 0.65 nominal), followed by own livestock rearing (USD 1.14 PPP; USD 0.31 nominal), agricultural paid labor (USD 0.98 PPP; USD 0.26 nominal), and other labor (USD 0.78 PPP; USD 0.21 nominal) (Figure 3).<sup>11</sup> Since UP women spend the largest share of time working in the lowest paid activity (other paid jobs), this implies a much lower return on their time spent in income-generating activities than non-UP women. In addition, formal employment is accessible to only few women due to low education levels.

Finally, Figure 3 also reveals important differential returns by gender across activities within UP households. Primary men earn more per hour in all activities outside the household: For other paid labor – the activity with the highest share of time spent working – the gender gap is the largest, where women receive USD 0.38 for every USD 1 men receive for returns to their work in this activity. For agricultural activities outside the household, women earn USD 0.53 per every USD 1 men receive, and USD 0.83 per every USD 1 men receive for formal/salaried employment. Why do UP households, and women in general, not allocate

> 11 Results reflect hourly earnings for the main activity groups over all individuals with non-missing earnings and positive hours in these activities. For livestock rearing we compute hourly earnings dividing household total earnings (revenue minus input costs) by the hours worked for all adult members in the household for the last four weeks. We did not collect as detailed data for agricultural businesses, with more seasonal revenues and costs.

9
Severity indexi =

-Peak-to-trough % changei + Number of years from peak to recovery of prior peaki _._

There are numerous other ways of gauging the severity of a crisis as it relates to output (including focusing on shortfalls relative to potential output). The simple measure described lends itself to comparisons across time and space (specifically to eras or countries where population growth is/was significantly greater than what we observe today, particularly in the advanced economies).

A key takeaway from Table 2 is that, apart from sharp output declines, “the worst of the worst” financial and conglomerate crises left a long-lasting imprint on economic well-being. As the table highlights, it often takes per capita GDP more than a decade (three decades is the record) to recover. Of course, in several of these episodes, economics is only a part of the story, as coexisting wars or civil unrest take their own toll on economic activity. For the full sample of 100 crises, Reinhart and Rogoff (2014) report that the average and median numbers of years to full recovery are 8 and 6.5, respectively.

# **_Post-COVID-19 expected recovery in historical context_**

The preceding evidence encompassed almost two centuries and included advanced and developing economies. I now focus more narrowly on the last wave of debt and financial crises in EMDEs during the 1980s.<sup>22</sup> Mexico’s famous default in August 1982 set off a domino effect in already extremely vulnerable developing economies. However, several smaller countries (Costa Rica, Honduras, Liberia, Sudan, Tanzania, Uganda, among others) were already in default in 1981. So, for comparison purposes to the COVID-19 shock, I benchmark 1980 as the precrisis equivalent to 2019 (i.e., 1980=2019=100).<sup>23</sup> In 1986, 55 percent of the EMDEs recorded real per capita GDP levels that were below those in 1980. By 1990 the situation had not improved, as 58 percent of EMDEs had real per capita GDP

> 22 The discussion that follows is based on the October 2020 World Economic Outlook database and projections.

> 23 As already noted, even before COVID-19, there were a number of countries in crisis.

16
In the last four columns of Table 7, we use a continuous variable capturing the intensity of the stock market liberalization. The measure represents the proportion of domestic stocks available to foreign investors at the industry-country level.<sup>22</sup> Based on data available in the Emerging Stock Market Factbooks of the International Finance Corporation, we compute the proportion of domestic stocks available to foreign investors by dividing the market capitalization of firms included in the IFC Investable index by the market capitalization of firms in the IFC Global index. These data are available at the level of 2-digit SIC industries while our international trade data are based on the SITC (Standard International Trade Classification) product classification. As there exists no direct concordance between those two classifications, we first apply the concordance tables from SIC industry classification to ISIC industry classification and subsequently apply the ISICSITC concordance tables. After this procedure, we are able to match the industry-country financial liberalization index to 632 out of our 1,299 product categories.<sup>23</sup> The purpose of the estimations in columns (3)-(6) of Table 7 is allowing for different levels of liberalization intensity both across countries and across industries. Similarly to our main dummy, the intensity measure equals zero before the government officially allows foreign investors to acquire any equity stakes in domestic firms. After the official liberalization event, the value of the intensity measure can take any value between zero and one and it can also differ across industries within the same liberalizing country. In columns (3) and (4) of Table 7, we use IFC indices available for a given industry-country pair in a year that is closest to the official year of the stock market liberalization. In columns (5) and (6) of Table 7, we use the average value for a given industry-country pair.<sup>24</sup> Throughout Table 7, our main interaction term remains positive and significant. In-

> 22 We are grateful to Christian Lundblad for suggesting this approach to us.

> 23 Our approach builds upon previous literature that has used IFC data to construct liberalization intensity measures at the country level (see, e.g., Bekaert 1995; Edison and Warnock 2003; Bekaert, Harvey, and Lundbland 2005; De Jong and De Roon 2005; Bekaert et al. 2007).

> 24 We thus abstract from the time variation in the liberalization intensity due to the limited availability of publicly available IFC data at the industry-country level across the time dimension. The focus on the cross-sectional variation is also in line with previous literature using a country-level measure of liberalization intensity in the context of international trade (Manova 2008; Defever and Suedekum 2014).

24
# **1 Introduction**

In the core of the monopolistic competition model with differentiated goods pioneered by Dixit and Stiglitz (1977), consumers benefit from having more varieties of final goods. These benefits stem from lower unit costs of imports and these lower costs are the source of the welfare gains. However, most studies focus on the conventional sources of gains from trade, such as productivity improvement due to increasing returns to scale, trade-induced innovation, technology spillover, and improved market efficiency because of import competition (Chen and Ma, 2012). These studies often assume a constant set of products over time, and this leads to systematically understated welfare gain calculations.

This paper estimates the comprehensive gains from import variety in emerging markets and developing economies (EMDEs), particularly for each of 28 Asian (including one Pacific Island nation) and East African economies during 1995-2021, following the seminal works by Feenstra (1994) and Broda and Weinstein (2006). The gains from variety for the economies were estimated, using six-digit harmonized system (HS) product data. We estimated more than 100,000 elasticities in total and with the elasticities, we constructed an exact price index to measure the welfare gains from variety growth. This method is consistent with the theory of monopolistic competition and is robust in empirical applications (Feenstra, 1994), from which the quantitative analysis of gains from variety started.

Feenstra (1994) showed how to estimate the elasticity of substitution of individual products, and using these elasticities he offered the formula for an exact price index that can account for entry and exit of varieties. By doing so, Feenstra (1994) demonstrated that new product varieties lead to an increase in consumer utility. However, a comprehensive measure of the gains from import variety puts tremendous demands on data availability and was not realized until Broda and Weinstein (2006).

Applying Feenstra’s estimation technique, Broda and Weinstein (2006) estimated the welfare gain that the United States enjoyed through trade liberalization over 30 years by computing the elasticities of substitutions of more than 30,000 products. Using the elasticities, 1