rafmacalaba/datause-encoder-data huggingface dataset:
Brown, Gillian (2000) “Evaluating the Impact of Water Supply Projects in Indonesia”, in Michael Bamberger (ed.) _Integrating Quantitative and Qualitative Research in Development Projects_ . Washington, DC: World Bank, pp. 107-113 Cartwright, Nancy (2007) “Are RCTs the Gold Standard?” _BioSocieties_ 2: 11-20 Deaton, Angus (2009) “Instruments of Development: Randomization in the Tropics, and the Search for the Elusive Keys to Economic Development” Working Paper No. 14690, National Bureau of Economic Research, Cambridge, Massachusetts Duflo **,** Esther and Michael Kremer (2005) “Use of Randomization in the Evaluation of Development Effectiveness” in George Pitman, Osvaldo Feinstein and Gregory Ingram, (eds.) _Evaluating Development Effectiveness_ New Brunwick: Transaction Publishers, pp. 205-31 Gibson, Christopher and Michael Woolcock (2008) “Empowerment, Deliberative Development and Local Level Politics in Indonesia: Participatory Projects as a Source of Countervailing Power” _Studies in Comparative International Development_ 43(2): 151-180 Guggenheim, Scott E. (2006) “Crises and Contradictions: Explaining a Community Development Project in Indonesia”, in Anthony Bebbington, Scott E. Guggenheim, Elisabeth Olson, and Michael Woolcock (eds.) _The Search for Empowerment: Social Capital as Idea and Practice at the World Bank._ Bloomfield, CT: Kumarian Press, pp. 111-144 Jha, Saumitra, Vijayendra Rao and Michael Woolcock (2007) “Governance in the Gullies: Democratic Responsiveness and Community Leadership in Delhi’s Slums” _World Development_ 35(2): 230-46 Kane, Mary and William Trochim (2007) _Concept Mapping for Planning and Evaluation_ . Thousand Oaks, CA: Sage Publications King Gary, Christopher J. L. Murray, Joshua A. Salomon, and Ajay Tandon (2004). “Enhancing the Validity and Cross-Cultural Comparability of Survey Research.” _American Political Science Review_ 98(1): 191-207 Morgan, Stephen and Christopher Winship (2007) _Counterfactuals and Causal Inference: Methods and Principles for Social Research_ New York: Cambridge University Press Morra, Linda and Ray Rist (2009) _The Road to Results: Designing and Conducting Effective Development Evaluations_ Washington, DC: World Bank Mosse, David (2005) _Cultivating Development: An Ethnography of Aid Policy and Practice_ London: Pluto Press NONIE (Network of Networks on Impact Evaluation) (2008) _Impact Evaluations and Development. NONIE Guidance on Impact Evaluation._ Draft prepared for the Cairo International Evaluation Conference, April 2009 27
Policy Research Working Paper 6119

# **Abstract**

This paper exploits the staggered rollout of Thailand’s universal health coverage scheme to estimate its impacts on whether individuals report themselves as being too ill to work. The statistical power comes from the fact that there is an average of 62,000 respondents in the labor force survey at each survey date and no less than 68 survey dates, most of which are just one month apart. The analysis finds that universal coverage reduced the likelihood of people reporting themselves to be too sick to work: the authors estimate the effect to be −0.004 one year after universal coverage and −0.007 three years after. The estimated effects are much larger among those age 65 and over. Universal coverage had a much larger effect on health (about four times larger) than the Village Fund scheme, which provided free credit to rural households through a subsidized microcredit scheme and which was rolled out around the same time as universal coverage.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at awagstaff@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1. INTRODUCTION**

The extensive research on US immigrants varies greatly in findings. Some studies provide optimistic depictions of immigrants’ performance on the US labor market<sup>1</sup> , while others question whether convergence between immigrants’ and natives’ performance occurs at all.<sup>2</sup> This paper investigates the convergence among country of origin performance patterns of skilled male migrants to the US. Relying on Censuses 1980, 1990 and 2000 it attempts to answer three questions. First, do the entry levels of occupational placement of skilled US migrants differ by country of origin when other factors are kept constant? Second, how does occupational placement by country of origin change over time? Finally, to what extent do country of origin attributes explain the temporal patterns of the occupational placement?

The first finding is that predicted entry-level occupational placement of the US skilled immigrants varies widely by country of origin. Second, for the majority of countries there is evidence of improvement in the occupational placement over a decade. Third, placements by country of origin are convergent: individuals from countries with lower predicted entry-level performance progress faster than individuals from countries with higher predicted performance at arrival. Fourth, the convergence loses intensity over time: the growth rate experienced by a cohort within the first ten years weakens over the following decade. Fifth, countries lower placed initially do not fully catch up within ten years to those already at an advantage immediately after arrival in the US. The sixth finding is that country specific attributes impact on the improvement in immigrants’ occupational placement mainly through their effect on initial performance: thus, they lose significance when initial occupational levels are added to the regressions.

The foremost reason for assessing immigrants’ performance by using occupational placements instead of earnings is because the former are less likely to vary by city or state within the US compared to the latter. This is a significant methodological advantage when capturing performance of immigrants spread throughout the country. In addition, occupations are less likely to be affected by changes in the labor market conditions over time. Consequently, the period effect biases which have to be addressed when focusing on earnings are less of a problem if employing occupational placement as a measure of performance.

The analysis of immigrants’ occupational placement lies in a cone of shadow when compared to the generous number of studies using earnings to evaluate performance. Nevertheless, researchers are becoming increasingly aware of the particular importance that

> 1 Chiswick (1978) 2 Borjas (1985), Lubotsky (2007), Kim (2009)

2
_Table 3: Soft Aid for Trade Facilitation : Bilateral-5Year Fixed Effects and Year Fixed Effects Included_

||1<br>|2<br>|3<br>|4<br>|5<br>|6<br>|7<br>|
|---|---|---|---|---|---|---|---|
||Baseline<br>– all<br>positive<br>trade|Baseline<br>with per<br>capita<br>GDP|Only<br>countries<br>receiving<br>AfTF /1/|Recipients<br>only;<br>trade with<br>recipients<br>& ROW|Recipients<br>with<br>developing<br>world only|Recipients<br>with<br>developing<br>& bilateral<br>donor; no<br>intra-<br>donor<br>trade|Base-line<br>(2) with<br>lags|
|ln_tariff|-0.199***<br>[0.035]|-0.199***<br>[0.035]|-0.245**<br>[0.104]|-0.221***<br>[0.048]|-0.214***<br>[0.043]|-0.201***<br>[0.036]|-0.289***<br>[0.072]|
|ln_rep_gdp|0.688***|1.574***|-4.733***|0.617|1.393***|1.508***|2.079**|
||[0.045]|[0.323]|[1.290]|[0.483]|[0.466]|[0.327]|[1.038]|
|ln_part_gdp|0.254***|-0.697***|-7.587***|-2.338***|-1.584***|-0.759***|-0.011|
||[0.036]|[0.270]|[1.742]|[0.494]|[0.407]|[0.274]|[0.610]|
|ln_soft_rep|0.004*|0.004*|0.068**|0.024***|0.004|0.004|0.024***|
||[0.002]|[0.002]|[0.031]|[0.005]|[0.003]|[0.002]|[0.007]|
|ln_soft_part|-0.002|-0.002|0.057*|-0.009|-0.007**|-0.002|0|
||[0.002]|[0.002]|[0.030]|[0.006]|[0.003]|[0.002]|[0.003]|
|ln_rep_gdp_cap||-0.882***|5.352***|0.041|-0.6|-0.814**|-1.596|
|||[0.320]|[1.274]|[0.474]|[0.451]|[0.324]|[1.046]|
|ln_part_gdp_cap||0.955***|7.919***|2.576***|1.720***|1.015***|0.312|
|||[0.264]|[1.726]|[0.482]|[0.396]|[0.267]|[0.599]|
|L.ln_soft_rep|||||||0.046***<br>[0.008]|
|L2.ln_soft_rep|||||||0.020***<br>[0.005]|
|L.ln_soft_part|||||||0.002<br>[0.003]|
|L2.ln_soft_part|||||||0.004<br>[0.003]|
|Observations|108304|107806|12424|52181|57861|104637|32592|
|Number of Bilat - 5year F.E.|39309|39125|6576|22181|25237|38386|13461|
|Robust standard errors in brackets||||||||

* significant at 10%; ** significant at 5%; *** significant at 1% /1/ A4TF “Aid for Trade Facilitation’ Includes Bilateral- 5 year fixed effects and year fixed effects

28
# **_5.4 Brazil and Regional Trends_**

We find that in 2009 and 2008, Brazil had higher NIM spreads than the LAC average, while the LAC average was higher than all other regions except Sub-Saharan Africa (Figures 8a & 8b). This indicates a LAC region wide problem of high NIM spreads. Despite LAC’s robust growth in the last few years NIM spreads remain the second highest of all regions in the world. Moreover, LAC had the highest micro-factors in the world and the second lowest macro behind South Asia in 2009. On the other hand, Brazil in 2009 had lower I&R spreads than the average in LAC but higher than the average macro spreads. In 2008, on average, LAC had the third highest micro spreads behind East Asia and SubSaharan Africa and the 6<sup>th</sup> highest macro spreads in 2008. However, in 2008, Brazil had higher I&R spreads than the LAC average but lower macro spreads.

**Figure 8a. Average I&R and macro (2009)**

**Figure 8b. Average I&R and macro (2008)**

<!-- Start of picture text --> Brazil Brazil<br>Latin America and the... Latin America and the...<br>East Asia and Pacific East Asia and Pacific<br>South Asia South Asia<br>Europe and Central Asia Europe and Central Asia<br>Middle East and North... Middle East and North...<br>Sub-Saharan Africa Sub-Saharan Africa<br>Midde East (Developed) Midde East (Developed)<br>Asia (Developed) Asia (Developed)<br>Europe (Developed) Europe (Developed)<br>North America (Developed) North America (Developed)<br>-0.5 1.5 3.5 5.5 7.5 9.5 11.5 -0.5 1.5 3.5 5.5 7.5 9.5 11.5<br>Average of I&R Average of Macro Average of I&R Average of Macro<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data In 2009 Sub-Saharan Africa had the highest NIM, perhaps because of the fragility of states, the uncertainty regarding repayments, and a much smaller banking sector. In 2008, Eastern Europe had the highest macro spreads because of the crisis in Eastern Europe resulting from the global crisis. Developed Asia has the lowest NIM in both 2008 and 2009. The year 2008 is the year of the crisis with all regions, except East Asia & Pacific. and marginally the Middle East & North Africa, having higher NIM compared to 2009. In Brazil, where the government in the latter part of 2008, and through 2009, introduced measures to help the flow of credit as explained by Takedaa and Dawid (2010), helping smaller banks sell their credit portfolios and relaxing larger banks regulations it experienced a marginal decrease in the NIM from 2008 to 2009.

The high R<sup>2</sup> of our regression results, show that our findings regarding the importance of micro-factors are robust. R<sup>2</sup> s are generally high and when they are low this can be interpreted that micro-factors are not important in explaining the total NIM (Figure 9a & 9b). In this case, the residual constant term (macro-factors) becomes more significant. This is the case for some developed countries, because the main drivers behind the NIMs are macro-factors since they have more competitive banking sectors.

22
**Table 2: Summary Statistics – Entries are proportion or means unless otherwise noted**

||**All**|**Rur**|**al**|**Urb**|**an**|
|---|---|---|---|---|---|
|||Poor|Non-Poor|Poor|Non-Poor|
|**Food budget shares**||||||
|Cereal|0.160|0.159|0.143|0.184|0.150|
||[0.001]|[0.002]|[0.002]|[0.002]|[0.002]|
|Meat/fish|0.262|0.242|0.257|0.261|0.303|
||[0.001]|[0.002]|[0.002]|[0.002]|[0.002]|
|Oil|0.086|0.092|0.089|0.084|0.074|
||[0.000]|[0.001]|[0.001]|[0.001]|[0.001]|
|Tubers/plantain|0.360|0.393|0.396|0.353|0.272|
||[0.001]|[0.002]|[0.002]|[0.002]|[0.002]|
|Other|0.132|0.114|0.116|0.118|0.200|
||[0.001]|[0.001]|[0.001]|[0.001]|[0.002]|
|**Aggregate prices (/1000)**||||||
|Cereal|1.003|0.995|1.016|0.989|1.023|
||[0.002]|[0.003]|[0.004]|[0.003]|[0.004]|
|Meat/fish|3.313|3.039|3.039|3.669|3.594|
||[0.009]|[0.019]|[0.018]|[0.014]|[0.010]|
|Oil|1.470|1.393|1.351|1.500|1.700|
||[0.006]|[0.009]|[0.009]|[0.012]|[0.014]|
|Tubers/plantain|0.667|0.548|0.563|0.717|0.923|
||[0.002]|[0.002]|[0.002]|[0.003]|[0.004]|
|Other|1.785|1.580|1.628|1.941|2.099|
||[0.005]|[0.008]|[0.013]|[0.011]|[0.010]|
|**Annual per Cap. food**<br>**expenditure (/1000)***|257.194|116.877|346.691|176.281|507.085|
||[1.437]|[0.657]|[3.199]|[1.001]|[6.060]|
|Observations|21,239|6,766|4,785|5,743|3,945|

*prices - Franc Congolais; food expenditure in 1000 FC per year standard errors in brackets, 100 bootstrap replications Source: 2012 1-2-3 Survey, DRC

Table 2 shows summary statistics of budget shares, aggregate prices for food commodities, and per capita food expenditure by area of residence and poverty status. Tubers and plantain are the main staple crop in the Congolese diet, representing the most important food commodities in terms of food budget. Households devote between 27.2 (urban non-poor) and 39.6 (rural non-poor) percent of their food budget to these food commodities. Meat and fish represent the second most important food commodities of both poor and non-poor households in urban as well as in rural areas. They account for relative budget share ranging from 24.2 (rural poor) to 30.3 (urban nonpoor) percent. Cereals represent an important budget share of the household food budget. Together, cereals, tubers and plantain, and meat and fish represent 78.2 percent of food expenditure of the households in the sample. Among the poor, this share is even higher, representing 79. 4 and 79.8 percent of the budget share of the rural and urban poor, respectively. It is important to point out that these food commodities constitute the bulk of the exempted food products. Other exempted foodstuffs are grouped in the “other foods” category; but the structure of the data set does provide us a means to further investigate the relative share of such goods. Annual per capita food expenditure varies between 116,877 and 507,085 FC. Aggregate food prices varies by commodity, with animal proteins being the most expensive, while cereals are the cheapest.

There are variations in food shares, aggregate prices, and expenditure levels by area of residence and poverty status. Less expensive food commodities tend to represent a relatively large 10
of workers are still in place, since effectively this is an exit barrier. It is also an entry barrier since it discourages entry by discouraging firms from hiring permanent workers who benefit from on-the-job training.

Following the work of Hall and Jones (1999), we will focus on the determinants of productivity levels rather than productivity growth rates. Hall and Jones have argued “levels capture the differences in long-run economic performance that are most directly relevant to welfare as measured by the consumption of goods and services.” Also, in this context they point to the recent evidence on the transitional nature of growth rate differences across countries, the empirical questioning by Jones (1995) of the relevance of endogenous growth models, and the theoretical support from the recent models that show the effect of policies on income levels and not on growth rates. They argue that countries in the long run differ in their income levels and not growth rates. Other important, recent papers that have focused on levels rather than growth rates are Frankel and Romer (1999), Irwin and Tervio (2002) and Rodrik, Subramanian and Trebbi (2002).

Using two-digit industry level data for the India states for the period 1988-2000, we are able to obtain some results, that we think are interesting and have important policy implications. We find certain interesting trends in the inequality of per worker and aggregate output and value added across industries and states that show imperfect interindustry and interstate labor mobility as well as misallocation of resources across industries and states. We find that trade liberalization increases productivity in all industries across all states. Productivity is also found to be higher in the less protected industries. The effects of protection and trade liberalization are more pronounced in states that have relatively more flexible labor markets. Similar effects are also found in the case of employment, capital stock and investment. We also find that labor market flexibility, independent of other policies, has a positive effect on productivity. Importantly, per capita state development expenditure seems to be the strongest and the most robust predictor of productivity, employment, capital stock and investment. Furthermore, industrial delicensing increases labor productivity but only in the states with flexible labor market institutions. Even after controlling for delicensing, trade liberalization is shown to have a productivity-enhancing 5
In this regression, we do not find a significant effect of local level of economic activity (measured by electricity consumption), nor do we find a relationship with output per worker. The lack of a longer series of data (we have only two years) and the proxy we use for economic activity may limit the analysis of the effect of economic growth and its structure in the formalization process, even though other studies have shown their key importance.

# _Interaction between Business and Labor Formality: Main Results_

Table 4 reports the results of our main regression analysis on the interrelationship between business and labor formality. Columns (1) and (3) display OLS regressions results, and columns (2) and (4) display 2SLS regressions results. The dependent variables are indicator variables for complying with municipal business licenses for operation and payroll registry of workers. For each formality variable, we include a proxy variable for the enforcement of regulations. For business formality, we use the distance (in logs) from the business location to the nearest SUNAT office as our proxy for enforcement of municipal regulations. For labor formality, we use the percentage of firms already registered in the Electronic Payroll Registry as our proxy for enforcement of labor regulations.

In addition, we include a set of covariates related to the probability of complying with business and labor formality regulations. In this set of covariates, we consider the (log) product per worker, (log) total number of workers, age of the firm, an indicator variable for the legal status of the firm (incorporated versus unincorporated businesses), dummies for industry, (log) population of the district, and a dummy variable for Lima and Callao (the capital city).

The findings indicate that business formality affects labor formality, but not the other way around. Specifically, the OLS regressions find that both types of formality reinforce each other, as the estimated coefficients for the payroll registry and license variables in columns (1) and (3) show. However, when we control for endogeneity using the instruments, the 2SLS regression results tell a somewhat different story: firms that comply with business formality increase their probability of complying with labor formality by 0.7 percentage points (or by 54 percent). Nevertheless, complying with labor formality does not have a statistically significant effect on the probability of complying with business formality.

18
- 13 -

countries' weights because the Japanese weights would give greater importance to products that are declining relatively in price.

Some evidence for the existence of the third sequence of events is provided by a comparison of annual rates of growth of world manufactures export price indexes based on world weights with world export price indexes based on U.S., German, and Japanese weights.

Table 4 Effect on Price Indexes for Developed-Country Exports of Manufactured Goods of Weighting by Export Patterns of the U.S., Germany, and Japan Average Annual Rate of Growth (Per Cent) of Price Index, 1953-1991

|||Weirhts of||
|---|---|---|---|
||1963|1975|1986|
|WeiEhts of||||
|Developed Countries|4.40|4.24|4.04|
|U.S.|4.41|4.26|4.03|
|Germany|4.39|4.18|4.20|
|Japan|4.00|3.95|3.80|

Sources: Appendix Tables 2 and 3 World export price indexes for manufactures weighted by Japanese export weights rose less rapidly than indexes based on world, or U.S. or German, export weights, no matter what year's export composition is used, although Japan's advantage in this respect is smaller with the later year's weights. In other words, Japanese exports were more concentrated than, for example, U.S. exports on products for which prices were rising less r..pidly than average or were even declining. Either Japan's exports were more heavily concentrated on products for which productivity was rising relatively fast everywhere or its rapid productivity growth was driving down world prices of the products in which its exports were concentrated, whether or not other countries' productivity was growing rapidly in these products.