rafmacalaba/data-use-mentions-extended huggingface dataset:
group of households receiving remittances. Airola (2008) finds a small negative correlation between remittances and labor supply in Mexico: the estimated elasticity suggests that, at the sample mean, male recipients of remittances worked 4.25 fewer hours per week.

Approaches used in the literature to address potential endogeneity range from limiting the sample to only some types of migrants, using panel or pseudo-panel data, using propensity score matching, to using instrumental variables. Instrumental variables (IV) are exogenous variables, often based on geographical variation, that are correlated with a household’s probability of receiving remittances but not correlated with its labor supply decisions. Amuedo-Dorantes and Pozo (2006b) use the location of Western Union offices in Mexico to estimate the effect of remittances on local labor supply. Similarly, Acosta (2006) uses outmigration rates at the county and village level in El Salvador as instrumental variables to predict receipt of remittances. Using a set of 11 countries in Latin America and the Caribbean, Acosta, Fajnzylber, Lopez (2009) build instrumental variables using the percentage of households that receive remittances in the country, and its interaction with household characteristics that are expected to affect the decision to migrate.<sup>16</sup> In each of these papers, addressing endogeneity through IV reduced the estimated negative impact of remittances on labor supply and in some cases, especially for men, erased it completely.

However, strong IVs are difficult to find in the context of limited data found in the Northern Triangle. Yang (2008) uses exchange rate shocks to instrument for changes in remittances, finding evidence of remittances leading to more entrepreneurship and more hours worked in self-employment in the Philippines. Taking advantage of the sampling strategy of the household survey, Kim (2007) builds pseudo-panels in Jamaica identified by sampling cluster and finds that remittances reduced labor force participation (but not hours worked). Funkhouser (2006) uses a 3-year panel from Nicaragua and finds no effect of migration or remittances on the labor supply of adults who did not
, Table 6 shows that the average underestimation rate for the treatment group plots declined by 64 percent (from 32 percent to 12 percent) while the corresponding overestimation rate only declined by about 12 percent (from 149 percent to 131 percent). These results clearly reveal asymmetric responses both at the extensive (Table 5) and intensive margins (Table 6).

**Table 6: Magnitude of information-induced changes in reporting behavior (treatment group plots only)**

||**Post-planting reporting behavior**<br>ME magnitude in<br>post-planting (%)|**Post-harvest r**<br>%Underestimation<br>in post-harvest|**eporting behavior**<br>%Overestimation in<br>post-harvest|Difference<br>between PP<br>and PH<br>|
|---|---|---|---|---|
|Overestimated in post-planting|149.1(214.9)|3.5(11.9)|131.3(222.3)|17.8<sup>*</sup><br>|
|Underestimatedinpost-planting|31.8(22.5)|11.67(18.2)|34.1(128.0)|20.2<sup>***</sup>|

Note: Values in bottom panel are mean percentage over/under-reporting in each round (standard deviations in parentheses).<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

> 16 In the pre-treatment survey, only 31 farmers accurately reported their plot size; this increases to 125 in the posttreatment survey .

19
ways. One of them, mimicking the traditional literature on growth convergence across countries, assumes that all k� indicators in a bucket have the same prior inclusion probability 1⁄k� . This helps to reduce the sensitivity of results (Rockey and Temple 2016). The other way is totally agnostic, and therefore each of the K indicators considered has an inclusion probability equal to 1⁄K . This other way is used as a robustness check.

Because our study covers hundreds or even thousands of localities, degrees of freedom are much less of a constraint than in the growth literature on which most studies of convergence have been based (Levine and Renelt 1992). The large number of observations also increases the stability of the estimated coefficients, which in the cross‐country literature have proven sensitive to small changes in the data (Ciccone and Jarociński 2010).

We also improve over the previous literature on the computational front. Until recently, computational constraints prevented the full implementation of this kind of analysis. For example, Sala‐i‐Martin, Doppelhofer and Miller (2004) ran 100,000 regressions at a time, at which point they replaced the prior probabilities with the estimated posterior probabilities and start again, until the process converged and probabilities stabilized. A compact algorithm developed for this paper allows us to run one billion regressions on an ordinary laptop computer in a few hours. The C++ code is available on request.

# 4. Data

We use household consumption expenditure per capita as the living standards indicator. Data are from the Household Consumer Expenditure modules of the 61st and 68th rounds of National Sample Surveys of India, hereafter identified as NSS 2004–05 and NSS 2011–12. These surveys were conducted between July 2004 and June 2005 and between July 2011 and June 2012, respectively (NSSO 2005, 2012). They report household consumption information on an itemized form. We use monthly consumption based on the mixed recall period and divide by household size to compute the monthly nominal household expenditure per
initial doses, followed by 3 booster doses that should be administered to children and adolescents with at least 4 years between each booster dose.<sup>21</sup> Although all surveyed economies provide the first three doses of DTP immunization for free during the first semester of life, only Chile and India provide subsequent booster doses of the vaccine free of cost.

The data on time from initial contact to onset of treatment was provided by a limited number of respondents. As a result, the evidence is not representative of the average citizen experience. The average time in practice reported by contributors to access to antenatal care treatment and to obtain DTP immunization is 2 days in 3 out of 4 pilot economies. The time to obtain a combined oral contraception in all four countries varies. Figure 3.5 shows the time it takes for a woman to access family planning services, to receive antenatal care, and to have access to DTP immunization for her children in all four economies.

# **Figure 3.5: Time to Receive Family Planning, Antenatal Care, and DTP Immunization Services in Public Health Facility (calendar days)**

> 20 UNICEF global databases, 2016, based on Multiple Indicator Cluster Surveys (MICS), Demographic and Health Surveys (DHS) and other nationally representative sources. No data available for Chile in 2014. Available at: <u>https://data.unicef.org/topic/maternal-health/antenatal-care/.</u> 21 Summary of WHO Position Papers – Recommendation for Routine Immunization (Updated on March 2017). Available at http://www.who.int/immunization/policy/Immunization_routine_table1.pdf.

22 | P a g e
I discuss the nature of the survey data used in this section, conduct event studies on six major capital control announcements in Brazil and measure the revisions of expectations in response to these announcements.

# **5.3.1 Data**

Survey data of market expectations used in this section comes from the Brazilian central bank’s Market Expectations Systems (MES). Since 1999, the MES collects forecasts of inflation, output growth, industrial production and the policy interest rate at different horizon from around 120 professional forecasters, most of whom are financial institutions or real-sector companies with a forecasting team. The MES only reports summary statistics of the distribution of forecasts. Individual forecasts are not publicly available.

I focus on daily median expectation of Brazilian quarterly real GDP growth for the current quarter, although median expectations for the previous quarter and the next three quarters are also analyzed. Individual forecasters do not submit their forecasts on a daily basis, so daily variation in the expectations data comes from the fact that different forecasters report to the MES system at different points in time. On average, the median daily expectation of quarterly GDP growth changes every 2-3 days.

# **5.3.2 Empirical methodology**

I use event study to measure the revisions to median quarterly GDP growth expectation in response to six major capital control news events highlighted in Table 6 and Table 7 in the appendix. Five out of the six events involve the use of tax on portfolio inflows. Announcement of these events are unexpected to market participants. Figure 7 shows Google search counts for the phrase _Brazil IOF tax_ between 2006 and 2013, and hikes in search counts are highlighted in the figure. A hike usually comes on or after the announcement date of a major capital control event, suggesting the unexpected nature of these announcements. A search of news on or near the six announcement dates yields no information of any other major economic policy announcements on these dix dates, which would confound the event study results.

29
What is notable about the FSU countries is that, when we rank countries both according to their HCI and their GDP PPP per worker, all FSU countries with the exception of Azerbaijan have a much better HCI ranking than a GDP PPP per worker ranking. This is shown in table 5, which gives rankings out of 141 countries. The nine FSU countries shown in the table have an HCI ranking which is 19 places higher on average than their GDP PPP per worker ranking. Of the four remittance receiving FSU countries in CASC, the Kyrgyz Republic has an HCI ranking which is 35 places better than its GDP PPP per worker ranking, that of Georgia is 26 places better, Tajikistan 16 places better and Armenia eight places better. There could be various reasons for these differentials. In some of the FSU countries, the capital stock per worker is lower than is typical for a county at similar levels of income: Armenia, Georgia and the Kyrgyz Republic each have low capital-output ratios compared to other lower-middle-income countries, although somewhat surprisingly, Tajikistan has a very high capital-output ratio. A second reason could be poor total factor productivity because of structural constraints in the economy. Whatever the reason, the fact that GDP per worker is lower in these countries than would be typical for a country with a similar endowment of human capital suggests that workers, which can take their human capital with them, could migrate to another country, with a higher capital stock per worker or with higher total factor productivity, and produce more output and thereby earn higher wages than they would in their home country.

To explore this issue further, we estimate a production function using cross-country data in which GDP PPP per worker is regressed on the HCI and the capital stock per worker for each country, with all data in logs. There are 141 countries for which there are both data on the HCI and the capital stock in the Penn World Tables. The GDP PPP per worker and the capital stock (also at PPP prices) per worker are both averages over
subsidies are not pro-poor because the better-off households capture the subsidies disproportionally. Consequently, expanding rural electrification from SHS programs is not considered to be an effective policy choice for addressing poverty issues, although little empirical evidence exists to validate such claims. The Bangladesh national survey data presents an opportunity to empirically analyze the distributional impact of SHS programs.

# 5.3.1 Policy simulation

The distributional consequences are illustrated using a policy simulation exercise under two assumptions. First, all households currently without grid-electricity are assumed to be entitled to the micro-finance scheme. Second, the private sector agencies who are responsible for implementing the SHS decide program locations at the upzila level. The choice of location is driven by the objective of maximizing SHS dissemination while minimizing the operational cost. Two indicators at the upzila level that are important from the perspective of delivery agencies include: (1) the affordability rate, and (2) the proportion of households living in non-electrified villages, both capturing the SHS market potential as well as the scale of the operational cost.

The distributional consequences are essentially determined by the location choices of the private sector delivery agencies. For example, the implementing agencies may choose to place SHS programs only in locations with a high affordability rate. But it is also likely that these locations have a high concentration of better-off households, in which case the SHS programs will be regressive because better-off households would receive the subsidies disproportionally.

# 5.3.2 Distributional impact

The distributional impact of SHS promotion program is analyzed using the concentration curve (CC). The CC plots the cumulative percentage of the SHS subsidies received by households against the cumulative percentage of household population, ranked by per capita income in ascending order.<sup>10</sup> Therefore, the CC graphically illustrates the share of program subsidies captured by different incomes groups.

The distributional impact of the policy simulation based on the two location choice indicators are presented in Figure 2. Clearly, targeting SHS programs based either on affordability rate or the proportion non-electrified households at the
1973|
||[0.153]|(0.006)|(0.012)|||
|_C. Respondent and Household Char_|_acteristics_|||||
|Suspects Malaria|0.587|-.021|-.014|0.765|1973|
||[0.493]|(0.032)|(0.056)|||
|Patient Answered Clinic Survey|0.464|0.039|0.004|0.249|1973|
||[0.499]|(0.023)|(0.026)|||
|Male|0.312<br>[0.464]|-.003<br>(0.024)|-.035<br>(0.029)|0.474|1973|
|Bambara|0.4<br>[0.49]|-.038<sup>_∗_</sup><br>(0.021)|0.026<br>(0.025)|0.093<sup>_∗_</sup>|1971|
|SkFh|0532|0037|045|0313|1973|
|peas renc|.<br>[0.499]|.<br>(0.03)|-.<br>(0.035)|.||
|Literate(inFrench)|0287|0048<sup>_∗∗_</sup>|-092<sup>_∗∗_</sup>|004<sup>_∗∗_</sup>|1973|
||.<br>[0.453]|.<br>(0.022)|.<br>(0.043)|.||
|PrimarySchoolorLess|0.433|-.044
the predicted wealth gap in school readiness at ages 7–10 can be accounted for by differences in home stimulation at the same age, even when controlling for early skills and initial endowments. Finally, there are also significant SES gaps in learning outcomes (math and early literacy) when the children are 7–10 years old, mapping closely onto the gaps for our school readiness outcomes. As with the readiness measures, the significance of the wealth gradients in learning outcomes is robust to the inclusion of lagged outcomes and initial endowments.

Our results align with previous evidence that measures of executive function and language would be potentially more sensitive to the effects of household wealth or maternal education. The differential widening gradient may be due, in part, to the differential timing of development of various regions of the brain (Lenroot and Giedd 2006), although we do not have the data to understand these mechanisms.

The timing and widening of the SES gradients confirms the importance of investing during the early years, because of the brain’s greater plasticity (i.e., capacity to benefit from environmental interventions) and the timing of physiologic development (Shonkoff 2011). These developmental benefits of early intervention are supported by a strong economic argument for intervening as early as possible (Doyle et al. 2009). During the first five years of life, children’s language, early understanding of mathematics and reading, and self-control emerge. The extent to which children master these skills during this sensitive period has implications for success in school (Lerner 1998). There is also evidence that performance in early measures of executive function is associated with performance in later measures of academic skill, such as early math and reading ability (Blair and Razza 2007).

In addition to highlighting the importance of addressing poverty in early childhood, we find that SES continues to play a critical role into middle childhood. Our estimates from Madagascar 17
an average annual rate of 1.9 percent per year among oil rich countries using the traditional decomposition, and this contribution is reduced to 1 percent per year when including natural capital as an additional input. In the case of countries abundant in minerals and metals, the contribution of TFP growth (1 percent per year) using the traditional method is wiped out when accounting for natural capital in the production function. In fact, its contribution becomes detrimental —as the TFP contracts at an annual rate of 0.3 percent. In sum, the contribution of physical and natural capital dominates the growth narrative of resource rich countries once we include natural capital in the production function.

# **3 Trade Integration in Sub-Saharan Africa**

This section examines the evolution of international trade integration in Sub-Saharan Africa by calculating the volume and the patterns of trade of Sub-Saharan Africa as well as the role of natural resources in their foreign trade baskets. Specifically, it examines three different dimensions of international trade integration: (a) the volume of trade (as percentage of GDP), (b) trade diversification (as proxied by indices of product and market concentration), and (c) the importance of natural resources in the export sector (where natural resource exports are normalized by either the country’s GDP or its total amount of exports).

**Table 1. Trade measures for Sub-Saharan Africa**

||Sub-Saharan<br>Africa|Reso|urce Rich|Non-Resource<br>Rich|
|---|---|---|---|---|
|||Oil|Metal||
|**Trade Openness**|||||
|Percent of GDP|||||
|1990 - 94|64|95|65|56|
|2010 - 14|77|94|93|70|
|**Herfindahl Index**|||||
|Product Concentration|||||
|1990
question ‘with whom’ data, but only 2 of the surveys in our sample do so. Thus, for the purposes of harmonization, we focus on primary activities and report the number of hours allocated to unpaid domestic work, a broader category that includes housework and childcare. This is a limitation of our analysis, since childcare and housework (mostly unpaid and performed by women) are also performed as secondary activities (Floro and Miles, 2003; Folbre et al, 2005; Folbre and Yoon, 2007a; 2007b). Hence, the unpaid domestic and care work category in this paper represents a lower bound estimate.

Time allocations are expressed in hours per day. The gender gap is calculated as the difference between the average number of hours per day men and women spend in the different activities on a daily basis. Our sample includes men and women aged 15 to 64 years of age.<sup>4</sup> Following the approach in the 2012 WDR, activity episodes are assigned a zero if not performed by the individual. This implies that we use the same sample of individuals to calculate the amount of time

> 2 Data on some specific activities are not available for a handful of countries: Argentina and Iraq do not include information on childcare activities, so unpaid domestic work only includes time allocated to household activities in these countries. Timor-Leste does not collect data on self-care and leisure, social and study activities. Argentina, Iraq and Mexico do not have information on time allocated to self-care.

> 3 This finding is consistent with previous studies such as the 2012 WDR (World Bank, 2011).

> 4 Argentina is an exception because the time use module was only applied to people aged 18 and above.

8
unsafe zones, or for data on illegal and informal activities not usually reported. For example, remote satellite sensing can capture spectral reflectance of different crops, thereby allowing calculation of land surfaces devoted to poppy cultivation (Lind, Moene, and Willumsen, 2014).

# <u>Data requirements and pitfalls</u>

There are three primary pitfalls related to use of geospatial data. The first is related to cost. Although much of the sensor-captured data on land cover, forest, and airborne pollution are public and free, some disaggregated data provided by public entities, or specific data provided by private firms, are costly. For example, access to the global annual cloud-free nighttime lights database costs $70,140 dollars, and the global nighttime light monthly increments cost $7,665 per year. Depending therefore on a study’s time frame type of information needed, geospatial data costs can escalate quickly.

The second drawback is data complexity and the skills needed for processing. Earth’s land area can be subdivided into hundreds of trillions of one-meter cells, making such high-dimensional data difficult to model even with linear functions (Donaldson & Storeygard, 2016). Also, satellite data can often be highly correlated among nearby units. Whether spatial dependence appears as a dependent or independent variable, statistical inference should be taken with caution and adjustments made to correct potentially biased coefficients.

The third pitfall is related to ethical concerns. While approaches have taken advantage of available data, such as nighttime lights, to measure variables such as welfare in developing countries (Donaldson & Storeygard, 2016; Henderson, Storeygard & Weil, 2012), or building type and size information (Marx, Stoker, and Suri, 2015), using very high-resolution imagery might raise individual privacy and regulatory concerns.

25
countries with poor governance systems might have high levels of pollution because of a lack of proper environmental regulations exacerbated with low compliance and, at the same time, have sizable informal sectors. One way to work around this endogeneity is to apply country and industry fixed effects as the data set has both country- and industry-level observations. It is important to acknowledge that estimations in this paper do not claim causal effects despite improving OLS coefficients by using country and industry fixed effects but only discuss correlations between emissions and informality. All these estimations are also conducted using country and sector weights, the results are not reported here as significance and sign of the coefficients remain the same. While regressions with country fixed effects have been used in other studies and produced similar results, this paper adds to the literature by including industry fixed effects and conducting industry-specific estimations.

The regressions include several control variables to take into consideration widely documented differences in capital intensity and production technologies across countries and sectors as well as to account for the relative importance of the sectors in the overall economy. At the same time, the choice of control variables is highly driven by the availability of data on a country and sector level, and the selected variables are extracted only from two data sets - GTAP and I2D2 - given the paucity of sector data in general. More specifically, the control variables include the natural logarithm of capital per worker, the share the respective industry value added contributing to the country’s total value added, and the share of imported intermediate goods in total consumption of intermediate goods in each sector. The capital intensity and share of imported intermediate goods in total aim at capturing the underlying differences in production technologies and the extent of the industry’s reliance on global value chains which can be indicative of their competitiveness and productivity. On average, capital-intensive sectors consume more electricity and, hence, are more likely to have high emissions as opposed to sectors with low capital. At the same time, sectors that rely more on imported intermediate goods might be more