rafmacalaba/datause-encoder-data huggingface dataset:
pollution from the burning of solid fuels kills over 1.6 million people (predominantly women and children) a year. More efficient electric stoves would reduce this death toll, which is almost as great at that caused by unsafe water and sanitation, and greater than that cause by malaria.

Better transportation networks also contribute to easier access to health care, particularly in rural areas. Recent data produced by national Demographic and Health Surveys in Sub-Saharan Africa show that a majority of women in rural areas rank distance and inadequate transportation as major obstacles in accessing health care (see African Union (2005)). In Morocco, a program developed in the mid-1990s to expand the network of rural roads led—in addition to reducing production costs and improving access to markets—to a sizable increase in visits to primary health care facilities and clinics (see Levy (2004)). In Malaysia and Sri Lanka, the World Bank (2005 _c_ , p. 144) found that the dramatic drop in the maternal mortality ratio (from 2,136 in 1930 to 24 in 1996 in Sri Lanka, and from 1,085 in 1933 to 19 in 1997 in Malaysia) was due not only to a sharp increase in medical workers in rural and disadvantaged communities, but also to improved communication and transportation services—which helped to reduce geographic barriers. Transportation (in Malaysia) and transportation subsidies (in Sri Lanka) were provided for emergency visits to health care centers. Moreover, in Malaysia, health programs formed part of integrated rural development efforts that included investment in clinics, roads, and schools. A similar approach was followed in Sri Lanka—better roads made it easier to get to rural health facilities. At a more formal level, Wagstaff and Claeson (2004, pp. 170-74) found, using cross-section regressions, that road infrastructure (as measured by the length of the paved road network) had a significant effect on a number of health indicators, such as infant and female mortality rates.

# **5.  Impact on Education**

A large body of evidence, based predominantly on microeconomic studies, has also documented the existence of a significant link between infrastructure and educational attainment. As summarized by Brenneman and Kerf (2002), these 19
of the GFC. In discussing capital regulations, it is important to keep in mind that the banking systems in ECA differ from the rest of the world in four key aspects.

First, banks in the ECA region are more interconnected through shared regulation, shared ownership structures and shared risk exposures. Due to geographical proximity, it is not uncommon to find banks operating in several ECA countries located nearby. Competing in the same markets exposes banks to common sources of market and credit risk shocks. Within the region, merger and acquisition (M&A) activity was at an all-time high prior to the GFC (Dermine, 2006) creating shared ownership structures within ECA. Banks located in countries that are members of the Euro Zone share a common currency and come under the regulatory purview of the European Central Bank and the European Banking Authority. All these interlinkages amplify risks and result in spillovers across national borders.

Second, banking sectors in ECA countries are highly concentrated. Figure 3 shows the share of assets held by the top three and top five largest banks. The figure shows average values for ECA countries grouped into different regions (see Table A1 for region classifications). Concentration is especially high in the EU group of countries (i.e., Western Europe, Southern Europe, Northern Europe) and in Central Asia. There is variation with some of the larger countries having more fragmented markets, whereas smaller countries are characterized by more concentrated banking sectors. The Russian Federation, in particular, has seen a substantial increase in bank concentration over the past 10 years.<sup>5</sup>

> 5 As reported in the BRSS 2019, part of the increase in concentration may be attributed to small bank failures during the period 2011-2016.

3
**Figure 5**

<!-- Start of picture text --> Proportion of final annuIty capital mcCUaUlated by gv,en age<br>With 15- year *nrnings gaps<br>so<br>40<br>*S .--- ... ...... .- >...... . ... o .e*. ............... S a *-"<br>-*-Gap 2G-42, 1:4X%, dY.O% -_-Gap 2G-42, .n2S, dY=2%<br>-4-Gao 49-063. 14N, dYsOM -Gp 49-63, Is2% dY-2%<br><!-- End of picture text -->

**Figure 6**

<!-- Start of picture text --> Proportion of fiLal annuity capital accusulated by given age<br>With 15-year earnings qaps Clog scale)<br>¶00<br>ib,,,,,,~~. .. , .,,,,, . I . .i ,,,, I,,,,,<br>-.Gap 2e-42, i=4X%, dY=O% Z SGa 2G-42, [=2%, dY=2%<br>-.- Gap 49-63, I=4%, CIY=O% I.tGsap 49-63, 1=2%, Cr=2X<br><!-- End of picture text --> The distributional outcomes of the LIFEMOD simulations reported in the tables above depend upon the interaction of the socio-economic characteristics of the LIFEMOD population with the specific details of pension scheme rules and assumptions. As Table 7 shows, the application of different retirement ages, real rates of return and rates of real earnings growth have a major impact on the contribution rate required for any given replacement rate for an _average_ individual; they are, in effect scaling 47
## 2. METHODOLOGY

To analyze poverty, we need to measure the economic welfare of each individual in the society. Although income i- widely used to measure economic welfare, it has many serious drawbacks.21 One major drawback of using income as a measure of economic welfare is that it may have substantial fluctuations which are averaged out in the long run. Therefore, it has been suggested that consumption is a better indicator of the actual economic position of a household than its current income.3/ The National Sample Surveys provide reasonably comparable time series data on the levels and distribution of household consumption expenditures. These data are available in grouped form, giving for each group: (a) the estimated number of persons, and (b) the average consumer expenditures in rupees per person. The monthly per capita expenditure levels are generally grouped into 13 to 14 experditure classes. To estimate poverty from such data, one needs to employ some intrapolation device. A commonly used procedure is to fit a density function to the entire consumption range and then compute poverty measures from the parameter of the fitted function. Most of the Indian studies have employed a two-parameter lognormal distribution (Minhas, Jain, Kansal and Saluja, 1987). The difficulty with this approach is that the lognormal distribution tends to overcorrect the positive skewness of

# 2/ For a detailed discussion of this issue see Kakwani (1986).

- 3" It would be more appropriate to use permanent income as a proxy for welfare or capability to escape poverty. Since there exists no reliable measure of permanent income, we have used household consumption expenditure, which is regarded as a more suitable measure of the household's economic welfare than the current income.
lower costs are at the top of their economic plans.

|**Table 1. Medians, Trade meas**|**ures in SSA**|**Sub Saharan Africa**|
|---|---|---|
||Region|Resource Rich<br>Non-Resource Rich|
|**Trade Openness**|||
|Percent of GDP|||
|1990 - 94|57.09|71.87<br>54.93|
|2010 - 14|76.39|87.23<br>64.81|
|**Herfindahl Index**|||
|Product Concentration|||
|1990 - 94|0.31|0.80<br>0.27|
|2010 - 14|0.21|0.54<br>0.16|
|Market Concentration|||
|1990 - 94|0.14|0.29<br>0.12|
|2010 - 14|0.13|0.21<br>0.12|
|**Natural Resources Trade**|||
|Percent of GDP|||
|1990 - 94|11.78|46.19<br>9.21|
|2010 - 14|7.88|23.58<br>5.98|
|Percent of total exports|||
|1990 - 94|91.43|98.63<br>83.57|
|2010 - 14|77.25|86.48<br>74.92|
|**Regional Trade**|||
|Inter-regional, Percent of GDP|||
|1990 - 94|31.91|40.36<br>26.74|
|2010 - 14|34.83|48.57<br>31.39|
|Intra-regional, Percent of GDP|||
|1990 - 94|20.04|19.40<br>23.88|
|2010 - 14|23.57|21.98<br>25.36|
|**Sectoral Trade**|||
|Primary|||
|1990 - 94|19.62|65.79<br>13.39|
|2010 - 14|22.3|38.71<br>19.51|
|Manufacturing|||
|1990 - 94|16.81|15.1<br>17.72|
|2010 - 14|21.87|23.88<br>21.56|
|_Sources: WDI, and author's calculations u_|_sing WITS, COMT_|_RADE_|

Trade diversification is proxied by the Herfindahl-Hirschman index (HHI) of exports across products and across markets. The Herfindahl-Hirschman index of export products (HHIP) is the sum of squared weights of goods in the export basket and it is calculated using the following formula: 7
**Annex Table 1.2. Bayesian Model Averaging**

|Dependent variable: Investment growth||
|---|---|
|VARIABLES|(1)|
|Lagged real GDP growth (percent)|0.558|
||[1.00]|
|Change in FDI inflows (percentage points of GDP)|0.703<br>[1.00]|
|Political stability|0.126<br>[0.83]|
|Lagged credit to GDP ratio (percent of GDP)|-0.067<br>[0.81]|
|Terms of trade growth (percent)|0.154<br>[0.99]|
|Large reform spurt|3.017<br>[0.53]|
|Sudden stop dummy|-3.777|
||[0.84]|
|Constant|-1.116|
||[1.00]|
|Observations|1,098|

Note: Estimation results are based on Bayesian model averaging. The sample is the same as in Annex Table 1.1. Probability of inclusion of each variable are in brackets.

# **Annex 2. Definitions and methodology**

**A. Investment‐less credit booms** . Data for the broadest definition of credit are provided by the Bank for International Settlements for 14 EMDEs from 1980 to 2015 (Argentina, Brazil, China, Hungary, India, Indonesia, Malaysia, Mexico, Poland, Russia, Saudi Arabia, South Africa, Thailand, and Turkey).

For other EMDEs, where credit from the domestic banking system remains the main source of credit (Ohnsorge and Yu 2016), annual data on claims by banks on the private sector, provided by the IMF’s International Financial Statistics, are used as proxies for credit to the nonfinancial private sector. This broadens the sample by another 41 countries, mainly from 2000 onwards. These include Azerbaijan, Bahrain, Bangladesh, Bulgaria, Bolivia, Botswana, Colombia, Chile, Costa Rica, Cote d’Ivoire, Croatia, Egypt, Gabon, Georgia, Ghana, Guatemala, Honduras, Jamaica, Jordan, Kazakhstan, Kenya, Kuwait, Mauritius, Mongolia, Namibia, Nigeria, Oman, Pakistan, Philippines, Panama, Paraguay, Peru, Qatar, Senegal, Serbia, Tunisia, Sri Lanka, Ukraine, Uruguay, República Bolivariana de Venezuela, and Zambia.

Advanced economies (AEs) included in the sample are Australia; Austria; Belgium; Canada; Denmark; Finland; France; Germany; Greece; Hong Kong SAR, China; Ireland; Israel; Italy; Japan; Republic of Korea; Luxembourg; Netherlands; New Zealand; Norway; Portugal; Singapore; Spain; Sweden; Switzerland; United Kingdom; and United States.

23
workers contracts with coverage of labor and social insurance regulations. As a result, if too few resources are allocated to these firms, the schooling composition of the demand for labor will tilt in the direction of workers with fewer years of education.<sup>2</sup> A large literature has analyzed the role of taxation, social insurance and labor regulations, credit frictions, market failures, and other factors, such as registration costs, in generating misallocation (IDB 2010, LaPorta and Schleifer 2008). In the case of Mexico, Levy (2008) and Busso, Fazio, and Levy (2012) emphasize the role of labor and social insurance regulations; Leal (2014), the role of taxation; and López-Martin (2015), the role of credit. In all these cases, misallocation results in too many low-productivity firms employing too many workers without coverage of labor and social insurance regulations, that is, in a large informal sector.

This paper does not model the frictions, market, or regulatory failures that generate misallocation. Rather, to test our hypothesis, a three-step approach is followed. First, a model of individual worker earnings that controls for all observable worker characteristics is estimated, but focuses on estimating the coefficients associated with observable firm characteristics.

Second, the implications for individual workers of eliminating misallocation are also considered, interpreted here as eliminating firm informality. To do so, a counterfactual earnings distribution is constructed keeping constant the unobservable and observable characteristics of individual workers, such as years of schooling, age, gender, and location, but assuming that the size and type distribution of firms mimics that of formal firms. The purpose is to measure how worker earnings are affected if, because of misallocation, there are too many informal firms in the demand side of

> 2 Bobba, Flabbi, and Levy (2017) develop a search and bargaining model where firms and workers form matches (jobs) that can be formal or informal. Modeling some of the distortions present in Mexico’s labor market, they show that the returns to education follow a path consistent with the findings in this paper, with the additional effect of a disincentive for individuals to invest in higher levels of education.

4
data are in logs, and for the PMR indexes we have re-scaled them to range from 1 to 8. The resulting regression is summarized in Table 6.<sup>32</sup> Table 5: Productivity and Regulation in Services, 2007

||productivity<br>US 1995=100<br>ln(A)|PMR<br>market access<br>ln(1+index)|PMR<br>price controls<br>ln(1+index)|
|---|---|---|---|
|Australia|4.741234|0.000000|0.565314|
|Austria|4.497638|1.178655|0.336472|
|Belgium|4.882857|0.000000|0.500775|
|Czech Republic|4.198892|1.299283|0.336472|
|Denmark|4.904586|0.405465|0.095310|
|Finland|4.680383|0.000000|0.182322|
|France|4.839986|0.405465|0.182322|
|Germany|4.873890|0.000000|0.765468|
|Hungary|4.299110|0.223144|0.587787|
|Italy|4.440765|0.510826|0.788457|
|Japan|4.250411|1.178655|0.875469|
|Slovenia|4.173714|0.864997|0.988611|
|Sweden|4.970974|0.510826|0.470004|
|United Kingdom|4.785076|0.000000|0.182322|
|United States|4.979338|0.000000|0.405465|

_basis: OECD and EU-KLEMS._ – Table 6: Regression analysis MFP and regulation

||OLS|robust<br>regression|
|---|---|---|
|discrimination against foreign frms|-0.3796***|-.3975 ***|
|domestic price regulation|-.3822 **|-.3735 *|
|intercept|4.9861***|4.9952***|
|summary stats|Rsq: 0.61||
||n:15|n:15|
||F(2,12)=18.93|F(2,12)=7.99|
||pr_>_F=.0002|pr_>_F=.0006|

_note: Regressions are based on 2007 MFP indexes. Indexes are rescaled OECD PMR sub-indexes. All variables are in logs. Significance levels: .01=***, .05=**, .1=*._ We assume that the coefficients capture, in a reduced form, the impacts of variations in openness and domestic regulation on MFP in the service sectors. On the basis of the coefficients above, we then construct an experiment wherein we move the Italian regulatory regime toward the North European norm, as proxied by the regulatory index values for the average for Northern European Members of 32The correlation of MFP against various OECD PMR sub-indexes is stronger for some than others. The correlation between MFP and openness (discriminatory procedures against foreign firms) is 0.6799. The correlation between MFP and domestic regulation (here represented by the price regulation index) is 0.4989.

30
|ln_part_gdp_cap|0.964***|7.618***|2.632***|1.628***|1.019***|0.376|
|---|---|---|---|---|---|---|
||[0.265]|[1.732]|[0.486]|[0.402]|[0.269]|[0.602]|
|L.ln_itfa||||||0.002|
|||||||[0.004]|
|L2.ln_itfa||||||0.006<br>[0.004]|
|L.ln_etfa||||||0.003*<br>[0.002]|
|L2.ln_etfa||||||0.001<br>[0.001]|
|Observations<br>108,304|107,806|12,214|51,652|57,855|104,644|32592|
|Robust standard errors in brackets|||||||
|* significant at 10%; ** significant<br>Includes Bilateral 5-years fixed effe|at 5%; *** si<br>cts and year|gnificant at 1%<br>fixed effects|||||

As part of our analysis we also examine whether the import effect may be due to tied-aid or because aid facilitates imports from rich (and therefore more likely donor) economies. First, we remove the donor economies completely from the sample (column 5). We observe that A4TF has facilitated imports from other developing countries, however, our regression result suggests that there was no effect on exports directed to the latter group. A4TF flows thus do not seem to have boosted trade among developing countries taken as a whole group (not just A4TF recipients). So, ‘south-south’ trade does not appear to benefit particularly from aid for trade facilitation.

We then include the bilateral flows between the recipient economies, other developing countries and the donors, but exclude intra-donor trade flows (column (6)). When intradonor trade is completely removed from the sample, the significance of A4TF for exporters returns. So, aid for trade facilitation may be more positively associated with exports to donor economies than to other developing countries.

Column (7) investigates whether time lags affect the efficacy of aid flows. One might argue that aid does not become effective immediately, but only after a certain period of time. Introducing time lags reduces the sample size due to several gaps in the aid flow data. Running the OLS regression with time and bilateral fixed effects on this smaller sample, yields coefficient magnitudes similar in profile to columns (1) and (2), but coefficients on the aid flows are not generally statistically significant.

In sum, the evidence suggests that aid for trade facilitation is positively related to both exports and imports of the recipients, that the relationship to importer’s trade flows is somewhat larger (which could indicate that aid flows to donors continues to be important). On the other hand, when considering trade between recipients and donors, excluding intra-donor trade, the relationship between aid and exports is relatively larger than the relationship between aid and imports suggesting the importance of A4TF for enabling exports to the developed world.

12
banks since we are interested in the barriers encountered by the average customer in each country. Survey responses were carefully “cleaned” through extensive follow-up with the banks whenever we had questions about the data provided. While we received a total of 253 responses from banks in 88 countries, to insure representativeness, we limited the analysis in this paper to countries for which the responding banks constitute at least 30% of the market in terms of total loans/total deposits or where we received a response from the largest bank.<sup>3</sup> This gives us a total sample of 193 banks across 58 countries.

Table 1 presents all the countries in our sample and shows their level of economic and financial development, as measured by GDP per capita in U.S. dollars and private credit to GDP, respectively. Also, the table contains information on the number of banks (out of the top 5 banks) that responded to our survey, along with the market share that they represent. Our sample comprises countries across all levels of financial and economic development. Countries range from Ethiopia with a GDP per capita close to 100 dollars to Switzerland, where GDP per capita exceeds 34,000 dollars. With banking sector credit at 2 percent of GDP, Mozambique is the country with the lowest level of financial development in our sample, while Denmark and Switzerland rank at the top with private sector credit exceeding 150 percent of GDP. In terms of regions, our sample coverage is also quite balanced. Our dataset includes 15 countries from Eastern Europe and Central Asia, 13 countries from Sub-Saharan Africa, 9 countries in Western Europe, 8 Latin American and Caribbean countries, 5 countries from the Middle East and North Africa, 4 countries in South Asia, 3 countries in East Asia and 1 non-European developed country (Australia).

> 3 We determined the market share using data from Bankscope.  We have data for the largest bank constituting less than 30% of the market in only one country, Swaziland. In Algeria too, we only have data for the largest bank, but this bank accounts for more than 30% of the market.

6
# **Aid, Service Delivery, and the Millennium Development Goals in an Economy-wide Framework**

**François Bourguignon, Carolina Diaz-Bonilla and Hans Lofgren**<sup>**†**</sup>

> † Development Economics, The World Bank. The authors also would like to acknowledge the initial inspiration of Luiz Pereira da Silva and significant contributions from Maurizio Bussolo, Ahmed Kamaly, Jeff Lewis, Hans Timmer and Dominique van der Mensbrugghe with assistance provided by Denis Medvedev and Shuo Tan. Comments and suggestions have also been provided by Ishac Diwan, Enrique Ganuza (UN), Pablo Gottret, Wafik Grais, Jee Peng, Sherman Robinson (University of Sussex), Agnès Soucat, and Rob Vos (UN). The views and findings in this paper are those of the author and not necessarily those of the World Bank, its Executive Board, or member country governments.
**Figure 10: The Structure of Social Returns**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>Primary Secondary Tertiary<br>Low income Middle income High income<br><!-- End of picture text --> _Private and social returns by region_ In our previous analysis (Psacharopoulos and Patrinos 2004a) we presented estimates from 83 countries and showed that the returns to education were highest in Sub-Saharan Africa, the region with the lowest levels of schooling. With near universal primary education in most regions, it is becoming difficult to estimate returns to primary schooling using recent surveys and the full discounting method. Our update – using estimates since 2000 – is dominated by such returns from high-income countries, which exclude returns to primary education (since the comparison group, that is, workers with no education, is absent). Overall, private returns to secondary and higher education in high-income countries are high, at 13 and 12 percent (see Table 5).

**-** **<u>Table 5: Private and Social Returns to Schooling by Region, post 2000 (%)</u>**

||<br>Priv<br>Secondary|<br>ate<br>Higher|<br>Soc<br>Secondary|ial<br>Higher|
|---|---|---|---|---|
|Average|13.2|12.4|10.2|10.6|
|Developing countries|--|--|10.2|16.4|
|Number of studies|50|54|59|61|

Note: For private returns, sample consists of high income countries plus Turkey; for social returns, two are from Africa, two are from Latin America, one is from Europe, one is from East Asia, and the rest are from high income countries.

In terms of social returns, these are higher than any plausible social discount rate though lower than private, across all income groups. The social returns to higher education are particularly high, but these are driven by returns in Africa, where the social returns to higher education are 35 percent in Malawi (Chirwa and Matita 2009) and 22 percent in South Africa (Salisbury 2015) – this of course implies that private returns to higher education are even higher.

13