rafmacalaba/datause-encoder-data huggingface dataset:
in some _municipios_ , the 2000 data are the only GDP figures available to us. If part of the impact of remittances is operating through income, then controlling for GDP may slightly bias downward our estimate of the impact of remittances on banking development.

Our primary independent variable of interest is the percentage of households in each _municipio_ receiving remittances. We measure this using the 2000 Population and Housing Census, implemented by the _Instituto Nacional de Estadística Geografía e Informática_ (INEGI). We use data from a sub-sample of Mexican households that responded to an “extended questionnaire” ( _cuestionario ampliado_ ), which, in addition to basic information on demographic and housing characteristics collected of all households, included questions on migration and nonwage sources of income, such as remittances. The sub-sample covered around 2.2 million households, or 10 percent of all households in the country, and was designed to be representative at the _municipio_ level.<sup>14</sup> Not every household had an equal chance of being surveyed, but we use sample weights provided by the census to aggregate all information to the _municipio_ level, including the percentage of households receiving remittances. The data on Table 1 indicate that on average 6.54 percent of households in a _municipio_ reported receiving remittances.<sup>15</sup> The data show wide variance in remittance receipts among the _municipios_ . Almost 7 percent of _municipios_ have no households reporting they receive remittances, while in more than 23 percent of _municipios_ the share of households receiving remittances exceeds 10 percent.

We also control for the density of population within the _municipio_ . Some _municipios_ have much larger land areas than others. For a given population, having a larger land area (that is, having lower population density) is associated with longer distances to any point in the _municipio_ . Longer distances imply longer travel times to and higher costs of using a bank branch. Hence, we expect that density should be positively associated with measures of bank branch development, bank breadth and depth. Land area is taken from INEGI. The average

> 14 INEGI (2000) provides a detailed description of the sampling methodology used to implement the extended questionnaire.

> 15 This is an unweighted mean of the municipio level data. The percentage of households reporting remittances in Mexico is less than 6.58% because those residing in smaller municipios are more likely to say they receive remittances.

11
10 over time and of the specific levels of dlfferent regions. Its main weakness, however, is its unabated tendency to give estimates mechanically decreasing with time, a consequence of the statistical models used to generate those estimates.

Immunization data refer to the vaccination of children under one year of age against tuberculosis (BCG vaccine; table Health 3), diphtheria, pertussis and tetanus (DPT vaccine, table Health 4), poliomyelitis (table Health 5) and measles (table Health 6). Our data was kindly supplied by the Expanded Programme on Immunization (EPI) from the World Health Organisation (WHO) in Geneva. Data is collected by the ministries of health in each country. Their districts collect the data on a monthly or quarterly basis and forwards them to the central government which compiles the data on an annual basis, as part of the routine national notification system. The figures quoted in the table are percentage of children immunized by 12 months of age.

# The trends

The data on life expectancy set a pattern of regional performance which remains valid for many other social indicators, even beyond the field of health. Most recent estimates (1988) put Africa at a low 51 years of age life expectancy (only weighted data used in the analysis), as against 56.5 for South Asia, 67.6 for East Asia, 63.1 for Europe, Middle East and North Afrlca, 66.8 for LatLn America and the Caribbean, and 71.5 for Eastern Europe.
The Structure of Social Disparities in Education: Gender and Wealth<sup>1</sup>

# I) Introduction

Universal primary education was enshrined as a human right in the United Nation's Universal Declaration of Human Rights in 1948. Forty years later the goal was still not in sight and a call on donors and governments to reaffirm their commitment to universal primary enrollment was part of the World Declaration on Education for All issued in Jomtien, Thailand in 1990. The year 2000 was set as the target for achieving this goal. It is now 1999 and we are still not near to achieving universal primary education - and as pointed out dramatically in a recent report by Oxfam International (1999) we do not appear to be closing in on it.

This paper uses a collection of internationally comparable household datasets to investigate the correlates of educational enrollment and attainment gaps within countries. The data from the Demographic and Health Surveys (DHS) for 57 surveys in 41 countries are used to carry out country specific analyses, which are comparable across countries. Specifically, the effects of gender, household wealth, the education of adult household members, and the presence of schools in the community on the educational outcomes of children are assessed in each country and compared across countries.

Using household based surveys allows the analysis to go beyond comparing country aggregates which are reported in several large "international databases" (e.g. UNESCO data or derivatives thereof such as Barro and Lee, 1993; Nehru, Swanson and Dubey, 1993; Dubey and King, 1994; Ahuja and Filmer, 1996). The DHS have a drawback in that they lack data on household consumption expenditures, the usual variable used to rank households by their socio-

> ' This paper has benefited greatly from comments from Jere Behrman, Jeff Hammer, Elizabeth King, Julian Lampietti, Andrew Mason, Lant Pritchett, Martin Ravallion, Jee-Peng Tan and participants at a workshop on Gender and Development in June 1999. Errors are of course my own. Please see http: /ww\.worldban]c.ortz/researcl/proiects/edattai n/edattainhtmn for more information on education gaps generated as a part of this project.

3
<!-- Start of picture text --> 250 MMR<br>200<br>150<br>CHN<br>LAO<br>100 CHN<br>MMR<br>KHM<br>IDN THA CHN<br>MNG MMR<br>MYS 50<br>MYS PHL THA IDN MNG MNG<br>PHL LA O KHM<br>LAO 0 THA PHL MYI DNS<br>-20 -15 -10 -5 0 5 KHM10 15<br>-50<br>Change in employment share (% of total) between 2000-2007 and 2008-<br>2016<br>Agriculture Industry Services<br>Source: World Development Indicators<br>2000-2007 and 2008-2016<br>worker (constant 2010$ USD, between<br>Percentage change of sectoral output per<br><!-- End of picture text -->

# **4. Misallocation across firms within a narrowly-defined industry**

Productivity analyses based on resource misallocation provide a new framework for policy makers and researchers. More conventional discussions about productivity center on technology and require a different set of policy prescriptions. For example, policies geared toward increasing technological diffusion and innovation include measures that promote research and development (R&D) and the creation of new ideas. These policies are usually complemented with those that incentivize learning, upgrading of skills, and investment in education. However, a complimentary approach focuses on the benefits of reallocation of inputs from less to more productive firms as an important component of aggregate productivity growth. Policies associated with allocative efficiencies emphasize correcting or reducing market distortions (see Box 1 for an example of power sector reform that disproportionately benefits firms and sectors with heavier electricity use).

What is resource misallocation in a narrowly-defined industry? In an economy with low levels of distortion, productive firms will have access to more resources—namely labor and capital— compared to less productive ones, leading to an increase in the overall productivity of the sector. In an economy with high levels of distortion, however, unproductive firms have disproportionately large access to resources, hence dragging down the overall productivity of that sector. The literature proposes two different approaches to detect this resource misallocation. The _direct approach_ method discussed in the box focuses on specific sources of misallocation and quantifies their impact on productivity, typically through structural models. The _indirect approach_ discussed in the rest of the section provides a comprehensive measure of misallocation, 14
increasingly competitive and changing domestic business environment and the effects of the regional financial crisis—both in leading Southeast Asian nations to reduce spending and investment and leading to a reduction in the cost of imports—might be leading to a higher mortality rate among private firms. It would also make sense that the ease with which one can now register an enterprise has led entrepreneurs to do so earlier than they used to, and sometimes to do so before having secured financing, equipment, or markets. As noted earlier, further research to identify which causes are most important would be of value to policy makers.

48. The most encouraging finding of this report is the strength with which employment is now growing in the domestic formal private sector. From 1995 to 2000, both the absolute number of people working for private companies and the formal private sector’s share of total employment have more than doubled. Over the same period, formal private sector employment grew at an impressive annual rate of 19 percent. If 1999 figures are to be believed, 2000 saw the private sector create almost 250,000 new jobs—an increase of nearly 45 percent.<sup>33</sup> It can be asserted with more certainty that the years 1999 and 2000 saw very impressive average annual labor growth of nearly 30 percent. Over the 1995-2000 period, state sector employment meanwhile grew at comparatively modest rate of 5.4 percent a year. As noted earlier, it is furthermore likely that employment in private companies is significantly under-reported.

**Table 5.5 Distribution of Labor Force, 1996 and 2000**

||1996|2000|
|---|---|---|
|Total Labor Force|35,866,175|38,643,100|
|Public Sector|8.9%|8.8%|
|- State Enterprises|5.2%|5.2%|
|- State Administration|3.6%|3.6%|
|Non-State|91.1%|91.2%|
|- Domestic Private|90.2%|90.0%|
|- Households and Farmers|89.1%|87.9%|
|- Non-agricultural|8.1%|8.1%|
|Household enterprises|||
|**- Private Companies**|**1.1%**|**2.1%**|
|FDI Enterprises|0.6%|0.6%|
|Collectives|0.3%|0.6%|

Source : GSO, 2001

# **Is the Formal Private Sector Diversifying Geographically?**

49. Private companies thrive not only in a good policy environment, but also when they are in the company of other dynamic private companies that can help provide needed services and to drive competition. As such, it is not surprising that private companies are often found in clusters. Given the large income differences seen in Vietnam across regions and between cities and the countryside, as well as the troubling problem of

> 33 Statistics gathered by CIEM through local DPIs shows creation of about 450,000 new jobs in 2000 and 2001.  These statistics, however, assume that all registered firms are indeed new firms, rather than re-registering firms.  These figures also do not include employment creation by existing private companies.  Significantly, half of all new jobs created by new firms were located in Ho Chi Minh City.

24
**Figure 1 – The share of total expenditure on subsidized products (left-panels) and the amount of subsidies per capita (right-panels) by percentile.**

<!-- Start of picture text --> 400<br> Sugar<br>.03 Flour<br> Sugar<br> Flour<br>300<br>.02<br>200<br>.01 100<br>0<br>0 .2 .4 .6 .8 1<br>0 Percentiles (p)<br>0 .2 .4 .6 .8 1<br>Percentiles (p)<br>.025 Gas LPG Gas LPG<br>Gasoline 4000 Gasoline<br>Diesel Diesel<br>.02<br>3000<br>.015<br>2000<br>.01<br>1000<br>.005<br>0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Percentiles (p) Percentiles (p)<br>.035 1000<br>.03 800<br>.025 600<br>.02<br>400<br>.015<br>200<br>.01<br>0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Percentiles (p) Percentiles (p)<br><!-- End of picture text --> Source: Authors’ estimations from household budget survey data.

11
Policy Research Working Paper 10444

# **Abstract**

This paper sets out to measure and analyze corruption risks, patterns of favoritism, and state capture in public procurement in Bulgaria. It draws on two main types of data: large-scale administrative data on public procurement and the list of politically exposed persons. The analysis rests on calculating individual corruption risk indicators (or red flags), such as single bidding in competitive markets, and creating a composite Corruption Risk Index based on these indicators. It maps the distribution of these red flags over time, across different regions and markets. The analysis finds that Bulgaria shows high corruption risk among other examined countries in the European Union, with weak institutions contributing to slow gross domestic product per capita convergence to Western European countries. The results point out that corruption risks have deteriorated over time. Combining suppliers’ political connections information with public procurement corruption risk data shows that connections are associated with higher risks, in particular connections to local government members and state-owned enterprises. The large-scale analysis of buyer-supplier contracting networks points at state capture patterns where groups of buyers and suppliers repeatedly connect in high corruption risk procurement contracts. Such groups have gained more power and control over a larger share of contracts since 2016. Finally, policy recommendations are provided in three areas: enhancing data scope and quality, introducing a data-driven approach to corruption risk assessment, and improving public procurement policy and practices to reduce noncompetitive tenders.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at misi.fazekas@gmail.com.

_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
is a lot more variation in the international sample compared to the sample of counties in the US.

Table 3: International Regressions of Global Tropical Cyclone Damages and Fatalities

|Impact|Constant|Log(Income)|Log(Population<br>Density)|Adj Rsq/<br>F Stat|N|
|---|---|---|---|---|---|
|Damages|15.17|0.415|-0.21|0.066|807|
||(22.77)|(6.44)|(3.04)|29.6||
|Fatalities|6.25|-0.477|0.07|0.158|1089|
||(18.20)|(14.01)|(1.86)|103.2||

Source: EMDAT 2009. The t statistics are in parenthesis. The functional form of the regression is log log.

The results in Table 3 also reveal that fatalities fall with income. The elasticity implies that a doubling of income reduces fatalities by 50%. Fatalities increase with population density but only slightly. A doubling of population density increases fatalities by only 7%. However, both the income and population density coefficient are significantly different from 1. That is, the 95 percent confidence interval for the income fatality coefficient lies between -.48 and -.47 and the confidence interval for the population density coefficient lies between -.01 and 0.22. The data suggest wealthier people protect themselves from tropical cyclones and that the risk per person falls as populations become more urbanized. Note that there are more storms that cause fatalities in the international data set than cause damages. This is because the international data set has a minimum damage requirement before storm damages are reported. It is not likely that this will bias the results because the bulk of damages done by tropical storms are caused by the most powerful storms.

We rely on the US regressions for the storm intensity coefficients. We rely on the international coefficients for the income and population density coefficients. We then calibrate the damage function for each country so that current baseline and current climate lead to observed national damages: 13
Policy Research Working Paper 10108

# **Abstract**

Agricultural productivity is hindered in smallholder farming systems due to several factors, including farmers’ inability to meet crop-specific soil requirements. This paper focuses on soil suitability for maize production and creates multidimensional soil suitability profiles of smallholder maize plots in Uganda, while quantifying forgone production due to cultivation on less-than-suitable land and identifying groups of farmers that are disproportionately impacted. The analysis leverages the unique socioeconomic data from a subnational survey conducted in Eastern Uganda, inclusive of plot-level, objective measures of maize yields and soil attributes. Stochastic frontier models of maize yields are estimated within each soil suitability class to understand differences in returns to inputs, technical efficiency, and potential yield. Only 13 percent of farmers are cultivating soil that is highly suitable for maize production, while the vast majority are cultivating only moderately suitable plots. Farmers cultivating highly suitable soil have the potential to increase their observed yields by as much as 86 percent, while those at the opposite end of the suitability distribution (with marginally suitable land) operate closer to the production frontier and can only increase yields by up to 59 percent, given the current technology set. There is heterogeneity in potential gains across the wealth distribution, with poorer households facing more heavily constrained potential. Assuming no change in technologies and management practices used by Ugandan farmers, there are limited economic gains tied to closing suitability class-specific productivity gaps, or even at the extreme reaching the average potential productivity levels observed in the high suitability class.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at sgourlay@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
Table I.2. Robustness Analysis for the Consumption Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.389***|0.445***|0.459***|0.366***|0.467***|0.428***|0.389***|0.445***|0.459***|0.366***|0.467***|0.428***|
||(0.129)|(0.173)|(0.158)|(0.118)|(0.152)|(0.135)|(0.129)|(0.173)|(0.158)|(0.118)|(0.152)|(0.135)|
|Obs. left|472|292|390|467|350|392|472|292|390|467|350|392|
|Obs. right|1,025|551|802|1,686|1,241|1,574|1,025|551|802|1,686|1,241|1,574|
|Mean values (Non-RAMV)|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|0.481***|0.354**|0.480***|0.419***|0.455**|0.418***|0.592***|0.825***|0.650***|0.558***|0.708***|0.582***|
||(0.181)|(0.178)|(0.186)|(0.154)|(0.181)|(0.158)|(0.218)|(0.289)|(0.246)|(0.175)|(0.242)|(0.184)|
|Obs. left|476|465|465|476|463|465|476|465|465|476|463|465|
|Obs. right|1,036|981|981|1,979|1,482|1,874|1,036|981|981|1,979|1,482|1,874|
|Mean values (Non-RAMV)|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|
|_Panel C: Polynomial of order tw_|_o_||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|0.570**|0.758**|0.669**|0.642***|0.674***|0.646***|0.658**|0.210|0.650***|0.379*|0.276|0.418**|
||(0.227)|(0.370)|(0.275)|(0.225)|(0.257)|(0.227)|(0.260)|(0.226)|(0.246)|(0.198)|(0.215)|(0.191)|
|Obs. left|531|360|446|380|360|375|531|360|465|380|360|375|
|Obs. right|1,154|657|895|1,839|1,786|2,082|1,154|657|981|1,839|1,786|2,082|
|Mean values (Non-RAMV)|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|1.373|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.