rafmacalaba/data-use-mentions-extended huggingface dataset:
277|0.307|0.343|-0.000|-0.030|-0.066**|
||[0.016]|[0.022]|[0.024]|[0.024]||||
|**Highlands region**|0.388|0.367|0.383|0.326|0.021|0.005|0.063**|
||[0.018]|[0.024]|[0.026]|[0.024]||||
|**Islands region**|0.154|0.178|0.152|0.200|-0.024|0.001|-0.046*|
||[0.013]|[0.019]|[0.019]|[0.021]||||
|**Momase region**|0.276|0.263|0.253|0.313|0.012|0.022|-0.038|
||[0.016]|[0.022]|[0.023]|[0.024]||||
|**Southern region**|0.182|0.192|0.211|0.161|-0.009|-0.029|0.021|
||[0.014]|[0.019]|[0.022]|[0.019]||||
|**Observations**|740|415|358|377||||

Notes: ***, **, and * indicate significance at the 1, 5, and 10 percent critical level. The values displayed for t-tests are the differences in the means across the groups.

**
br>-15<br>-20<br>-25<br>Bangladesh Bhutan India Pakistan Sri Lanka<br>Agriculture Manufacturing Construction Services<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset.

The region has not experienced a considerable creation of more regular employment between 2005 and 2015. As shown in Figure 9, the only strong expansion in regular employment happened in Bangladesh, where the share of regular wage jobs in the working-age population increased by 4.5 percentage points. Regular wage employment also increased in Bhutan, but only by 2 percentage points. In terms of other types of employment, casual work and unpaid employment declined across the region in the same period. In Pakistan, for example, casual employment decreased by almost 8 percentage points. In India and Bangladesh, unpaid employment decreased by 4 and 5 percentage points respectively.

Worldwide, a vast majority of jobs is created by the private sector. And the public sector can create only a limited number of jobs in countries where government revenue is modest, as is the case in South Asia. In Bangladesh and Pakistan, the share of public sector employment in the working-age population declined 13
An analysis using similar specifications as before suggests that the association of informality and poverty depends on the type of informal job. Columns 1 and 2 in Table 12 show that informal workers are more likely to be in the bottom quintile of the per capita household income distribution, even after controlling labor market characteristics. However, the statistically significant coefficient becomes insignificant and substantially smaller once we restrict the sample to salaried workers and control for other job characteristics such as firm-size (column 3).

These results show, again, a fundamental difference between salaried and self-employed informal workers. Like in the case life satisfaction (see Table 11), when excluding self-employed workers from the sample, informal employment is not statistically associated to lower household income. Our results suggest that all informal workers have lower wages than formal workers (see Table 8), all else equal, but salaried informal workers are not necessarily poorer at the household level. On the other hand, self-employed informal workers are associated with certain negative consequences such as lower subjective well-being, and dissatisfaction with pay, all of which together with lower wages, may lead to a situation that leads (or stems) from household poverty.

# **V. Conclusions**

In this paper, we utilize multiple data sources on informal employment in Russia and observe a long-term increase in informality from the early 2000s through 2016, with a short period of stability or even decline in the second half of the 2000s. Encouragingly, this trend appears to be consistent across different data sources and definitions of informal employment, including the official measure of informal employment monitored by Rosstat, which is presented alongside our estimates for comparison purposes.

We assess the comparability of sources in terms of the measurement of informal employment, and maintain a legalistic definition of informal employment throughout all data sources, which is in line with the majority of Russian literature and deemed more appropriate for transition economies. Throughout the analysis for this paper, in most cases the results are qualitatively consistent across the three surveys under study.<sup
# **Box 1:** Lessons from Latin America‘s Authoritarian Transition?

After a prolonged period of authoritarian rule, democratization in Latin America or, redemocratization in some cases, took place in a relatively short period of time. From 1979 to 1986 seven countries democratized in South America, five in Central America and six more in the Caribbean. After democratization in Argentina, Brazil and Uruguay quickly transitioned to electoral governments. Brazilian and Uruguayan demands for direct elections were closely tied to the events in Argentina. Later, the three countries pressed together for democratization in Paraguay and Chile. And, although the 1989 coup in which Stroessner was overthrown by his son-in-law General Andrés Rodríguez was the product of infighting, the new president soon faced considerable international pressure to call for elections (Zagorski, 2003). In 1990, Chile, Panama and Paraguay transitioned to democratically elected regimes. El Salvador and Guatemala became fully democratic after the settlement of their civil conflicts in 1992 and 1996 respectively.

This pattern of accelerated regime change has been described as a domino effect or a diffusion process. Przeworski, Limongi et al. (1996) explain that economic factors are not the only ones that matter for the durability of democracy and that the larger the proportion of democracies in a region, the more likely democracy is to survive in a country that is part of it. They highlight that contagion operates independently of the direct influence of Western governments and international institutions.

Similarly, Brinks and Coppedge (2006), using worldwide data on transitions to democracy from 1972 to 1996, show that region matters to predict the likelihood of democratization in a given country. In their work, diffusion is a determinant of the magnitude and direction of regime change since countries tend to follow the direction in which the majority of countries are moving and change to match the average degree of democracy of the contiguous countries, or ̳neighbor emulation.‘ The size of the gap in the level of democracy between a country and its neighbors explains the direction and strength of
% of the population in our scenarios.

To account for development, we use DHS data to explore the relationship between household income and exposure to diarrhea. We find a threshold at $15,600 per year, and we assume that only households with income below this level are affected by impact of climate change on diarrhea.

Further, we assume that fast progress in access to water and sanitation in the _prosperity_ scenario would halve the number of cases, which is consistent with a recent assessment in India (Andres et al. 2014). Of course, this assumes that the new water and sanitation infrastructure can continue to perform well in 2030 and beyond – in other words, that development has been climate‐informed. For that to occur, the uncertainty in climate projections would need to be accounted for in the design phase, as would the extra funds needed to invest in more resilient infrastructure (possibly factoring in safety margins and retrofit options) (Kalra et al. 2014).

We find that with high climate change impacts, in Vietnam, in the high‐impact case, 350,000 people would be pushed into poverty in 2030 in the _poverty_ scenario and 200,000 would be pushed below 4usd per day ( Figure 4). However, in the _prosperity_ scenario and in the low‐impact case, impacts are very small because of the positive impact of malaria (actually, the average income of the bottom 40% slightly increases in the low‐impact case, see Table 5).

As for the impact of high temperatures on labor productivity (also based on results presented in chapter 4 in _Shock Waves_ ), we assume that in hot countries, people working outside or without air conditioning will lose between 1 and 3 percent in labor productivity due to this change of climate, compared with a baseline with no climate change. To assess the number of people affected, we estimate the shares of people working outside or without air conditioning in the two socioeconomic scenarios. Our results show 14
– “tail bias”, by which the poorest and richest are not adequately surveyed either they are not present in the survey at all, or they choose to either not respond or misreport about their income or consumption levels. Bollinger et al. (2019) find, indeed, a U-shaped relationship between earnings nonresponse rate in the main US household survey (CPS) and actual earnings as recorded in administrative sources. However, the non-response rates they find cannot mirror a “bunching in the middle” like the one found for subjective ranking in LiTS. For instance, in LiTS round III, corresponding to year 2016, only 0.57% of the respondents position themselves in decile 10. Under the assumption that the response rate of individuals in decile 5 -which represent 30% of the sample- is 100%, this would imply that the nonresponse rate in decile 10 is close to 98%. For decile 9, the implied nonresponse rate would be 97%. For decile 1, the poorest decile, the implied nonresponse rate would be 86%. The maximum nonresponse rate Bollinger et al. find in the top-most and bottom-most percentiles in the United States is 30% - and only for percentiles 95 and above (well into decile 10) and percentile 10 and below (decile 1). Thus, the nonresponse rates compatible with a bunching in the middle as the one observed in the subjective ranking variable are well beyond those factually observed in the extreme percentiles of the income distribution in the United States. Also, if the LiTS survey would be missing almost entirely the top two deciles and the bottom decile of the income distribution (plus sizeable portions of other deciles) of each country then survey’s claim of representativeness at the country level would be seriously in doubt. Gimpelson and Treisman (2018) analyze this point in an indirect way by contrasting LiTS round II respondents’ assets information -car and second house ownership- with their subjective ranking: in the poorest countries included in LiTS round II -
Policy Research Working Paper 10780

# **Abstract**

This paper studies the consequences of a U.S. cabotage law for Puerto Rico (PR). Data on ship arrivals in PR show that the fleet of U.S. vessels that call there lacks capacity for carrying non-containerized freight. Empirical estimation using trade data shows that PR’s imports of sea-shipped final products are biased against U.S. mainland sources. This bias is strongest for heavy products and products not typically shipped in containers. Among upstream products, a strong bias against imports of sea-shipped products applies to all sources. Estimated tariff-equivalent costs among final products imply static annual welfare losses of 1.1 percent of household consumption ($203 per person). The same tariff-equivalent cost estimates imply that the law raises the cost of investment in PR by 3.0 percent. The observed bias against sea-shipped inputs in PR’s imports may result from long-run industry location decisions that have been influenced by the law’s presence.

This paper is a product of the Development Research Group, Development Economics. 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 rhillber@purdue.edu or jimene19@purdue.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of
being savings driven, total global investment is driven by total global savings, with the amount of investment in a given country being a function of both domestic savings as well as the current account balance, which is determined exogenously. The additional implication of the savings driven investment assumption is that that as dependency ratios fall in a given country, domestic savings will rise, which in turn will boost investment. The opposite would hold true for a country where dependency ratios are rising.

While LINKAGE provides the economy-wide effects of demographic change over time, the GIDD microsimulation framework (Bussolo et al., 2010; Bourguinon and Bussolo, 2013) will be used to generate income distributions under the various scenarios. GIDD draws on household level survey data benchmarked to 2010 to estimate income distributions by country that account for demographics, household characteristics (e.g. age, gender, and education of different members), sector of employment, skill premia on wages, and income.<sup>10</sup> Using the simulated income and employment under future scenarios from LINKAGE, and accounting for the demographic shifts characterized in the UN (2015), GIDD is able to generate income distributions by country that are consistent with both the more ‘aggregated’ changes under the CGE simulations and also what is known about households from survey data. In addition to incorporating the changes in key variables from the LINKAGE scenario results, the GIDD methodology updates the household survey data for the terminal year of the simulation. This is done by reweighting the population characterized by the base year household surveys using non-

> 10 Table A1 in the annex provides information on the household surveys used in the micro-simulation.

11
.

Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. _Journal of Personality and Social Psychology, 92_ (6), 1087.

- Duflo, A., & Kiessel, J. (2017). Evaluating the Teacher Community Assistant Initiative in Ghana. Retrieved on 14 October 2018. <u>https://www.povertyaction.org/study/evaluating-teacher-community-assistant-initiative-ghana.</u>

- Duflo, E., Dupas, P., & Kremer, M. (2011). Peer effects, teacher incentives, and the impact of tracking: Evidence from a randomized evaluation in Kenya. _American Economic Review_ , _101_ (5), 1739-74.

- Duke, N. K., & Pearson, P. D. (2009). Effective practices for developing reading comprehension. _Journal of Education_ , _189_ (1-2), 107-122.

- Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. _Psychological Science in the Public Interest_ , _14_ (1), 4-58.

- Durkin, D. (1978). What classroom observations reveal about reading comprehension instruction. _Reading Research Quarterly, 14_ , 481–533.

Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta‐analysis of school‐based universal interventions. _Child Development_ , _82_ (1), 405-432.

47
# **Empirical features for Mexico**

In the study of Mexico a regional agricultural production model and a macroeconomic (CGE) model were developed.<sup>4</sup> Both models focus on the Río Bravo Basin, but the CGE model also incorporates additional regions and sectors, comprising the entire Mexican economy. The detailed and technical description can be found in Dinar and Asad (2006), Yunez-Naude and Rojas Castro (2008), and Howitt and Medellin-Azuara (2008).

# <u>The Regional Farm production model</u>

The regional production model is linked to a hydrological model<sup>5</sup> of the Río Bravo Basin <u>(http://www.crwr.utexas.edu/riogrande.shtml). The regional agricultural model is based on</u> production models of representative farm types. The production models were calibrated to small (area<10ha), medium (10> area <50ha and large farms (area >50ha), based on the farm sample that was used by FAO. Then, using CONAGUA statistics on farm distribution in the study area, the farm models were extrapolated to construct the regional model that represents the agricultural sector in the 4 Rio Bravo Riparian states Chihuahua, Coahila, Nuevo Leon, and Tampaulipas. Several sets of policy interventions were simulated. The production model, a partial equilibrium framework, distinguishes between farm types and is used to assess the impact of the following policy issues:

1. Reduced water availability

2. Increased water cost 3. Smaller elasticity of crop supply (to reflect ―water secured‖ crops with lower sensitivity to changes in crop prices) + change in water availability 4. Smaller elasticity of grain crop supply + increased water cost 5. Reduced labor availability 6. Increased labor cost 7. Changes in crop price support programs While specific policy intervention results are discussed in Dinar and Asad (2006), and to a greater detail in Howitt and Medellin-Azuara (2008
5.0%|119|5.6%|
|Zimbabwe|2005|60|82||
||2010|57<br>1.0%|84|-0.5%|
|Average||3.6%||4.2%|

Source: Demographic and Health Survey website‘s STATcompiler tool, supplemented by figures from DHS reports for the most recent surveys, which are not yet accessible via STATcompiler. See References section for details. All figures correspond to the 5-year period prior to the survey. For surveys covering multiple years, the first year is recorded as the survey year.
# **VI. Conclusions**

This paper uses nationwide information on ENLACE achievement tests to test whether the PROSPERA program, which has been shown in numerous evaluation studies to increase school enrollment, leads to significant increases in learning. We construct a panel of sixth graders and follow their achievement up through twelfth grade, comparing achievement of beneficiaries to non-beneficiaries. The data set we construct also allows us to estimate impacts of PROSPERA on enrollment and on the type of school in which PROSPERA beneficiaries enroll. We study impacts by gender for the entire sample of sixth graders in schools in medium to very high poverty communities and for the subsample of sixth graders in indigenous schools, which we use as a proxy to measure impacts for the indigenous.

Estimated enrollment effects confirm large effects of participating in the PROSPERA program. Although positive effects have been documented before, our results are innovative in that they are based on administrative rather than parental self-reported information on children’s school attendance. The results are however quite similar to previous studies based on parental reports, showing important and significant effects on enrollment both in secondary and high school, for girls and boys and for the indigenous and non-indigenous. The analysis of type of school enrolled showed that, for secondary schools, the program increases the probability of enrolling in a tele-secondary school but does not affect the probability of enrolling in a general school. At the high school level, the probability of attending a general school increases relative to the probability of attending a technical school.

To estimate effects on achievement, we use specifications where the outcome measure is the difference in test scores. These differenced models essentially allow for individual fixed effects, which are assumed to be sufficient to control for selectivity in test-taking. Such control is necessary because some children are induced to take the achievement tests by the PROSPERA program. Our results indicate increases in achievement on the order of 0.03 to 0.1 standard deviations both for boys and girls in the sample of children in marginalized areas and on the order of 0
# **1. Introduction**

The global recession has placed aid budgets of donor countries under significant strains. In addition, aid effectiveness as it relates to trade is at the forefront of development policy discussions. There is also increased attention to project planning and focusing aid for trade in areas of maximum impact and return on investment. Moreover, there is very limited empirical evidence on the impact of aid to service sectors. Aid for trade has rapidly gained importance in trade and development circles as well as in the donor community. Launched at the Hong Kong SAR, China WTO Ministerial Conference in December 2005, the aid for trade initiative aims at helping developing countries, particularly least developed countries, overcome their supply-side constraints, so as to expand trade and benefit from global integration. Despite enjoying preferential market access and facing lower tariffs, several developing countries —especially low-income countries— have seen their share of world exports diminish over the past years. Clearly, the reduction of tariff and non-tariff barriers faced by developing countries is an essential condition for their export growth, but not a sufficient one. These countries face supplyside constraints that severely limit their ability to reap the benefits from global trade integration.

Evidence on the effectiveness of aid for trade in improving trade-related performance in developing countries is still limited. An important limitation prevalent in any econometric assessment is the potential reverse causality between aid and trade. Indeed, aid for trade is expected to have an impact on exports, however, aid may also be determined by export performance. If, for instance, better performing countries are rewarded with more aid, estimates of aid for trade coefficients would be biased upward.

In this paper, we propose an identification strategy that helps to overcome the reverse causality of the trade-aid relationship. Using input-output data, we exploit the differential 2