rafmacalaba/datause-encoder-data huggingface dataset:
in 2019 and 2021. The share of TPOs that spent more than 50 percent on non-exporters doubled between 2019 and 2021, but still represents a small share of the TPOs in the sample in 2021 (12 percent). The expenditure on new or occasional exporters is slightly larger. More than 15 percent of TPOs report spending more than 50 percent on new and occasional exporters in both years, and “only” around 35 percent of TPOs spend less than 10 percent on new and occasional exporters. By far the largest expenditures focus on established exporters. Around 40 percent of TPOs report spending more than 50 percent on established exporters, and the pandemic brought a a slight decrease in that share of TPOs.

In the bottom panel of Figure 11 we report the distribution of TPOs’ expenditures on established exporters by income group. There are some interesting differences. In highincome countries the share of TPOs spending more than 50 percent on established exporters is almost 70 percent in 2021. In low-income countries the same share is 20 percent, while it is 28 percent in lower-middle-income countries and 23 percent in upper-middle-income countries. Interestingly the distribution of this share shifts to the right between 2019 and 2021 in high-income countries, whereas it shifts to the left in other income groups. Thus, there is a much higher concentration of expenditures on established exporters in high-income countries, and the focus on established exporters increased between 2019 and 2021 in highincome countries, whereas it tended to decline in other income groups.

There are two potential explanations for why TPOs focus their efforts on established exporters rather than new or potential exporters where returns are larger. The perhaps more obvious reason is a political economy explanation. Established exporters have better – connections and have therefore better access to TPO programs see box 5.4 in WorldBank (2022). An alternative explanation is that TPOs are risk-averse, and focusing on established exporters is less risky. Arguably, the COVID shock reduced the perceived risk of investing in new or potential exporters as more established exporters were unable to trade. The fact that TPOs in high-income countries moved further towards established exporters, while TPOs in low-income countries moved towards new and potential exporters, suggests that the stronger focus on established exporters in low-income countries is perhaps better explained by risk22
**Table 1: Country-Specific Sibling Correlation Estimates (Full Sample)**

|**Country**|**Sibling**<br>**Corr.**|**S.E.**|**N**|**Country**|**Sibling**<br>**Corr.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.662|0.006|10521|Benin|0.600|0.010|5480|
|Indonesia|0.625|0.004|33209|Burkina Faso|0.620|0.013|3498|
|Philippines|0.595|0.007|15064|Burundi|0.539|0.013|3897|
|Vietnam|0.664|0.011|3692|Cameroon|0.627|0.012|3663|
|||||Chad|0.735|0.013|2710|
|Europe & Central Asia||||Congo, Rep.|0.532|0.018|1781|
|Albania|0.471|0.020|2311|Cote d'Ivoire|0.584|0.018|1565|
|Armenia|0.546|0.018|3215|Ethiopia|0.696|0.007|7047|
|Kyrgyz Republic|0.379|0.031|1455|Gabon|0.549|0.023|1212|
|Tajikistan|0.439|0.020|3238|Ghana|0.588|0.014|3313|
|Turkey|0.547|0.009|8292|Guinea|0.552|0.016|2589|
|||||Kenya|0.541|0.010|7559|
|Latin America & Caribb|ean|||Lesotho|0.581|0.011|4382|
|Bolivia|0.678|0.009|6971|Liberia|0.532|0.019|1771|
|Brazil|0.698|0.012|2967|Madagascar|0.769|0.010|4376|
|Colombia|0.606|0.006|17607|Malawi|0.598|0.010|6164|
|Dominican<br>Republic|0.566|0.009|8190|Mali|0.604|0.013|3910|
|Guatemala|0.713|0.007|6553|Mozambique|0.540|0.016|2910|
|Haiti|0.680|0.008|6022|Namibia|0.524|0.019|2581|
|Peru|0.617|0.004|34974|Niger|0.675|0.015|1505|
|||||Nigeria|0.699|0.007|11380|
|Middle East & North Af|rica|||Rwanda|0.536|0.010|6848|
|Egypt, Arab Rep.|0.542|0.005|27042|Senegal|0.598|0.012|3850|
|Jordan|0.481|0.008|17023|Sierra Leone|0.517|0.019|2209|
|||||South Africa|0.490|0.020|2407|
|South Asia||||Tanzania|0.528|0.012|5619|
|Afghanistan|0.563|0.009|7585|Togo|0.521|0.020|1615|
|Bangladesh|0.641|0.007|12031|Uganda|0.620|0.012|4176|
|India|0.629|0.002|151142|Zambia|0.643|0.010|5595|
|Nepal|0.623|0.010|5574|Zimbabwe|0.589|0.015|3369|
|Pakistan|0.633|0.003|40964|||||

_Notes_ : This table presents the sibling correlation estimates for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the full sample.

35
households and Muslim households within geographic areas.<sup>5</sup> This paper estimates that, while there is a substantial unconditional association between religion and sanitation practices, a comparison of Hindu households and Muslim households within similar locations finds more similar sanitation practices. The estimates suggest that household religion itself has less influence on sanitation practices, as compared to characteristics of household location. Sanitation practices are substantially better in urban settings, with better access to sanitation infrastructure, for both Hindus and Muslims.

The paper proceeds as follows. Section I describes the data, and Section II presents descriptive statistics. Section III describes the estimating equations, and Section IV presents the results. Section V concludes.

# **I Databases on Indian Households: IHDS, DLHS-3, NFHS-3**

The analysis uses data from three nationally-representative surveys of households in India: the first wave of the Indian Human Development Survey 2004–05 (IHDS), the third wave of the District Level Household and Facility Survey 2007–08 (DLHS-3), and the third wave of the National Family Health Survey 2005–06 (NFHS-3). These data sets each contain different information on sanitation practices, and provide complementary insights on the relationship between sanitation practices and religion. The IHDS includes the most data on various sanitation practices, but is a smaller sample. The DLHS-3 has more households, but has fewer detailed questions on sanitation practices. The NFHS-3 provides an opportunity to follow migrants’ behaviors, though district-level identifiers are not available.

Three sanitation practices are studied: (1) whether an individual or household owns or uses a latrine, (2) whether the respondent washes hands with soap after defecation, and (3) whether the interviewer observed fecal matter (human or animal) around the dwelling of the respondent at the time of the survey.<sup>6</sup> The analysis is limited to households in which the

> 5There is also evidence of differences in contraceptive use among Muslim women and Hindu women who report not wanting additional children (Dharmalingam and Morgan, 2004; Dharmalingam, Navaneetham and Morgan, 2005), though other evidence suggests similar female autonomy within the household for Muslim women and Hindu women (Jejeebhoy and Sathar, 2001; Desai and Temsah, 2014).

> 6In the analysis, “latrine” refers to all types of latrines or toilets. The IHDS and DLHS-3 ask the

4
all available surveys and dummy variables for each survey. While the results from the pooled regressions gives us an indication of the average effect across surveys, they do not allow for slope differences across countries. In addition to using country-specific weights, we weight by the inverse of the respective population in each country in the case of the pooled regression.

We include a wide array of other individual characteristics that might explain variation in the use of financial services (see Table 8). Appendix Table A2 presents the descriptive statistics for all characteristics, for each country. First, we control for geographic location by including a dummy variable _Rural_ indicating whether the individual lives in a rural district. Geographic barriers such as larger distance to the nearest bank office would suggest a negative relationship between _Rural_ and use of formal financial services, while the use of informal financial services might not necessarily vary across different geographic areas. We control for the education level of individuals, by including dummy variables that indicate whether the individual has (i) no education or less than primary, (ii) primary completed, (iii) secondary level completed, and (iv) at least an undergraduate college degree. We expect individuals with higher levels of education to be more likely to use formal and informal financial services. We also include the _Age_ of the individuals, in logs. While there might be a positive relationship between the age of individuals and the use of financial services, this relationship might be non-linear and turn negative at higher ages when individuals leave the labor market. We also include an income measure where available. With the exception of Kenya 2006 (no income), Kenya 2009 (individual expenses), and Uganda (household income), we include the log of individual monthly income.<sup>8</sup> We expect higher-income individuals to be more likely to be able to afford formal financial services, while the relationship of income with the use of informal financial services is not clear _a-_

> 8 In the case of the pooled regression, we convert all income measures into USD, using average-year exchange rates.

21
- 2 -

# **I. INTRODUCTION**

In many of the poorest countries, livestock farming is one of the important industries to develop for not only economic growth but also poverty reduction and environmental protection. The livestock industry contributes about 1.2 percent of the global GDP—as much as 5 percent for some countries—and is growing by about 2.5 percent per annum. Lowincome and lower-middle income countries are relatively heavily dependent on the livestock industry (Table 1).<sup>1</sup> About one billion people in rural areas are still dependent on livestock for their livelihood in developing countries; 75 percent of the world’s poor live in rural areas (OECD, 2006).<sup>2</sup> Given the rapidly increased demand for livestock products led by vigorous growth and urbanization, a key challenge is how to ensure efficient production in the livestock sector. It is still unclear whether it could be achieved by establishing institutions, such as agricultural credit and trade agreements, or providing physical infrastructure, such as slaughterhouses and transport infrastructure (e.g., roads and ports).

<u>Table 1. Contribution of Livestock Production to GDP, 2001</u>

|Production<br>(Billions ofU.S. dollars)|Share of GDP<br>(Percent)|
|---|---|
|World<br>380.1|1.2|
|High income<br>160.6|0.6|
|Upper middle income<br>44.7|1.9|
|Lower middle income<br>147.9|4.9|
|Low income<br>26.9|3.1|
|Memorundum item:||
|Agriculture production<br>1,138.7|3.6|

Source: Author’s estimation based on _FAOSTAT_ and _World Development Indicators_ .

Using trade data from the European Union (EU), this paper aims at examining the effects of international trade preferences and public infrastructure on productivity changes in livestock production. A fundamental question is this: Infrastructure is intuitively essential for reducing

> 1 National accounts data on the gross production value of livestock products may not be published in most developing countries. These figures in Table 1 are estimated from livestock—i.e., beef and veal, buffalo meat, mutton and lamb, pig, chicken, duck, goose, turkey, and goat meat—production data from _FAOSTAT_ and the regional average export prices of individual items, which are calculated under the law of one price in internal and external markets. In the case of Botswana, which is one of the upper-middle income countries in Africa, its national accounts data indicate that the livestock share of GDP was about 2 percent in 2002/03.

> 2 In 2006, OECD POVNET has published a policy guidance note, “Promoting Pro-Poor Growth: Agriculture.”
of the price of traditional exports, indicating that the latter may be the likely driver. Traditional exports have accounted for 69-75% of total exports in the period with the bulk of its share accounted by the handful of primary products that dominate the price index depicted -- mainly copper products, fishmeal, non-ferrous metal transformation and refined petroleum products.

A more detailed look at the evolution of the index reveals two distinct phases during the 1993-2009 period. The first one from 1993 to 2001 is associated with a lower rate of growth in the price of traditional exports and a slower decline in diversification. The second phase coincides with the high growth rates of international prices that begin in 2002 and is when diversification declines more sharply. It is important to note that despite the general trend towards lower diversification, towards the end of the period there seems to be a temporary improvement (in 2007 and 2008) which is the likely result of the declining growth in international mining prices and the relative stagnation of export volumes.

**Figure 8: Export Concentration Index: 1993-2009**

<!-- Start of picture text --> 0.22 350<br>0.21 300<br>0.20 250<br>0.19 200<br>0.18 150<br>índice HH<br>0.17 100<br>índice precios (1994=100)<br>0.16 50<br>0.15 0<br>1993 1995 1997 1999 2001 2003 2005 2007 2009<br>HH Prices of traditional exports<br><!-- End of picture text --> The results of this analysis is largely consistent with the result reported in a recent study by UNECLAC (2010), which compares changes in the average concentration over years 1991-1994 and 2006-2009 for a sample of Latin American economies. While it finds that on aggregate for the region, export diversification has increased between the early 1990s and the end of the 2000s, it finds that Peru, Venezuela, Ecuador, Bolivia, Chile, Panama and Paraguay have bucked that trend -- mostly economies that are well known for their oil, gas and mining exports.

As the aggregate analysis may be clouding important dynamics in some export categories, Figure 9 displays the evolution of the Herfindahl-Hirschman Index applied only for the grouping of non-traditional exports. The overall trend is remarkably different to that of aggregate exports, indicating greater diversification secularly for most of the period, except for the years between 2001 and 2006. This trend may reflect two possible competing hypotheses. One is that Peru may be exporting the same nontraditional products in 2009 and in the early 1990s, but with a new distribution in which the proportion of 14
Sized Enterprises Development Organization” (KOSGEB-governmental institution) with nonrefundable support for <u>SMEs up to $50,000 to pay for listing costs.</u> _Source:_ WBG compilation of exchange information.

# 3. Exchange Incentives and Support—Outreach and Training

As noted earlier, many SMEs in EMCs do not want to list, because they do not understand the benefits, do not want to share information or ownership, and find listing to be expensive and complicated.

# _i. Outreach_

The exchange has a role to play in educating companies about the benefits of going to the market compared with other sources of finance, and in which circumstance an equity market listing is the preferred route for an entrepreneur. For many exchanges, outreach is a daily activity.

- China’s Shenzhen Stock Exchange did outreach to more than 5,000 potential companies.

- NSE Emerge offers the NSE Capital Market Awareness Program for SME promoters to make them aware of the capital market for capital raising.

Outreach is often in partnership with intermediaries, auditors, and commerce and industry associations. In some cases, the exchange has involved nongovernmental organizations and government to support the outreach efforts. An exchange may need to create a national campaign to bring the full range of supporters around the topic. For example, Borsa Istanbul’s “National IPO Campaign” partnered with Chamber of Commerce offices in cities around Turkey. BIST trained the liaison officers in each city.

Investor information days are useful to promote the exchange and showcase listed companies, providing investors with an efficient opportunity to meet several companies in one day. They are instructive for SMEs, who learn what investors are looking for.

# _ii. Training_

Exchanges often offer special training programs to educate SMEs on the benefits of listing. Pre listing training covers corporate governance, information disclosure, and capacity building, and includes support for preparing offering documents and advice on valuation, pricing, and how to attract investors. Post listing extends to managing investor relations, exchange compliance, and ongoing investors’ coverage and visibility. For example:

- AltX offers mandatory Directors Induction Courses. The JSE has duplicated the NASDAQ Issuer Services Model, bringing together education and training to executives of listed companies, investor relations, and public relations executives. Training includes listing requirements, targeting investors, investor relations, and financial media management. Services also includes providing venues for financial results announcements, Annual General Meetings, or any other activity companies may wish to host. The JSE also has a media center that houses Bloomberg (yet to go live), CNBC Africa, SABC, and a radio station (Power FM). The JSE has a customer relationship management team, with one person focusing on AltX companies. This function is outside

37
|i|n|ioe[|d<br>oe|
|---|---|---|---|
||{s.e.)|<br>(3.6.<br>]|(3.2.]|
|In{ population<br>|0.132°"*<br>|—0.1s7"TM*<br>|—0.215TM<br>|
|In{ population<br>Linnegibornoos)<br><br>|||<br>|0.158"<br>oo<br><br>|0.7845**<br>|ou)<br><br>|
|In{populationin<br>|magiboroedidonder)<br><br>||<br>|<br>0.333"<br>(oom)<br><br>|<br>—1.58**<br>36)<br><br>|
|<br> <br>In{populationin<br>|mugibereeaBadordey)<br><br>|<br>||<br>|<br>—0).128"<br>oss)<br>|<br>1.387***<br>|iy<br>|
|<br> <br>In{pop. in square}<br>[indeim<br><br>In{pop.|<br>eete<br>|<br>cmp<br>||<br>0.0257<br>ia)<br>aay|
|in neighborhood)<br>[indbimn<br><br>In{pop.|<br>cet<br>|wemp<br>||<br>—U.197""<br>ia)<br>**o**se)|
|in neighb. 2nel order)<br>[indtianeetwemptel<br><br>|<br>|<br>||<br>|<br>s<br>oor<br>|
|<br>In{pop.<br>in neighb. 3rd order!)<br>[Sndtta<br><br>In{population|<br>ces<br>|emp<br>||<br>—v.670-"<br>tad<br>uy|
||<br>0.0493|O.0492|<br>0.123|
|In{cliatance|O.0834°**|O.14a6"|2.18***|
|Border|Lis|110°"|114"|
|Road type 1:|—().628***|—U.7su"*|—) 708"|
|Road type 2:|-16¢°TM"|—1.64"TM|—1.74-TM|
|Proximity of previous|6.11"|6.18"|6.11***|
|Proximity of previous event|—).904"*|—0.s14*|—).6l9|
|Proximity of previous event|—U.0097|—0.0054|—0.176|
|In distance to|—0.494"|—v.494"*|—.4s4"|
|No of. failures|2291|2291|2291|
|ll<br>nullmoclel|—30571.32|—i0571.32|—d0571.32|

Table 1: Cox Regression Estimation Results, Risk of Conflict Event, Population Size and Concentration Models, 1960-2004
Using a panel survey data set from the Pakistan Social and Living Standard Measurement Survey conducted in 2007/08 and 2010 and data from a household survey administered by IFC, this paper presents the first empirical evidence on the benefits of reliable electricity on households in Pakistan. The study uses a two‐stage propensity score–weighted fixed‐effects model to control for unobserved village‐ and individual‐specific effects that may simultaneously affect electrification status and the welfare outcomes of interest. The results show that electrification is associated with a broad range of social and economic benefits in Pakistan, including income and expenditure, better health outcomes for children, improved school enrollment and school completion for boys (but not girls), and increased women’s labor force participation and decision‐ making power.

All these benefits from expanding and improving electricity supply are important, although not all of them can be quantified in monetary terms. The potential gains in income growth alone are substantial. According to the 2014 Household Integrated Economic Survey, the average rural household in Pakistan earned PRs 26,452 ($253) a month in fiscal 2014. With estimated average income gains of 37 percent a year, the increase in monthly household income would be about PRs 9,787 ($93). Assuming the marginal cost associated with electricity generation and transmission is about PRs 12.2 ($0.12) per kWh, annual average per capita electricity consumption is 471 kWh, and the average household includes 6.7 people, the net per capita gain from gaining access to electricity is estimated at PRs 11,782 ($113) a year.

There is no consensus on the access rate of electricity in Pakistan. The official estimate based on household surveys suggests that about 5 million people remained off‐grid in 2016. Data from the 2017 census and utility connections lead to an estimate that is almost 10 times as high: almost 50 million people (36 percent of the population). Based on the more conservative figure of 5 million, connecting the entire off‐grid population would raise income by $565 million a year. Based on the higher figure, the annual income gain could reach $5.7 billion.

Improving the reliability of electricity supply would add to these gains. Anecdotal evidence suggests that power cuts have been reduced over the past few years, thanks to additions of generation capacity and low global oil prices. Lacking official estimates of load‐shedding hours, 12
# **Table 2. Probabilities of Being Poor**

|||**Specific**|**ation 1**|||**Specificatio**|**n 2**||
|---|---|---|---|---|---|---|---|---|
|||**FHH Type 2**||**FHH Type 4**||||**FHH Type 4**|
||**FHH Type 1**<br>**Self-reported**|<br>**Majority-female-**<br>**adult**|**FHH Type 3**<br>**Potential**|<br>**Most-educated-**<br>**female-adult**|**FHH Type 1**<br>**Self-reported**|**FHH Type 2**<br>**Majority-female-adult**|**FHH Type 3**<br>**Potential**|<br> <br>**Most-educated-**<br>**female-adult**|
|Self-reported FHH|-0.007***<br>(0.00)||||-0.044***<br>(0.00)||||
|Self-reported FHH # Number of children|0.008***<br>(0.00)||||0.006***<br>(0.00)||||
|Share of female adults>0.5||0.052***<br>(0.00)||||0.032***<br>(0.00)|||
|Share of female adults>0.5# Number of||<br>-0.007***||||<br>-0.014***|||
|children||(0.00)||||(0.00)|||
||||-0.027***|||-0.017***|||
|Potential FHH|||(0.00)|||(0.00)|||
||||0.014***|||0.011***|||
|Potential FHH# Number of children|||(0.00)|||(0.00)|||
|Educated females||||0.002<br>(0.00)||||-0.006**<br>(0.00)|
|Educated females# Number of children||||<br>0.013***<br>(0.00)||||<br>0.013***<br>(0.00)|
|Household head`s|||||||||
|characteristics|N|N|N|N|Y|Y|Y|Y|
|Household characteristics|N|N|N|N|Y|Y|Y|Y|
|Country FE|Y|Y|Y|Y|Y|Y|Y|Y|
|<br>Survey year FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N|214931|214931|214931|214931|211069|211069|211069|211069|

Note: ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Robust standard errors are in parentheses. The full regression results using the linear probability model are provided in Appendix A, Table A.10.

34