rafmacalaba/data-use-mentions-extended huggingface dataset:
14 woman becomes a widow, it is likely that she does not marry again given a lack of suitable partners of similar age.<sup>8</sup> Finally, we calculate a demographic conflict proxy that captures the extent of deaths across age groups (SEXRATIO). The data on sex ratio comes from two secondary sources: the 1991 Census (which is matched with the 1992 RDHS) and the 2002 Census (which is matched with the 2000 RDHS and the 2005 RDHS). Women are assigned the average sex ratio (defined as the ratio of males to females) in the cohort of their potential partners in a given province, taking into account the typical age difference between spouses in Rwanda. More precisely, sex ratios in a woman’s five-year age group, one younger age group and two older age groups were averaged. These provincial, age group-specific sex ratios are the closest approximation to the local marriage market possible with publicly available data. Still, sex ratios derived from census data overestimate the number of men potentially available on the marriage market, as tens of thousands of male perpetrators of genocide were in jail (Ministry of Finance and Economic Planning et al. 2003).

It is important to note that our analysis is based on a sample of survivors. Households in which all (female) members died during the genocide are by definition not accounted for. In other words, the impact of the genocide is underestimated in the present analysis. However, given that the focus of this paper is on the impact of the genocide on the fertility of the survivors in the post-genocide period (and not on estimating excess mortality), this does not bias our analysis.

# **5 Estimation strategy**

We employ two slightly different estimation strategies to explore the impact of the two channels – replacement and marriage market – on fertility. This is due to the fact that proxies for conflict exposure measuring replacement effects are only available for post-genocide RDHS data, while conflict proxies measuring marriage market effects are
Figure 4. Lorenz curve, Malawi Fourth Integrated Household Survey (IHS4): comparing overall inequality across household per capita consumption expenditure, household per capita wealth, and individual wealth <u>Note: based on the full sample in the IHS4 (26,079 adults).</u> Table 10. Comparison of inequality measures, for individual-level wealth, across the Malawi IHPS 2016 (LSMS+) and Malawi IHS4 2016/17

||Among fu|ll sample:|||Among only<br>|those owni<br>|ng any asset<br>|s (value>0)<br>|:<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||Gini in<br>|dex<br>|Gini<br>|index<br> <br>|75/2<br>|5<br>|GE(0): T<br>|heil L<br>|GE(1):<br>|Theil T<br> <br>|
||IHPS<br>(LSMS+)|IHS4|IHPS<br>(LSMS+)|<br>IHS4|IHPS<br>(LSMS+)|IHS4|IHPS<br>(LSMS+)|IHS4|IHPS<br>(LSMS+)|<br>IHS4|
|_For IHPS:_<sup>_(1)_</sup> Missing values|94.3***|88.3***|92.0***|79.2***|9.0***|5.4***|2.7***|1.4**
In addition, we also asked about the ability of people to work together through the survey question (we call this the “work-compatibility” question): _Do you feel that the ability of different religions, nationalities, and races to work together for a common cause is:_ _1 = Very low_ _2 = Low_ _3 = Neither low nor high 4 = High 5 = Very high_ We aggregate the responses into a binary variable that differentiates between “high” or “very high” (4 or 5) and “very low”, “low” and “neither” (1, 2, or 3). As such, we are again able to measure those respondents whose opinions of others are positive relative to those who are neutral or negative. On average, around 40 percent of respondents feel that the ability of people from different backgrounds to work together is “high” or “very high”; again, the relationship between the response to this question and the distance from the fault-line will nuance this overall summary statistic.

Finally, helpfulness and kindness comes from the following question asked of all respondents in the household survey: _After the earthquake, your opinion of the helpfulness and kindness of {name} is: 1 = Much better than before 2 = Better than before 3 = Same 4 = Less than before 5 = Much less_ Again, we split the Likert scale into a binary variable; “better” (1 or 2) and “same or worse” (3, 4, or 5). Thus, when presenting our findings, we are measuring the fraction of respondents whose perceptions of helpfulness and kindness have increased following the earthquake and 13
rather than prices facing ...rms. In modern, developed economies with high rates of income and indirect taxation, the gap between household and ...rm TFP can be considerable. Finally, in an open economy, the change in welfare will also re‡ect present and future changes in the returns on net foreign assets and in the terms of trade. However, these latter terms disappear if absorption rather than GDP is used as the output concept for constructing TFP, and TFP and the initial capital stock are again su¢ cient statistics for measuring welfare in open economies. Most strikingly, these variables also su¢ ce to measure welfare level di¤erences among countries, with both variables computed as log level deviation from a reference country.

We apply these results to measuring welfare growth rates and gaps in a sample of developed countries. For reasonable assumptions about ...scal policy, we ...nd that over our sample period the UK and Spain are the leaders in welfare growth rates. Throughout our sample period, however, the US is the world leader in welfare levels. The UK converges steadily towards US levels of welfare, and is within a few percent of catching the US by the end of our sample, 2005. At the start of our sample, several countries show evidence of convergence to the US, but by the end almost all countries are diverging away from US welfare levels. This divergence is particularly stark for Japan and Italy, which end the sample with less than half the per-capita welfare of the US.

37
In summary, declining labor shares are of second-order importance in quantifying the effects of changes in productivity gaps across sectors on aggregate productivity. Using disproportionate changes in labor shares increases the share of actual growth accounted for by changing distortions at the upper end.

# **6 Conclusion**

The goal of this paper is to examine the role of distortions in a historical perspective. Over the last few decades countries have seen sizable changes in GDP per capita over time. A fundamental issue of interest is to understand whether changes in distortions over time have been important drivers of growth or not. To quantify the effect of changes in distortions over time in accounting for actual growth, I develop a simple model of resource allocation in which these gaps arise due to distortions in the form of asymmetrical taxes on labor inputs across sectors. A model of resource allocation is essential in contrast to a shift-share analysis of aggregate productivity because productivity in both sectors change as a result of resources flowing from the low productivity sector to the high productivity sector.

Using sector level data from WDI database and labor share estimates from GTAP database, I estimate distortions in Industry and Services relative to Agriculture for a number of countries that span a spectrum of world income distribution. The main finding of the paper is that there is limited evidence supporting a dominant role of changes in distortions in accounting for actual growth. For the median country, implied growth is less than 2.5% of actual growth across all periods. However, changes in distortions can account for larger share for some countries. To the extent that reductions in distortions represent a _lower bound_ on possible gains because large reductions in barriers are yet to be realized, I perform a second counterfactual in which barriers across countries are reduced to what is observed in rich countries. Once again, I find only modest gains in productivity for most countries. The findings of the paper are also robust to changes in labor shares over time. However, disproportionate changes in labor shares across sectors somewhat increases the share of actual
of four years. As mentioned in the introduction, this kind of analysis has been done, traditionally, with aggregated, country level data, which are not plagued with our identification issues but at the same time lack the micro-economic support and policy relevance that firm-level data provide. This kind of disaggregated data is inherently noisier, but strong patterns should emerge through the noise, as is the case in this analysis, with high statistical significance.

In order to gain a relatively clear picture of the structure and composition of Lao exporters as well as a preliminary understanding of their survival-related mechanisms, we first looked at several descriptive characteristics of this very useful dataset. We found that multi-product, multidestination firms dominate exports in value terms and that products with a low potential for diversification tend to increase their already large presence in Lao exports.

We have also documented the great deal of exploration that goes on every year on the extensive margin (new firms, new products and new destinations) and found that this is dominated, in dollar terms, by the incumbent exports that make up the intensive margin. In terms of survival, there is a relatively high rate beyond the first year, when compared to the few countries for which we have similar data. Nevertheless, only around 10 percent of the firm-product-destination triplets last through all of the four years of our dataset. Thus, churning and realignment of product and destination portfolios within firms is happening at a very rapid pace.

The regression analysis of the factors that determine whether exporting firms survive past the first year of activity and the dollar value at which they start exporting shows that Lao firms profit from focusing on a more limited range of products and destinations. One important and rather surprising fact is that firms benefit when others do the same: the agglomeration of exporters serving the same market with the same product has a large positive impact on the odds of survival and on the initial export value. This means that there are positive spillover effects from the activities of competitors, effects similar to external economies of
0.285|0.010|9,025|
||_(0.456)_|_(0.452)_|||

* p < 0.05, ** p < 0.01, *** p < 0.001 _Notes:_ Standard deviations in parentheses. T-tests with robust standard errors clustered at the school level. All figures are rounder to three decimal places.

_Source:_ Own survey data, administrative data, school year 2015/2016, own calculations.

_Table A2.2: Impacts on Deliberate Practice Beliefs and Grit, Balanced Sample (Z-Scores)_

|Deliberate Practice Beliefs<br>(1)|Grit<br>(2)|Grit: Effort<br>(3)|Grit: Interest<br>(4)|
|---|---|---|---|
|Treatment 1<br>0.162***|-0.064***|0.051**|-0.127***|
|“Student Self-Learning”<br>_(0.019)_|_(0.020)_|_(0.021)_|_(0.019)_|
|Treatment 2<br>0.236***|-0.032|0.062***|-0.094***|
|“Teacher Delivery”<br>_(0.018)_|_(0.022)_|_(0.019)_|_(0.022)_|
|N<br>18,718|18,718|18,718|18,718|
|N Control<br>7,286|7,286|7,286|7,286|
|N Treatment 1<br>5,424|5,424|5
# **5. Further analysis**

# **_5.1. Potential mechanisms and projected impacts under future climate change_**

Having demonstrated strong evidence of temperature effects on poverty and inequality at the subnational level, we further explore agriculture as a potential mechanism. Agriculture plays an important role in poverty reduction for various reasons. The majority of the global poor live in rural areas where agriculture is the predominant form of economic activity and agricultural growth is more effective at reducing poverty than non-agricultural growth; moreover, poorer households are more vulnerable to increases in food prices (Hertel and Rosch, 2010; Hallegate _et al._ , 2016). We analyze the global dataset of historical yields from Iizumi and Sakai (2020), which provides actual crop yields at 0.5° resolution for the period 1981-2016. Using the panel FE and long differences models as with Equations (1) and (2), we find negative effects of higher temperature on different crop yields including rice, maze, and soybean, as shown in Table A11 (Appendix A). Again, we find the long differences model estimates to be smaller than the panel FE model estimates, which are in line with previous studies showing potential adaptation in the long run (e.g., Chen and Gong, 2021). We also document the heterogenous effects of hotter temperature and discuss the results in Appendix D.<sup>9</sup> We next provide projections of the effects of future temperature on poverty to better understand potential effects under different scenarios. We focus on different climate change scenarios taken from the recent released Coupled Model Intercomparison Project Phase 6 (CMIP6).<sup>10</sup> Table A13 (Appendix A) provides a summary of the projected changes for

> 9 As another potential mechanism, we explore subnational migration flow between the period 2005 and 2010. Using a simple OLS regression, we find some suggestive evidence that hotter temperature could lead to more migration (Appendix A, Figure A8
aid program for pregnant women in the Estate Sector.

# **Accessibility**

When governments make data widely available, they empower individuals to make better choices through more information and knowledge. The digital revolution has increased the potential and ease with which information can be shared. One example is providing agricultural information, often through text messages, to smallholder farmers to increase their productivity. Such data transmission can improve existing extension services, which rely on in-person agricultural advice and are more costly to sustain and whose quality is more difficult to assure. A meta-analysis suggests that providing agricultural information increases yields by 4 percent and also increases farmers’ probability of using productivity-enhancing agrochemical inputs (Fabregas et al. 2019). With more than 2 billion people living on smallholder farms, these numbers can have major consequences for global poverty and shared prosperity.

Likewise, providing real-time price data to farmers allows them to sell their products at the right time and place. In Ethiopia, small-scale farmers lack access to reliable price information, and thus often receive prices far below market value. In 2008 the Ethiopian Commodity Exchange opened, providing price information to farmers through text messages, hotlines, and online 17
Republic, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan). Our focus is the ratio of gross domestic savings (GDS) to GDP, with GDS derived residually from the current account deficit and gross domestic investment, using internally consistent World Bank national accounts statistics for all components.

Table 1 reports the gross domestic savings rates and the fiscal deficit. Pre-transition savings rates (1989 for central Europe, 1990-91 for the FSU and the Baltics) were among the highest in international comparison, averaging about thirty percent, before sharply declining to about ten percent in the early transition years, with a slight increase in the latter years.

The savings data for transition economies suffer from two potential problems. First, it is possible, indeed likely, that the national account statistics capture only a subset of activity. If both expenditure and income data are incorrectly measured, the net effect on savings is ambiguous. Second, the nominal data are deflated by price indices that exclude activity in the informal sector. Depending on the extent of the black-market spread, the pre-transition price level is thus understated, and thus savings (and the monetary overhang defined vis a vis the measured price level) are consequently over-stated [Dornbusch and Wolf (2000)].

# **3. Involuntary Savings**

The welfare implications of the collapse of measured savings depends crucially upon whether the pre-transition savings were voluntary and stable; voluntary but driven by expectations of the systemic change --- for example, reflecting the expectation of sharp relative price changes and greater availability of goods --- or involuntary, reflecting binding limits on consumption spending below desired levels. Of these three explanations, the second commands least empirical support: savings rates in socialist were high throughout the 1980s.

2
mapping of tertiary and residential road networks. Here, we make two hypothesises: 1) settlement clusters with denser highways/motorways also have denser tertiary/residential roads and 2) strong differences in percentile rankings between the two network types, speficially lower percentile ranks for tertiary and residential roads, suggest incompleteness in the OSM road network. Our results show that approximately 44.7% of the clusters have percentile ratios greater than one, with a maximum ratio of 21.3. This suggests that the density of tertiary and residential roads for many settlement clusters is relatively lower compared to the network density of higher-level roads. Therefore, we decided to exclude tertiary and residential road types in this study due to incompleteness in the mapping of tertiary and residential roads (3.3% and 92.7% of the clusters contain no records for tertiary and residential roads respectively) as well as imbalance in network density distribution.

In addition, we examine the same percentile rank patterns for clusters that belong to economies at different development stages and find that clusters that belong to developed regions (e.g., G7<sup>6</sup> countries) have relatively higher road network densities than those that belong to emerging and less developed economies (Fig. 3c-i). Such results are aligned with the findings from Barrington-Leigh et al. and raise the issue of data bias in this study. To minimize the error introduced by this bias, we remove clusters with inadequate network coverage in addition to excluding tertiary and residential roads from extracted OSM. For each cluster, we calculate the percentage difference between the area of the convex hull created from the OSM data set and that created from the original settlement cluster boundaries. Clusters with percentage differences above the inflection point or “knee”<sup>74</sup> are removed, resulting in 3,128 clusters (Supplementary Figure 2). Furthermore, 564 clusters (among the 3,128 clusters) have insufficient speed limit assignment and are left out in the subsequent
Second, we evaluated survey representativeness. For each country, we compared programs in the survey and others in the universe based on characteristics such as HEI governance (public / private; for-profit), tuition, enrollment, and institution type (university, non-university HEI, non-HEI). Although a few differences were statistically significant, they were small in magnitude (Table A4). We recalibrated the sampling weights used in the estimations to account for them. As a result, our results can be viewed as representative for the SCP universes.

Self-reporting could have biased our survey data if, for example, program directors had expected to receive some benefit from the educational authorities based on their responses, in which case they would have had incentives to misreport information. To mitigate this problem, we followed best practices for self-reported data collection and applied them to all programs. First, respondents received a letter from the research team indicating that all responses would remain confidential and anonymous and would be reported only in an aggregate fashion—thus favoring truthfulness—as in Bloom et al. (2020). Second, the letter informed respondents that the survey was being conducted exclusively by The World Bank for research purposes and made no mention of governmental units. Third, questions were designed to avoid common biases associated with self-reporting. For example, questions referred to specific time periods (such as the previous academic year) to avoid memory biases, and, where possible, included specific response options.

To address potential biases in self-reported program outcomes, we conducted an outcome validation exercise using country-specific administrative data and household surveys. On average, we found that directors’ outcome reports did not differ substantially from outcomes obtained from these other sources. This result was more salient when we restricted the comparison of average labor market outcomes to the age group that was more likely to be enrolled in SCPs (Table A5).

13
metal mining (-48.7%/+1<sup>_◦_</sup> C) and timber (-41.2%/+1<sup>_◦_</sup> C) (Chen and Yang, 2019). Losses in agricultural TFP in China are estimated at 2.6% for every additional day with a temperature of more than 33<sup>_◦_</sup> C during the year (Chen and Gong, 2021).

These findings are consistent with county-level payroll evidence from the US indicating that labor productivity in highly-exposed non-agricultural industries, such as utilities, mining, and manufacturing, declines by as much as 50% on a workday above 95<sup>◦</sup> F (35<sup>◦</sup> C), a ninefold impact relative to less exposed industries, although there seems to be adaptation when evaluating cross-county differences (Behrer and Park, 2017).<sup>xxxv</sup> In indoor controlled environments, productivity tends to be similarly negatively associated with temperature, often in a non-linear, U-shaped way. A meta-analysis of indoor office, laboratory, and classroom settings finds task performance to increase until reaching a ceiling at 21<sup>_◦_</sup> C- 22<sup>_◦_</sup> C, with further increasing temperature from 23<sup>_◦_</sup> C to 30<sup>_◦_</sup> C resulting in a 9% productivity loss (Seppanen et al., 2006). Student performance among college entrants in China declined by 2.9% of a standard deviation for a 1<sup>_◦_</sup> C increase in mean temperature of 23<sup>_◦_</sup> C during the exam period—with a more pronounced effect for highperforming students (Graff Zivin et al., 2020)
compensation because it doesn’t include fringe benefits and employer social security contribution. Following Hsieh and Klenow (2009), we inflate the labor cost by a factor of 3/2.

> 6Note that industries in Ethiopia, Ghana and Kenya are classified according to ISIC Rev 3.1, ISIC Rev 3 and ISIC Rev 4, respectively. Industries in Cˆote d’Ivoire are classified according to NAEMA (equivalent to ISIC Rev 3) whereas the industrial data for US is reported based on 1987 SIC and 1997 NAICS classifications. We use appropriate concordance tables to match the datasets. We keep firms that correspond with the US data at four-digit levels.

11
views, and family circumstances.

In instances in which discrimination in the workplace appears to be the result of statistical discrimination (for example, employers believe that female applicants are still, on average, less qualified or less productive than male applicants), Tunisia’s National Employment Agency <mark>(Agence nationale pour l'emploi et le travail indépendent) could strengthen its labor market programs and job intermediation services between employers and job seekers, e</mark> specially in workplaces where most workers are men <mark>. Offering internships and on-the-job training c</mark> an serve

> 18 https://wbl.worldbank.org/.

> 19 World Bank, Women, Business and the Law database, 2019, https://wbl.worldbank.org/.

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