rafmacalaba/datause-encoder-data huggingface dataset:
# **4.  Relating Outreach Indicators to Household and Firm Data**

How well do our outreach indicators predict the actual use of savings and loan services by household and firms? To a large degree the usefulness of the macro-level banking sector outreach indicators we propose will depend on whether they track the micro data that we ultimately care about. Regressing user-based data from household and firm surveys on our indicators of deposit and loan use, we show the predictive power of our aggregate outreach indicators.<sup>8</sup> Specifically, we use country-level data on the percentage of households that have a bank account constructed from different household surveys and compiled by Claessens (2005) and Gasparini et al. (2005) and country-level data on the share of small firms with bank loans from the World Business Environment Survey (WBES).<sup>9</sup> While the household surveys are based on thousands of observations, WBES samples on average 120 firms per country, 40% of which are small.<sup>10</sup> We therefore expect a much lower degree of precision and predictive power when relating firm-survey based user data to our aggregate indicators than when using householdsurvey based measures. While we tried different empirical specifications, below we present the model with the highest R<sup>2</sup> .

A regression of the share of households with bank accounts (Household share) on the log of number of deposit accounts per 100,000 (Ln deposits per 100,000) and the log of average size of deposits in US dollars (Ln average deposit size) yields the following result (robust standard errors in parentheses):

> 8 We are grateful to Patrick Hohonan for this suggestion.

> 9 WBES is a database of firm level surveys, which we discuss further in Section 6.1.

> 10 Given the small sample size and the size-stratified nature of WBES – 40% small, 40% medium and 20% large enterprises, independent of the actual size distribution -, we focus on the group of firms most likely to be affected by cross-country variation in banking sector outreach.  When we use the overall share of firms with bank loans or focus on small and medium enterprises, we obtain similar results, but at lower significance levels and with lower R<sup>2</sup> .

13
<!-- Start of picture text --> _8<br>‘ oucy<br>3<br>|<br>va<br>é<br>Ey<br>F Fl a<br>“<br>ie aLTy<br>3 enon Huy *eALR emiL EST<br>eer’ 8 MOE gum *TUR<br>0 1 Fy 3 4<br>Average ROA 2008-2007 (8)<br><!-- End of picture text -->

<!-- Start of picture text --> q<br>_<br>A<br>3 eur<br>- aLty oA8<br>ont<br>6 on oct ie — Fie ll<br>40 50 60 70 80<br>Share of loane made to firma(mea 2007-2009,%)<br><!-- End of picture text --> <!-- Start of picture text --> g<br>eUKR<br>a<br>al<br>8<br>| euT<br>2 HUN<br>GEO<br>ar eA eLTy<br>- acy eenay etua«=e BX cer Shay<br>20 30 40 50 60<br>Share of loans made to households (mean 2007-2009,%)<br><!-- End of picture text -->
<!-- Start of picture text --> Urban Share<br>0.8<br>0.6 .<br>More Developed — 80 years Less Developed<br>0.4 e<br>*.<br>e<br>*<br>*<br>Pl<br>.<br>0.2 ”<br>1700 1750 1800 1850 1900 1950 2000 2050 2100 2150 2200<br><!-- End of picture text -->
**Figure 7: Female labor share, trade participants vs. non‐participants, by country**

<!-- Start of picture text --> Exporting vs. non‐exporting firms<br>60%<br>lva ukr<br>50% rus bgr mda mng<br>bih tjk mmr<br>40% jamcolkgz srbhrvidn lkablrmarrou<br>30% ecupolttocri mexchngtmkazpanperaze namuzbnic armgeo slv mkdeth<br>zar bolchllbn tun<br>20% argprytza zweuga ken bgd vnm<br>ngagha zmb<br>10% indegy sen jor<br>irq pwbgyemak npl<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Female labor share (%), exporting<br>Importing vs. non‐importing firms<br>60%<br>ukr<br>bih<br>50% nic<br>col lkabgrblr mng tjk<br>hrv rus mda<br>40% idn mar<br>30%20% nplzar gha tzapryarglbnugazwechlbolttocripolkazpanchnkengtmazeecumexnamper jamuzbarmkgzbgdsrbgeo tunvnmslvethlva mkdrou<br>wbg<br>nga<br>10% indegy zmbsen jor<br>irq pyakem<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70%<br>Female labor share (%), importing<br>Female labor share (%), non‐exporting<br>Female labor share (%), non‐importing<br><!-- End of picture text --> Source: The female labor shares are averages using firm employment as weights. For country abbreviations, see Organization for Standardization (ISO), https://www.iso.org/obp/ui/#search.

Foreign ownership status, on the other hand, shows a different relationship with the female labor share, as suggested by Figure 8 (bottom panel). While the majority of countries are still located below the y = x line, implying a positive association between FDI status and the female labor share in more countries, the composition of countries looks different compared to expording, importing and GVC participation. Some of the countries with a strong gap in female labor share among non‐trading firms, such as Sri Lanka, Morocco, and Mongolia, are now located above the dotted line, i.e. the female labor share appears to be higher among domestically‐owned firms. While the low share of foreign firms in the sample of 7% could bias the results, another explanation may be that FDI is less common in female‐intensive sectors like apparel.

15
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21
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38
_Low-Income Countries’ Access to Private Debt Markets_

**Figure 2 Private debt issuance by low-income countries, 1970-2007**

<!-- Start of picture text --> billions of constant $2007 percent<br>10<br>20<br>8<br>Share of total (right axis)<br>15<br>6<br>10<br>4<br>5 2<br>0 0<br>1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> _Source:_ World Bank Debtor Reporting System.

Comparing private debt flows to low- versus middle-income country aggregates is somewhat arbitrary for many of the issues at hand, especially for countries close to the low-income cut-off (GNI per capita of $935 in 2007). In 2007 GNI per capita in 11 countries was within $100 of the low-income cutoff (figure 3). It seems unlikely that access to private debt markets would vary appreciably over such a narrow range. A more meaningful approach would be to examine how access to private debt markets varies with income (GNI per capita), focusing on the lower end of the income distribution.

**Figure 3 GNI per capita in developing countries, 2007**

<!-- Start of picture text --> $US<br>12500<br>Upper-middle<br>income<br>$3706 to<br>10000<br>$11,456<br>7500<br>Lower-middle<br>income<br>936 to $3705<br>5000<br>Low income<br>< $935<br>2500<br>0<br><!-- End of picture text --> Source: World World Development Indicators.

5
years of repeated cross-sectional Integrated Living Conditions Survey of individuals from 2007 to 2016, where respondents answered a set of questions about local infrastructure quality. The second method uses the Demographic and Health Survey from 2015-2016. Since DHS allows for approximate household location identification, I match the locations with a unique road quality dataset and use topographic control variables. In order to address the problem of endogeneity and reverse causation, the study uses an instrumental variable strategy based on historical military and postal routes in Armenia from a 1903 map, when Armenia was part of the Russian Empire.

The analysis with both datasets and methods shows that road quality is positively associated with non-agricultural employment in rural areas. Households living further from good quality roads are more likely to be employed in agriculture and less likely to be employed in seasonal employment. The DHS analysis shows negative association between distance to good quality roads and employment outcomes. People are 5.7 percentage points less likely to work in the non-agricultural sector and 5.1 percentage points less likely to be engaged in skilled manual employment with 1 log increase in distance (or approximately 2.7 fold increase in distance). Women are particularly affected in the type of pay they receive for work. The results show that 1 log increase in distance decreases the probability of getting cash earnings for work by 9.3 percentage points. These results are particularly interesting in the prism of women’s empowerment, where financial independence is one of the key factors.

The result on seasonal employment is particularly interesting for its unexpected opposite sign. The analysis from both datasets shows a positive association of seasonal employment with better quality roads. The analysis on ILCS shows that people reporting poor road quality leading to towns and markets are 5.2 percentage points less likely to engage in seasonal work. The analysis on DHS shows 5.5 percentage points less likelihood of seasonal employment with one unit increase in log distance from a good quality road (approximately 2.7 fold increase). These results could be explained by the outcome that people living nearby improved roads are more likely to work outside the village. Better quality road infrastructure stimulates mobility and commuting through two main channels: increased productivity, and reduced job search and commuting costs. Therefore, people living closer to better roads might be more likely to find additional jobs during less 30
incidence and intensity of catastrophe in health spending. We also present measures that capture the degree to which catastrophic health spending is concentrated among the poor. Section 6 addresses the issue of impoverishment-the extent to which people are made poor-or more poor-by health spending. We present measures that capture the impoverishing effects of health spending, distinguishing between the incidence and intensity of impoverishment, and showing how one can assess the extent to which greater intensity is due to people being made even poorer by health spending or by people becoming poor through such spending. In our coverage of both catastrophic health spending and impoverishment, we illustrate the measures with data on out-of-pocket payments from Vietnam for both 1993 and 1998. In the case of impoverishment, we show the differential impacts of hospital costs and other health care spending. Section 7 contains a summary and offers some conclusions.

# **2. Progressivity and income redistribution**

One approach, then, is simply to measure the degree of progressivity of the payments distribution and the income redistribution associated with it. Some theoretical results from the tax literature help clarify the relationship between these concepts, as well as the link between them and ability to pay.

## _2.1._ **_Progressivity and redistributive_** _effect:_ **_Some theoretical results_**

## **2.1.1. Progressivity**

Let pre-payment income (the analogue of pre-tax income in the tax literature) be x, and health care payments be _T_ (the analogue of taxes). There are two useful results from the tax literature. The first concerns progressivity. We can measure the progressivity using Kakwani's (1977) index. Denote Kakwani's index of progressivity of health care payments on pre-payment income by z', which is defined as twice the area between the Lorenz curve for pre-payment income, _Lx(p),_ and the concentration curve for health care payments, _Lc(p)._ (Thep in parentheses here indicates the person's or household's rank in the pre-payment income distribution.) The concentration curve for payments is formed by plotting the cumulative share of payments on the vertical axis against the cumulative proportion of households (or individuals) ranked by pre-payment income on the horizontal axis (Figure 1). Thus we have: (1) _,rTK =2 [L_(p)-LT(P)Pp=CTGX,_ _S_
||**Appendix**|**Table A5: **|**Panel Regre**|**ssion Results**|**, 1962-2007**|||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Dependent Variable:|Fertility<br>Rate|Fertility<br>Rate|Fertility<br>Rate|Female<br>Labor<br>Force Par-<br>ticipation|Female<br>Labor<br>Force Par-<br>ticipation|Female Ed-<br>ucational<br>Attainment|Female Ed-<br>ucational<br>Attainment|
|(Log) Female Labor|-0.56***|-0.28***|-0.24***|0.03|-0.05|0.62***|0.30***|
|Need of Exports<br>(FLNX)|(0.043)|(0.047)|(0.042)|(0.059)|(0.030)|(0.080)|(0.079)|
|(Log) Openness|-0.06***|-0.09***|-0.02|-0.05**|-0.03**|0.14***|-0.09***|
||(0.016)|(0.018)|(0.017)|(0.022)|(0.015)|(0.037)|(0.033)|
|(Log) GDP per capita|-0.35***|-0.27***|-0.19***|-0.09***|0.01|0.59***|-0.06*|
||(0.009)|(0.020)|(0.022)|(0.012)|(0.018)|(0.021)|(0.036)|
|(Log) Population|-0.06***|-0.42***|-0.07*|-0.04***|0.20***|0.08***|0.97***|
||(0.008)|(0.021)|(0.038)|(0.011)|(0.049)|(0.016)|(0.087)|
|Country Fixed Efects|No|Yes|Yes|No|Yes|No|Yes|
|Year Fixed Efects|No|No|Yes|No|Yes|No|Yes|
|_R_<sup>2</sup>|0.610|0.927|0.936|0.083|0.968|0.512|0.950|
|Observations|1,247|1,247|1,247|819|819|1,102|1,102|

Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. _Fertility Rate_ is (log) total births per woman; _Female Labor Force Participation_ is (log) female labor participation rate (% of female population ages 15+); _Female Educational Attainment_ is the (log of) Barro-Lee measure of female educational attainment (average years of schooling among female population ages 15+ at five-year intervals). All of the variables are five-year averages over the period 1962-2007, except for _Female Labor Force Participation,_ which is averaged over the period 1980-2007. Variable definitions and sources are described in detail in the text.
**Figure 14: Population Pyramid in Nepal**

<!-- Start of picture text --> 95<br>Female<br>90<br>Male<br>85<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1500K 1000K 500K 0K 0K 500K 1000K 1500K<br>Male Population Female Population<br>Age Group<br><!-- End of picture text --> Source: Nepal Population Census 2011

# **6. Conclusions**

The earliest records of poverty in Nepal indicate that the country was one of the poorest in the world at least until the early 1990s. Poverty in Nepal was chronic and plagued by high population growth, sluggish economic growth, a very low level of exports and rural population dependent on subsistence agriculture. The only glimmer of hope for poverty reduction at that time was to slow down population growth and formulate effective policies focused on boosting agricultural productivity in rural areas, which accounted for 95 percent of the poor population.

Contrary to the prediction, there was a remarkable reduction in the poverty rate in Nepal in a span of a decade and a half between 1995 and 2010. While the poverty headcount rate remained virtually unchanged at approximately 40 percent until 1995, it declined to 31 percent by 2003 and as low as 12 percent by 2010. Many non-income dimensions also exhibited significant improvements, often at a pace faster than neighboring countries in South Asia. The impressive poverty reduction record led the government to revisit and raise the poverty line in order to better reflect the living standards of contemporary Nepal. Even with the revised poverty line that increased in its real value by 35 percent from the previous line, the poverty rate in 2010 was estimated at 25 percent.

While improving agricultural productivity was long regarded as instrumental to lifting the living conditions of Nepal’s impoverished rural areas (World Bank 1991; World Bank 1999), a bulk of the observed poverty reduction appears to have come as a result of an exogenous improvement in economic opportunities for poor Nepalis outside of Nepal’s borders. International migration increased dramatically in the last 15 years; a daily average of 1,500 young Nepalis 24
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<!-- Start of picture text --> 100%<br>a Very Important Important<br>80%<br>60%<br>40% Hy om 5%<br>20% 51% gag 6% Th 46%<br>"35% 39% | 949% peo, 20% = S748 35% nay 30%<br>18% 31% 24% 31% 3804, 590,<br>20% 21% 44%, 16%<br>0%<br>PP8 gE2 woBOPgo gt oF OPeeMg& ot Sg¢8id«@<br>eeaSre)Ry ”oe Ro aSn x) PSSS«s\ onTM. aetas? raCS Pa nnENCMry a % ox?<br>x?cer eo oe ew roe onde of eo kK we eo w? & oo ws<br>eo ofa Weoo coo® oxeo ? oea? oe¢ Piaseoao vex &<br>ow of oe o®<br>ee GCC Non-GCC<br><!-- End of picture text -->
Competitive pressure is the driving force behind productivity improvements. In closed economies with sheltered markets enterprises have little incentive to invest in new products or cost saving measures. But in open, highly competitive economies enterprises must constantly strive to improve their competitive position to survive. The competition measure uses a series of perception questions regarding the importance of domestic and foreign competition for decisions to introduce new products and to reduce the costs of existing products. It also includes a question on the number of competitors in firms’ main product line as a gauge of market power.

**Table 3 Mean Indicators of Competition by Country**

|Indicator|Kyrgyz<br>Republic|Moldova|Poland|Tajikistan|Uzbekistan|
|---|---|---|---|---|---|
|_Importance of domestic_<br>_competition for decisions_<sup>_a_</sup>||||||
|To introduce new products|2.7|2.6|3.3|2.7|2.4|
|To reduce costs|3.0|2.9|3.3|2.6|2.1|
|_Importance of foreign_<br>_competition for decisions_<sup>_a_</sup>||||||
|To introduce new products|2.4|2.2|2.9|2.0|1.8|
|To reduce costs|2.4|2.5|2.9|2.0|1.7|
|Competitors in main product||||||
|line|28.8|43.6|63.1|47.8|32.3|
|Average net inflows of foreign<br>direct investment (percentage<br>of GDP),1996–2001|2.9|4.0|5.1|1.9|0.8|

a. Average ranking by firms on a scale of 1 (not at all important) to 4 (very important). _Source:_ For foreign direct investment, World Bank, World Development Indicators database; for all other data, World Bank, Business Environment and Enterprise Productivity Extension Survey, 2003.

By all measures based on managers’ perceptions, Poland has a much more competitive economy than the Central Asian countries or even Moldova (table the 3). On average Polish managers ranked pressure from domestic competitors as 3.3 in importance (with 3 meaning fairly important and 4 very important), while in other countries average rankings ranged between 2.14 and 3.0. As expected, the lowest scores appeared in Uzbekistan, 14
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