rafmacalaba/data-use-mentions-extended huggingface dataset:
2,002|1,978|1,978|
|R<sup>2</sup>|0.243|0.244|0.264|0.264|0.321|0.321|
|_δpv −βpv_||0.299||2.138||0.0213|
|S.E.||0.201||1.327||0.887|

_Notes_ : * p _<_ .1, ** p _<_ .05, *** p _<_ .01. Household-level data. All columns include fixed effects for if a household was located within 10 km. of a population center with at least 20,000 inhabitants, the state-level administrative center, and a market. S.E.s allow for clustering at the level of enumeration area (EA). Borno state is excluded.

33
development related indicators. The dependent variables thus are just a linear combination of 3 related independent variables, including θw .

in which, Y is the outcome variables, X is a set of covariates and is the stochastic error term.

# **3. Data and Key Variables**

# **3.1 Data**

This study uses the Pakistan Social and Living Standards Measurement Survey (PSLM) 2005-06 to analyze the impact of women’s decision-making power on household expenditures, the composition of caloric availability, and child education. PSLM is a large household survey on a range of social issues, including education, health, immunization, women’s decision making, pre/postnatal care, and household consumption. The module on women’s decision making applies only to married women age 15 through 49; thus this study can use only a subset of the whole sample.

More specifically, this study uses two samples to understand three different dimensions of human development in relation to women’s decision-making power in Pakistan. The first sample is at the household level, restricted to households that include married women age 15 through 49; the dependent variables are budget share controlled by each household head and caloric availability, including calories from different sources. The sample size is 7,938 households. The

> 3 Some recent literature (eg. Felkey A. J.  (2005) and Basu K. (2006) and Lancaster et. al. (2006)) shows that the relationship between women’s decision-making power and household outcomes is neither linear nor monotonic. Though the non-parametric graphical analysis in this paper shows non-monotonic relationship for a couple of outcome indicators, the parametric analysis assumes that the relationship is linear and monotonic.

7
To measure _SP_ we use the household registry of each entity with records on date of each household membership and location. We match the number of households registered in _SP_ by municipality and quarter obtained from the administrative data with the panel data. Figure 2 shows the trend in the number of households and individuals with _SP_ .

Figure 2. Number of Individuals and Households with _Seguro Popular_ <!-- Start of picture text --> 2002q3 2003q3 2004q3 2005q3 2006q3 2007q3 2008q3<br>date<br>individuals with SP (millions) households with SP (millions)<br>Source: Seguro Popular Registry for municipalities in Labor Surveys<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> In turn, figure 3 shows the share of municipalities that have rolled out _SP_ using different thresholds (at least 5 households, 2 percent and 5 percent of the population in the municipality covered). The program started being deployed in a few municipalities by 2002 and its coverage went steadily up over the years. By 2005, about 60 percent of the municipalities had at least 5 people enrolled and in 40 percent of the municipalities coverage was above 2 percent of the population. By 2009, the program is present in all municipalities, with more than 5 percent of the population covered.

14
p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, conditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

57
# **5 Summary and conclusions**

“Why doesn’t capital flow from rich to poor countries?” asked Robert E. Lucas, Jr. One view is that, precisely because emerging markets lack capital, many highly profitable, investable, and commercially viable projects fail to receive financing owing to frictions that impede the flow of capital across international markets. Such frictions could include government regulations, limited banking sector development, or rich country investors’ misperceiving investment in poor countries to be riskier than it actually is. A counter view attributes lack of capital in poorer countries to poor (or extremely poor) risk-adjusted returns to investing. A major challenge to distinguishing between these hypotheses has been the lack of data that would enable a systematic evaluation of risk-adjusted returns to private equity markets over long periods of time and across many countries. In the presence of idiosyncratic country shocks, only with data for many years and many countries is it possible to test for persistent differences in returns between rich and poor countries.

Data from the IFC portfolio on the return to private equity across 58 years and 130 countries provide the best available evidence yet on these questions. In contrast to previous macroeconomic literature, notably Caselli and Feyrer (2007) and Chari and Rhee (2020), we find significant differences in returns across countries. Quantitatively, a less integrated market appears to be more important than economic growth for returns: a one standard deviation decrease in financial openness or banking system depth within a country is associated with a substantially greater increase in return than the corresponding association with a one percent increase in cumulative annualized real GDP growth over the life of an average investment. This finding suggests that the extent of capital market integration, rather than differences in underlying economic performance, is most useful in explaining differences in private investment returns across economies, consistent with the model elaborated in Section 2.

It is worth noting that the data do not allow us to measure the extent to which the IFC invests in businesses that otherwise would not receive funding,
- Hall, Bronwyn H. 1987. “The Relationship between Firm Size and Firm Growth in the U.S. Manufacturing Sector.” _Journal of Industrial Economics_ 35: 583–606.

- Hausmann, Ricardo, Dani Rodrik, and Andrés Velasco. 2005. “Growth Diagnostics.” John F. Kennedy School of Government, Harvard University, Cambridge, MA.

- IDB (Inter-American Development Bank). 2007. _Living with Debt: How to Limit the Risks of Sovereign Finance._ Economic and Social Progress Report. Washington, DC: IDB.

Jovanovic, Boyan. 1982. “Selection and Evolution of Industry.” _Econometrica_ 50: 649–70.

- Klapper, Leora, Luc Laeven, and Raghuram Rajan. 2004. “Business Environment and Firm Entry: Evidence from International Data.” NBER Working Paper 10380, National Bureau of Economic Research, Cambridge, MA.

19
levels of consultation with other village institutions and women in particular, and a high tendency to take illegal payments for hearing disputes. Village heads did well only in the area of making court decisions publicly available.

We also draw on household survey data from our control villages to consider whether there is systematic evidence that village heads are (or are perceived to be) biased against villagers who share different political opinions in the distribution of food aid and the resolution of court cases. Respondents are coded as having different political opinions from the village head based on their responses to a direct question about this.<sup>11</sup>

> 9See Sokwanele (2011, p. 11).

> 10Interview with community leader in village 31729.

> 11Specifically, respondents were asked whether they agreed or disagreed with the statement “My own political views are very similar to those of the village head.” Unfortunately, even though the district authorities initially approved a questionnaire containing this question, we were asked to remove it two-thirds of the

12
While evidence for developing countries is thin, the recent World Development Report (World Development Report 2019) shows that the variance of the labor-saving effect is so large that it is hard to conclude that robots will indeed decrease the net demand for labor. Furthermore, as highlighted by Acemoglu and Restrepo (2018, 2019a, 2019b), at the aggregate level, the job displacement effects will push wages down and help introduce new tasks that are labor-intensive.

Evidence about firm- and country-level job effects from technology adoption are only available for a handful of middle-income countries. A World Bank study (Dutz, Almeida, and Packard 2018), which summarizes findings for Argentina, Brazil, Colombia, Chile, and Mexico, shows that across these economies except Brazil, ICT adoption by firms is associated with increases in total employment and in employment of low-skilled labor (Brambilla and Tortarolo 2018; Dutz, Almeida, and Packard 2018; Iacovone and Pereira-López 2018; Almeida et al. 2017; Dutz et al. 2017). This paper advances the literature by providing evidence about the effect of digital-technology adoption on factor demand across a large sample of formal manufacturing enterprises in developing countries and by identifying the channels through which factor demand is affected. The two channels are factor-saving productivity improvements and scale effects, which reflects the impact of digital-technology adoption on a customer base.

# **3 Data**

The empirics rely on panel data of manufacturing firms from the World Bank Enterprise Survey Database (WBES). The estimation sample covers 82 countries from a maximum sample of 90 countries in the six regions where the World Bank operates: Europe and Central Asia - ECA (30), Sub-Saharan Africa - SSA (27), Latin America and the Caribbean - LAC (18), East Asia and Pacific - EAP (6), South Asia - SA (6), and Middle East and North Africa - MENA (3).

8
additional recommended content as is the case on platforms like Youtube. Additionally, the training episodes were embedded in Qualtrics, and an individualized Qualtrics link was sent to each participant as a WhatsApp text message. Hosting the videos on Qualtrics allowed tracking participants’ progress and interactions with the training, which I report on below.

# **4 Sample Description**

In this section, I provide a description of the sample, including an overview of participants’ demographic information, gender norms, digital access, and business characteristics. These are summarized in table 1.

**Demographics:** The average participant is a 41 year old married woman with children. Education is relatively balanced in the sample, with close to a quarter of women having less than a high school degree, half having a high school degree, and another quarter having a college or advance vocational degree. These levels are comparable to the national average for Jordanian women, noting that women in the sample are 10 percentage points more likely to have a high school degree as opposed to no degree<sup>17</sup> , compared to the national average.

**Mobility and Gender Norms:** Baseline data suggests limited mobility among study participants. For example, 41% of women said they would be unable to take weekly trips outside of their local neighborhood to attend a training or take business-related tasks; 44% reported that they would be unwilling to operate their business outside the house, even if rent were paid for; and the average woman reported leaving the house alone less than 7 times a month. These revealed mobility patterns are consistent with respondents’ reported views around women’s work. More than 70% of study participants agreed with a statement saying that if a mother works outside the home, her children suffer. This is slightly less agreement than the national average of 84% among Jordanian women, as measured in the World Value Survey (2018). Additionally, 44% of study participants reported believing that society judges
# 1. Introduction

Climate disasters reverse gains and limit progress in efforts to reduce poverty. Global extreme poverty is increasingly concentrated in Sub-Saharan Africa (SSA) where drought is a major risk, a risk that is increasing in many parts of the continent because of climate change. Investments to reduce the impact of drought on welfare are thus essential to poverty reduction efforts in the region. Quantifying the welfare needs that arise when climate shocks occur is an important part of targeting investments to reduce their impact (Clarke and Dercon 2016). This requires vulnerability or damage functions that relate losses in welfare to climate conditions (Auffhammer 2018). While these are widely available for the impact of floods and cyclones on buildings, this is not the case when it comes to quantifying the impact of drought on household consumption in Sub-Saharan Africa.

This paper pilots a regional approach to estimating vulnerability functions for drought and consumption using methods widely employed in the microeconomic literature (Dell, Jones, and Olken 2014). The analysis uses survey data from close to 100,000 households across nine countries and five livelihood zones, allowing considerable variation in climatic conditions to be utilized in estimation. The result is stable estimates that are not driven by any one climate event. We find that consumption is reduced by 10-20 percent in the worst weather observations in our surveys (corresponding to about a 1 in 10-year drought event). We use the estimated vulnerability functions and the full hazard distribution in each household location to calculate the risk drought poses to welfare outcomes. The results indicate that risk is large. Poverty is 1–12 percent higher under the worst weather conditions relative to the best conditions observed in the past 13 years. This amounts to an increase in the total national poverty gap that ranges from US$4 million to US$2.4 billion (2011 PPP) across countries. To our knowledge, these are the first crosscountry estimates of the poverty impact of drought in Africa.

There are several ways in which damage functions for rainfall deficits have been
and Cadastral Geographic Information System of the National Interconnected System ( _Sistema De Informa ̧c ̃oes Geogr ́aficas Cadastrais Do Sistema Interligado Nacional, SINDAT_ ) of the Brazil’s Operator of the National Electricity System ( _Operador Nacional do Sistema El ́etrico, ONS_ ). As in Lipscomb, Mobarak, and Barham (2013), we define electricity access in a given year as the share of municipality area located within 50 kilometers of a transmission line, distribution substation, or autonomous power plant.

Table 1 shows variable means for population, gross-valued added, and capitallabor ratio across municipalities, overall and for the industry sector, in the study’s four periods.

> 5See `www.ipeadata.gov.br` .

> 6More precisely, the road segment is defined as one of the following: ‘Pavimentada’,‘Duplicada’, and ‘Em obra de Duplica ̧c ̃ao’.

> 7This includes the state of Tocantins and its capital, Palmas, even though Tocantins had not not yet been created in 1970.

> 8Radial highways include road segments with labels ‘010’, ‘020’, ‘030’, ‘040’, ‘050’, ‘060’, ‘070’, ‘080’, ‘090’. Coastal highways include segments with labels ‘101’ and ‘116’.

5
extended labor force, or 11.4% (respectively, 12.8%) of the population aged 15+. The informality ranking of the four European regions remains unchanged: the highest prevalence of informal employment is found in the South (more than one quarter of extended labor force); in the West and in the East this proportion is one sixth, whilst in the Nordic countries it is between one ninth and one eighths (Table 2, panel B, right). Note these are _lower bound estimates_ , because respondents employed during the survey were not asked about their past activities.

Figure A1 (in Annex), based on results of ESS rounds 2, 3 and 4, summarizes main findings on the prevalence of informal employment in the four European regions for the whole period between 2004 and 2009.<sup>12</sup> Overall size of informal employment decreases from the South to West to East to North, but the median prevalence of dependent informal employment is higher in the East than in the West. For each of these country groups, the median (across space and time) level of informal self-employment is higher than that of informal dependent employment. The East and the North are much more homogeneous in terms of informal employment than the West and the South.

> 12 Like in all Figures hereafter, countries in Figure A1 are not weighted by population size.

12
while simulations in this paper suggest that the poor earn a far greater share of the total increase in incomes from tourism.

These findings strongly suggest that the tourism sector must be seen as an important sector in the efforts of Panamanian authorities to reduce poverty in this highly dual economy. But as suggested by the global experience, poverty benefits from the tourism sector are not automatic, and whether or not they receive a significant share of the benefits depends on the way the supply chains are structured and the way tourists spend their money.

Given the importance of the tourism sector and its potential to result in large benefits for the poor, devising a set of indicators and effective monitoring schemes is of paramount importance. These indicators fall into three categories: (1) accurately measuring the flows of tourist expenditures to provinces and to the indigenous areas _comarcas_ , which was so far not possible with the available data; (2) measuring the impact of tourism on local employment and wage levels at hotels, restaurants and other tourism-related enterprises; (3) measuring the extent to which tourism establishments affect local food and beverage production and devising complementary projects to enhance agricultural productivity and improve access of local, particularly smallholder and poor farmers, to the supply chains; (4) stimulating production and availability of quality handicrafts and artisan products in areas with high potential for these industries and measuring impacts of tourism on these sub-sectors; (5) measuring availability of infrastructure availability and services to assess the degree of connectivity to markets, and the ease of travel within the country’s more peripheral areas.

The household level data and data from tourism surveys (Satellite Tourism Accounts) that are needed for monitoring local impacts of tourism growth are already available in Panama. This paper has identified the data needs in order to carry out a more comprehensive and accurate assessment of local impacts of aggregate growth in tourism expenditures—survey data at the level of tourism destinations. No data are available to help determine which specific types of employment and specific subsectors of the economy the incremental
rabbit (=1)|0.04|0.03|0.03|0.05|
|Owns pig (=1)|0.12|0.13|0.13|0.11|
|Owns chicken (=1)|0.11|0.06|0.10|0.12|
|Owns sheep (=1)|0.05|0.06|0.05|0.05|
|Owns cow (=1)|0.28|0.17|0.24|0.32|
|Livestock (TLU)|0.56|0.37|0.54|0.62|

_Source:_ VUP household survey, 2009, 2011, 2014.

17
<!-- Start of picture text --> ”<br>5<br>=<br>==<br>oS<br>5<br>@<br>So<br>N<br>8<br>s~<br>2<br>=<br>a<br>°<br>Rural Urban Total<br>HE Working-age Women HEE Employed Women<br><!-- End of picture text --> <!-- Start of picture text --> ~ So<br>58<br>ES<br>=<br>no] So<br>as<br>=<br>38<br>So<br>Za<br>3"<br>8<br>><br>BO<br>Qa<br>g<br>Se<br>38<br>ec38<br>2.<br>Eggo<br>é¢<br>: Rural Urban Total<br>WS «2009-2001 (MMM 2018-2009 [EMM 2018-2001<br><!-- End of picture text -->
sup> and the World Bank Survey on Applied Policies in Services<sup>33</sup> .

# **Barriers to establishment**

# **Form of establishment Score 0.5**

All forms of legal entities (sole proprietorship, limited liability partnerships, general partnership, individual enterprise, limited liability companies and public limited companies) are permitted for both domestic and foreign firms.

However, according to the Engineering Registration Act no. 63 Para 12: “1) No person or body of persons not citizen of the United Republic shall be registered as a local consultant or consulting firm unless–(a) in the case of natural person, he is a citizen of the United Republic; (b) in the case of a company, it is incorporated in Tanzania and the majority of its shares are owned by the citizens of the

> 31 The work on the estimation of the ad valorem equivalents of the barriers in engineering services was done by Josaphat Kweka with the guidance of Nora Dihel.

> 32 The regulatory surveys were conducted by local consultants who interviewed the professional associations in the examined East African countries in 2009.

> 33 The policy surveys were conducted by The World Bank Research Department in 2008-2009.

112
on the effects of these two dimensions are more uniform. MNC affiliates with some level of host economy participation (such as joint ventures) are commonly found to use more local suppliers. Sanchez-Martin, De Pinies, and Antoine (2015) use firm-level data from the World Bank Enterprise Survey (WBES) for a large number of developing countries and identify a negative effect of the percentage of foreign ownership of MNC affiliates on the percentage of inputs sourced in host economies. Wei et al. (2012) and Amendolagine et al. (2019) report a similar negative effect for samples of firms in China, Sub-Saharan Africa, and Vietnam.

> 6 There are recent indications that the effect of the investment motive may be weakening or becoming less clear. One issue is that the level of industry aggregation that is used in studies can affect the classification of MNC affiliates as horizontal or vertical FDI (Alfaro and Charlton 2009). Another issue is that an increasing number of MNC affiliates have both market-seeking and efficiency-seeking characteristics, subject to variation across industries and regions in the world economy (Baldwin and Okuno 2014).