rafmacalaba/data-use-mentions-extended huggingface dataset:
<u>Table 2: Diference in Means of Control Variables</u>

||No mission|Mission|Diference|
|---|---|---|---|
|Log(Distance to coast)|5.76|4.01|1.76<sup>_∗∗∗_</sup>|
|Log(Distance to main river)|5.29|5.89|_−_0.60<sup>_∗∗∗_</sup>|
|Percentage of area within 10 km of water|0.07|0.10|_−_0.03<sup>_∗∗∗_</sup>|
|Malaria Ecology Index|11.64|6.82|4.83<sup>_∗∗∗_</sup>|
|Caloric Suitability Index / 1000|1.33|1.57|_−_0.24<sup>_∗∗∗_</sup>|
|Terrain Ruggedness Index|17.18|26.42|_−_9.25<sup>_∗∗∗_</sup>|
|Mean elevation|714.05|750.26|_−_36.22|
|Tropical dummy|0.86|0.56|0.31<sup>_∗∗∗_</sup>|
|Log(Distance to explorer route)|3.62|4.20|_−_0.58<sup>_∗∗∗_</sup>|
|Log(Distance to colonial railway)|5.28|3.59|1.68<sup>_∗∗∗_</sup>|
|18th-century population|11.46|23.48|_−_12.02<sup>_∗∗∗_</sup>|
|Precolonial
# **TABLE A2. Correlations between Shock in 2002 and Maternal Mental Health Index in 2006/07**

||(1)|(2)|(3)|
|---|---|---|---|
||MHI-1|MHI-2|MHI-3|
|Experienced shock of crop or livestock|0.176|0.354|0.335|
|loss in 2002|(0.126)|(0.256)|(0.239)|
|Observations|1,095|1,095|1,095|
|Mother controls|Yes|Yes|Yes|
|Child controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|R-squared|0.074|0.075|0.076|

Table A2 presents correlations between maternal mental health indexes and the main shock under analysis experienced by mothers of our sample. Sample is restricted to our group of interest. Mental health indexes are the standardized measures described in the data section using the SRQ-20 items.

34
**Figure 6. Decomposition of percentage change of (sales-weighted) average markup, 2008–17**

_Source:_ World Bank staff calculations based on SBS data set.

**This result could be interpreted as a sign of consolidation of market power among existing firms enabled by remaining product market regulations that restrict competition, despite previous reforms.** Several policy-related factors can explain this result, among them, government interventions and regulations that restrict competition by reinforcing dominance or limiting entry, facilitating collusive outcomes or increasing the cost to compete in markets, and discriminating and protecting vested interests.<sup>24</sup> In this regard, Pop and Ore Monago (2020) provide illustrative examples about the presence of regulations that still restrict competition in Romania, drawing from the OECD–World Bank Group (WBG) product market regulation (PMR) indicators data set, which provides information across 70 countries.<sup>25</sup> The assessment uses the latest PMR round for Romania from 2013 and includes Romania’s performance projected on the basis of information updated in 2017. Results suggest that the regulatory framework as it is in the books in Romania is less restrictive than that of countries such as Poland and the total sample average, but it is more restrictive than that of the OECD and EU15 countries. However, from an economywide perspective, the overall restrictiveness of regulations in Romania is mainly associated with the state control of the economy and several remaining barriers to entry and rivalry (see figure 7).

> 24 In principle, greater competition outcomes are enabled through an effective competition policy framework, which involves three key pillars: (a) fostering pro-competition regulations and government interventions, (b) promoting competitive neutrality and non-distortive public aid, and (c) enabling effective competition law and antitrust enforcement.

> 25 The current PMR database used for this study includes Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Bulgaria, Canada, Chile, China, Colombia, Costa Rica, Croatia
Figure A.12: The Impact of Reductions in Commission Fees and Tax Withholding Rates Difference-in-Difference Estimation on Monthly Data

# A. Number of Card Transactions

<!-- Start of picture text -->
-10 -5 0 5 10<br>Months to Reform<br>Retail Wholesale<br>OLS DD (Log): 0.036 (0.014). N Retailers=4352, N Wholesalers=821<br>B. Volume of Card Transactions<br>-10 -5 0 5 10<br>Months to Reform<br>Retail Wholesale<br>OLS DD (Log): 0.031 (0.016). N Retailers=4352, N Wholesalers=821<br>C. Having a POS<br>-10 -5 0 5 10<br>Months to Reform<br>Retail Wholesale<br>OLS DD (Log): 0.011 (0.002). N Retailers=8662, N Wholesalers=5552<br>1.1<br>1.05<br>1<br>Normalized Log Outcome<br>(Relative to Pre-Reform Average) .95<br>1.06<br>1.04<br>1.02<br>1<br>Normalized Log Outcome<br>(Relative to Pre-Reform Average) .98<br>1.1<br>1.05<br>1<br>Normalized Log Outcome<br>(Relative to Pre-Reform Average) .95<br><!-- End of picture text -->

Notes: These graphs implement a DID estimation similar to the one from Section 5.1, equation 2, except that the post-reform period starts in January 2012, when withholding rates applied by credit/debit card companies on card purchases from non-CEDE firms were reduced (see Figure A.7) and commissions charged by credit/debit card companies were lowered (
prospective investors. The List underwent significant changes in 2010, imposing more restrictions on foreign investment into the telecommunications sector, for example. On the other hand, several sectors, such as hospital services, underwent greater liberalization at this time.

Cross-country comparisons suggest that restrictiveness towards FDI in services is relatively high in Indonesia. The OECD compiles an FDI Regulatory Restrictiveness Index measuring the openness of 55 economies to FDI. It has been conducted intermittently since 2003, backdated to 1997, and on an annual basis since 2010. It includes four primary components: restrictions on foreign key personnel, equity restrictions, screening and approval requirements, and other operational restrictions (e.g.: land use restrictions).<sup>6</sup> As foreign equity limits are understood to be the most onerous – although certainly not the only – restrictions on FDI, the index is strongly weighted according to these limits. According to the 2012 iteration of the index, Indonesia is the third most restrictive FDI regime of those surveyed, ranking behind only China and Saudi Arabia.

While the Index measures a country’s policy stance on FDI, and therefore the focus is placed firmly on discriminatory measures toward foreign investors, the index may also register the impact of non-discriminatory measures that are burdensome to investment regardless of its origin.<sup>7</sup> In this sense, the index may be viewed to a certain extent as a proxy measure for a broad range of policy variables.

In the aftermath of the East Asian crisis, for instance, there was an economic imperative and political support for reforms in Indonesia. One notable example are the 1999 reforms of the Indonesian Telecommunications sector. Such ‘big bang’ reform processes are not atypical when looking at the international experience; reforms in the Indian service sectors in the 1990s are another example of such ‘packaging’.<sup>8</sup> This suggests that the evolution of the indicator may well be indicative of more wide-ranging reforms, of which a changed FDI
.033)|
|=1 if Unconditional Schoolgirl|-0.186|-0.454|0.005|0.017|-0.088|-0.357**|-0.406|-0.091|0.013|0.008|0.010|
||(0.180)|(0.425)|(0.068)|(0.086)|(0.054)|(0.163)|(0.344)|(0.093)|(0.219)|(0.093)|(0.041)|
|p-value UCT vs. CCT|0.084|0.240|0.776|0.954|0.042|0.044|0.225|0.17|0.196|0.508|0.845|
|p-value Treatment|0.145|0.490|0.845|0.964|0.118|0.087|0.432|0.358|0.006|0.471|0.971|
|Mean in Control Group|0.000|9.743|0.699|0.541|0.258|0.000|1.42|0.352|0.000|0.647|0.052|
|Sample Size|543|543|543|543|543|539|540|543|542|541|457|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. The husband quality super index is a standardized index of all other outcomes in this table (except HIV as it is defined on a smaller sample). All variables are constructed so that higher values are better, except for HIV.
technology abandoned it. A 1990 survey found that only 29 percent of the farmers who first used fertilizer under the program kept using it (Tripp, 1993, p. 2012).

Similar programs were launched for maize in Ethiopia, Ghana, Kenya, Malawi, Mozambique, Sudan, Zambia, and Zimbabwe (Smale, 1994; Brinkley, 1996; Howard et al., 2003; Smale and Jayne, 2011). Some programs showed early success before reaching an adoption plateau; however, often adoption gains were reversed and new technologies abandoned. Frequently, adoption depended heavily on controlled prices or subsidies that proved too expensive to maintain. Similarly, researchers are finding that several recently launched campaigns to promote MV technologies with subsides are proving expensive with marginal benefits at best (Ricker-Gilbert et al., 2013; Jayne and Rashid, 2013).

# **7.1.2 Heterogeneous conditions and technologies**

An alternative explanation is that the circumstances that affect the profitability of a given technology are heterogeneous, so it is unlikely that all farmers will decide to use the same technology. In this sense, the choice of technology is endogenous (Mundlak, 1988; Mundlak, Butzer and Larson, 2012). Differences in natural endowments, for example soil fertility or water availability, affect fertilizer response rates, yields, and therefore the profitability of a given technology (Larson and Leon, 2006; Marenya and Barrett, 2009; Jayne and Rashid, 2013). Variable weather adds to the risk of a technology, even if the technology is profitable on average (Larson et al., 2016). Spatial differences in transport and transaction costs affect relative profitability (Xu et al. 2009). In the specific case of maize, Suri (2011) concludes that in Kenya, the differences in the costs and benefits of using maize hybrids explain the differences in choices made by adopters and non-adopters of MV technologies. In short, the market prices and relevant shadow prices farmers face differ, which generates variation in pursued
.819|0.819|0.829|0.846|0.848|

_Source_ : Author’s analysis based on the 2006 round of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS) from the National Council of Applied Economic Research. _Note_ :<sup>*</sup> _p <_ 0.1,<sup>**</sup> _p <_ 0.05,<sup>***</sup> _p <_ 0.01. Robust standard errors clustered at the village level are reported in parentheses. This table provides the coefficients on demographic and farm characteristics estimated in tables 2 to 5, baseline specification. I do not report the gender of the head of household and the share of land owned, as they are not significant. For soil characteristics, I only report the variables that have a significant impact on productivity at the 5 percent level.

# **Table S1.2.** First Stages—Specification 2, Tables 2, 3, and 4

|**Dependant variable:**||**Educ (**|**survey)**||
|---|---|---|---|---|
|_Group: Caste_|||||
|Educ (listing)|0.72<sup>*** </sup>|0.72<sup>*** </sup>|0.72<sup>*** </sup>|0.72<sup>***</sup>|
||(0.016)|(0.016)|(0.016)|(0.017)|
|_Group: Caste & Occup_|||||
|Educ (listing)|0.72<sup>*** </sup>|0.72<
||view images for overall energy<br>efficiency<br>predictions<br>and<br>building attributes|-<br>Morphology<br>description<br>-<br>Google Street View<br>(GSV)||
|---|---|---|---|
|Fehrer and Krarti<br>(2018)|Use of nighttime light imagery<br>from VIIRS as a predictor of<br>building energy use intensity<br>within states, counties, and cities<br>in the United States|-<br>Satellite<br>data<br>from<br>VIIRS|US (several<br>levels)|
|Berger<br>and<br>Worlitschek<br>(2018)|Estimating<br>residential<br>space<br>heating demand via maps of<br>population density distribution<br>combined<br>with<br>norm<br>temperature profiles and the<br>definition of heatingdegree days|-<br>Population<br>-<br>Temperature<br>-<br>Energy<br>supply/consumption<br>-<br>Buildings|Switzerland|

_Source: Own elaboration based on literature review._

## **Big data sources might also help generate new insights into energy spending, consumption patterns,**

**and related social norms and behaviors.** For example, Fergus and Chalmers (2021) use data from smart meters, consumer access device (CAD) data, and machine learning algorithms to detect and monitor fuel poverty. They also generate new insights into energy consumption, behavior, social norms, and expectations of energy sobriety.

**Big data approaches are promising but require advanced data science skills and tools.** Big data approaches allow for real-time tracking and extremely granular measurement of energy poverty. At the same
# **1. Introduction and motivation**

It is well established in the literature that exporters are larger and more productive than nonexporting firms. Recent literature has also shown that exporters achieve better labor market outcomes than non-exporters. Firms that have a larger share of exports in total sales are found to have higher levels of employment and pay higher wages (Bambrilla, Lederman and Porto 2012). Cebeci, Lederman and Rojas (2013) attempt to identify a causal effect of changes in the structure of sales on labor market outcomes using exogenous fluctuations in exchange rates combined with firms’ initial exposure to various markets as instrumental variables. Under this approach, increases in exports as a share of total sales in Turkish firms are associated with increases in employment levels within firms, but a higher export share generates no observed impact on wages.

In this analysis, we explore whether changes in firms’ sales structure – exporting versus selling to the domestic market, as well as by export destination – matters for labor outcomes. We use firmlevel data for Georgia to estimate the quasi-elasticity of employment and wages with respect to the share of exports in total sales. Following the methodology of Cebeci, Lederman and Rojas (2013) allows us to test for a causal effect of firms’ growth in exports relative to domestic sales (measured by the change in a firm’s share of exports in total sales<sup>2</sup> ) on their labor market outcomes, namely employment level (total employment, as well as female employment) and average wages (average for total firm employment, and for female employment).<sup>3</sup> In addition, we assess whether exporting to different destination markets such as the European Union or high-income countries has different impacts on labor-market outcomes.

The remainder of the paper proceeds as follows. Section 2 presents the methodology and Section 3 describes the firm-level data source used. Section 4 discusses stylized facts about exporters in Georgia. Section 5 concludes with the results of the analysis.
Milanovic (2016) and Chandy and Siedel (2017a, 2017b). We find that such adjustments have little effect on global poverty measures, but substantially revise upwards both global and national inequality, and significantly change our understanding of the distributional nature of growth and ‘shared prosperity’.<sup>3</sup> Notably, the relationship between observed levels of inequality and country income levels changes significantly, strengthening evidence of a Kuznets curve where inequality first rises and then falls with economic development (Kuznets, 1955).

The remainder of the paper proceeds as follows. Section 2 presents the data compiled for the analysis presented in this paper. Section 3 discusses reasons why gaps exist and presents our analysis of the differences between survey means and national accounts estimates (both in terms of levels and change over time) based on our newly compiled data. Section 4 considers two adjustments to survey data to reconcile these differences and describes the implications of each for global poverty and inequality measurement; the final section offers some concluding thoughts.

# **2. Data**

# **_National accounts_**

Our main comparison between survey and national accounts data focuses on comparing the survey household consumption aggregate with the component of national accounts that corresponds to household expenditure, known as household final consumption expenditure (HFCE) and established in the 1993 System of National Accounts.<sup>4</sup> While per capita HFCE is the variable that conceptually most closely corresponds to the measures from household surveys,<sup>5</sup> we also compare survey estimates to per capita gross domestic product (GDP) estimates, for two reasons. First, GDP is a more frequently cited indicator of economic development and may be measured with less noise than HFCE, which is sometimes measured as

> 3 Shared prosperity is an indicator of shared growth developed by the World Bank, measuring the income growth of the bottom 40 percent of the distribution in each country. It is often compared to the average growth of the country.
for these where _Yict_ is the outcome of interest for individual _i_ connected to a girl in club _c_ at time _t_ , Boys _c_ is an indicator that the boy is in a community that offers the _Boys_ intervention, Goal _i_ is an indicator that the female who is connected to the male was invited to the _Goal_ treatment, and Post _t_ is an indicator for the period after treatment is implemented. _Xict′_<sup>is</sup> a vector of controls that includes individual characteristics equivalent to the controls for the females’ models, except we control for whether the male speaks to his father about sexual reproductive health topics rather than his mother. Location fixed effects in _αc_ are at the region level to account for the level of stratification of treatment assignment to the _Boys_ arm.

At endline, males were asked their opinion on statements, such as “A woman should tolerate violence from her husband/partner,” in order to elicit their attitudes toward violence. We also ask males the same violence questions we ask females, but in terms of their perpetration of it, and construct equivalent IPV indices. Table 3, which is organized the same way as Table 2, presents estimates from equation 2. Table 3, column 1, shows that the _Boys_ arm has a strong impact in reshaping IPV attitudes, with males in this arm being 17.4 percentage points ( _p_ =.001) less likely than males in control communities to agree that women should tolerate violence from their husband or partner. The violence attitudes index suggests that males in the _Boys_ treatment arm have 0.290 standard deviations “better” attitudes regarding violence ( _p_ =.016). The magnitude of the attitudinal change is similar to RCT results from Dhar, Jain and Jayachandran (forthcoming), who engaged adolescents in classroom discussions about gender equality.<sup>17</sup> Table A6 further shows that improvements in attitudes were larger among boys who enrolled in Grassroot Soccer, with an improvement in the Violence Attitudes
**Figure 7: One year survival probability for infants, by birth cohort and location. From birth histories in the Cambodia Demographic and Health Survey 2000**

<!-- Start of picture text --> 1<br>0.95<br>0.9<br>0.85<br>0.8<br>0.75<br>70-74 75-79 80-84 85-89 90-94 95-99<br>Birth cohort<br>Urban Rural<br>One year survival probability<br><!-- End of picture text --> 27
Figure 2: Real Median Family Income and Gap between the Treated and Synthetic Control <!-- Start of picture text --> (a) Real Median Family Income (b) Gap between Treated & Synthetic<br>Pre Post<br>1995 2000 2005 2010 2015 Pre Post<br>Year<br>1995 2000 2005 2010 2015<br>Treated (oil, gas, energy) Synthetic Control Year<br>(c) post/pre RMSPE ratio (d) Gap between Treated & Synthetic<br>0 5 10 15<br>Post/pre RMSPE ratio 1995 2000 2005 2010 2015<br>(e) Placebo Sanctions in the Year 2000<br>Pre Post<br>1995 2000 2005 2010 2015<br>Year<br>Treated (oil, gas, energy) Synthetic Control<br>.4 .1<br>0<br>.2<br>-.1<br>0<br>-.2<br>-.2<br>-.3<br>log (demean) Family Income -.4 Gap in prediction error -.4<br>-.5<br>-.6<br>-.6<br>2 .4<br>.2<br>1.5<br>0<br>1<br>Density<br>-.2<br>.5<br>Gap in family income prediction error<br>-.4<br>0<br>-.6<br>.4<br>.2<br>0<br>-.2<br>log (demean) Family Income -.4<br>-.6<br><!-- End of picture text --> _Source: Iranian Households Income and Expenditure Surveys (HIES)._ Note: The figure reports synthetic control method analyses as
_Brazilian spatial labor dynamics Page 6_ neighboring dynamic areas. Agroclimate is postulated as an exogenous determinant of employment growth. This allows for the possibility that agricultural labor is shifting between agroclimatic zones – and perhaps, especially, out of less favorable zones – in response to changing agricultural market conditions.

We model capital investment as

# ∆ ln _K_ = ∆ ln _K_ ( _MARKETACCESSt_ − 1, _GOVERNANCE_ , _AGROCLIMATE_ , ∆ ln _MP_ ) ()3

Better market access (measured by lower transport costs to São Paulo and to the nearest state capital) is associated with lower price levels and hence higher returns to capital. Better local government is expected to increase the município’s attractiveness for investment. Increases in the market potential indicate location near a dynamic region; this potentially endogenous instrument must however be instrumented.

Because we lack reliable data on capital, we substitute (3) into (1). While we have data on mean years of education of the work force, we lack information on employees’ educational quality. Moreover, a dynamic município may experience changes in mean educational level due to compositional effects, depending on the relative educational level of in-migrants, out-migrants, and stayers. Hence we use initial educational level of workforce, and initial educational level of public school teachers (a proxy for local educational quality) in place of educational change in (1). We interpret the result as describing the demand curve while allowing for the endogenous response of capital investment to initial conditions.

# **Data**

We examined patterns of employment and wage change over the period 19912000. The basic spatial unit of analysis was the município. A complication, however, is that some municípios experience splits over the study period, the total number growing from 4491 to 5507. In complicated cases, two municípios are reorganized into three. To address this problem, we merged municípios into 4267 Minimum Comparable Areas (MCAs) – consisting of sets of one to three municípios whose borders were constant over the study period.

Much of the data stem
years, and a number of lagged explanatory variables which proxy for the steady-state of the neoclassical growth model and capture the effects of various policies on long-run growth: investment, population growth, trade openness, inflation, and the black market premium. We estimate this model using non-

> 1 The World Bank's Unified Survey projections, and the IMF's World Economic Outlook projections are produced in this way. Both organizations also use large macroeconometric models: the World Bank's Global Economic Model (GEM) is used to produce forecasts appearing in the Bank's annual Global Economic Prospects publication, and the IMF maintains MULTIMOD for research and simulation purposes.

I