rafmacalaba/datause-encoder-data huggingface dataset:
(registered) firms with 5 or more employees. The surveys cover manufacturing and services firms excluding extractive industries and agriculture. The survey is implemented using stratified random sampling with location (within country), sector, and size as the strata. Respondents of the surveys are for the most part owners or top managers of the firms. The use of a standardized survey instrument and methodology allows for comparisons across economies. The surveys cover a range of topics including corruption, access to finance, infrastructure, competition, crime, labor, business environment obstacles and firm performance.<sup>2</sup> A female-managed firm is defined as a firm where the top manager is female.<sup>3</sup> We focus on female management, rather than ownership, because it is a well-defined concept even for firms with multiple owners or shareholders, and because previous studies have argued that female decision-making is more strongly associated with productivity than female participation in ownership (e.g. Aterido and HallwardDriemeier 2011; Hallward-Driemeier 2013).

Table A1 provides the percentage of female-managed firms by country in the sample. The economies in the sample with the highest percentage of firms with top female managers are Thailand (67 percent) and Cambodia (60 percent), followed by the República Bolivariana de Venezuela (50 percent). On the other end of the spectrum are the Republic of Congo, Guinea, and Madagascar, which have no firms with female top managers in the sample. The average for the whole sample including all countries is 18 percent.

# _Mean differences between female-managed and male-managed firms_

Table 2 provides descriptive statistics and means tests of several variables between female-managed and male-managed firms. The outcome variable for the analysis is labor productivity, defined as sales per worker. There is a statistically significant (at 5 percent) unconditional gender productivity gap, with labor productivity being approximately 11 percent lower among female-managed firms than among malemanaged firms.<sup>4</sup> This is higher than the gender gap found by Hallward-Driemeier for the formal sector

> 2 Details of the methodology can be accessed through the following link: http://www.enterprisesurveys.org/methodology.

> 3 The survey question is “Is the Top Manager female?” and an affirmative response classifies the firm as femalemanaged.

> 4 The US dollar equivalent in average log sales per worker is 17,500 (9.77 in logs) for female-managed firms and 19,732 (9.89 in logs) for male-managed firms.

7
Panel D is constructed similarly, but it is instead taken from a private post-job survey conducted for oDesk company contacts. The results in both panels indicate that there are no performance differences for diaspora-based contracts relative to their peers. Effects are very small in economic magnitude and not statistically significant. The appendix shows that this null result holds regardless of whether employer search is used and for four other measures of performance: obtaining a wage rate increase on the contract, being hired again on oDesk, being rehired by the same company contact, and the worker’s wage rate on the next contract that he or she signs.

We interpret these results as suggesting that workers in India operate in a competitive environment where they are paid market rates, regardless of whether or not a contract is diaspora-based. These results have strong implications for our four hypotheses of what determines initial location choice. First, they are consistent with taste-based preferences existing on the part of company contacts, but they are not consistent with significant levels of taste-based preferences among workers in India. Second, the null results for performance, especially the lack of rehiring of workers, do not align with stories about ethnic Indians having special information advantages or screening capabilities for workers in India. Similar to observable traits examined in Table 6, the future performances of the hired workers are not different for ethnic Indians. Third, the very small wage declines suggest that greater bargaining power by ethnic Indians in their home region is not likely. Finally, the overall lack of wage or performance consequences is not consistent with explanations related to productivity advantages of ethnic collaborations.

Table 8 next repeats this analysis, but instead uses variation across contracts initiated by ethnic Indians living outside of India. Conceptually, this analysis shifts from the worker’s perspective to that of the hiring ethnic Indian. This sample provides a nice contrast with Table 7. This analysis is also informative about whether ethnic Indians have a general “technology” advantage in their work that is being captured in our study of their connections to India, but would also be reflected if we looked at other locations. The wage effects in Table 8 are much stronger than in Table 7 and suggest that ethnic Indians pay about 7.5% less when outsourcing to India than to other locations. Interestingly, we also see some suggestive evidence of performance declines compared to other locations, but these shifts are much smaller than the wage effects. Comparing Tables 7 and 8 suggests that the advantages of the diaspora connection, at least using 19
economywide performance of employment and output in the manufacturing sector is not driven by large firms in the formal sector. Instead, it appears that the growing share of small, less productive firms is dragging down the economywide productivity performance of the manufacturing sector.

To determine whether this interpretation applies to other African countries, we turn to the UN Industrial Development Organization data, which we use to compute growth in real output per worker, real value added per worker and real exports to gauge the health of the formal manufacturing sector. The results of this analysis are presented in Table 1. The data are organized into four columns: employment growth, growth in real output per worker, value added per worker, and exports. The data coverage across the countries of Africa is admittedly sparse, but we do have information for the six countries with at least 100,000 workers in the formal manufacturing sector. We also include four Asian comparator countries: Bangladesh, Lao PDR, Sri Lanka and Vietnam.

In considering Table 1, first notice that growth in real value added per worker (or output) is positive in all the African countries. This is consistent with what we reported for Ethiopia and Tanzania and suggests that the poor productivity performance of economywide manufacturing is not a result of subpar performance in the formal manufacturing sector. Second, apart from Ethiopia and Kenya, employment growth in formal manufacturing is considerably weaker in Africa than in Bangladesh, Lao PDR and Vietnam. This gap is despite the fact the levels of manufacturing employment are considerably higher in the Asian comparator countries; for example, total formal manufacturing employment was around 300,000 in Ethiopia in 2017, while it was more than 6 million in Vietnam.

The upshot of these comparisons is that the formal manufacturing sectors in the African countries for which we have data appear to be performing reasonably well. This evidence is consistent with a large literature of on the productivity of Africa’s larger manufacturing firms.<sup>2</sup> But the formal manufacturing sector in Africa, unlike their counterparts in Asia, does not appear to be absorbing significant amounts of labor– at least not yet -- except perhaps for Nigeria. Teal (2016) succinctly describes the issue in Ghana as follows: “It is the inability of larger firms, particularly those employing more than 100, to grow in numbers and employment that needs to be explained if the inability of Ghana to produce more productive jobs in its manufacturing sector is to be understood.” As a step towards a

> 2 For example, John Sutton in his Enterprise Maps argues that the reasonable performance of manufacturing firms serving domestic markets can be explained by the discipline imposed by having to compete with imports.  And _Made in Africa: Learning to compete in industry_ (2016) demonstrates the relatively high productivity of large African firms compared to firms with fewer than 10 employees.

10
# **Appendix 1: Timeline**

August 2013: Program launched October 1, 2013 – December 26, 2013: Online application process for interns December 29, 2013 – January 5, 2014: Screening for eligibility January 2014: Random selection of potential interns for training February-March 2014: two-day basic skills training March-May 2014: matching of treatment group interns to firms, and interviews by firms March-June 2014: start of internships September 2014: Houthi rebels take control of capital of Sanaa, UN peace deal announced in which they agree to withdraw once national unity government is formed.

August-December 2014: end of internships January-February 2015: Houthis seize state TV; President Hadi resigns and flees to Aden; Houthis take charge of government March 2015: Follow-up survey conducted March 2015: Islamic state carries out suicide bombings (March 20), Saudi-led coalition launches air strikes and imposes naval blockade (March 26), clashes in Aden and other cities, by April widespread conflict.

# **Appendix 2: Pooled Estimates**

## **Appendix 2: Pooled ITT Impacts Post‐Intervention in Panel**

||Worked<br>at all|Hours<br>worked<br>per week|Average<br>monthly<br>earnings|Truncated<br>monthly<br>income|
|---|---|---|---|---|
|Assigned to Intern Program|0.065|4.730**|1950|1776|
||(0.047)|(2.361)|(2350)|(2243)|
|Feb 2015 dummy|0.015|0.231|‐23400***|‐22300***|
||(0.022)|(1.149)|(2426)|(1998)|
|Sample Size|918|918|906|906|
|Control Mean|0.484|19.167|18663|18418|

Notes: Robust standard errors in parentheses, after controlling for strata fixed effects. and clustering at the individual level.

*, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

20
contaminated water attended fewer days of school per year compared to those who drank arsenic-free water. The study further found that young adults (aged 19-21 years) who relied on arseniccontaminated water had six months less schooling, on average, compared to their counterparts who drank arsenic-free water (Murray & Sharmin, 2015). On productivity, one study estimates that lowering the concentrations of retained arsenic among Bangladeshi prime-aged males to those levels found in unaffected countries would increase earnings by 9 percent (Pitt, et al., 2012).

Although there is significant evidence on the damaging effects of arsenic on cognitive function and educational attainment, the bulk of the literature notes these effects among adolescent-aged children and young adults, with relatively little focus on very young children. Yet, exposure to arsenic could be most harmful during the initial critical windows of growth when the developing brain is most sensitive (Rice and Barone, 2000; Kile, et al., 2016; von Ehrenstein, et al., 2007). Therefore, reduced cognitive and educational attainment in later childhood and adulthood could be an accumulative result of arsenic exposure during in utero and early childhood. Before a child even sets foot in school, he or she may already have vastly different cognitive abilities than his or her classmates, which may have significant implications for future individual health, education, and livelihood.

In this paper, we seek to measure the magnitude of the effect of varying levels of arsenic concentration in drinking water on early childhood development skillsets in children aged 3-5 using nationally representative household survey data. We ask: does arsenic exposure through drinking water lead to relatively poorer performance in skills related to healthy early childhood development? Such an effect would be consistent with evidence on the impact of arsenic poisoning on intellectual function and educational attainment and further motivate the prioritization of arsenic mitigation in Bangladesh for not only water and health policy, but also for early childhood development and education policy. We additionally attempt to determine the particular types of skills that arsenic poisoning can inhibit. We specifically examine the effects of arsenic on physical, social-emotional, literacy-numeracy, and learning skills domains and observe differences in effects experienced by the poor and non-poor.

We present an econometric analysis using cross-sectional data from the 2012-2013 Bangladesh Multiple Indicator Cluster Survey (MICS). To examine early childhood development, we analyze scores from the Early Childhood Development Index (ECDI), a novel 10-item module developed by UNICEF to monitor performance indicators for basic skills related to good early childhood development. In measuring childhood exposure to arsenic, we use MICS results from arsenic testing at a child’s household water source used for drinking. We quantify arsenic exposure in three ways: 1) based on the WHO guideline for safe levels of arsenic for human consumption of less than 10 parts 3
resulting from changes in national trade restrictions as simply the negative of the consumptionweighted global average of the T<sup>�</sup> i’s. To avoid dealing with interaction terms, we convert all proportional changes into log-change form and decompose the change in the domestic price in country i into the change in the world price and in the country’s own protection: Equation 3 We are particularly interested in what the percentage change in the domestic price would be in each country if all countries had refrained from insulating. This is obtained by estimating the change in the international price that would result from all countries reversing their insulating actions, plus the change from reversing country i’s own interventions. The proportional change in p from its initial level can be recovered by recalling that, if we set the initial price level at unity, Equation 4 ∆ln p= ln(1 + p�) If, for instance, all countries reduce the power of the protection rate applied in their market by 10 percent, the world price will rise by 10 percent. This means that a country that changes its national protection by less than the weighted average global change in trade taxation will experience a change in domestic prices that is larger than it would have experienced absent insulation by all countries.

Estimates of the impacts of price-insulating behavior on international prices are reported in the first column of Table 3.<sup>3</sup> These are derived from annual protection changes estimated

> 3 To test the sensitivity of those results, column 2 of Table 3 reports the results if the supply elasticity is as high as unity instead of zero in each country and the common demand elasticity is set at -0.2. Clearly, using these different demand and supply elasticity assumptions makes little difference to the estimates (assuming still that those elasticities are uniform across countries). This result is not unexpected given that net trade is a small share of

8
Table 5: Specialization/diversity levels for industries based upon district locations

|||S|pecializatio|n||Diversity||
|---|---|---|---|---|---|---|---|
|||1989-|2000-||1989-|2000-||
|NIC|IndustryDescription|1994|2010|Change|1994|2010|Change|
|15|Food products and beverages|1.37|1.34|-0.02|1.39|1.59|0.13|
|16|Tobacco products|0.84|1.10|0.27|1.37|1.51|0.10|
|17|Textiles|0.94|0.86|-0.09|1.39|1.59|0.13|
|18|Wearing apparel; dressing and dyeing<br>of fur|1.39|13.97|1.64|1.40|1.76|0.23|
|19|Leather; luggage, handbags, saddlery,<br>harness and footwear|1.71|1.48|-0.14|1.41|1.61|0.13|
|20|Wood and wood products, except<br>furniture; straw and plating|1.26|59.25|1.92|1.39|1.65|0.17|
|21|Paper and paper products|2.07|24.05|1.68|1.40|1.56|0.11|
|22|Publishing, printing and reproduction of<br>recorded media|1.30|1.07|-0.20|1.39|1.58|0.13|
|23|Coke, refined petroleum and nuclear<br>fuel|4.99|5.38|0.08|1.37|1.53|0.11|
|24|Chemicals and chemical products|1.32|1.04|-0.23|1.39|1.58|0.13|
|25|Rubber and plastic products|1.49|1.22|-0.20|1.40|1.57|0.11|
|26|Other non-metallic mineral products|1.41|1.40|-0.01|1.39|1.58|0.13|
|27|Basic metals|0.95|1.67|0.55|1.39|1.56|0.12|
|28|Fabricated metal products, except<br>machinery and equipments|1.32|1.08|-0.20|1.39|1.58|0.13|
|29|Machinery and equipment, n.e.c.|1.40|1.15|-0.20|1.39|1.58|0.13|
|30|Office, accounting and computing<br>machinery|6.70|97.93|1.74|1.38|1.62|0.16|
|31|Electrical machinery and apparatus,<br>n.e.c.|1.60|1.34|-0.18|1.41|1.58|0.11|
|32|Radio, television, and communication<br>equipment and apparatus|5.10|31.57|1.44|1.43|1.46|0.02|
|33|Medical, precision and optical<br>instruments, watches and clocks|3.03|3.06|0.01|1.42|1.60|0.12|
|34|Motor vehicles, trailers and semi-<br>trailers|2.18|1.82|-0.18|1.43|1.61|0.12|
|35|Other transport equipment|1.86|1.96|0.05|1.43|1.59|0.10|
|36|Furniture, manufacturing n.e.c.|1.02|1.11|0.08|1.39|1.59|0.13|
||Traditional|1.31|1.34|0.02|1.39|1.48|0.06|
||Modern|2.08|2.06|-0.01|1.40|1.48|0.05|

Notes: See Table 1a. "n.e.c." stands for Not Elsewhere Classified. Change is measured in relative terms to average values across 1989-2010. "Modern" industries are comprised of the following (NIC 98 2-digit): 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 35. Average values for Traditional/Modern are calculated by aggregating all plants in these industries before indices are measured.
Table 10: Correlates of Politicians’ Integrity

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||All|Chaiperson|Contenders|Councillors|
|Voter Attachment to National Party - 2011|0.304|2.720*|-1.341|0.290|
||(0.498)|(1.468)|(1.218)|(0.584)|
|Average Herfndahl index - Councilor|-0.017**|-0.047**|-0.002|-0.014*|
||(0.007)|(0.019)|(0.018)|(0.008)|
|Herfndahl index - Chairperson|-0.003|0.005|0.004|-0.005|
||(0.004)|(0.012)|(0.012)|(0.005)|
|Number of radios (HQ)|0.008|-0.106*|0.076*|-0.007|
||(0.020)|(0.056)|(0.041)|(0.025)|
|Poverty rate 2013|-0.638|-1.047|0.112|-0.723|
||(0.473)|(1.158)|(1.433)|(0.548)|
|Log Total Population|-0.016|-0.065|-0.054|-0.024|
||(0.096)|(0.287)|(0.294)|(0.110)|
|Urban population share|-1.614**|0.736|-2.489|-1.377*|
||(0.706)|(2.035)|(1.742)|(0.833)|
|Average Score UCE - 2011|0.010|0.062|-0.011|0.017|
||(0.020)|(0.049)|(0.050)|(0.024)|
|District Age 10-19 years|-0.203|-0.642|0.072|-0.250|
||(0.159)|(0.405)|(0.319)|(0.201)|
|District Age <= 10 years|-0.022|-1.064**|0.333|0.032|
||(0.135)|(0.408)|(0.297)|(0.168)|
|Female|-0.038|1.271**|0.437|-0.028|
||(0.086)|(0.519)|(0.336)|(0.093)|
|Age in years|0.012***|0.004|0.010|0.014***|
||(0.004)|(0.014)|(0.009)|(0.005)|
|Household members|-0.010|-0.003|-0.004|-0.021*|
||(0.008)|(0.017)|(0.015)|(0.012)|
|Less than College|0.041|-0.963|0.018|0.024|
||(0.107)|(0.713)|(0.430)|(0.117)|
|Complete College|0.064|-1.259*|0.119|0.107|
||(0.119)|(0.634)|(0.389)|(0.134)|
|Masters/PhD|0.329**|-0.970|0.366|0.166|
||(0.162)|(0.701)|(0.447)|(0.257)|
|Asset index - total|0.010|0.230|-0.040|0.006|
||(0.042)|(0.189)|(0.106)|(0.050)|
|Constant|-0.020|-1.462|1.680|-0.320|
||(1.545)|(4.015)|(3.967)|(1.877)|
|Observations|768|82|181|503|
|R-Squared|0.142|0.510|0.173|0.152|

_Note:_ This table reports regressions using the Integrity Index as explanatory variable, with the sample restricted to politicians. Column (1) includes all politicians, while columns (2) - (4) restrict the sample to Chairpersons, non-elected Contenders and Councillors, respectively..

61
5

# 2.  A New Data Set on International Migration, Remittances and Poverty

Our evaluation of the impact of international migration and remittances in developing countries is based on a new data set that includes information on international migration, remittances, income inequality and poverty for 74 “low income” and “middle income” developing countries.<sup>7</sup> These countries were selected because it was possible to find relevant migration, remittances and poverty data for all of these countries since the year 1980.<sup>8</sup> Since it was not easy to assemble this data set, and data problems still plague this (and all other) studies on migration and remittances, it is useful to spell out how this information was assembled.

In the case of migration, few, if any, of the major labor-exporting countries publish accurate records on the number of international migrants that they produce. It is therefore necessary to estimate migration stocks and flows by using data collected by the main labor-receiving countries. For the purposes of this paper, the main labor-receiving countries (regions) include two: United States and the OECD (Europe), excluding North America and Asia.<sup>9</sup> Unfortunately, no data are available on the amount of migration to the third and fourth most important labor-receiving regions in the world, the Arab Gulf and South Africa.

Because of their importance to labor-exporting countries, remittance flows tend to be the best measured aspect of the migration experience. For instance, the International Monetary Fund (IMF) keeps annual records of the amount of worker remittances received by each labor-exporting country.<sup>10</sup> However, as noted above, the IMF only reports data on _official_ worker remittance flows, that is, remittance monies which are transmitted
34. **The following illustrative policy scenarios are considered** :

1. **_A uniform, globally applied CO2 price of $25 per tonne, with no special compensation for developing countries_** .<sup>27</sup> The emissions price corresponds to the medium damage scenario studied in AGF (2010) and is approximately consistent with a recent US inter-agency assessment of environmental damages per tonne (US IAWG 2010). The CO2 price is higher, but only moderately so, than the current (September 2011) emissions price in the EU ETS of about $19 (EUR 13) per tonne of CO2.<sup>28</sup> A $25 per tonne emissions price would add about US 6 cents per liter, or about 8 percent, to the price of jet fuel.<sup>29</sup> Although unlikely perhaps for the foreseeable future (not least because it provides no developing country compensation), this policy scenario still serves as a useful benchmark as it representations a  ̳worst case‘ scenario (given the emissions price) in terms of potential adverse impacts on low-income countries.

2. **_As (1) but developing countries keep all revenues from fuels taken up in their jurisdiction (through rebating of receipts from taxes or allowance auctions)_** _._ In terms of potential revenue for climate finance, this policy scenario is very similar to scenarios when developing countries are either exempt from the pricing regime or could join voluntarily (but keep revenues they collect). The environmental effectiveness of the policy would be somewhat weaker in these latter cases, however.

3. **_As (2) but with an emissions price of $15 per tonne of CO2_** _._ This policy could reflect a scenario with lower assumed environmental damages. Alternatively, from a climate finance perspective, it could approximate a case when countries agree on a minimum price floor, but with individual countries free to impose higher prices (through higher taxes or more stringent trading schemes) and keep extra revenues for themselves.

4. **_As (1) but with a CO2 price of $40 per tonne_ .** This policy reflects a higher scenario for environmental damages or more aggressive target for climate stabilization. For practical purposes however, this scenario is even less realistic than policy (1), given the tension between aggressive pricing and reaching international agreement (the price level is about double the prevailing price in the EU ETS).

# **B.   Environmental Effectiveness**

35. **A price of $25 per tonne of CO2 might reduce travel demand by 2–4 percent below baseline levels** . An 8 percent increase in aviation fuel costs would add about 2 **–** 4

> 27 All prices are expressed in year 2011 units.

> 28 See www.pointcarbon.com.

> 29 Combusting a litre of jet fuel produces 0.0025 tonnes of CO2 emissions (EIA 2011a).

24