rafmacalaba/data-use-mentions-extended huggingface dataset:
the state with the highest per capita income (Goa in the case of the Tenth Commission). Goa is taken to be the same as for the state with the second highest per capita income (Punjab) from that of the next one (Maharashtra) since otherwise Goa will not get any share at all.

Step 4. Allocate 5 percent of the pool on the basis on area, i.e., the ith state gets A,/A of the 5 percent, where A, is the ith state's area, and A is the country's total area. An adjustment is however made so that no state gets a share higher than 10 percent or less than 2 percent.

Step 5. _5_ percent for infrastructure is on the basis of an aggregate index computed by an expert group. The details are given in Appendix 5 to the Tenth Commission's report.

Step 6. 10 percent for tax effort is allocated using the ratio of per capita own tax revenue to the square of per capita income with the respective products being scaled by population as in the distance criterion. That is, the ith state gets **PiEi/EjPjE** of the 10 percent where E, 2 is the ith state's effort index defined as E, = (R,fP,)/(Y,/P,).

12 The author would like to thank Mr. S. Guhan for providing me with detailed information on the Finance Commission formula.

20
The large number of potential incentive targets and anti-corruption actions listed in Table I raise two questions:

* how to establish the priorities of anti-corruption campaign, and

* which actions should be used to meet those targets.

Economic theory suggests that high priority programs are those that address large welfare losses caused by corruption. Two practical considerations make prioritization based on welfare losses difficult: quantifying corruption losses is often not possible and large losses are often the result of multiple governance failures. The result is that prioritization of anti-corruption campaigns must rely on analysis of an individual country's economic, political and bureaucratic conditions.

Prioritization based on welfare losses highlights one of the problems with using survey data to construct an anti-corruption program. High visibility corruption, which is likely to be identified by surveys, may have significantly lower welfare costs than less visible corruption. It is possible, for example, that the public is highly aware of corruption causing marginal losses in traffic enforcement, utility hook-ups, or business permits but largely ignorant of the economic distortions caused by revenue losses through tax evasion by wealthy individuals and large corporations.

In determining which actions should be used, Table I suggests actions which vary depending on the weaknesses of a specific government. For a country in which the government that is disproportionally large, actions which reduce the size of government are likely to reduce the scope for corruption. For a country in which a few officials appear to have become very wealthy in public service, actions should aim to reduce the gross gains from corruption. For a country in which few are held accountable for corrupt activities, anti-corruption efforts should focus on judicial independence resources.

Empirical evidence for individual anti-corruption efforts is listed in Table 2. Widespread corruption, however, is likely to be the result of multiple governance failures so successful anti-corruption campaigns are likely to be multi-pronged. For example, an official's cost-benefit analysis suggests that raising public sector wages, by itself, is unlikely to lead to lower corruption.<sup>9</sup> A program that ties
individual’s beliefs with regard to the reasons why there are people in need in society, whether it be luck, laziness, an inevitable part of modern life, or injustice in society. Finally, the _Xi_ vector also includes country dummies, such that estimates rely on within-country variation. The full set of controls is listed in Table 3.

# **5. Results**

Before proceeding with the discussion of the results related to the hypotheses outlined in section 4, I return to the fact that the estimates are based on the second wave of the LiTS survey from 2010, and a newer (2016) wave of the survey was published recently. As noted above, the analysis relies on the 2010 round of the survey because the key variable in the analytical set-up, namely a question whether someone has access to connections, was not repeated in the 2016 survey questionnaire and is only available for the 2010 data. Nevertheless, it is worth commenting on the persistence over time of beliefs regarding inequality of access to opportunities, or beliefs about future mobility to see whether the beliefs expressed in 2010, and hence some of the conclusions that could be drawn from the empirical analysis in this paper, are still relevant six years hence.

Figure 1 presents the distribution of responses to the need for connections questions, separately for each domain, for the 2010 and 2016 survey rounds. In the overall sample, the distribution of beliefs with respect to the need for connections remains largely unchanged. If anything, the views are more pessimistic still in 2016, as measured by the share of adults who think that connections are either very important or essential across various domains of opportunity. Across countries, the average share of adults who believe access to opportunities to be unequal in the sense of the definition in this paper, the views are quite similar in countries in which the incidence of inequality of access to opportunities was already relatively high in 2010; for many of the countries in the lower end of the 2010 distribution one can see significant
female illiteracy is estimated with a significant bias in the CPHS (in selected states the mean values from the CPHS-2019 are as much as 45 percentage points lower than what is observed in the NFHS-5).

Figure 4 compares adult education levels (ages 15 to 49) in CPHS and PLFS for 2017 to 2019. The share of adult education attainment at the state level observed in the CPHS is plotted against the shares observed in the benchmark PLFS survey. Estimates above (below) the diagonal indicate states where education shares are estimated to be higher (lower) in the CPHS relative to the PLFS. Panel (a) of Figure 4 shows population shares of adults with below primary level education (which includes those with nonformal education as well non-literates). Panels (b) to (d) compare state level shares of primary, secondary and higher educated adults, while panel (e) plots the share of adults with graduate, certificate or post-graduate levels of education.

15
# Box 1: Effects of Changing Remittances Modeling in El Salvador

Some refinements on how non-labor income is projected may result in a more accurate estimation of the changes along the income distribution. This study modeled international remittances as neutral distribution using the inflows growth rate between the baseline year and the simulated year from the World Bank's Macro Poverty Outlooks. However, as stated in the methodology presented earlier in this paper, international remittances can also be modeled using the proposed method in Olivieri et al. (2014). This methodology consists of a two-step assignment rule. First, the projected amount of international remittances is calculated as the initial level of remittances times the change between the baseline and the projected year; second, the difference in the amounts between the base year and simulated year is randomly assigned to households within each region, considering population growth while maintaining the original regional distribution of international remittances.

To assess the effect of these two different international remittance approaches on income distribution, this document simulates the poverty and vulnerability headcounts and the Gini coefficient for El Salvador 2017 - 2019. It is important to clarify that El Salvador is selected due to its high level of international remittances. In 2019, this non-labor income component accounts for 61.6% of the average per-capita non-labor income of the country<sup>23</sup> ; hence, a change in the way international remittances is modeled is expected to impact the overall performance of the model. **Figure 11** presents the results for this exercise using the _new model without rescaling_ . In the results, Neutral distribution corresponds to the first approach while Random Allocation refers to the methodology in Olivieri, et al. (2014).

<!-- Start of picture text --> Figure 11 Neutral Distribution vs. Random Allocation to Simulate Remittances in El Salvador<br>A) Differences Between Projected and Actual B) Poverty MSE<br>Data<br>2.50 2.00<br>2.00<br>
40% fewer vacancies. The drops were significant in sectors such as hotels and restaurants, and entertainment, as well as in occupations that are more difficult to perform from home. They also find that users reduced job-search intensity and seemed to have re-directed their searches to occupations that are more likely to be performed from home and more resilient to the crisis.

Alstadsæter et al. (2020) study the impacts of COVID-19 on layoffs in Norway, a country that has strongly relied on unemployment insurance benefits to mitigate the impacts of the crisis. Their analysis based on UI claims data shows that almost all layoffs up to April 19 were temporary. Even if accounting for only 10% of the total number of layoffs, permanent layoffs generated a 1.5 percentage point increase in unemployment, a significant month-to-month variation for Norway. They also show that layoffs affect populations that are already financially vulnerable (low-income, low-educated, immigrants) and are more common in jobs that require physical proximity, especially in the initial phases of the crisis. Similarly, they find that in the early stages of the crisis, the impacts were mostly felt by women and young workers, but as time passed men and older workers were also significantly impacted.

The Korean experience also provides interesting insights as the government mainly relied on testing and tracing and less on lockdowns to contain the spread of the virus. Aum et al. (2020) find that a one per thousand increase in infections leads to an almost 3% decrease in local employment. They compare these effects to those in the United Kingdom and the United States, where lockdowns were introduced, and note that employment losses were almost double in these countries. Shedding light on the channels driving these results, the authors find that employment losses were mainly due to a slowdown in hiring by firms and to transition of workers out of the labor market rather than to unemployment. The authors note that at the time of publication, the Korean government had not implemented any public furlough scheme. The paper also shows that
The choice of aggregation does not alter previous findings, as seen in Figure 9. All four social sustainability components have correlations with GDP, poverty, and inequality that follow the same patterns as indices aggregated following an arithmetic mean.

A final robustness check explores the extent to which social sustainability components, both individually and simultaneously, are associated with the variation of national poverty that cannot be explained by income level. This is an alternative way to empirically document the associations between social sustainability and income. Specifically, we regress national poverty rates against log per capita GDP, isolate the residual of that regression and test whether that residual is statistically associated with each the social sustainability components. Results, available upon request, show that there are some statistically significant associations between social sustainability components and the poverty residual. However, these associations are neither systematic nor robust to the choice of poverty rates, namely, national poverty lines and international poverty lines at US$1.90, US$3.20 and US$5.50 (2011 PPP) per person per day.

# **5. Conclusions**

Sustainable development is impossible without all three pillars of sustainability: economic, environmental, and social. Yet despite growing awareness among policy makers, the social dimension of sustainable development has been often overlooked, poorly understood, and insufficiently analyzed. COVID-19 has raised the profile of social sustainability as the international community pledges to pursue a green, resilient, and inclusive recovery. Whether this emerging policy commitment remains aspirational or becomes a transformational paradigm in practice remains to be seen. In the meantime, more foundational work to improve our understanding of social sustainability is needed.

The complexity of social sustainability when it comes to its components, interactions and goals —contrasted, for example, with monetary poverty, fiscal and debt deficits, or greenhouse-gas emissions—has led to incomplete definitions and long lists of principles, attributes and conditions that a country, a city or a community must display to be socially sustainable. Such lists include aspects as wide-ranging
, Italy, Germany, and France. Compared to rows 1 and 2, rows 3 and 4, labeled " _HVaR_<sup>+" and "</sup><sup>_H_</sup> _ES_<sup>+", reveal that the degree</sup> that correlation decreases during market upturns is generally less than the degree that correlation increases during market downturns.

Panel B reports the results of the _AH_ statistics. Signs of the _AH_ statistics using the VaR-implied correlation are incongruous, but only the positive statistics are significant. The _AH_ statistics using the ES-implied correlation are all positive and significant at 10% level and higher, implying that correlations are generally higher during market downturns and lower during market upturns than the linear correlation. The downside _AH_ statistics using the VaR-implied correlation are significant at 5% level only for the relation between the US and Canada and the relation between the US and Japan. The downside _AH_ statistics using the ES-implied correlation imply that correlations between the US and Canada, the UK, Italy, and France during market downturns are significantly underestimated by the linear correlation at 1% level, and correlations between the US and Germany and Japan during market downturns are underestimated by the linear correlation at 10% level. The upside _AH_ statistics using the VaR-implied correlation demonstrates a positive and significant relation between the US and Germany, and the upside _AH_ statistics using the ES-implied correlation show that all correlations decrease significantly at 10% and higher.

19
0.50|
|Main income source before the earthquake|||
|Agricultural production|0.12|0.32|
|Trade|0.37|0.48|
|Unskilled work|0.26|0.44|
|Skilled work|0.17|0.38|
|Transfer|0.03|0.18|
|Other income source|0.04|0.19|
|Aid/transfers from abroad|0.12|0.32|
|Aid/transfers from Haiti|0.18|0.38|

Source: Own computations using June 2010 (EFSA II) surveys. Note: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake.

# **5.4.Pre-Earthquake Conditions**

Table 16 presents summary statistics by quintile of pre-earthquake wealth. The poorest households lived mostly in the East of the affected area (Léogane, Gressier, Jacmel, Petit Goâve, Grand Goâve and Croix-des-Bouquets). They were mostly agricultural households: 66% of them were practicing agriculture, compared to only 5% among the wealthiest, who were mostly concentrated in Port-au-Prince or Pétionville. The poorest households lived in houses with no electricity and no toilets, and do not have access privately to water. They had no car and no oven for cooking. Only few of them had a TV or a fan. Most (66%) had tools or materials for production. Compared to other groups, they took more part in associations be they religious ones (28%) or not (19%). In the population, very few people (around 1% to 2%) were part of cash and food for work 41
49 Figure II: Rice Yields and Imports in Sri Lanka over time <!-- Start of picture text --> 2012 2014 2016 2018 2020 2022 Jan/2020 Jan/2021 Jan/2022<br>year Month<br>(a) Rice Yields (Maha season) (b) Rice Imports<br>4500 40<br>4000 30<br>20<br>3500<br>Imports (in million $)<br>Paddy yield kg per hectare<br>10<br>3000<br>0<br><!-- End of picture text --> _Notes_ : Panel IIa shows the rice yield (in kg/ha) for the Maha season between 2013 and 2022. The Maha season for rice is from September to March of the following year. The red line in 2021 marks the year fertilizer import bans were introduced. The data comes from the DCS. Panel IIb shows monthly rice imports, in millions (MM) of USD. The first red line in May 2021 marks the beginning of the ban, and the second red line in November 2021 marks the end of the ban. The data comes from the S&P Global Market Intelligence’s Panjiva data platform.
Policy Research Working Paper 10448

# **Abstract**

This paper estimates worker, firm and assistance programs’ responses to foreign shocks in Brazil exploiting quasi-experimental variation in firms’ foreign demand resulting from the Global Financial Crisis. It shows these transitory shocks have permanent effects in a setting characterized by a large informal sector. While the informal sector provides a buffer mitigating long-term scarring, it is not sufficient to compensate worker and firm scarring. Scarring occurs for incumbent workers, not just displaced workers. Linking employer-employee data with the national registry of low-income households, unemployment insurance disbursement records, and worker-level training, the paper finds that assistance programs fail to mitigate adjustment. Training does not respond, unemployment insurance compensates 4 percent of workers’ wage losses and welfare programs 2 percent. Firms in highly concentrated sectors or state-owned do not bear the burden of adjustment. Other firms bear the burden and either exit or restructure, downsizing employment and productivity, scarring incumbent workers and increasing long-run inequality.

This paper is a product of the Development Research Group, Development Economics and the Office of the Chief Economist, Human Development Global Practice. 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 afernandes@worldbank.org and jsilva@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
job offers then had to go through the onboarding process at the firms which typically consisted of a training that lasted for several days or weeks depending on the hiring firm. This training typically focused on teaching technical skills related to fabric handling, stitching, pressing, packing and quality control among others.

# **3.2. Data**

The data collection for the baseline survey took place at screening centers directly after the eligibility checks of individual job applicants in order to minimize attrition and to ensure that the treatment and control groups are fully comparable. All eligible job applicants who agreed to take part in the study were interviewed at baseline and, in total, information from 935 respondents was collected between June-August 2016. Approximately eight months after the baseline data collection, an effort was made to re-interview all baseline respondents during the follow-up data collection. Among the baseline respondents, 827 were successfully tracked, which results in an attrition rate of 11.6 percent. The information from respondents interviewed both at baseline and at follow-up comprises the panel data set and only the sample of panel respondents will be at the core of the impact analysis.

Table 1 indicates that on average study participants randomized into the control group were roughly 24 years old at baseline. Around one-quarter of the respondents were married and slightly less than one-quarter were mothers at baseline. As expected, the number of migrants among applicants for factory work is high. Nearly 70 percent of the respondents had migrated to Addis Ababa. On average, respondents have more than nine years of completed formal education and nearly 10 percent of the sample is enrolled in some form of educational program at baseline. It should also be noted that the vast majority of study participants has had experience from either self or wage employment, with nearly 77% reporting to have at least one regular wage or salary paying experience (not shown in the table). We also find that around 30 percent of our sample has had some cash income within the 30 days leading up to the baseline survey. In
**COVID-19 containment and economic support policies.** Policies that aim to restrict community mobility to stem the spread of COVID-19 have also impeded economic activity and revenue mobilization which puts pressure on public, private, and financial balance sheets. Many countries have adopted fiscal stimulus measures to stimulate the economy. Some of these measures directly support the financial sector through guarantees and may therefore necessitate or complement a financial sector policy response. The data are taken from the Oxford Government Policy Response Tracker (see Appendix A for more detail).

**Economic development.** The level of economic development can be interpreted as a broad proxy of available economic resources and buffers to respond to COVID-19, as well as differences in economic structure, financial development, and institutional frameworks. We expect countries with higher economic development to be more active in their policy response as highlighted by Benmelech and Tzur-Ilan (2020). The notable exception could be the policy response on the _Payments Systems_ category. As more developed economies have more developed payment systems,<sup>13</sup> additional policy measures in this category could be less needed. Therefore, the observed correlation between economic development and policy response might be negative.  We use GDP per capita (expressed in current international dollars converted by purchasing power parity (PPP) as per the end of the year 2019 as a measure of economic development. The data are taken from the World Bank’s World Development Indicators.

**Macro-financial fundamentals.** On the one hand, the current account, fiscal deficits, and high levels of public and private debt may limit the capacity to mount an effective policy response. On the other hand, external and debt sustainability vulnerabilities as well as crowding out effects could amplify the economic shock of the COVID-19 prompting authorities to respond faster and at a larger scale. We use four indicators: the 2019 current account balance from the IMF World Economic Outlook (April 2020); the 2019 general government net lending (borrowing) taken from the IMF World