rafmacalaba/data-use-mentions-extended huggingface dataset:
The effects of information about the income distribution on redistributive preferences continue to be debated. In a study in Buenos Aires, Cruces, Perez-Truglia and Tetaz (2013) found that households who were informed that they were poorer than they thought responded by increasing their support for redistribution, much like standard economic reasoning suggests. However, in another study, people who were informed of their relative poverty responded by _reducing_ their support for redistribution (Hoy and Mager 2020). Findings from other studies appear to be similarly mixed (Fehr, Mollerstrom, and Perez-Truglia 2022; Karadja, Mollerstrom, and Seim 2016; Kuziemko et al. 2015; Nair 2018). To this debate, we contribute evidence from the first large-scale survey of beliefs about inequality from India.

In contributing descriptive evidence of beliefs about the income distribution and inequality from a sample spanning all major states, religions and caste groups, we add to the research on distributional concerns and the political salience of income inequalities in India (Gaikwad, Hanson, and Tóth 2019; Jaffrelot 2015, 2016; Kohli 2012; Suryanarayan 2019; Thachil 2014; Thachil and Herring 2008). Further, our findings join a nascent literature in questioning how standard political economy models have been applied to the study of redistributive preferences in developing countries (Holland 2018; Kasara and Suryanarayan 2020; Thachil 2014). We emphasize the need for scholars to take the role of group-specific public goods, and the implicit sharing of resources within communities, seriously when examining beliefs about inequality.

# **2. Research Design and Data**

Our analysis is based on data from the Consumer Pyramids Household Survey (CPHS), conducted every four months with a panel of 175,000 households by the Centre for Monitoring the Indian Economy. The CPHS collects monthly data on household income, expenses, and assets, among other economic indicators. We computed the mean income reported by 5
are working.<sup>19</sup> We use the CHNS to contrast average work hours in 1991 with 2009, and also include data from the 2008 CHARLS pilot.

From the unconditional plots in Panel A of Figure 3, a gradual reduction in average work hours after age 45 is apparent for both men and women in urban areas. Also evident is a steeper decline in work hours for women between 45 and 55 and for men between 55 and 65. Turning our attention to Panel B, a gradual decline in work hours is evident for men and women who continue working beyond age 55, but it is not steep. By 2009, urban men between 55 and 60 who are employed, report spending an average of 45 hours per week working. By age 70, urban men who continue in productive employment are spending just 20 to 30 hours per week employed while their employed rural counterparts of the same age are still working more than 40 hours per week. The difference across urban and rural areas in the work-intensity of those working beyond mandatory retirement age suggests that urban residents may have the ability to retire gradually, while rural residents, if they have the physical capacity, may indeed continue to work full-time into their 70s.<sup>20</sup> When comparing work-intensity of older workers across countries, it is evident that China’s residents over 60 work fewer hours than their respective urban and rural counterparts in Indonesia and Korea (Figure 4). Conditional on working, average working hours for urban men and women (Figure 4, Panel B) are more similar to the UK, where hours drop off more quickly with age, than for Indonesia, Korea and the USA.

# **Retirement in China, Indonesia and Korea**

The descriptive patterns shown above make use of data from early stages of longitudinal studies from China and Korea which are modeled on the Health and Retirement Study (HRS) from the US and the English Longitudinal Study on Aging (UK). New
26 The alternative tack for addressing omitted variable bias is the usual "kitchen sink" robustness test by adding to the regression all the cluster level variables for which we can create measures. The first row of table 6 shows the 'base case" estimate while the following rows show the estimate of social capital with different sets of cluster specific variables added.

Adding the cluster averages of all the household level variables already included in the regression only slightly lowers the estimate (and raises the t-statistic).

|Table 6:<br>Robustne<br>using the|ss of the estimate on social capital to inclusion of other variables,<br>HRDS household level data.|
|---|---|
|Coefficient (t-statistic)<br>on social capital|Variables included|
|.193<br>(2.31)|Base set (table 4, column 1)|
|.178<br>(2.61)|Base set plus cluster averages of education, assets, household<br>size, female headship, self-employed in agriculture.|
|.267<br>(2.89)|Base set plus land quality variable from SCPS|
|.273<br>(2.88)|Base set plus land quality variable from HRDS|
|.155<br>(2.01)|Base set plus district population density and financial institutions<br>per person.|

Notes: Full regressions in appendix 1.

The most plausible candidate for a variable that could cause both higher incomes and higher social capital and is excluded from our base case regression is land quality. As has been argued by Binswanger, Khandker and Rosenzweig (1993), higher quality land leads to higher output, greater density of population, and more physical and financial infrastructure. These greater levels of economic activity might in turn lead to greater social capital. We
|**8.9**|

Data source : _China Statistical Yearbooks_ and authors’ calculations _._ The data of Chongqing are included in Sichuan for a better coherence; Hainan (an island and a Special Economic Zone) and Xizang are excluded for their special characteristics.

As argued by Léon (1998), the development in China is polarized in the coastal region. The infrastructure investment has not kept pace with the demand, which hinders the spillover of the economic development to the entire territory. The difference in the cumulative regional growth results in the widening of the intra-national disparities.

6
The ENIGH data only includes 6 time periods, so with the inclusion of cohort fixed effects, identification of the slope coefficient comes from within-cohort changes in income over this small number of periods. As a result, the standard errors are large, giving wide confidence intervals for conditional mobility. Nevertheless, the coefficient of 0.08 for urban primary wage income is very close to the 0.05 coefficient obtained using the ENEU data.

# 6 Interpretation

Our results show rather limited absolute mobility in income and expenditure in Mexico, but rapid conditional mobility. In order to interpret this result further, recall the data generating equation for household income at time t given in (1), written to explicitly include the individual fixed effects: This can be rewritten as: This partitions current household income into a term due to the household’s fixed effect in income growth, a term which represents the effect of initial differences in household income, and a term which represents the cumulative impact of shocks to labor earnings. Comparing the current income of households i and j, we then have that: 24
expect the upstream shock variable to be positively associated with export growth in the exporting countries (i.e. a positive 6 ). Disruptions in industrial production in source countries are expected to be linked to declines in exports in the exporting country, as exporters face bottlenecks in imported inputs. ββ

# **2.3 Data**

For the estimations we use monthly bilateral trade data for a total of 28<sup>13</sup> exporting countries covering the period from January/February to June 2020, the first phase of the pandemic. Data were collected from the Covid-19 Trade Watch (World Bank, 2020): specific sources of data are, respectively, customs for China, Eurostat for the European Union, Ministry of Finance for Japan, and U.S. International Trade Commission for the United States. Export data are aggregated at the ISIC Rev. 3 4-digit level, which consists of over 140 sectors. Bilateral annualized export growth in a sector ( _growthijkt_ ) is computed based on export levels for a month in 2020 relative to export levels of the same month in 2019. The estimations exclude mining sectors such as oil and coke from the sample. We also winsorize<sup>14</sup> the export growth data and exclude Serbia as partner country in order to deal with extreme outliers.

To assess the demand and supply shocks that economies experience as a result of Covid-19 we use monthly information from the Google mobility data from the Covid-19 Global Community Reports<sup>15</sup> which are published on a daily basis for 132 countries. The Google mobility growth rate captures peoples’ movement trends across different places and is provided relative to the median daily value from the 5 ‐ week period from January 3 to February 6, 2020 (the baseline day). We select two components of Google mobility, namely work mobility ( _work mobilityit_ ) and retail and recreation mobility ( _retail mobilityjt_ ) to measure, respectively, the supply and demand shocks of the pandemic. Work
|Mid|High|
|Traditional wood|14|74|420|109|293|1150|
|Improved wood|25|103|470|92|311|1565|
|Traditional charcoal|8|91|451|64|255|1181|
|Improved charcoal|8|81|303|65|245|1033|
|Kerosene|15|67|200|63|220|857|
|Propane|63|123|250|107|269|907|
|Electricity|108|374|789|142|512|1446|

Calculations based on Jeuland and Pattanayak (2012).

As can be seen from Table 3, the ranges of potential direct costs for each technology are quite large. The figures suggest that in general more advanced technologies tend to be less costly, because of improved thermal efficiency and higher energy density, even though capital costs and fuel costs may be higher. Less anticipated perhaps is the very wide range of costs. The figures also emphasize the substantial impact of cooking times on opportunity costs.

Tables 4 and 5 present estimated differences in the cost of acute respiratory illness induced by exposure to household air pollutants. Table 4 shows the relative capabilities to reduce acute respiratory illness of the other technologies in Table 3, compared to traditional wood stoves. To translate these relative risk reductions into economic benefits per household, we need the baseline incidence of acute respiratory illness <u>(number of cases per person per year), to which the</u> **10 |** P a g e
, the NNS 2015 did not implement food consumption modules and second, the NNS 2018 was not publicly available at the time at which this work was undertaken.

The data includes 160,888 individuals, from 33,400 households, which were surveyed in NNS 2013. However, some modules were only asked of some subsets of the full sample. For example, the Infant and Young Children Feeding (IYCF) module included 4,628 individuals. This, though, approximately matched the number of infants and young children (0-23 months) in the full Individual Socio-Economic module (4,938). Further, the Maternal Health and Nutrition module included 6,232 women, which was also roughly in line with the number of pregnant women and mothers with the youngest child being 0-36 months in the Individual Socio-Economic module (6,419 individuals). Finally, and of most importance, the Dietary (Household) module only 8
# **3. Empirical approach**

## **3-1. Conceptual framework**

The pandemic brought serious health and economic impacts that reduced urban households’ incomes, leading to welfare losses. As described in the previous section, the results from the HFPS indicate severe employment and income impacts in Ethiopia and Kinshasa. An important channel of these income losses is the reduction in urban residents’ mobility. For instance, the government restricted public gatherings in Ethiopia and people entering the CBD area in Kinshasa. Reduced mobility constrained workers’ labor performance because of the difficulty in commuting to their workplace. Moreover, it constrained business operations, job search, and work-related interactions. In low-income countries like Ethiopia and the DRC, few can work remotely.

Most importantly, labor impacts of the COVID-induced mobility shocks are expected to be spatially heterogenous. A key question to be addressed in this paper is how COVID-19 impacts on workers’ labor outcomes vary depending on their locations—for example, which towns and where in the towns. For town characteristics, we focus on population and population density. Given the expected higher contagion risks, the government may have implemented tighter non-pharmaceutical interventions in larger and denser towns. Fearing about contagion risks, people may also tend to travel less in such towns. Moreover, mobility and accessibility are probably more important for workers in larger and/or more densely populated towns, as their labor income premium stems from agglomeration economies. Within a city, workers living far from job opportunities, such as those who live in suburbs and peri-urban areas, are likely to rely more on freedom of movement to access jobs. With their movement more severely affected, impacts on labor outcomes are likely to be larger.

## **3-2. Data**

Our empirical analysis primarily relies on panel household phone surveys that have been regularly collected since the outbreak of the COVID-19 pandemic in Ethiopia and Kinshasa. In addition, we use Facebook movement data to construct an index to capture mobility trends.

_Ethiopia and Kinshasa high-frequency phone
# **3. Data and Stylized Facts**

This paper uses data from the International Monetary Fund–International Financial Statistics (IMF-IFS) database. Lack of data forced the exclusion of Algeria, the Comoros, Lebanon, Libya, Oman, Qatar, Somalia, and the United Arab Emirates. Many data points were also missing for other countries, including Djibouti, Morocco, Saudi Arabia, the Syrian Arab Republic, and Tunisia.

The countries’ foreign assets and liabilities grew rapidly over the past four decades, increasing from about 20 percent of GDP in 1970 to 205 percent in 2016, as is shown in figure 1. NFA rose from about 20 percent of GDP in 1970 to about 90 percent in 1994 before falling to 40 percent in 2015 (figure 2).

**Figure 1 Global ratio of total assets and liabilities to GDP, 1970–2015**

**Figure 2 Global ratio of net foreign assets to GDP, 1970–2015**

<!-- Start of picture text --> 2.5 0<br>2.0<br>‐20<br>1.5<br>‐40<br>1.0<br>0.5 ‐60<br>0.0 ‐80<br>‐100<br>Total assets/GDP Total liabilities/GDP<br>1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015<br>Percent of GDP<br>percent of GDP<br>Net foreign assets as<br>1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015<br><!-- End of picture text --> _Source_ : International Monetary Fund– International Financial Statistics (IMF-IFS) database.

_Source_ : International Monetary Fund– International Financial Statistics (IMF-IFS) database.

Net foreign assets and/or liabilities are seen as the reflection of a country’s external position and trade and investment with the
can be diluted when weather outcomes are more coarsely averaged over time.<sup>2</sup> Second, by normalizing daily rainfall values relative to a location- and day-of-year specific climate distribution, our measure effectively summarizes deviations in the timing of rainfall throughout the year. For example, a year where precipitation is exactly equal to the long-run daily average on every day of the year would have zero dry days and zero wet days. But a year with the same total annual precipitation, but where the timing of rainfall throughout the year is significantly shifted, would have many dry and wet days. As a result, when we use dry (wet) days as our measure of precipitation shocks, we can capture effects on firms that result from disruptions to the normal timing of rainfall throughout the year.

# **2.2 Empirical Approach**

The empirical approach exploits cross-sectional variation in the ES data combined with precipitation shocks at the ADM2 geographical unit, while accounting for sub-national ADM1 fixed effects. The main analysis estimates the effect of precipitation shocks at the ADM2 level on sales at the firm level while controlling for temperature and other firm-level controls. The following regression is estimated: i i 1 i i i i (1) ii ii ii ii ii YY = ββ SSho oo + γγXX + SS + αα + ηη + εε Where: _i_ indexes firms, _a_ indexes sub-national ADM1 units, g indexes ADM2 units, s indexes sector (2 digit ISIC level) and _t_ indexes survey-years. Note that ADM2 is more geographically disaggregated unit than ADM1. i i is the log of sales (in USD). i represents measures of precipitation shocks. The ii main precipitation shock is number of dry days for a specific ADM2 unit. Average temperature is also YY SSho oo accounted for in the estimations.

> 2 In this way, our dry (wet) days measure is similar in to the `degree-days’ temperature variables used in Schlenker and Roberts’ well-known analysis of U.S. maize yields
interplay between trade shocks and factor adjustment costs. We have tted our model to household survey panel data and plant-level panel data from Argentina and recovered measures of the adjustment frictions faced both by workers and rms. Using the structural parameters, we have simulated the response of the model, both of rms and workers, following a tari cut episode.

Our paper combines labor market frictions with a complex model of capital adjustment costs, which few papers do. This matters for the patterns of responses of investment, employment and wages. Following a tari cut in textiles, factor market frictions amplify the labor market responses so that the long-run responses of real wages are larger than the short-run responses. There is no overshooting of real wages, as in xed capital models. Our results suggest that the cost of adjusting capital is a fundamental driver of these patterns. After losing protection, real wages decline on impact. Moreover, as rms disinvest in response to the negative trade shock, labor productivity continues to decline during the transition, and real wages further decline as the economy approaches the new steady state. The results are also consistent with the recent ndings in the empirical trade literature, notably those reported by Dix-Carneiro and Kovak (2017). By combining capital and labor adjustment costs, our analysis complements the growing literature on the role of factor market frictions in shaping the way employment and labor adjust to the elimination of taris.

# References

Ackerberg, D., K. Caves, and G. Frazer (2015). Identication Properties of Recent Production Function Estimators, Econometrica, vol. 83(6), pp. 24112451.

Artuc, E., S. Chaudhuri, and J. McLaren (2010). Trade Shocks and Labor Adjustment: A Structural Empirical Approach, American Economic Review, 100(3), pp. 100845.

Artuc, E., D. Lederman, and G. Porto (2015). A Mapping of Labor Mobility
such as imports and GVC participation are included only in the latest wave.<sup>2</sup> Not all countries implemented all waves of data collection. Since our goal is to capture both the immediate impact of COVID-19 and the recovery, we consider only those countries that have implemented at least two rounds of data collection, using the wave 1 questionnaire _and_ either of waves 2 or 3. Since the severity of COVID-19 does not follow a systematic pattern across countries, and the evolution of cases has been rather randomly distributed in terms of the timing, we combine countries that implemented wave 2 or 3 of the questionnaire and label the period as the latest round.

The analyses in this paper rely on two main BPS samples: the _full_ sample of firms in 45 countries where data on the exporting status of the firm in 2019 is collected and the _restricted_ sample of 11 countries where the trade module was implemented in wave 3 and more in-depth measures of global engagement can be constructed. The sample of 45 countries includes 44,059 firms in the first round and 43,156 in the latest round while the sample of 11 countries with trade module information includes 12,882 firms.<sup>3</sup> Appendix Table A1 shows the number of observations for each country included in the sample.

To confirm the robustness of the results on trade outcomes in BPS data, we rely on firm-level monthly export and import customs data sets for the 2019-2020 period for 20 countries collected as part of the expansion of the _Exporter Dynamics Database_ (Fernandes et al., 2016). See Appendix B for the number of observations per country with customs data, as well as a brief data cleaning description.<sup>4</sup> The severity of the pandemic across countries and over time is captured using data from Google mobility reports around transit stations (Google, 2021). For countries without available data, we impute the severity based on the Oxford Government Response Tracker index (Hale et al.,
# **Figure 2: Schooling and age distributions by year.**

Panel A. Education.

<!-- Start of picture text -->
1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0 5 10 15 20<br>Years of Education<br>1995 2003 2012<br>Fraction in the population aged 18-65<br><!-- End of picture text -->

Note: The distributions of years of education are calculated for all the individuals over the estimating sample (formal, informal and self-employed of ages 18-65). The year 1995 includes 1995 and 1996 PNAD samples, the year 2003 includes 2002 and 2003 PNAD sample, and the year 2012 included 2011 and 2012 PNAD sample.

**Figure 3: Minimum Wage and the Distribution of Earnings**

<!-- Start of picture text --> Panel A. The evolution of real minimum Panel B. Earnings distributions and minimum<br>wage and earnings: 1995-2012. wage spikes by year.<br>2.03<br>2.0 1.0<br>1.8<br>0.8<br>1.6<br>1.43 0.6<br>1.4<br>1.26<br>1.2 0.4<br>1.14<br>1.0 0.95<br>0.2<br>0.8 0.82<br>0.6 0.0<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 4 5 6 7 8<br>Year log(Earnings).2005 Reals<br>Average Earnings Median Earnings Minimum Wage 1995 2003 2012<br>1995=1 kdensity<br><!-- End of picture text -->