rafmacalaba/data-use-mentions-extended huggingface dataset:
Next, I examine whether the potential increase in the wife’s inheritance results in partition of joint households by checking for higher rates of migration among couples in reform states married post-reform. Table 6 shows the coefficient for being exposed to the reform on the outcome variable _joint._<sup>23</sup> The first column shows that Hindu women who were ‘treated’ are significantly less likely by about 4.3% to reside in joint families, while the second column shows that there is no such effect for the nonHindu women. A placebo test identical to specification (3), which assumes that the amendments occurred a couple of years prior to their actual implementation, shows no such significant effect for the ‘treated’ Hindu women. This suggests that the significantly lower propensity of Hindu women to reside in joint families post-reform is not simply a function of some such pre-existing trend in the reform states and can in fact be attributed to the amendments.

# (c) _Is part of the effect driven by the negative effect on men’s inheritance?_

Finally, I explore whether this shift in household structure is partially driven by the detrimental effect of the reform on men’s inheritance. These are the men with sisters who were eligible to benefit from the reform. Unfortunately, the NFHS data doesn’t allow me to calculate the number of siblings of each man since I do not observe any siblings who are not residents of the same household. To overcome this shortcoming, I make use of the Rural Economic and Demographic Survey (REDS) conducted in 1999. The REDS data provides information on household structure that is similar to the NFHS but also contains additional information on all the children of household heads who are not residents of the household. In particular, it provides the exact years of marriage of any daughters of the household head, thereby allowing me to identify whether these women, and in turn their brothers, are actually exposed to the reform. The partition channel predicts that men are
## 6% by non-technical measures.

Table 8: Number (share) of products affected by at least one active NTM in 2017

||Technical|Non-technical|
|---|---|---|
|ARG|4,479 (81)|5,205 (95)|
|BOL|1,930 (35)|945 (17)|
|BRA|3,960 (72)|1,221 (22)|
|CHL|3,309 (60)|107 (2)|
|COL|2,963 (54)|3,254 (59)|
|CRI|1,777 (32)|322 (6)|
|CUB|5,363 (98)|5,362 (97)|
|ECU|2,937 (53)|2,198 (40)|
|GTM|1,233 (22)|54 (1)|
|HND|1,633 (30)|588 (11)|
|MEX|1,848 (34)|837 (15)|
|NIC|1,898 (35)|752 (14)|
|PAN|1,717 (31)|111 (2)|
|PER|2,045 (37)|84 (2)|
|PRY|1,527 (28)|393 (7)|
|SLV|1,937 (35)|88 (2)|
|URY|2,555 (46)|367 (7)|
|VEN|4,891 (89)|4,803 (87)|

Source: Author’s calculation based on UNCTAD’s NTM data. Note: The total number of products at the HS 6-digit is 5,400 (HS Combined).

# **3.4 Colombian Firms and Non-Tariff Measures in LAC**

We now study the extent to which Colombian
dimension is obtained by taking the unweighted average of all indicators in this dimension, including the indicators for the system of national accounts in use, national accounts base year, classification of national industry, CPI base year, and classification of household consumption (Appendix A, Table S1).

A score for each pillar is subsequently computed as the average score of the dimensions in that pillar. For pillars 1, 2, 4, and 5, the unweighted average of the dimensions within each pillar is taken. For pillar 3 on data products, we take a weighted average of the dimensions, where the weights are based on the number of SDGs in each dimension (6 SDGs in dimension 3.1 on social statistics, 6 SDGs in dimension 3.2 on economic statistics, 2 in dimension 3.3 on environmental statistics, and 2 in dimension 3.4 on institutional statistics. SDG 14 - Life Below Water - is omitted because land-locked countries do not report on these indicators.). This reflects a perspective that all SDGs are of equal importance, and therefore the dimensions are weighted accordingly. Additionally, for Pillar 4 on data sources, censuses and surveys are given separate weights, so that censuses, surveys, administrative data, and geospatial data each receives a weight of 1/4. While censuses and surveys are in the same pillar in the framework, and therefore each would typically only receive a weight of 1/6 in this dimension, because of their importance in producing many indicators, they are given extra weight such that each gets a weight of 1/4. (Using a weight of 1/6 for censuses and surveys provides very similar results. In particular, the correlation between the SPI overall score under the preferred approach and the alternative approach is 0.998.) The score for each pillar (S c ) is calculated as follows SS. S LL cc S c = ∑ <u>c</u> ×S c <u>c</u
In columns (8) to (10) in table 2 we look at this in a regression setting with plant and time dummies. In column (8) the OLS coefficient of 0.127 implies increasing the adoption of management practices by 1 percentage point would be associated with about a 0.1% increase in output. In column (9), we see the impact of management instrumented with the intervention displays a higher significant point estimate of 0.346. As with quality and inventory the IV estimator is again notably higher than the OLS estimator, again indicating an endogenous adoption of better management when output falls. Finally, in column (10) we look at the ITT and see a point estimate of 0.056, implying a 5.4% increase in output (exp(0.056)-1), although this only significant at the 11% level.<sup>22</sup> Looking at the small-sample standard errors we find the IM and permutation tests are all significant at the 5% or 10% level.

There are several reasons for these increases in output. Undertaking routine maintenance of the looms reduces breakdowns. Collecting and monitoring the breakdown data also helps highlight looms, shifts, designs and yarn-types that are associated with more breakdowns. Visual displays around the factory floor together with the incentive schemes motivate workers to improve operating efficiency. Finally, keeping the factory floor clean and tidy reduces the number of untoward incidents like tools falling into machines or factory fires. Again the experience from lean manufacturing is that the collective impact of these procedures can lead to extremely large improvements in operating efficiency, raising output levels.

# **_V.D Results by plant_**

We can also examine the difference in quality, inventory and output after treatment on a plant by plant basis. Figure 6 plots the histograms of the before-after changes in our performance measures for the treatment and control plants. No outliers are driving these differences, with all treatment plants improving their quality (top-left
35

# <u>References</u>

Adams, Jr., Richard. 1991. The Effects of International Remittances on Poverty, Inequality and Development in Rural Egypt. Research Report 86. International Food Policy Research Institute, Washington, DC.

- Adams, Jr., Richard.  1992.  “The Effects of Migration and Remittances on Inequality in Rural Pakistan.” Pakistan Development Review, 31, 110-133.

- Adams, Jr., Richard.  1995.  Sources of Income Inequality and Poverty in Rural Pakistan.  Research Report 102.  International Food Policy Research Institute, Washington, DC.

- Adams, Jr., Richard.  1998.  “Remittances, Investment and Rural Asset Accumulation in Pakistan.” Economic Development and Cultural Change, 47, 155-173.

Deaton, Angus. 1992. Understanding Consumption. Oxford: Clarendon Press.

- Deaton, Angus.  1997.  The Analysis of Household Surveys:  A Microeconomic Approach to Development Policy.  Baltimore:  Johns Hopkins Press.

- Glewwe, Paul and Jacoby, Hanan.  2004.  “Economic Growth and the Demand for Education:  Is There a Wealth Effect.”  Journal of Development Economics, 74, 33-51.

- Psacharopoulos, George.  1985.  “Returns to Education:  A Further International Update and Implications.”  Journal of Human Resources, 20, 583-604.

- Psacharopoulos, George.  1994.  “Returns to Investment in Education:  A Global Update.”  World Development, 22, 1325-1343.

Ratha, Dilip. 2004. “Enhancing the Developmental Effect of Workers’ Remittances to Developing Countries.” In World Bank, Global Development Finance, pp. 169-173. Washington, DC.

- Stahl, Charles.  1982.  “Labor Emigration and Economic Development.”  International Migration Review, 16, 868-899.

Stark, Oded. 1991. The Migration of Labor. Cambridge, MA: Harvard University Press.

- Stark, Oded, Taylor, J. Edward and Yitzhaki, Shlomo.  1986.  “Remittances and Inequality.”  The Economic Journal, 96,
>50<br>40<br>30<br>20<br>10<br>0<br>SSA Average SSA Median SSA Weighted Avg.<br>excluding Nigeria<br>2007 2013 2018<br><!-- End of picture text --> _Source: IMF – World Economic Outlook Database, October 2018_ Public debt has been rising across all income groups in the region, with the largest increase recorded by lower-middle-income countries (LMCs), an average increase of 26 percentage points of GDP since 2013. By 2018, the (simple) average public debt of LMCs in the region reached 74

> 2 Eritrea, Somalia, and South Sudan are excluded due to the lack of reliable data.

> 3 Using GDP-weighted averages does not change this narrative. Although debt increased only moderately between 2007 and 2012, debt accumulation accelerated significantly in the region thereafter in GDP-weighted terms.

4
Malawi. Model predictions achieve values of 0.57 in Malawi and 0.42 in Mozambique at fine spatial levels, despite the lack of available geo-features. This is a large improvement over the 0.15 to 0.17 RR<sup>2</sup> reported by [29] in comparable settings, and demonstrates the feasibility of combining transformer models with imagery to estimate wealth RR<sup>2</sup> changes at granular levels, given sufficient training data.

The results point to the benefits of applying transformer models that incorporate geospatial features to generate highresolution predictions of asset wealth. This in turn underscores the importance of developing tools, documentation, and training materials to make estimation feasible for national statistics offices, international organizations, and other data providers. In addition, developing and evaluating methods for estimating the uncertainty associated with predictions is crucial to facilitate implementation.

The results also highlight the importance of having access to a Page 7 of 11
Schooling (years)|0.007|0.025|0.022|0.014|0.016*|
|Economic complexity|0.093*|0.135*|-0.024|-0.021|0.034|
|Commodity exporter|0.030|-0.065|-0.124|-0.052|-0.077*|
|Trade (% GDP)|0.134*|0.123|-0.033|-0.009|0.055|
|Investment (% GDP)|-0.208|-1.109**|2.133***|1.010***|-0.153|
|Law and Order|0.033|0.010|0.052|0.011|0.046***|
|Observations (All)|61|69|78|79|287|
|_Note:_|||_∗_p_<_|0.1; _∗∗_p_<_0.05;|_∗∗∗_p_<_0.01|

Note: Decade dummies are used in the panel specification, but no fixed effects are included. Initial productivity is the average of log productivity over the 10-years in the preceding decade. Productivity growth calculated as the change in average log-productivity between the two decades (10-year average growth). For the decade beginning in 2010, productivity is assumed to grow at its average rate between 2010-7 for the final two years of the decade, 2018 and 2019. All conditioning variables are lagged decadal averages. Market exchange rate results are also provided for comparison.

# **PPP-effects on productivity growth**

To establish the effects of differences in relative prices on the level of output across economies, the Penn World Tables draw on multiple years of data from the World Bank’s ICP, with data beginning in 1970.
Policy Research Working Paper 8015

# **Abstract**

Uninsured risk constrains households in their production decisions in many developing countries. Similarly to crop insurance, employment guarantees can support farmers in managing agricultural production risks. Evidence from representative panel data of Andhra Pradesh, India, suggests that the National Rural Employment Guarantee Scheme (NREGS) reduces households’ uncertainty about future income streams because it provides employment opportunities in rural areas independently of weather shocks and crop failure. Because the NREGS makes an ex-post labor supply response to agricultural shocks more efficient, households with access to the NREGS can shift their production towards riskier but also more profitable crops. The observed shifts in agricultural production do considerably raise the profitability of agricultural production and hence the incomes of smallholder farmers. The findings are not driven by changes in the labor or cost intensity of those crops, which supports the idea that the causal mechanism underlying the observed changes is indeed an insurance effect.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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://econ.worldbank.org. The author may be contacted at esther.gehrke@unigoettingen.de..

_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
The standard approach used by economists to adjust for differences in cost-of-living is to use purchasing power parity exchange rates, and to use regional price deflators. These help to adjust for differences in the price of a set basket of items in different locations, and then adjusting for living costs becomes largely a measurement issue. However, migration does induce a couple of additional issues into this adjustment. The first is that temporary migrants might earn income at destination with the intention of spending it at home, so that migrants may not be spending all of their income on the more expensive goods at destination (Dustmann et al, 2021), and so cost-ofliving adjustments may understate the gains from migration. However, conversely, Atkin (2013) shows that inter-state migrants in India carry their food tastes with them, paying more for food as they buy products from their origin state that are more expensive than the local food bundle. Costof-living adjustments may then undercount the costs of migrants being able to obtain the same utility from their consumption expenditure as at home. Nevertheless, in many cases these cost-ofliving adjustments will be small compared to the large income gains from migration.

Addressing the issue of differences in productivity between migrants and natives requires measuring what the change in income is for those individuals who actually migrate. One approximation of this is carried out by Clemens et al. (2019), who use survey data to compare the incomes earned by migrants in the U.S. to the (purchasing power parity adjusted) incomes of individuals of the same age, gender, and education in their home countries. They find a wage gap of PPP$13,710 per year, with U.S. incomes more than 5 times that in the average sending country. Gibson and McKenzie (2011, 2012), consider high academic achievers from Ghana, Micronesia, New Zealand, Papua New Guinea and Tonga, and compare the (cost-of-living adjusted) incomes of migrants to those with similar skill levels who did not move, finding income gains of
run measurement of top incomes in France, and subsequently this area of research has exploded.

In this paper we have surveyed this literature, focusing on the challenges that have been identified when trying to measure the top of the income and wealth distributions, as well as the solutions that have been used to overcome these challenges. Our focus has been low- and middle-income countries, although we have discussed research from high-income countries, particularly when no research on a specific topic has been undertaken in any LMIC.

There are multiple challenges that researchers measuring the top end of the wealth and income distributions face. We focused first on the challenges when using household survey data, given that this is the most common form of data on incomes and wealth available in LMICs and that other forms of data (like administrative tax microdata or even tax tabulations) are uncommon. These include missing high income or wealth individuals due to non-random non-response (item or unit) or due to sampling error and measurement error (either from respondents themselves, proxy respondents or 30
0.01*|-0.00|-0.00|
||(0.00)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Constant|0.84***|0.68|0.56|-1.44**|2.16***|0.61|-0.02|
||(0.12)|(0.49)|(0.62)|(0.72)|(0.63)|(0.54)|(0.89)|
|Observations|1,292|1,267|3,426|605|465|3,389|3,396|
|Number ofgroups|1,019|1,001|1,624|506|391|1,615|1,622|

_Source:_ Author based on DHS 2000/2005/2010/2015. _Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Ref. group for wealth is poorest.

27
) exhibited no variation across projects. Second, some indicators used in IPF drew directly on project data in natural units rather than the ordinal score (e.g., 0, 5, 10) assigned to projects in the MCE approach. Third, some values maintain their ordinal scores, but are transformed so that more positive scores are recorded with higher values (in the MCE, lower scores are attributed to better performance on sub-indicators). The resulting indicators are described in Table 4, and relevant transformations are described in Table 5.</mark> 25
search engines and websites. Included interventions must have been carried out in low- or middle-income countries using recognized experimental or quasi-experimental estimation strategies.

**SEMI-STRUCTURED AND UNSTRUCTURED INTERVIEWS WITH IE PRODUCERS AND CONSUMERS, INDIVIDUAL RESEARCHERS, INSTITUTIONAL REPRESENTATIVES, AND POLICY MAKERS** – Thirty-three face-to-face or telephone interviews were conducted with a purposive sample of impact evaluators, impact evaluation funders, policymakers, and individuals specializing in cost-benefit and cost-effectiveness methodologies to learn about the range of institutional barriers and incentives of integrating Value for Money analysis into impact evaluations.

**SURVEY OF POLICYMAKERS** —In partnership with the World Bank’s Strategic Impact Evaluation Fund (SIEF), we designed a survey that was emailed to 3,623 individuals on a contact list maintained by SIEF of policy-minded individuals. These individuals were asked

> 4 A list of the World Bank impact evaluations that were included in the sample is available from the authors.

5
|0.789|0.538|0.512|0.470|
|1960-1969|0.298|0.213|0.913|0.576|0.649|0.555|
|1950-1959|0.308|0.210|0.995|0.620|0.511|0.451|
||||**So**|**ns**|||
|1980-1989|0.469|0.393|0.622|0.505|0.450|0.407|
|1970-1979|0.431|0.357|0.394|0.275|0.499|0.455|
|1960-1969|0.311|0.256|0.906|0.633|0.626|0.529|
|1950-1959|0.310|0.240|1.035|0.704|0.585|0.502|
||||**Daug**|**hters**|||
|1980-1989|0.543|0.447|0.783|0.585|0.482|0.438|
|1970-1979|0.553|0.439|0.913|0.605|0.580|0.529|
|1960-1969|0.425|0.326|1.105|0.653|0.758|0.626|
|1950-1959|0.499|0.349|1.275|0.620|0.612|0.555|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) Each row uses a subsample of children who were born in the given birth cohort and the given
<!-- Start of picture text --> Urban/Rural Male-Headed/Female-Headed<br>Ethiopia Malawi Ethiopia Malawi<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>.2 .4 .6 .8 1 0 .2 .4 .6 .8 1 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Nigeria Uganda Nigeria Uganda<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Specialization Index Specialization Index Specialization Index Specialization Index<br>Rural Urban Male Female<br>Fractional Index 3<br>Ethiopia Malawi Ethiopia Malawi<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 .2 .4 .6 .8 1<br>Nigeria Uganda Nigeria Uganda<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8
Given this insight, and based on our motivating example of Figure 1, a natural question arises: how much of the fall in the monetary poverty during the Golden Decade in LAC was permanent and how much was cyclical? As shown in Figure 1, emerging regions such as LAC made tremendous progress in terms of reducing monetary poverty during this period, with the headcount ratio for the 5.50-dollar poverty line falling by almost 20 percentage points (from 42.1 percent in 2003 to 23.4 percent in 2014). Needless to say, the relative importance of permanent versus cyclical reductions in poverty has critical implications for (i) assessing the long-lasting magnitude of the fall in poverty, and (ii) the public policies that may be put in place to deal with permanent versus cyclical falls in poverty.

To decompose the fall in monetary poverty between permanent and cyclical components, we follow the very influential methodology proposed in a seminal paper by Datt and Ravallion (1992). Using microdata from household surveys, Datt and Ravallion (1992) decompose the change in poverty between two points in time into (i) a “growth component” and (ii) a “redistribution component.” The growth component is identified as the change in monetary poverty between two years that would have occurred if the income of each household member had changed in the same proportion as the national mean income, keeping constant the shape of the income distribution (measured by the Lorenz curve). The redistribution component reflects the change in monetary poverty that would have occurred if the income distribution had changed as it did, but with no changes in mean income.<sup>18 19</sup> Applying Datt and Ravallion’s (1992) methodology to the period 1995-2010, Ferreira _et al._ (2013) show that monetary poverty – using a poverty line of 4 dollars a day (2005 PPP) – fell about 17 percentage points, of which 66 percent was due to the growth component and the remaining 34 percent to