rafmacalaba/data-use-mentions-extended huggingface dataset:
implications of India’s vaccination campaign on individual employment outcomes. Due to the limitations of the data and the scope of this paper, we do not attempt to capture the grave health impacts of the pandemic (Jha et al., 2022), which are likely to have affected the labor market.<sup>2</sup> For the panel data analysis, we follow two fixed cohorts from the CPHS data – a Covid-affected cohort and a pre-Covid cohort for comparison – over a span of 12-16 months. Keeping each self-contained cohort fixed allows us to track the same individuals at different points in time. Further, the pre-Covid cohort acts as a plausible counterfactual for the Covid-cohort so that we have a baseline reference for “normal” labor market dynamics. The Covid-cohort consists of individuals interviewed between November 2019 and February 2020, right before the pandemic, and we track their outcomes every 4 months until March–June 2021. The pre-Covid cohort consists of individuals interviewed either between November 2017 and February 2018 or November 2018 and February 2019 as baseline, and then followed for 12-16 months. The outcomes of these two cohorts are then compared with reference to their own baselines using a differencein-difference regression framework, to assess the impact of the pandemic on labor market dynamics. We further examine whether individual circumstances like education and location mitigated the impacts of pandemic-related labor shocks.

Unemployment peaked at an unprecedented 24.3 percent in April 2020 and recovered quickly to 7.3 percent by July 2020. It rose again in May 2021 to 11.4 percent during India’s devastating second wave of Covid but came back to around 7 percent between July and December 2021. The quick recoveries in unemployment however mask an overall decline in labor force participation. Moreover, while Covid-19 was an economy-wide shock, women, youth, and historically disadvantaged castes experienced relatively more adverse effects, partly due to the vulnerable and informal nature of their pre-pandemic occupations. Covid-19 has induced further shifts into insecure forms of employment. In the immediate aftermath of the national
statistics are not comparable across sources, (see Section 3). Even when external agents try to centralize and standardize records, they need to rely on the availability of each country’s public debt data. For instance, the WB’s IDS are supposed to cover public sector PPG external debt. However, as shown in section 2, the majority of LICDs only have central government debt data available. Additionally, external agents cannot easily validate the accuracy of the coverage of data shared and are left to assume that it coincides with their requests. The lack of homogeneity and comparability may impact the analysis of debt statistics that

> 37 E.g., the upcoming Review of Data Provision to the Fund for Surveillance Purposes in 2021 could consider proposals to extend the minimum data provision obligation under Article VIII, Section 5 of IMF Articles of Agreement to include data on general government debt and the broader non-financial public sector to enhance IMF capacity to assess fiscal space and analyze debt risks.

> 38 The WB is examining DRS reporting requirements for domestic debt data—launching a survey on domestic debt reporting, with pilots from November 2020—and broadening external debt data requirements, e.g., the collateralization features of loans.
Share Manufacturing Industry|-0.2%|0.6378|13.0%|13.2%|
|Share Electricity, Gas , Steam, Water and Sewerage|0.1%|0.4251|1.1%|1.0%|
|<br>Share Construction|-1.2%|0.0005|8.5%|9.7%|
|Share Commerce and services|-1.3%|0.0113|22.3%|23.6%|
|Share Others|0.4%|0.4353|21.1%|20.8%|
|Registered with Any SS institution related to your main job|-1.1%|0.0785|62.5%|63.5%|
|Share of workers registered with any social security<br>|-0.8%|0.1496|51.6%|52.4%|
|institution related to your main job|||||

_Notes_ : *In 2014, the LFS no longer captures information about children 14 and under. N2013 = 21,467 for most variables, N2014=22,791 for most variables. Variables are at the household level. _Source_ : LFS 2013 & 2014. World Bank author’s calculations.

32
extra-marital sexual intercourses. More specifically, according to column 4, when the partner works in the mines, a woman is almost 2 percent points more likely to report extra-marital sex. This result is robust to the inclusion of controls such as age, age squared, urban status, education and wealth. Among the other findings, age positively influences the dependent variable, while the coefficient on age squared is negative, suggesting, as expected, that women in the middle stage of their life are more likely to report extra-marital affairs. Having older partners increases the likelihood of engaging in extra-marital relationships. Furthermore, educated women are more likely not to engage in extra-marital sex. The coefficient on assets owned by the household is negatively correlated with the dependent variable, suggesting that richer women are less likely to engage in extra-marital sexual affairs. Finally, Catholic women are more likely to engage in extra-marital relationships compared to Protestant.

[Insert table 6] We next test whether, ceteris paribus, miners and women with a partner employed in the mines are less likely to use condom. The Swaziland DHS asks about condom use during the last intercourse to women and men in the sample, while in Lesotho, the survey investigates the last contraceptive method used by males and whether a condom was used at the last intercourse in the female sample. Furthermore, the survey elicits information on whether the last sexual intercourse occurred with a spouse or with another partner. This question is crucial to comparing prevention behaviors inside and outside the marriage.

Marginal effects of probit coefficients for the probability of using a condom are reported in table 6. The first notable result in column 1 shows that, after controlling for a large number of 15
withheld on their card machine statement switches from being blank to carrying a number. Noticing the application of withholding for the rst time may raise the salience of possible enforcement interventions like desk audits, and lead rms to increase compliance. Consistent with this, columns 5 and 6 show that rms experiencing a withholding rate increase from zero exhibit a larger increase both in their reported tax liability and in their total tax paid than rms experiencing a similarly-sized rate increase from a positive to a larger rate.<sup>55</sup> This signicant interaction does not disappear when additionally interacting with the self-employed and reclaimer indicator, and is always larger than the latter. We thus label the increase in reported tax liability among treated rms the salience of enforcement eect, as per the panel title in Figure X.<sup>56</sup>

> 54See Figure XII for an illustration of the type of information provided by card processing companies in Costa Rica to aliated businesses.

> 55We exclude from the From-Zero Rate Increase group all rms that experience the maximum rate increase of six percentage points, to ensure that the average rate increase in this group is not larger than the average rate increase among other treated rms.

> 56An alternative explanation might posit that rms are unaware of their true tax liability and thus report with error, but start to report more correctly after the withholding rate reform, as the withholding system provides them with information of their true tax liability. However, this explanation would be consistent with our results only if rms systematically (but non-strategically) underestimated their tax liability.

30
economic growth is still more important in explaining changes in poverty; the R-squared increases when including sector growth GVA variables.

Table 28 illustrates results using local fiscal expenditures, and there are even less explanatory items. Which is consistent with regressions on total central and local expenditures, that local expenditures are less correlated to changes in poverty. Interestingly, Anatolian tiger regions have low amounts of central spending compared to the Developed West or the Rest of the Country (Figure 27).

# **4. Conclusion**

This paper examined poverty rates, poverty convergence, and determinants for Turkey at the NUTS2 level from 2006 to 2013. There is strong regional variation in regional poverty reduction. Poverty reduction across the country benefited from growth in the agriculture sector from a GVA standpoint. On the other hand, employment numbers show that increases in the services sector have a statistically significant relationship with poverty reduction across the entire country. When comparing these two effects, it is also important to bear in mind that job creation, which is captured by employment, was the main driver of poverty reduction (consumption based) in Turkey between 2002 and 2012.

In terms of government spending, increases in central spending are significantly more correlated with poverty reduction than the rise in expenditure at the local level. However, the causality of this relationship is not clear. Spending at the local level might be increasing as a response to higher poverty rates in the region. Another complicating factor is the high level of aggregation in the spending data. Despite the analysis conducted at the functionally disaggregated levels, a deeper breakdown that identifies expenditure 27
part of the paper analyzes the dynamics of growth in the post-HIPC era. The second part draws on a standard macroeconomic model that accounts for financing of deficits and debt structure in the post-HIPC to assess long-run growth. The model is applied to Ethiopia. The baseline simulation of the model produces slow growth and accumulating external debt if current economic conditions continue as they are. Baseline projections are constructed, after which some experiments are run to explore the growth implications of higher foreign aid and/or lower interest payments on foreign debt, under different scenarios. The simulation results show that the optimal outcome in terms of higher economic growth depends on the sustainability and size of foreign aid and the proportion of external assistance allocated to public investments.

This research is undertaken at a time of the global review of progress toward the MDGs, almost a decade into the adoption of these goals (Go et al., 2010; Bhagwati, 2010).<sup>6</sup> It is motivated by the wave of euphoria that has accompanied the resurgence of growth in the post-HIPC across Sub-Saharan Africa. In effect, global experts not only in development institutions, but also in academia have gone as far as likening Africa’s recent positive growth rates to those that have transformed Asian emerging market economies into global players (Sala-i-Martin and Pinkovskiy, 2010; Devarajan and Shetty, 2010).

Although the success of Asian emerging market economies, most notably illustrated by large current account surpluses, has been predicated on major structural shifts in production possibility frontiers and industrial transformation, most African countries remain excessively dependent on natural resources and primary commodity exports (Fofack, 2010)]. Hence, the paper also attempts to shed some light on the nature and

> 5 According to IMF’s estimates published in the World Economic Outlook Database, growth rates averaged 2.47% across Sub-Saharan Africa in 2009, significantly below the rates enjoyed in the pre-crisis years (about 5.5%) between 2004 and 2008.

> 6 On the occasion of that
.

At the time of writing, raw data for the more recent release of NFHS data (2019–20) was not yet available. However, the fact sheets so far released reveal figures on home and/or land ownership by females (aged 15–49), either alone or jointly. As shown in Table 2, this data suggests a growing incidence of property and/or land ownership among females (aged 15–49) for some states overall and for urban areas within states. For example, as per NFHS 2019–2020, the share of the property or land claimed as owned by females<sup>6</sup> in Karnataka increased by about 15.8 percentage point to 67.7 percent in comparison to 2015– 2016. In urban Karnataka, this increase was almost 20 percentage points. Likewise, NFHS reports an increased share of females owning property and land in Gujarat; in 2019–2020, the share of females owning property or land was 42.6 percent, an increase of about 15 percentage points in comparison to 2015–2016 data. However, data for Maharashtra, Delhi, and Odisha all show a decrease in female ownership between 2015–16 and 2019–20 of between 11 and 20 percentage points, as shown in Table 2. The overall picture for India and urban India shows a small increase in female property and land ownership over this five-year period, with a slightly larger overall increase in female ownership in rural areas. Across the states mentioned, and in India overall, ownership is highest in rural areas, though the gap is narrowing in some places, such as Karnataka.

Table 2: Proportion of females (aged 15–49) owning a house and/or land _(_ alone or jointly with others) (%), in select Indian states

|**Geography**||**2019**–**20**|||**2015**–**16**
We treat the 2006 and 2008 household surveys as repeated cross-sections and estimate the following regression specification of the difference-in-difference model for individual outcomes: where _Yijt_ is the HTC outcome of individual or couple _i_ living in facility _j_ ’s catchment area in year _t_ ; _P4Pj_ is a dummy variable that takes value 1 if facility _j_ belongs to Phase I (i.e. started receiving P4P in 2007) and 0 otherwise; α _j_ is a facility fixed effect; γ _2008_ is a fixed effect for 2008; _I2008_ is a dummy variable that takes value 1 if the year of observation is 2008 and 0 otherwise; the _Xkijt_ are individual characteristics; and ε ijt is a zero mean error term. We compute robust standard errors clustered at the district by year level to correct for correlation of the error terms across facilities within districts.

# **4. Results**

Table 5 reports the estimated P4P program impacts on HTC outcomes using the individual as the unit of analysis. We present analyses for the entire sample and then conduct sub-group analyses by marital status. In all of the estimated models, we control for age, education, and household assets and for gender and marital status when relevant. Assets are measured as the value of land, durables in the house, farm animals, farm equipment, and microenterprise equipment.

In column (1) of table 5, we find a positive and statistically significant impact at the 10% level of 6.1 percentage points in the probability to have ever been tested with respect to the comparison group. This represents a 10.6 percent increase over the control group. When we restrict the sample to individuals living in a couple (column 2), we find a positive impact of 10.2 percentage points that is statistically significant at the 5% level, representing a 14.5 percent increase from baseline. However, there is no impact on individuals not currently in a couple regardless of whether they are sexually active or not (columns 3 and 4). Those
Montenegro and Patrinos, 2014). Although historically most research found the highest payoffs came from primary education, recent evidence from a study comparing 139 economies (Montenegro and Patrinos, 2014) suggests that the returns to tertiary education surpass those at the primary and secondary education levels. These findings may be an indication of important shifts taking place, whether as a result of massive expansion in educational attainment, because of so-called skill-biased technological change (Katz and Murphy, 1992; Autor, Katz and Krueger, 1998) or because of rising inequality in the wage structure (DiNardo and Pishcke, 1997; DiNardo and Card, 2002).

However, while findings on the returns to schooling have proven to be robust and useful (Heckman, Lochner, and Todd, 2001), there are limitations (i.e., unobserved ability and effort, endogeneity, etc.) and methodological challenges associated with using schooling as a proxy for estimating schooling returns (Card, 1999). For example, Hanushek et al. (2013) have pointed out that Mincer’s formulation assumes that schooling is the only systematic source of skill differences.<sup>5</sup> But research shows that other factors, such as an individual’s ability, family inputs, and school and labor market characteristics also determine skill acquisition (Card, 2001 Heckman, and Vytlacil, 2001). Further, not all skills are acquired or demonstrated through years of formal schooling. Cognitive and noncogntive skills are important determinants of labor market success (Bowles, Gintis, and Osborne, 2001; Borghans et al., Duckworth, Heckman, and ter Weel, 2008; Cawley, et al., 1996; Heckman, Stixrud, and Urzua, 2006). Using schooling alone as a proxy for general skills severely underestimates the returns to human capital and obscures our understanding of how the labor market rewards various skills.

In response to the conceptual and methodological limitations of the returns-to-schooling studies noted above, a substantial literature has examined how cognitive skills
migrated, while columns (3) and (4) split this sample between male and female migration. It appears that the differences between nonmigrant households (column 5) and migrant households are very stark: households with no migrants are more likely than migrant households to reside in urban areas, they generally have less children and are richer and more educated. The comparison between households in which the mother migrated and the father stayed and households in which the father migrated and the mother stayed (column 6) further reveals that these differences are amplified for households with migrant mothers; these are indeed more likely to live in rural areas, to be less educated and poorer, and to have more children, though these are on average older than those of migrant fathers. The last row of table 4 finally shows differences in terms of degree of risk aversion of the head of household.<sup>13</sup> This measure is a 1–4 score derived from the answers to a sequence of questions in which respondents are faced with hypothetical lotteries with high stakes.<sup>14</sup> It appears that migrant households are on average more risk averse than nonmigrant ones, while there is no significant difference between households who choose female migration and those who choose male migration.

**Table 4.** Descriptive Statistics, Households with Children. IFLS 2007.

||(1)<br>Nonmigrant<br>households|(2)<br>Migrant<br>households|(3)<br>Migrant<br>father|(4)<br>Migrant<br>mother|(5)<br>Difference<br>(1)-(2)|(6)<br>Difference<br>(3)-(4)|
|---|---|---|---|---|---|---|
|Size ofhousehold|4.623
0|0|0|0|0|1|3|
|**2**|Scientific<br>article|Journal with peer<br>review|Experimental|Students|1|1|0|1|1|1|1|1|9|
|**3**|Scientific<br>article|Journal with peer<br>review|Cross-sectional:<br>serological<br>survey<br>and KAP|MSM|1|1|1|0|1|1|1|1|9|
|**4**|Scientific<br>article|Journal with peer<br>review|Cross-sectional:<br>serological survey|Female sex workers|1|1|1|0|1|1|1|1|9|
|**5**|Scientific<br>article|Official bulletin|Cross-sectional:<br>serological survey|Oil workers|1|0|0|0|0|1|0|0|2|
|**6**|Scientific<br>article|Official bulletin|Cross-sectional:<br>serological<br>survey<br>and KAP|Female sex workers|1|0|0|0|1|1|0|1|5|
|**7**|Scientific<br>article|No<br>information<br>available|Cross-sectional:<br>KAP survey|Adolescent females|1|1|0|0|0|1|0|0|3|
|**8**|Technical<br>report|Technical<br>publication|Cross-sectional:<br>demographic<br>and<br>reproductive health<br>surve|General population|1|1|1|1|1|1|1|1|11|
||||y|||
of individuals in this group reporting trust in executive institutions.<sup>21</sup> In addition, institutional trust is slightly higher in rural areas than in urban areas, with respectively 58 and 54 percent of the population reporting trust in government.

These socio-demographic factors have a cumulative impact: our statistical model approximates that a high-income male age 51-54 has an 81 percent probability of trusting the government, compared to 46 percent for a low-income female age 20-24.

# **5 Conclusion**

This paper shows, using a large-scale survey on trust in Morocco, that positive perceptions of policy outcomes and positive perceptions of policy processes are both associated with trust in institutions but that the e↵ect of process-based drivers appears to be greater. Policy outcomes indeed matter, as positive perceptions of the delivery of public goods and services are positively associated with Moroccan citizens’ trust in institutions; yet, we find that the relation between trust and process-based drivers such as policy credibility and public integrity is stronger.

This paper also contributes to the broader understanding of institutional trust by documenting the substantial variation in trust over individual-level demographic and socioeconomic factors. Data analysis provides empirical insights on these questions and shows that institutional trust is contingent on individual-level social capital and characteristics. Specifically, the paper finds that individuals with lower levels of social trust, women, individuals living in rural areas, middle class individuals and youth are less likely to express trust in national institutions.

Some of the paper’s findings related to perceptions of public action or socio-demographic factors and institutional trust are surprising and could warrant further research. For example, findings show that trust in institutions is not correlated in a linear way with measures of income. Institutional trust according to households’ income level follow a U shape, where households with the lowest income and the highest income express the highest levels of trust in institutions. The reason for this non-linearity is not instantly clear. Also, it appears that improved access to services sometimes generates a lower trust dividend than could be expected.
estimates, most of which went up to 1987 or 1988, to take account of the substantial downward movement in the real value of the Rupee which started in about 1987, and of the the July 1991 devaluation. Secondly, extensive and unprecedented liberalizations of the trade and other policies affecting manufacturing had either been recently implemented or were in prospect, and it was felt that the book should take account of the direct and indirect impact of these changes on agricultural incentives. Thirdly, it was strongly urged that the protection and incentive estimates for the 1 980s and later should be put in a longer term perspective which, among other things, would allow for fluctuations and longer term trends in world commodity prices. Finally, it was suggested that the study should recognize India's potential impact on world prices in the cases of commodities - notably rice - where Indian production or consumption is a large fraction of world production or consumption, and where changes in Indian imports or exports are likely to perceptibly affect world prices.

In order to accommodate the suggestions for a longer and updated time series of agricultural incentive indicators, it was decided where possible to use some shortcuts to extend the detailed estimates of effective protection and effective subsidy coefficients (see below for a description of these concepts) up to 1990, but to confine the longer term series to nominal protection estimates for the years 1965 to 1993. Fiscal 1993 was the first full year following the July 1991 devaluation and the first year to include the trade policy and other reforms introduced in March and April 1992. Subsequently, for most but not all of the crops, the nominal protection estimates were extended up to 1995. These long period nominal protection estimates for agriculture have in turn been used as one input into the analysis of the extent of exchange rate overvaluation inherent in India's trade regime, which is a key element for quantifying the relative incentives over time between agriculture on the one hand and manufacturing, minerals and non-traded activities on the other. Nalin Kishor and subsequently Anju Gupta have