rafmacalaba/datause-encoder-data huggingface dataset:
# **5 Conclusion**

Quantifying who benefits and who loses from trade reform and by how much is of crucial policy interest, but often challenging because of a lack of suitable data. The Household Impacts of Tariffs (HIT) data introduced in this paper are a publicly available household survey based data set covering 54 developing countries that enables researchers to analyze the distributional impacts of tariffs. It contains granular data for each percentile of the income distribution on both the income derived from and consumption of 53 agricultural products. In addition, it keeps track of spending on five different types of manufacturing goods and services, as well as transfers, and wage income disaggregated by 1-digit sector, 10 different types of non-farm household enterprise sales and various types of transfers.

Using a stylized agricultural model and a first-order effects approach we have illustrated potential applications of the data and shown that the prevailing structure of agricultural tariffs represses household incomes by 2.50% percentage points across countries. However, the costs of protectionism vary enormously across countries and across households within countries, because households in different parts of the income distribution tend to have very different income and consumption portfolios; the average standard deviation of the gains from trade within a country is 1.01 percentage points. We also show that using disaggregated data is important, because using more aggregated data yields biased estimates of the average gains from trade.

While we have focused on the elimination of agricultural tariffs, the HIT data set has a much wider set of potential applications and can accommodate richer and more sophisticated modeling assumptions. Examples of potential applications include assessing how EU and U.S. agricultural tariffs or regional trade-agreements, such as AGOA, impact households in low-income countries.<sup>13</sup> An analysis of poverty and inequality impacts of food price shocks is also possible. Moreover, the data can be used to study issues that are not related to trade reform, such as food subsidies or value-added tax reforms.

> 13In the online Appendix we analyze the impact of non-tariff barriers.

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35 number is difficult to conceive. Even here there is return but only when refugees find themselves in even greater peril abroad. This was the case, for example, of the 1.2 million Iraqis who fled to Syria, many of whom returned when an even more threatening civil war broke out in Syria. By contrast, with the important exception of persecuted minorities, return to countries not in conflict as in the Western Balkans is possible.

Some key economic indicators relating to the three groups of countries can also be found in the tables in Annex 2. The tables show that, while some countries’ economies – such as Syria and the Central African Republic are devastated by conflict – several of the countries in conflict show some progress on social indicators, and sustained moderate growth and/or sporadically rapid economic growth. This suggests that even in those countries, for a select few, or in some locations, voluntary return is not always out of the question. But again, these are exceptions, and very unlikely to become mass movements.

# b) Financial Incentives

This takes us to the question‐ can financial incentives make a difference in inducing failed asylum seekers and refugees to return to these difficult environments? In a recent blog, Harvard's Lant Pritchett<sup>37</sup> (a former World Bank researcher) writes "the fact that there are massive wage gains to workers moving from developing country to developed country labor markets (on the order of PPP adjusted $15,000 per year, per low‐skill worker) is now one of the most robust facts in development economics. This magnitude of wages gains, which imply workers would increase their average earning by roughly a factor of four, is consistent with observational data, econometric comparisons of equivalent workers across labor markets and adjusting for selection, occupational data, randomized experiments, and macroeconomic data.” This difference in real wages persists even though immigrants are paid less and have lower employment rates than comparable natives.

How much would it take to compensate the worker who returns home to a developing country? For example, Pasha and Altaf (1987) estimate lifetime earnings in the Gulf in the early 1980’s and compared them to home earnings in Pakistan. Accounting for average age and other observables, they conclude that only if wages of immigrant workers in the Gulf were cut by over 50%, would they induce return.

Applying Pritchett’s more recent estimate of the difference in earnings at home in a developing country and abroad in a developed country, if the immigrant is fully employed throughout their stay and their working time horizon is 25 years, the present value of the immigrant’s foregone earnings if he or she returns is approximately $262,000 at a discount rate of 3%. This sum is at least 10 times larger than the highest known grant offered to

> 37 Lant Pritchett, Labor Mobility and Wages of the Rich Country Poor, Part One: Analysis and Implications of the Mariel Boatlift, Center for Global Development, June 2017
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Table 5: IV results, robustness checks

||Omitting strength|Non-seasonal inversion|State clustering|Without winsored|
|---|---|---|---|---|
|Mean PM2_._5|-0.0090<sup>_∗_</sup>|-0.0094|-0.0075<sup>_∗∗_</sup>|-0.0085<sup>_∗∗_</sup>|
||(0.0050)|(0.0062)|(0.0032)|(0.0037)|
|N|6,142|5,575|6,142|6,069|
|**Weather controls:**|||||
|Temperature|Y|Y|Y|Y|
|Precipitation|Y|Y|Y|Y|
|Relative humidity|Y|Y|Y|Y|
|Surface pressure|Y|Y|Y|Y|
|Wind speed|Y|Y|Y|Y|
|Wind direction|Y|Y|Y|Y|
|**Fixed efects:**|||||
|Fiscal year|Y|Y|Y|Y|

Notes: The outcome is the first difference within a district of the log of annual GDP measured at the district level. We instrument for annual average of PM2 _._ 5 measured at the district level during a fiscal year from April to March and weighted by population with inversions and inversion strength. Pollution data comes from the Van Donkelaar group. Inversion data comes from the MERRA re-analysis data and measures the incidents when temperature at atmospheric levels above the surface is higher than at the surface by district for each fiscal year. Inversions are measured as the share of days with an inversion. Inversion strength is the average difference in temperature when there is an inversion. All inversion data and weather controls are population weighted averages across districts. The unit of observation is the district year. In all columns we control for the first difference of the number of days in one of fifteen precipitation bins, the number of days the mean temperature is in one of twenty-five bins, a second order polynomial of relative humidity and surface pressure, four wind speed bins, ten wind direction bins, and interactions of relative humidity and the square of relative humidity with all of our temperature bins. All columns include fiscal year fixed effects. In all columns we cluster standard errors at the district level. We winsorize GDP growth at 1%. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

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willing to contribute to productivity-enhancing goods such as roads, electricity, irrigation, and credit, females are the only ones who even in the reserved period contribute to water, schools, health, and sanitation. This is consistent with gendered differences in the preference for different types of public goods found in the literature and earlier evidence regarding the agenda of public meetings. Second, in all cases, coefficients in the immediate post-reservation period are larger or equal in magnitude and more significant than during the reserved period, supporting the notion that reservation has significant longterm effects. While it is not clear whether the negative impact of the second lag for all groups with the exception of credit is due to the smaller sample, defects in implementation, or other reasons, it suggests scope for further study. Taken together, these findings suggest that positive impacts of reservations on certain public good outcomes as reported in the literature may have materialized not because of higher quality of officials but because this policy empowered women to more actively contribute to public goods and monitor in specific areas of particular interest to them.

# **5. Conclusion and policy implications**

Our analysis contributes to the debate on the extent to which reserving political positions for females can improve political outcomes. India‟s adoption of far-reaching policies -allocated randomly across villagesto overcome the country‟s high level of gender bias and social stratification some time ago makes this particularly suitable for our analysis. A nation-wide sample with a rich set of information from individual respondents allows us to contribute to the literature methodologically and substantively.

Methodologically, we note that differentiating between male and female respondents can be relevant if reservation-induced effects on different groups are of comparable magnitudes but opposite sign. We find pronounced, but strongly gender-differentiated, impacts of female reservation on meeting attendance in the short term. Information on all elections after the 73<sup>rd</sup> Amendment came into force allows testing for persistence and long-term impacts of such policies with results confirming that in many cases the full impact of this policy materializes only with some lag.

Substantively, this allows us to complement a focus on outcomes in the short term with attention to longer-term impacts and the mechanisms that may be at work, in particular individuals‟ willingness to contribute to public goods, participation in political processes, and ability to access, process, and use information to hold leaders to account. As they put less experienced and qualified individuals into positions of leadership, it should not come as a surprise that the immediate effect of reservations on the quality of public service provision is negative. In our case, and in line with the importance of learning, such negative effects have to be weighed against significant and in most cases persistent impacts (some 18
question would stick out as very different in sensitivity; iii) could be seen by respondents as not easily inferable by the researchers (since if we knew the true answers for the household for each of 1 through 3, then we would be able to infer individual responses for question 4 in group A); and iv) that we thought that people would think it was plausible that many people would answer that at least one question was true, but that few people would be likely to regard all three statements as true.

# **3.2 Surveys**

In order to trial this methodology, we took advantage of surveys in four countries that were being taken for other purposes and added this list randomization question. The surveys in Ethiopia and Morocco were surveys on Migration and Development conducted by Maastricht Graduate School of Governance, and oversampled households with migrants. The Philippines survey was conducted by the World Bank, Innovations for Poverty Action and the University of Michigan as a baseline survey for an experiment intended to help lower barriers to international migration. The Mexican survey was conducted by the World Bank and Innovations for Poverty Action as a follow-up survey for a financial literacy experiment. We describe the population of interest in each study.

The Ethiopian sample was a study explicitly designed to study migration and so over-sampled areas with high concentrations of migrants. Sampling was based on a two stage purposive sampling technique. The five regions of Amhara, Oromia, SNNP, Tigray, and Addis Ababa were chosen, and within each region, three Woredas were selected for enumeration: one urban Woreda and two rural Woredas. The Woredas were selected to represent a city within the region (urban area), a rural area Woreda close to an international border area, and a rural area farther from the border. The final criteria for selection were Woredas that were known to have migration. Within each Woreda, the Kebeles selected for the initial household listing were selected based on accessibility. After listing, the survey randomly chose households to survey, oversampling households with current or return migrants (Kuschminder, 2012). Surveying took place between February and April 2011. The resulting sample consists of 1,251 households given the list randomization question.

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use these estimates to identify three key parameters of the model: the elasticity of labor demand, the elasticity of substitution between native and immigrant workers, and the elasticity of native labor supply. The first two parameters determine the sign and magnitude of the immigration induced shock to native labor demand. The third, the elasticity of native labor supply, governs the degree to which this demand shock is reflected in wage or employment changes. Using these parameter estimates we are able to identify the effect of immigration on wage and employment levels at a local labor market and national level, as well as, on the returns to education and associated inequality. Finally, we are able to simulate the effect of changes in various immigration policies on these outcomes.

We implement our methodology using Malaysian data. The vast majority of papers in the literature use data from OECD countries, even though half of all immigrants reside in developing countries (Artuc et al., forthcoming). Malaysian data enable us to present rare evidence from a developing country that is also a prominent destination for migrants.<sup>6</sup> A second key advantage is that the Malaysian experience is uniquely suited to understanding the labor market impact of low-skilled immigration. Immigrants to the United States, and numerous other OECD countries, are overrepresented at both the higher and lower end of the skills distribution (Docquier, Ozden and Peri, 2010). They also have the opportunity to assimilate, obtain permanent residency, and eventually become citizens. This assimilation process creates another potential source of heterogeneity as older immigrants may become more like natives.<sup>7</sup> In contrast, immigration to Malaysia is

> (2001), Card and DiNardo (2000), Cortes (2008), and Peri and Sparber (2009). Altonji and Card (1991) and Ottaviano, Peri and Wright (2012) use variation across industries. Our main specifications combine these approaches and use variation across both regions and industries.

> 6In terms of both income and immigration levels Malayasia is close to a median country.

> 7The high degree of heterogeneity among immigrants in the US likely explains why, as highlighted by Cortes (2008), it is diffi cult to identify the effect of immigration on immigrant wages for the US.

4
As we can see in Table 4, the risk rating of the country also explains the level of investment; the lower the risk of the country, the greater the level of investment in private participation in Infrastructure Investments, even if we take out most of the controls.

In the specifications of column (5) the overall r-squared is over 65 percent; a high value for a panel data model.<sup>10</sup> Also, the between r-squared is very high with a value around 75 percent. The variables are statistically significant at 95 percent confidence with the exception of GDP growth. This presents a positive impact but only with 90 percent confidence. Openness is not significant.

**Table 4: The Effect of Country Risk on PPI Commitments**

|**VARIABLES**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|Country Risk|0.0531***|0.0359***|0.0274***|0.0280***|0.0300***|
||(0.00508)|(0.00491)|(0.00554)|(0.00563)|(0.00574)|
|Log GDP PPP||1.461***|1.475***|1.484***|1.419***|
|||(0.0952)|(0.0990)|(0.103)|(0.119)|
|Log GDP Growth|||0.131***|0.0981*|0.100*|
||||(0.0505)|(0.0526)|(0.0538)|
|Log Inflation||||-0.0846**|-0.0808**|
|||||(0.0401)|(0.0409)|
|Log Openness|||||0.134|
||||||(0.212)|
|Constant|2.866***|-32.02***|-32.21***|-32.25***|-31.34***|
||(0.203)|(2.269)|(2.366)|(2.485)|(2.562)|
|R2 within|0.0729|0.2228|0.2150|0.2266|0.2262|
|R2 between|0.3158|0.7218|0.7300|0.7328|0.7535|
|R2 overall|0.2842|0.6211|0.6296|0.6373|0.6475|
|Observations|1,518|1,464|1,283|1,219|1,201|
|Number of countries|127|124|120|120|119|

*** p<0.01, ** p<0.05, * p<0.1 Source: Authors’ calculations.

Note: These results are from the random effect model as well. Appendix 3 provides the results for the fixed effect model. The results show the same message. As robustness check, we also estimate the equation with three, four and five lags, which results are in the Appendix 5.

10 Values over 10 percent are accepted in the common literature, due to the bi-dimensional nature (countries and time) of the panel data model.

11
53% after the scheme. To the extent that the two groups of users (new and continuous) are comparable, there is a notable shift in their travel expenditure patterns, which complements the household-level results presented above.

In interpreting the treatment effect, we note that if paid bus ridership was widespread among women prior to the policy, the eliminated fare would directly reduce their expenditures. The Delhi Survey, however, documents the presence of a large segment of new users following the introduction of the policy. For these women, free bus ridership may imply new travel demand or substitution from other modes of transport. Either of these changes are likely to impact time use, which we turn to next.

# **H Impact on Time Use**

Our first set of main results pertains to time use. To reiterate, the CPHS reports daily time spent on work for employed women, household chores, and travel time, without breaking down the latter by motive for travel. Due to various substitution possibilities between travel modes, it is not clear whether total travel time will increase, decrease, or remain unchanged. It is possible that women switch from paid to free buses and there are no changes in travel time. However, if women who were previously budgetconstrained switch from walking to taking free buses, travel time will be reduced. Yet a third possibility is that the price effect helps women who would have otherwise not gone out to take more buses, increasing their travel time. While our ability to shed direct light on mode substitution is limited by the CPHS data, empirical results could help discern the overall impact. Finally, reallocation from travel time may affect time spent on household chores, and the possibility of cheaper commutes may affect labor supply. In what follows, we first focus on employed women and document time use changes that vary by marriage status and skill level. Subsequently, we offer a simple model of intra-household time allocation, corroborating its implications by showing results for men’s time use. Our final analysis documents results for unemployed women.

11
country official statistical offices and other multilateral organizations (see Annex).<sup>3</sup> The annual GDP growth rate for the world as a whole is assumed to be 3.5% per year in the period 20112020, while that for MENA is 4.3%. Oil importing countries are assumed to grow at 3.5% per year, while developing oil exporters and GCC countries are assumed to grow at 4.6% and 4.5%, respectively.<sup>4</sup> Demographic trends are taken from the 2009 UN Urbanization Prospects.

**Table 4: Unit costs of infrastructure by sector (US$)**

|**Sector **|**Cost per unit**|**Unit**|**Depreciation rate (%)**|
|---|---|---|---|
|**Electricity generation**|2,000|kW|4|
|**Paved roads**|410,000|Kilometer|4.7|
|**Unpaved roads**|50,000|Kilometer|7.2|
|**Rails**|900,000|Kilometer|4|
|**Rural water and sanitation**|150|person|3|
|**Urban sanitation**|150|person|3|
|**Rural sanitation**|130|person|3|
|**Urban water**|80|person|3|
|**Main telephone lines***|127-580|line|8|
|**Mobile lines***|127-451|line|8|
|**Access to electricity**|195|person|4|
|**Ports**|348|TEU|4|
|**Wastewater treatment**|120|person|3|

Source: Yepes (2008). *Varies by region.

In order to assess the full budgetary allocation needed by the various sectors, estimates of the associated maintenance needs are included as well. Maintenance is needed for any investment to meet its assumed lifetime so a commitment to maintenance is built into the costbenefit analysis and the calculation of the social rate of return of the investments. In this paper, maintenance costs are estimated by multiplying the stock value in the previous period by a depreciation rate. A fixed annual depreciation rate is assumed for each sector and shown in Table 4. Estimates of total annual expenditure needs by sector, sub-group and region are presented in Table 5. In the absence of data for a sector or subsector in a country, regional averages of investment were imputed to obtain total investment needs.<sup>5</sup>

> 3 In some cases recent trends of annual growth of the same series are used to fill in missing values. United Nations National Accounts Main Aggregates Database is used for historical GDP and for the value added components of agriculture and manufacturing. Additional data on container port traffic is taken from the Containerization International Yearbook (1970-2006). Extra information on roads and paved roads is available for Oman. GDP projections are from the World Bank.

> 4 When past growth rates were not available, a Hodrick-Prescott filter (with a smoothing factor of 100) was used to obtain the long-run growth rate trend of the past ten years (1999-2009) resulting in an implied annual growth rate equivalent to that of the World Bank projections. Growth rates are then used to obtain 2010-2020 GDP scenarios. 5 Regional averages of investment as a share of GDP were used in the following cases: roads in Syria, electricity in West Bank and Gaza, water and sanitation in Bahrain, sanitation in Saudi Arabia and Ports in Bahrain, Iraq, Libya, Qatar, Syria and West Bank and Gaza.

6
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Yamauchi, Futoshi, 2005, Why do Schooling Returns Differ? Screening, Private Schools and Labor Markets in the Philippines and Thailand, _Economic Development and Cultural Change_ 53: 959-981. -----------------, 2007, Social learning, neighborhood effects, and investment in human capital: Evidence from Green-Revolution India, _Journal of Development Economics_ , 83: 37– 62.

-----------------, 2008. 'Early Childhood Nutrition, Schooling, and Sibling Inequality in a Dynamic Context: Evidence from South Africa, _Economic Development and Cultural Change_ 56: 657-682. Yamauchi, Futoshi and Yanyan Liu, 2011, Impacts of an Early Education Intervention on Students' Learning Achievement: Evidence from the Philippines, IFPRI Discussion Paper No. 1121, Washington D.C.: International Food Policy Research Institute.

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