rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2 – Balance Check with Multiple Bandwidth Sizes**

|_Bandwidth_<br>_size in months_|Obs|Primary<br>school|High<br>school|College|Gender|Skin<br>color|Household<br>size|<br>Age|Start<br>working<br>age<br>|Firms’<br>location (=1<br>if outside<br>home)|<br>Firms’ location<br>(=1 if<br>exclusively<br>outside home)<br>|<br>Borrowed<br>money from<br>relatives to start|Used owned<br>resources|Asset (=1<br>for owned<br>dwellings)|<br>_Joint test_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|3|1476|-0.0070|-0.0079|0.024|-0.035|0.079|-0.31|-0.41|-0.66|0.026|-0.072**|0.034|0.0064|-0.061||
|||(-0.14)|(-0.17)|(1.08)|(-0.69)|(1.52)|(-1.64)|(-0.36)|(-0.97)|(0.53)|(-2.46)|(0.99)|(0.12)|(-1.32)|[0.075]|
|4|1748|-0.0039|-0.017|0.016|-0.034|0.059|-0.22|0.0026|-0.70|0.041|-0.062**|0.034|-0.0097|-0.061||
|||(-0.086)|(-0.43)|(0.88)|(-0.75)|(1.28)|(-1.34)|(0.0025)|<br>(-1.14)|(0.94)|(-2.31)|(1.15)|(-0.21)|(-1.48)|[0.021]|
|5|2114|0.0018|-0.025|0.019|-0.034|0.071*|-0.16|0.18|-0.87|0.026|-0.048*|0.022|0.011|-0.058||
|||(0.044)|(-0.68)|(1.16)|(-0.83)|(1.73)|(-1.06)|(0.19)|(-1.51)|(0.67)|(-1.95)|(0.83)|(0.27)|(-1.57)|[0.019]|
|6|2427|-0.020|-0.019|0.024|-0.013|0.057|-0.20|0.30|-0.85|0.026|-0.040*|0.0086|0.029|-0.029||
|||(-0.53)|(-0.56)|(1.53)|(-0.34)|(1.48)|(-1.45)|(0.36)|(-1.61)|(0.72)|(-1.74)|(0.35)|(0.76)|(-0.84)|[0.023]|
|7|3011|-0.035|-0.0066|0.037**|-0.018|0.046|-0.085|0.036|-0.61|0.0093|-0.034|0.0015|0.043|-0.00091||
|||(-1.04)|(-0.21)|(2.53)|(-0.52)|(1.33)|(-0.68)|(0.047)|(-1.29)|(0.29)|(-1.64)|(0.069)|(1.24)|(-0.030)|[0.001]|
|8|-0.042|0.0040|0.037***|-0.031|0.061*|-0.076|0.32|-0.37|0.026|-0.013|-0.020|-0.0011|0.037|-0.0045||
||(-1.27)|(0.13)|(2.59)|(-0.94)|(1.83)|(-0.63)|(0.43)|(-0.81)|(0.53)|(-0.40)|(-1.00)|(-0.050)|(1.10)|(-0.15)|[0.000]|

Note: ***, **, * statistically significant at 1, 5, and 10 percent. Robust t-statistics in parenthesis. The coefficients are from a linear regression of each covariate on a constant, a (treatment) dummy that is 1 for firms created after the threshold and 0 otherwise, and the assignment variable Z. Robust t-statistics in parentheses. P-value of the joint test in brackets.

12
||(1)<br>Dif gr|(2)<br>Actual gr|(3)<br>Forecast gr|(4)<br>Dif gr|(5)<br>Actual gr|(6)<br>Forecast gr|
|---|---|---|---|---|---|---|
|Lag disc dum,two years|-0.833<sup>_∗∗∗_</sup>|-0.095|0.458|-1.556<sup>_∗∗_</sup>|-0.952<sup>_∗_</sup>|1.018<sup>_∗_</sup>|
||(0.308)|(0.325)|(0.299)|(0.645)|(0.572)|(0.603)|
|Inst*lag<br>disco||||0.138|0.162|-0.159|
|||||(0.140)|(0.123)|(0.099)|
|Fixed efects|CFE YFE|CFE YFE|CFE YFE|CFE YFE|CFE YFE|CFE YFE|
|Sample|Full|Full|Full|Full|Full|Full|
|Obs- Country Year)|3781|5413|4773|2807|3972|3536|
|R-sq|0.06|0.05|0.03|0.06|0.06|0.04|
|F-test|13.78|8.95|7.34|13.11|8.82|6.63|

Time trend, Standard errors clustered at country level _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01

||(1)<br>Dif gr|(2)<br>Actual gr|(3)<br>Forecast gr|(4)<br>Dif gr|(5)<br>Actual gr|(6)<br>Forecast gr|
|---|---|---|---|---|---|---|
|Lag supergiant dum|-1.766<sup>_∗_</sup>|-0.336|1.138<sup>_∗∗∗_</sup>|1.677|-4.615<sup>_∗∗∗_</sup>|1.800|
||(1.062)|(0.890)|(0.351)|(4.801)|(0.816)|(1.272)|
|Inst*lag<br>~~S~~uper<br>~~d~~isco||||-2.690|1.332<sup>_∗∗∗_</sup>|-0.590|
|||||(3.584)|(0.136)|(0.714)|
|Fixed efects|CFE YFE|CFE YFE|CFE YFE|CFE YFE|CFE YFE|CFE YFE|
|Sample|Full|Full|Full|Full|Full|Full|
|Obs- Country Year)|3781|5413|4773|2807|3972|3536|
|R-sq|0.06|0.05|0.03|0.05|0.06|0.03|
|F-test|14.18|8.80|7.62|16.10|12.81|7.05|

Time trend, Standard errors clustered at country level _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Table 3: Main results, all sample, five year cumulative discoveries.

# **3.3 Synthetic controls**

In this section we present synthetic control estimates for a variety of different countries. The use of synthetic controls allows us to demonstrate the variation in the effect of discoveries on different countries with different prior characteristics. Since our identification strategy uses the exogenous timing of a major discovery, we can use this to show effects at the country specific level.

We follow the literature in generating our synthetic controls Abadie et al. (2010). We use five predictor variables to construct each synthetic control based on conditions in the country prior to discovery: ethnic fragmentation, population in year prior to discovery, GDP per capita one, three, and five years before the discovery event.

Figures 5 and 6 gives illustrative examples of the short run GDP per capita effects of a giant oil 15
# Annex figure 2. The aggregate social spending

<!-- Start of picture text --> % of GDP<br>1 6 - - - - - - - --------<br>15<br>14<br>13<br>12<br>11<br>10<br>8<br>7<br>8 / SoCaI Services Cash<br><!-- End of picture text --> <!-- Start of picture text --> Social Wetfare GMP<br><!-- End of picture text --> <!-- Start of picture text --> s~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I-<br>1980181 199W91 1994i95<br><!-- End of picture text --> <!-- Start of picture text --> 1971/72<br><!-- End of picture text -->

# Annex figure 3. Health spending

<!-- Start of picture text --> % of GDP<br>/H^alth Cash<br>4<br>3~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>3 I E i<br>1971172 1980/81 1990191 1994195<br><!-- End of picture text --> 51
**Table 1: Estimated percentage decline in employment across sectors**

|**Sector**|**Scenario 1**|**Scenario 2**|
|---|---|---|
|BuildingMaterials,Ceramics and Glass Industry|-17.3|-23.5|
|Mechanical and Electrical Industry|-16.59|-22.5|
|Textile,Clothingand Leather Industry|-21.25|-28.8|
|Miscellaneous Industries|-15.21|-20.6|
|Buildingand Civil Engineering|-21.47|-64.22|
|Commerce|-4.63|-7.7|
|Transport|-19.1|-31.7|
|Hotels,Cafés and Restaurants|-28.63|-47.5|
|Various Merchant Services|-9.99|-16.6|

Source: WB estimates and Author’s calculations

**Figure 7: Projected contraction in employment (%) in sectors**

<!-- Start of picture text -->
-7.7<br>-22.5<br>-31.7<br>Scenario 2<br>-28.8<br>-4.6<br>-16.6<br>-19.1<br>Scenario 1<br>-21.3<br>-50.0 -45.0 -40.0 -35.0 -30.0 -25.0 -20.0 -15.0 -10.0 -5.0 0.0<br>Commerce IME Transports Textiles Construction Tourism (Hotels, restaurants)<br><!-- End of picture text -->

Source: WB estimates and Author’s calculations Note: We have used sectoral growth projections estimated by WB combined those with growth elasticity of employment to generate projected employment contract in “high-risk” sectors.

With the projected sectoral and employment contraction discussed above, poverty is projected to increase by 7.3 percentage points under Scenario 1, and by 11.9 percentage points under Scenario 2 (Table 2). This implies a more than 50 percent increase in poverty in Scenario 1 and almost a doubling of the poverty rate in Scenario 2, thus reversing the trend of declining poverty over the past decade. What is worse, many more are expected to lose income and become vulnerable to falling into poverty in the future. The poverty gap, which measures the poverty deficit of the entire population, would increase from 14
Policy Research Working Paper 10209

# **Abstract**

Estimates of the number of people living in extreme poverty, as reported by the World Bank, figure prominently in international development dialogue and policy. An assumption underpinning these poverty counts is that there are no economies of scale in household size—a family of six needs three times as much as a family of two. This paper examines the sensitivity of global estimates of extreme poverty to changing this assumption. The analysis rests on nationally representative household surveys from 162 countries covering 98 percent of the population estimated to be in extreme poverty in 2017. The paper compares current-method estimates with a constant-elasticity scale adjustment that divides total household consumption or income not by household size but by the square root of household size. While the regional profile of extreme poverty is robust to this change, the determination of who is poor changes substantially—the poverty status of 270 million people changes. The paper then shows that the measure that accounts for economies of scale is significantly more correlated with a set of presumed poverty covariates (years of schooling, literacy, asset index, working in agriculture, access to electricity, piped drinking water, and improved sanitation).

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at djolliffe@worldbank.org and stettehbaah@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
slightly higher proportion of workers on PW earn less than the stipulated wage rate than of workers on non-PW work. However, this is deceptive, given the greater compression of PW wages. This is clear if we calculate the proportion of workers earning less than 75% of the stipulated wage rate. For example, in R1, only 14% of PW workers earned less than 75% of the stipulated wage rate, as compared to 46% of non-PW workers. In R2 the corresponding proportions were 21% and 45%. It appears that BREGS is able to provide participants a “fuzzy wage floor” that is not available for other casual work.

Figure 6 examines whether there is a difference in the wage floor for men versus women. We use data from Block 1 in the individual questionnaire, which is based on one-year recall and therefore has more observations for a gender-wise disaggregation. In means and medians, the ratio of the BREGS wage rate to the stipulated wage rate was similar for both men and women, in both rounds.<sup>24</sup> However, the gap widens at lower proportions of the stipulated wage rate (as is evident from the cumulative distributions in Figure 6).

The proportion of women earning considerably less than the stipulated wage rate is higher than for men in both rounds. In R1, 19% of women were earning less than 75% of the stipulated wage rate, as compared to 13% of men. The gap narrowed slightly in R2, with 15% of women earning less than 75% of the stipulated wage rate, versus 11% of men. It is clear that BREGS is even less effective in providing a wage floor for women than for men.

# **5. Forgone earnings**

Recall that to measure forgone earnings we asked counterfactual questions of BREGS participants in our surveys, to obtain their assessment of how many days they would have otherwise worked and what they think they would have earned if they had not been doing the BREGS work during that period (Section 2).

We found that forgone opportunities varied considerably across workers. Table 5 summarizes the types of activities that BREGS-participants identified as being displaced by their BREGS work. For men, about 14% in R2 (less in R1) said they would have migrated if not for BREGS; this was only true of 1% of women in R2. Casual work in agriculture was identified as the forgone work opportunity for about 22% of men and 25% of women in R1. Casual non-farm

> 24 The mean ratio was 90% for men in both rounds; for women the means are 87% and 89% in R1 and R2, respectively. Medians were similar, with half the workers (of both genders) earning less than 91% of the stipulated wage rate in R1 and 96% in R2.

18
of development trajectories, the similarities are so close as to suggest a dominant role for inherited attitudes and institutions.

# **8 Conclusion**

Much of the growth literature stresses differences in the capacity to generate, import, and apply new technologies as central to explaining relative growth performance. In particular, Aghion et al. (2005) document that different frontier adjusted endowments of human capital broadly construed dictate very distinct growth trajectories and Howitt & Mayer-Foulkes (2005) argue that at the time of a radical shift in technological progress, such differences lead to convergence clubs of high growth and stagnant economies. To date, however, there has been little data generated on the higher level human capital and institutions required to make this possible, especially in a historical context, and none to attempt to verify its impact on income differentials.

This paper first generates national level engineering data for the Americas in 1900 that suggests that differences in innovative capacity plausibly can explain the disparate development trajectories of countries which, at the time, had very similar levels of income. This measure, compiled using graduates of local engineering universities, professional associations and censuses, is arguably the first to offer international comparability for this key period around the Second Industrial Revolution. We see it as a measure of higher level scientifically oriented human capital per se and supporting institutions, as well as arguably a more general measure of technological sophistication of the entrepreneurial class.

To more conclusively establish the link, we employ county level data from the US and the eighteenth century an institution capable of furnishing the youth of the colony with the barest essentials of a secular education.” The majority of the relatively few university educated members of the Brazilian elite at independence trained at Coimbra in the reinstated legal theological tradition (Carvalho, 1982). In Ecuador, (Hurtado, 2007, p. 115) argues that education quality even among the elite was poor and unfocused on practical elements. Again, he cites the American ambassador to Quito in the 1850s who found “convents instead of presses, barracks in place of schools.” See comparable accounts in Will (1957) for Chile; Safford (1976) for Colombia; and Lopez Soria (2012) for Peru.

34
randomization process was built into the CAPI (Computer Assisted Personal Interview) questionnaires administered in the surveys. As our research hypothesis suggests stronger effects of nudges for more vulnerable populations, we focus on IDPs for the main analysis. The availability of the HFS sample provides a comparison group of non‐IDP households for the experiment, which will be used for robustness checks. The treatment and control groups are relatively balanced. There is a higher share of male headed households in the treatment group, which have also more members, though in practical terms these differences are relatively small. As gender of the household head and household size are potentially correlated with poverty, these variables are included in the regression models and interacted with the treatment to control for potential impacts (Lanjouw & Ravallion, 1995).

_Table 1: Balance across treatment and control arms (IDP sample)._

||Control|Treatment|Difference, p‐value|
|---|---|---|---|
|Household size|4.835|5.098|0.003***|
||(0.060)|(0.064)||
|Gender of household head|0.492|0.448|0.005***|
||(0.011)|(0.011)||
|Literacy of household head|0.507|0.529|0.155|
||(0.011)|(0.011)||
|Household head completed some primary school|0.540|0.563|0.133|
||(0.011)|(0.011)||
|Is the household head employed|0.328|0.319|0.555|
||(0.010)|(0.010)||
|Share of children in household|0.364|0.373|0.309|
||(0.006)|(0.006)||
|Share of elderly in household|0.011|0.010|0.582|
||(0.002)|(0.001)||
|First Component of Asset Principal Component Analysis|‐0.126|‐0.194|0.162|
||(0.037)|(0.032)||
|N|2079|2066||
|Proportion|0.502|0.498||
|_Standard errors in parentheses; **p<0.05, ***p<0.01_||||

# 4. Empirical Strategy

To assess the effect of our prime on reporting behavior, we can formulate following simple regression equation.

Y� �β� �β�T� �Ti ∗Xiβ� �X�β� �γ� �α� �ε�, (3) 9
tourists as well as the interaction with the type of product exported, while the third column also introduces the lagged expenditure per day of tourists, along with the interaction. Columns four and five present the same specifications as in column three, but they differ from it in that the dependent variable considers total exports of the 19 products to countries other than India (column 4), and to India only (column 5).

**Table 3: Estimation output**

|**Variables**|**Exports**|**Exports**|**Exports**|**Exports**<br>**Outside**<br>**India**|**Exports to**<br>**India**|
|---|---|---|---|---|---|
|Lagged Log Tourists||-0.844|-0.343|-1.316|-0.119|
|||-0.546|-0.538|-2.057|-0.788|
|Lagged Log Tourists*Touristic Export||||||
|Products||2.416***|1.723***|3.506**|-0.934|
|||-0.511|-0.402|-1.659|-0.787|
|Lagged Log Expenditure per day Tourists|||-0.119|2.733*|0.0285|
||||-0.567|-1.307|-0.497|
|Lagged Log Expenditure per day||||||
|Tourists*Touristic Export Products|||1.151**|-0.538|-1.049|
||||-0.494|-0.986|-0.681|
|Log World Imports of the Same Product|1.179***|0.955***|0.711***|0.657**|0.518|
||-0.108|-0.164|-0.196|-0.243|-0.384|
|Log Real Exchange Rate|-0.838***|-0.754***|-0.726***|-0.908***|-0.580***|
||-0.106|-0.123|-0.113|-0.165|-0.165|
|Constant|-10.65***|-23.04***|-15.76|-18.8|4.328|
||-1.265|-6.767|-9.359|-14.12|-7.154|
|Observations|362|350|336|305|147|
|R-squared|0.372|0.38|0.385|0.453|0.106|
|Number of Products|19|19|19|19|14|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Consider first the results of the simple export supply function reported in Column (1). Exports appear to be responsive to world demand and to relative prices, with the expected signs. Increases in world demand push Nepalese exports upwards, while increases in the real exchange rate, which imply that inflation is higher than the nominal exchange rate depreciation, reduce Nepalese exports. These results are interesting in their own right, given the generalized perception that export stagnation is largely explained by supply side constraints related to inadequate infrastructure. The recent appreciation of the rupee against the dollar may have been a detrimental factor for export growth, particularly for exports outside India. When it comes to the size of the effects, we cannot reject the hypotheses of exports being unit-elastic to world demand and to relative prices.

12
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31
also remove loss-making firms with negative valued and finally exclude industries with less than 4 observations.

The two databases were reduced after the “cleaning” process and this was mainly due to a lack of capital stock data for a large group of firms. For instance, “Machinery” was dropped from both surveys, although this industry only represents about 5 percent of the manufacturing sector’s value added (UNIDO). A critical point is to assess whether the final sample is not too different from the original (representative) sample. Inklaar, Lashitew, and Timmer (2015) proposes a rule of thumb to address this issue: if the remaining database has less than 40 observations or if it represents less than 40 percent of the original sample it should preferably not be used for empirical analysis. WBES2007 respects both criteria. The final database has 312 observations which represents more than 50 percent of the initial sample. Concerning WBES2013, the first criterion is fulfilled (61 observations remained), however the second one is not (the final sample represents around 30 percent of the initial sample). We aknowledge that representativeness could have been importantly affected for the WBES2013 and these results should be taken with caution. For this reason, as a robustness exercise, we apply an alternative methodology to compute resource misallocation (Bartelsman, Haltiwanger, and Scarpetta, 2013) that uses less theoretical restrictions and does not require a capital stock measure, allowing then to use a higher share of the initial sample for the empirical exercise.

# **5 Computing Distortions and Potential Gain from Reallocation**

To be able to compute the degree of resource misallocation and the potential gain from resource reallocation a calibration exercise is required. Following HK and as is common in this literature, the rental price of capital ( _R_ ) is set to 0.10 (the idea is to account for a 5% real interest rate and for a 5% depreciation rate). HK demonstrates that the level of _R_ affects only the level of factor distortion but not the TFP gains from resource reallocation. The elasticity of substitution between firm value-added ( _σ_ ) is set to 3. HK shows that the gains from liberalization are increasing in _σ_ so the choice of 3 is a conservative one (in the literature the estimates for _σ_ go from 3 to 10, see for instance Broda and Weinstein, 2006, and Hendel and Nevo, 2006).

As HK, _α_ is set to be 1 minus the labor share in the corresponding industry in the United States. Since we cannot separately identify the average capital distortion and the capital production elasticity in each industry we need to pick these shares from a relatively undistorted economy.

One more variable is needed to be able to compute the TFP gains associated to liberalization: _Asi_ , defined in equation (11). The problem here is that we do not observe real output for each firm ( _Ysi_ ) but rather its nominal output ( _PsiYsi_ ). HK notes however that firms with high real output must have a lower price to explain why buyers would demand the higher output. Then, _Ysi_ can be simply 12