rafmacalaba/datause-encoder-data huggingface dataset:
similar firms either in the region or in other regions, and whether consumers have access to that information.

In Ethiopian markets, like most of Africa, information asymmetries are widespread. Both consumers and firms must incur search costs, and sometimes other transaction costs, to access reliable information about prices, buyers, sellers, quality of products and quantities supplied or demanded. This introduces inefficiency through costs of bargaining, time and money costs of supervision and enforcement in dealing with prices. The more widespread access to price information changes customer behavior and induces manufacturing suppliers to face intensified competition. As such, their incentives are affected in a way that reduces markups and price dispersion, while also boosting productivity as well as wages and employment. The higher TFP can be a source of faster formal job creation.

The period following the rollout of 3G broadband networks coincides with a more robust growth in Ethiopia, often putting the country among the fastest growing economies in the world. Though, the adoption of broadband technologies itself could be an outcome of this fast growth, it has also played a significant role in facilitating the growth of industries and reinforcing market competition, hence productivity, particularly in the manufacturing sector. This paper examines the impact of access to 3G broadband networks on firm productivity in Ethiopia through its impact in closing gaps and information asymmetries about market prices.

# **3. Description of Information Sources and Data**

This study uses two different sources of data in estimating the effect of expanded mobile communication access in local markets on firm performance in Ethiopia: Large and Medium Manufacturing Industries (LMMI) and Household Consumption and Expenditure Surveys (HCES). LMMI is a firm-level census panel data collected by the Ethiopian Central Statistical Agency (CSA) from 2000 to 2014. The census covers all public and private manufacturing firms that have more than 10 employees and use electricity for production. The data set provides detailed information on sales, production, wages and benefits, number of employees, cost and quantity of raw materials, types of ownership, and location. The firms are categorized into the International Standard 11
# **New Estimates of Extreme Poverty for Children**<sup>**1**</sup>

_David Newhouse_ _Pablo Suarez-Becerra_ _Martin C. Evans World Bank Data for Goals Group_ _Keywords: Poverty, Children, Scales_ _JEL code: I3, O1_

> 1 David Newhouse is Senior Economist, Poverty and Equity Practice, World Bank. Pablo Suarez-Becerra is Economist Consultant, World Bank. Martin Evans is Economic and Social Policy Specialist at UNICEF. The authors can be contacted at <u>dnewhouse@worldbank.org, psuarezbecerra@worldbank.org, and mcevans@unicef.org.</u> The Data for Goals Group comprises approximately 20 staff and consultants from the World Bank’s Poverty and Equity global practice who collectively created the Global Micro Database, and is led by Andrew Dabalen, Joao Pedro Azevedo, and Nobuo Yoshida. The authors thank Kathleen Beegle, Jose Cuesta, Samuel Freije, Pedro Olinto, Frank Borge Wietzke and Maika Schmidt for their review of earlier draft and their comments and suggestions; and thank Antonio Franco Garcia for research assistance and Andrew Dabalen, Ana Revenga, and David Stewart for their support.
# _Data_

We use data from 159 Demographic and Health Surveys (DHS) from the 65 countries listed in Table 1. The DHS are particularly useful datasets for us as they cover a very large number of countries with a consistent questionnaire and survey methodology. Table 1 gives the years, sample sizes, and country of each survey. Our data contain the complete retrospective fertility histories of 1.3 million women in the 65 countries as well as relevant socio-economic information such as educational attainment, ownership of durable goods, and household location.

Frequently, we focus on the similarity or difference of patterns across geographic regions in the developing world. For this purpose, we construct regions as defined by the World Bank: Sub-Saharan Africa (AFR), Latin America and the Caribbean (LAC), South Asia (SA), East Asia and the Pacific (EAP), Europe and Central Asia (ECA), and Middle East and North Africa (MENA).<sup>6</sup> Note that the countries observed in the EAP region, namely Cambodia, Indonesia, Philippines, Thailand, and Vietnam, only comprise the smaller region traditionally referred to as Southeast Asia. In addition, the majority of countries we observe in ECA are located in Central Asia and relatively few in Eastern Europe. The countries in this group comprise: Armenia, Kazakhstan, Kyrgyz Republic, Moldova, Turkey, and Uzbekistan.

In general, we weight observations in each survey by their expansion factors, which reflect differences in the probability that households are sampled in the DHS.<sup>7</sup> When

> 6 In Latin America and the Caribbean, the list of countries is Bolivia, Brazil, Colombia, Dominican Republic, Ecuador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Paraguay, Peru, and Trinidad and Tobago; for Middle East and North Africa, Egypt, Morocco, Tunisia, and Yemen; for South Asia, Bangladesh, India, Nepal, Pakistan, and Sri Lanka; and for Sub-Saharan Africa, Benin, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Comoros, Congo, Cote d'Ivoire, Ethiopia, Gabon, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Niger, Rwanda, Senegal, South Africa, Sudan, Tanzania, Togo, Uganda, Zambia, and Zimbabwe.

> 7 When there is more than one survey for a country, we first pool all surveys and adjust the sampling weights such that each survey is equally weighted. For example, DHS surveys were administered in Cambodia in 2000 and 2005.

8
<!-- Start of picture text --> q<br>N<br>2<br>a<br>©<br>-<br>+<= PRESSR<br>CRF<br>Ns<br>= WNS<br>- Fd<br>cLR<br>[0]<br>AGR<br>©<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> a AGR<br>i]<br>N<br>2<br>oe<br>a<br>7<br>N<br>= CLR<br>MOP<br>=<br>ELM<br>©. TCHCRF<br>BAe<br>©<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> ~<br>cLR<br>CRF<br>- FRE<br>2 tf<br>eo<br>AGR<br>a<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> ©<br>~<br>AGR<br>x<br>TCH<br>- BeeARE<br>«9 PRF<br>MOP<br>©<br>s<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> wm<br>° AGR<br>ry<br>ive)<br>N<br>“ TCH<br>2<br>~<br>BAR<br>< cLR<br>ELM<br>© MOP<br>1995 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> wm<br>o<br>ry<br>©<br>N MoP<br>N<br>2<br>ELM<br>r<br>cLR<br>BBR<br>MNG<br>wW 468<br>1995 2005 2015<br><!-- End of picture text -->
|P070973|2001|2006|5/4/2006|15 Closed|Azerbaijan|
|---|---|---|---|---|---|
|P070973|2001|2007|7/13/2006|16 Closed|Azerbaijan|
|P070989|2003|2003|6/30/2003|1 Closed|Azerbaijan|
|P070989|2003|2004|6/16/2004|4 Closed|Azerbaijan|
|P070989|2003|2005|3/28/2005|6 Closed|Azerbaijan|
|P070989|2003|2006|8/15/2005|7 Closed|Azerbaijan|
|P070989|2003|2006|2/28/2006|8 Closed|Azerbaijan|
|P070989|2003|2007|2/22/2007|9 Closed|Azerbaijan|
|P070989|2003|2008|9/12/2007|10 Closed|Azerbaijan|
|P070989|2003|2008|6/18/2008|11 Closed|Azerbaijan|
|P070989|2003|2009|12/19/2008|12 Closed|Azerbaijan|
|P070989|2003|2009|6/26/2009|13 Closed|Azerbaijan|
|P070989|2003|2010|6/26/2009|13 Closed|Azerbaijan|
|P070992|2004|2004|6/30/2004|1 Closed|Bosnia and Herzegovin|
|P070992|2004|2005|4/16/2005|3 Closed|Bosnia and Herzegovin|
|P070992|2004|2006|8/4/2005|4 Closed|Bosnia and Herzegovin|
|P070992|2004|2006|3/6/2006|5 Closed|Bosnia and Herzegovin|
|P070992|2004|2020|9/18/2019|6 Closed|Bosnia and Herzegovin|
|P070995|2001|2001||0 Closed|Bosnia and Herzegovin|
|P070995|2001|2002|6/21/2002|2 Closed|Bosnia and Herzegovin|
|P070995|2001|2003|6/2/2003|4 Closed|Bosnia and Herzegovin|
|P070995|2001|2004|6/28/2004<br>|6 Closed|Bosnia and Herzegovin|
|P070995|2001|2005|3/4/2005<br>|8 Closed<br>|Bosnia and Herzegovin<br>|
|P070995|2001|2005|6/9/2005|9 Closed|Bosnia and Herzegovin|
|P070995|2001|2006|10/26/2005|10 Closed|Bosnia and Herzegovin|
|P070995|2001|2006|3/1/2006|11 Closed|Bosnia and Herzegovin|
|P070995|2001|2007|7/10/2006|12 Closed|Bosnia and Herzegovin|
|P070995|2001|2007|11/27/2006|13 Closed|Bosnia and Herzegovin|
|P070995|2001|2007|5/9/2007|14 Closed|Bosnia and Herzegovin|
|P070995|2001|2008|6/3/2008|15 Closed|Bosnia and Herzegovin|
|P070995|2001|2009|12/24/2008|16 Closed|Bosnia and Herzegovin|
|P070995|2001|2009|6/24/2009|17 Closed|Bosnia and Herzegovin|
|P070995|2001|2010|8/31/2009|18 Closed|Bosnia and Herzegovin|
|P070995|2001|2020|4/22/2020|19 Closed|Bosnia and Herzegovin|
|P070999|2005|2005||0 Closed|Madagascar|
|P070999|2005|2006|7/1/2005|1 Closed|Madagascar|
|P071001|2002|2002||0 Closed|Bosnia and Herzegovin|
|P071001|2002|2003|6/19/2003|3 Closed|Bosnia and Herzegovin|
|P071001|2002|2004|6/17/2004|5 Closed|Bosnia and Herzegovin|
|P071001|2002|2005|3/24/2005|7 Closed|Bosnia and Herzegovin|
|P071001|2002|2006|10/21/2005|8 Closed|Bosnia and Herzegovin|
|P071001|2002|2006|6/27/2006|9 Closed|Bosnia and Herzegovin|
|P071001|2002|2007|4/28/2007|10 Closed|Bosnia and Herzegovin|
|P071001|2002|2008|12/27/2007|11 Closed|Bosnia and Herzegovin|
|P071004|2003|2003||0 Closed|Bosnia and Herzegovin|
|P071004|2003|2004|5/19/2004|3 Closed|Bosnia and Herzegovin|
|P071004|2003|2005|4/7/2005|5 Closed|Bosnia and Herzegovin|
the international databases provide some of the outcome data in flows of services and some in stocks. Taking the changes in stocks (as in the case of roads) as an indication of services is, however, of limited interest because they do not necessarily reflect the services associated with the stock. Second, matching outcomes with expenditures is not as simple as it sounds. Capital expenditures are typically “oversized” in the short run because they are designed to meet long-term demand. We have no way of knowing, however, the average lead time of demand built in to the existing capital stock in any given year. There is thus a risk of overestimation of the efficiency problems simply because we are not picking up the timing issue properly. Third, the expenditure data are clearly problematic as well. To have a reasonable sample size, we need to work with central government expenditure data. We have no way of assessing correctly how much public-sector expenditure by subnational governments this assumption ignores. It should not be a major problem for infrastructure stocks because such a large portion is generally financed by the central government, not the entire government. It could be a much bigger problem for services, however, because subnational governments often take the responsibility for a fair share of those.

The relevance of the private sector’s role is also ignored by the approach followed here because nobody really monitors this role in any of the sectors.<sup>16</sup> Finally, there are very basic data issues. For many countries, we found data that did not pass some basic commonsense tests, let alone basic statistical tests, whether on expenditure or outcome. We ended up dropping many countries because of this concern about data quality. We dropped a few more after the conversion of all the monetary data into purchasing power parity (PPP) because it also created outliers. The conversion to constant prices of data on energy and fuel was particularly problematic since a large proportion of the changes in expenditure levels can be traced to prices rather than quantities.

# **3. Emerging facts on the evolution of central government expenditure**

Here we summarize the basic information on central government expenditures for the various sectors and the various country groupings. The country coverage for each subsector is shown in table 3. The distribution of countries across income groups is reasonably even, with 20–25 countries for almost each group. The sample size for low-income countries is somewhat smaller (with 16 countries), but it is still statistically significant.

> 16 WHO may have the best set of data on the role of the private sector (in health), and Calderon and Servén (2004) have the best data set, but only for investment in Latin America. There is no other quantitative crosscountry information on the relative importance of the private sector in any of the sectors covered here. There is actually very little qualitative information on this topic.

8
|**Appendix Table 5.1: Impact on Bu**|**siness Practices C**<br>Tradit|**onditional on**<br>ional Business|**Survival**<br>Practices|Verified|Verified||
|---|---|---|---|---|---|---|
||Finance &|Marketing &|Operations &|Traditional|Digital|Overall|
||Accounting|Sales|HR|Practices|Marketing|Index|
|**Panel A: Impacts in First Follow-u**|**p Survey (One-ye**|**ar after interv**|**ention started)**||||
|Assigned to Insourcing|0.096***|0.074**|0.028|0.110***|0.080***|0.066***|
||(0.031)|(0.032)|(0.018)|(0.030)|(0.024)|(0.018)|
|Assigned to Outsourcing|0.049|0.055*|-0.006|0.094***|0.093***|0.047**|
||(0.032)|(0.030)|(0.020)|(0.028)|(0.025)|(0.018)|
|Assigned to Training|0.002|-0.027|-0.010|0.018|0.012|-0.005|
||(0.032)|(0.033)|(0.019)|(0.026)|(0.025)|(0.019)|
|Assigned to Consulting|0.065**|0.009|0.011|0.080***|0.048**|0.034*|
||(0.032)|(0.032)|(0.019)|(0.029)|(0.024)|(0.019)|
|Mean of Control Group|0.646|0.602|0.493|0.231|0.227|0.485|
|Sample Size|659|659|659|659|659|659|
|P-value: all treatments zero|0.004|0.008|0.201|0.000|0.000|0.000|
|P-value: all treatments equal|0.010|0.006|0.125|0.004|0.007|0.000|
|<br>**Panel B: Impacts in Second Follow**<br>|**-up Survey (Two**<br>|**-years after in**<br>|**tervention start**<br>|**ed)**<br>|||
|Assigned to Insourcing|0.027|0.073**|0.021|0.121***|0.078***|0.049**|
||(0.031)|(0.034)|(0.018)|(0.029)|(0.024)|(0.019)|
|Assigned to Outsourcing|0.030|0.075**|0.023|0.099***|0.077***|0.052***|
||(0.031)|(0.032)|(0.018)|(0.028)|(0.024)|(0.019)|
|Assigned to Training|-0.026|-0.038|-0.006|0.034|-0.001|-0.016|
||(0.034)|(0.034)|(0.019)|(0.027)|(0.024)|(0.020)|
|Assigned to Consulting|0.064**|0.045|0.031*|0.122***|0.037|0.044**|
||(0.032)|(0.032)|(0.018)|(0.030)|(0.024)|(0.019)|
|Mean of Control Group|0.710|0.616|0.481|0.195|0.245|0.504|
|Sample Size|627|627|627|627|627|627|
|P-value: all treatments zero|0.065|0.001|0.215|0.000|0.000|0.000|
|P-value: all treatments equal<br>Notes:|0.051|0.001|0.236|0.006|0.002|0.001|

Notes: Regressions control for stratification batch fixed effects and baseline value of outcome. Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels. **Finance & Accounting** is the proportion of 10 practices in this area that the firm uses; **Marketing & Sales** is the proportion of 9 practices in this area that the firm uses; **Operations &HR** is the proportion of 11 operations and human resources practices the firm uses; **Verified Traditional practices** is the proportion of 10 of these first 30 traditional practices that we could physically verify as implemented; **Verified Digital Marketing** is the proportion of 11 practices in this area that the firm uses; and **Overall index** is the proportion of all 41 practices that the firm uses. Equality of 1 & 2 year effects tests the joint hypothesis that the 1 year and 2 year treatment effects are equal for the different treatments over time, but not necessarily equal across treatments. Estimation for sample of firms in operation at the time of the survey.

66
not adapt effectively in the face of environmental variability. For instance, cooperatives may need to ensure members some minimum level of well-being in order to retain members; this may hinder a cooperative’s ability to dramatically cut fishing effort when environmental conditions negatively impact fish populations.

In this paper, we develop a dynamic model of cooperative decision-making and use rich data from Mexico’s Gulf of California fisheries to examine how fishing cooperatives exercise property rights, with a focus on three issues: 1) Whether cooperatives with property rights manage a resource differently from those without; 2) How such differences depend on the scale of the resource; and 3) How such differences respond to predictable environmental fluctuations associated with ENSO (El Niño/Southern Oscillation) events. Mexico is a natural setting for the analysis. The country’s marine ecosystems are rich in biodiversity, which provides an opportunity to examine how fishing cooperatives shift behavior when fishing on species that vary in key traits, such as mobility. Moreover, ENSO events have important impacts on fisheries, with the direction and magnitude of these impacts differing among species.

We begin our analysis with a dynamic model of cooperative decision-making. Cooperative leadership can engage in more aggressive management by lowering the price that is paid to cooperative members for a specific species and, therefore, disincentivizing fishing of that species. The model yields three testable implications for how cooperative price and resulting catch change in response to external factors. Specifically, compared to other cooperatives, a cooperative with stronger property rights manages effort more aggressively, decreases the relative aggressiveness of management if a species is highly mobile, and restricts effort more when environmental forcing (e.g., ENSO events) limit the population growth rate of a species. Throughout, by “strong property rights” we mean the ability to exclude noncooperative fishers from the cooperative’s established fishing grounds.

We test these implications using daily logbook data from three cooperatives in the Gulf of California region, in northwest Mexico. One cooperative, operating on the Pacific coast, retains an exclusive concession for some species and is able to exclude outside fishermen for all other species (Cota-Nieto 2010; McCay et al. 2014). The other two cooperatives are located close to La Paz, the state capital of Baja California Sur (B.C.S.), and compete with other cooperatives and noncooperative fishermen for fish (Basurto et al. 2013; Sievanen 2014). Analysis using the fishing team-level logbook data reveals that cooperative members respond to cooperatives’ chosen prices as posited by the model. Exploiting the fact that one cooperative has stronger property rights than 3
Since employment information was asked at the individual and household level, both formal and informal employment are included. This is an advantage of using a household survey as opposed to a firm survey, which only consists of formal firms. Employment data are categorized to 60 two-digit sectors (the sector list is shown in Appendix A1). The two-digit sectors are grouped in five main broad sectors: Agriculture, Mining, Utility and Construction, Manufacturing and Services. Services consist of Commerce, Transport and Communication, Finance, and Other Services. VHLSS of 2002 includes 74,346 households covering the information on characteristics of households, employment and income (it is called the income module). Of 74,346 households, only 29,529 households have information on expenditure (it is called the expenditure module). This means that 29,529 households have information on both expenditure and income. Similarly, the VHLSS of 2004 contains 45,944 households with information on income and 9,189 households have information on both expenditure and income. The VHLSSs of 2002 and 2004 form a panel data set of income, covering 21,271 households appearing twice, of which 4,167 households have information on both expenditure and income. This study uses the measures of share of employed household members to total number of household members of working age, so households with no members of working age will be removed from the data set. We also control for change in share of employment at the province level between 1998 and 2002. Since VLSS 1998 covers 59 out of 61 provinces, this further reduces our observations in the data set.<sup>2</sup> Therefore, the final panel data sets include 18,957 unique households with information on income, of which 4,092 unique households have information on expenditure.

> 2 Our results remain unchanged when we do not control for change in share of employment at province level between 1998 and 2002.

9
weekly cross-sectional estimates to create the synthetic ‘market comparable’ measure which does not have as much time-series variation as the actual distance to default measure.<sup>14</sup> _4.2 The factors that affect the correlation between the parent and subsidiary distance to default_ Table 8 explores how subsidiary characteristics affect the association between the Merton distance to default of foreign bank subsidiaries and that of their parents. We find that the parent bank’s distance to default has a smaller impact on the subsidiary’s distance to default when subsidiaries have higher capital, deposit funding, and profitability ratios. Also, the association between the parent and the subsidiary distance to default is lower for countries that are physically distant or culturally closer. Most of these results survive even when we combine all variables together as in column 8.10.

How should we interpret the significance of geographical distance and cultural proximity? We view geographical distance and cultural proximity as proxies for more independent management of the subsidiary from its parents. Though, we are unable to conclusively confirm this hypothesis, we are able to offer some suggestive evidence for a subset of banks. In particular, for 47 subsidiaries, using information obtained from banks’ annual reports, we were able to construct a proxy for subsidiary management independence from the parent: the _share of declared independent board members_ (i.e., ratio of members identified as being independent because they own no or a small number of shares in the bank, are not clients or suppliers of the bank and do not have family members working in the bank). The share of independent board members interacted with the parent distance to default is positively and significantly correlated with the interaction of parent distance to default with the measures of

> 14 We have also replicated the analysis of publicly traded firms using synthetic measures of distance to default.  We also find lower correlations between the parents and the subsidiaries for this smaller sample of publicly traded banks.

16