rafmacalaba/datause-encoder-data huggingface dataset:
exports/output) reported in table 2.8 The average import penetration increased from 5.7 % in 1987 to 11.6 % in 1998. The export to output ratio increased from 9.7% to 11.2% in 1998. While the import penetration has almost doubled during this period, it is worthwhile to note that the import penetration in Brazil continues to be relatively low when compared to a country such as Colombia that liberalized during the same period. Colombian manufacturing import penetration was about 21% in 1984 and significantly exceeded 30% after the 1990 tariff reductions (Pavcnik and Goldberg (2001)). This difference could potentially be attributed to a large size of Brazil relative to a country such a Colombia. Moreover, the import penetration increases in Brazil varied significantly across sectors. Figure 2 shows the evolution of industry import penetration and tariffs from 1987 to 1998. Industries with the largest surges in import penetration are clothing (industry 23), transport (industry 12), textiles (industry 22), machinery (industry 8) electronics (industry 10), and pharmaceuticals (industry 20). These are also industries that experienced large tariff declines.

# **2.2 Labor Force Data**

We combine the trade exposure measures with labor market data _Pesquisa Mensal de Emprego_ (PME) from _Instituto Brasileiro de Geografia e Estatistica_ (IBGE), the Brazilian Statistical Bureau from 1987 to 1998. The data set covers the 6 largest metropolitan areas in Brazil: Sao Paulo, Rio de Janeiro, Porto Alegre, Belo Horizonte, Recife, and Salvador. These metropolitan areas account for about 31.9 million people of the economic active age out of a total of 79 million. Moreover, in 1997, the states of the 6 surveyed metropolitan areas produced

> 8 Data on import penetration and export to output ratio was obtained from Muendler (2002) at http://socrates.berkeley.edu/-muendler/. We made adjustments similar to those for tariffs explained in previous footnote so that the trade exposure information corresponds to the level of industry aggregation in the labor force data. We use industry level trade exposure measures that are weighted by the import penetration of the less disaggregated nivel 80 industry data. However, the correlation between the weighted import penetration series and the import penetration series based on simple averages is .99. Similarly, the correlation between the weighted export to output series and the export to output series based on simple averages is .99.

9
observe how the program affects the moderately poor (top half of beneficiaries in terms of asset wealth) versus the extremely poor (bottom half). To capture asset wealth, we carry out a principal components analysis (PCA) using dummy variables for ownership of 13 assets.<sup>12</sup>

# **4.3 Outcome of the randomization**

A comparison of baseline sample means in treatment and control villages reveals balance on most outcomes (Appendix Table A1, Panel A). Across 41 outcomes, for only six (three) are there significant differences at the 10 percent (5 percent) level. There are no overall differences in health between treatment and control villages, but 0–5 year olds in treatment villages were 6 percentage points more likely to be ill or injured and had 0.51 more sick days in the last month than those in control villages. Weight-for-age z-scores were slightly lower in treatment villages. And 0–18 year olds in treatment villages were 10 percentage points less likely to own shoes than were those in control villages. Households in treatment villages also spent slightly more on insurance. However, we see balance on all other outcomes of interest. Table A1, Panel B shows similar balance on individual and household demographic characteristics and village characteristics. The only significant difference is that treatment households are less likely to have an improved floor. We use individual (or household, for outcomes that vary at that level) fixed effects to account for baseline imbalances.<sup>13</sup>

# **4.4 Attrition**

If attrition were correlated with treatment status, one might worry that attrition had compromised the internal validity of the results.<sup>14</sup> Fortunately, this is not the case, as shown in Table A2. Columns (1)–(4) consider household attrition, columns (5)–(8) consider individual

> 12These include whether the household owns an iron, refrigerator, television, mattress or bed, radio, watch or clock, sewing machine, stove, bicycle, motorcycle, car or truck, wheelbarrow or cart, and mobile phone. 13 In the case of anthropometric outcomes, we use village _×_ cohort fixed effects. These results are robust to instead using individual fixed effects.

> 14Between baseline and midline, 8.6 percent of households attrited from the sample, and between baseline and endline, 13.2 percent of households attrited.

11
7

# III. Methodology for Estimating Levels of Migration

Since few, if any, of the major labor-exporting countries<sup>keep accuraterecords on</sup> either the number or the educational characteristics of<sup>migrants,it is necessaryto estimate</sup> these variables by using data collected by the main labor-receiving<sup>countries.For the</sup> purposes of this paper, the main labor-receiving countries<sup>(regions) includetwo:United</sup> States and the OECD. The third large labor-receiving<sup>region -the ArabGulf- does not</sup> publish any data on the number or characteristics of migrants,<sup>andthus willnot be</sup> included in this study.

# lIIa. Estimating Migration to the United States

Following the pioneering methodology of Carrington and Detragiache<sup>(1998),9</sup> this study employs four steps to estimate migration rates<sup>and the educational attainment</sup> of migrants to the United States.

The first step is to use data from the newly released files of<sup>the 2000 U.S.</sup> Population Census on the "place of birth for the foreign-born<sup>population." Fortunately,</sup> these data are disaggregated by country of birth for about<sup>50 specificcountries.</sup> However, it is not clear whether all of the "foreign-bom"<sup>populationare, infact migrants.</sup> For example, a person born in Mexico and brought to the United States<sup>as an infant</sup> would probably not consider himself as a migrant. Moreover, it is also<sup>not clear how</sup> many of those who enter the United States illegally are,<sup>in fact, included in the "foreign-</sup> born" population figures. As some observers have suggested,<sup>the U.S.Census data may</sup> be grossly undercounting the actual migrant population<sup>that is living -legally or illegally</sup>

**9** While the Carrington and Detragiache (1998) study is based upon data from the 1990<sup>U.S.Population</sup> Census, this study uses newly released data from the 2000 U.S. Census.
<mark>Twyman, J., Useche, P., & Deere, C. D. (2015). Gendered perceptions of land ownership and agriculturaldecision-making in Ecuador: Who are the farm managers?.</mark> _<mark>Land Economics</mark>_ <mark>,</mark> _<mark>91</mark>_ <mark>(3), 479-500.</mark> United Nations Development Program. (2018). Human Development Report. Retrieved from - <u>http://hdr.undp.org/en/2018 update.</u> World Bank. 2012. World Development Report 2012: Gender Equality and Development. Washington DC.

<mark>Yount, K. M., & Li, L. (2009). Women's “justification” of domestic violence in Egypt.</mark> _<mark>Journal of Marriage and Family</mark>_ <mark>,</mark> _<mark>71</mark>_ <mark>(5), 1125-1140.</mark> Zimmerman, M. A. (1995). Psychological Empowerment: Issues and Illustrations. _American Journal of Community Psychology_ , 23, 581-599.

36
characterized for the value added nest, for example, by the substitution elasticity _σi,V A_ . The terms _avai,r_ and _aoi,r_ are productivity parameters applied to the value added and final production nest for the block of equations (6), (7), and (8). For the experiment here we will use our estimates of MFP gaps linked to regulation – total 66.3 percent, domestic regulation 38.0 percent, and openness 20.5 percent – and apply them through the coefficient _avai,r_ . The basic macroeconomic impact of our productivity experiment is summarized in Table 8; the effects on sectoral value added shares is reported in Table 9.

Table 8: Macroeconomic Impact of Service Sector Reform

||total<br>impact|openness<br>to foreign<br>establishment|domestic<br>regulatory<br>environment|
|---|---|---|---|
|GDP, % change|21.86|5.00|16.87|
|equivalent variation|23.58|5.48|18.09|
|consumer prices|-10.02|-2.45|-7.58|
|producer input prices||||
|for commercial services|-21.27|-5.26|-16.01|

_source: Own estimates from CGE model and database._ Table 9: Comparison of Value Added Shares

||EU26|Italy 2007<br>pre-experiment|Italy<br>post-experiment|
|---|---|---|---|
|primary|0.03|0.03|0.03|
|manufacturing|0.22|0.25|0.27|
|services|0.76|0.72|0.70|

_source: Own estimates from CGE model and database._ Starting from the value added composition of the Italian economy as summarized in Table 4, the large impact on GDP should not be surprising. The market (commercial) services sector accounts for 42 percent of Italian value added. As such, a 66.4 percent productivity shock implies a 27.8 percent gain in value added, which is close to the GDP and equivalent variation changes in the CGE model – between 22 and 24 percent. Even here, the gains are not quite 27.8 percent, reflecting substitution effects in the model (adjustments in input mixes, and trade substitution effects) as well as underlying distortions not reflected in the back of the envelope estimate. As we move away from simple microeconomic comparisons, the more detailed estimates from the CGE model provide greater insight. For example, consumer prices drop substantially with regulatory reform in services (Table 8). They also drop with better conditions for foreign establishment. However, the greatest impact follows from domestic rules and regulations. Table 10 focuses on the resulting shift in the Italian structure of production. Here, 32
**–** **<u>Table A4: Marriage quality women in their first marriage.</u>**

||Co-resides<br>with husband|Polygamy<br>|Husband works in<br>formalsector<br>|
|---|---|---|---|
|Rural area before current marriage|-0.001|0.096<sup>***</sup>|-0.136<sup>***</sup>|
||(0.022)<br>|(0.024)<br>|(0.022)<br>|
|Ever been to French school|-0.061<sup>***</sup>|-0.148<sup>***</sup>|0.187<sup>***</sup>|
||(0.023)|(0.026)<br>|(0.022)|
|Polygamous father|0.004|0.088<sup>***</sup>|-0.006|
||(0.019)|(0.022)|(0.022)<br>|
|Fostered before age 15|-0.022|-0.023|0.061<sup>**</sup>|
||(0.026)|(0.031)<br>|(0.029)<br>|
|Serere|-0.031|-0.059<sup>*</sup>|0.065<sup>*</sup>|
||(0.029)<br>|(0.035)<br>|(0.034)|
|Poular|0.051<sup>**</sup>|-0.100<sup>***</sup>|-0.039|
||(0.024)|(0.026)<br>|(0.026)<br>|
|Other ethnicity|-0.006|-0.079<sup>***</sup>|0.075<sup>**</sup>|
||(0.027)|(0.030)|(0.029)|
|Mean of dep. var|0.77|0.36|0.34|
|N|1,941|1,936|1,826|
|PseudoR2|0.010|0.048|0.085|

Note: Logit model. Marginal effects shown.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12.

**-** **<u>Type of Marriage Sample: Women in their first marriage</u>**

||Has a civil<br>contract<br>|Lives with in-<br>laws<br>|
|---|---|---|
|Rural area before current marriage|-0.181<sup>***</sup><br>(0.018)<br>|0.052<sup>**</sup><br>(0.023)|
|Ever been to French school|0.143<sup>***</sup>|-0.016|
||(0.017)|(0.025)|
|Polygamous father|0.008|-0.012|
||(0.017)|(0.021)|
|Fostered before age 15|-0.003|-0.027|
||(0.023)|(0.030)<br>|
|Serere|0.037|-0.064<sup>*</sup>|
||(0.027)|(0.034)|
|Poular|-0.014|0.026|
||(0.021)|(0.024)<br>|
|Other ethnicity|0.024|-0.130<sup>***</sup>|
||(0.023)|(0.032)|
|Mean of dep. Var|0.20|0.28|
|N|1941|1941|
|PseudoR2|0.147|0.016|

Note: Logit model. Marginal effects shown.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12.

Source: Authors’ estimations using PSF1.

51
# **Reference**

Basu, K. 2013. “Shared Prosperity and the Mitigation of Poverty In Practice and in Precept.” _Policy Research Working Paper_ 6700. World Bank, Washington, DC.

Bourguignon, F. 2003. “The Growth Elasticity of Poverty Reduction: Explaining Heterogeneity across Countries and Time Periods.” In T. Eichler and S. Turnovsky (eds.). Growth and Inequality. Cambridge. MIT Press.

Chandy, L., N. Ledlie, and V. Penciakova. 2013. “The Final Countdown: Prospects for Ending Extreme Poverty by 2030.” Brookings Institution. Global Views. Policy Paper 2013-04.

Chen, S., and M. Ravallion. 2010. “The Developing World is Poorer than We Thought, but No Less Successful in the Fight Against Poverty.” _Quarterly Journal of Economics_ 124 (4): 1577-625.

Chen, S., and M. Ravallion. 2012. “More Relatively-Poor People in a Less Absolutely-Poor World.” _Policy Research Working Paper_ 6114. World Bank, Washington, DC.

Dollar, D., and A. Kraay. 2002. “Growth is Good for the Poor,” _Journal of Economic Growth_ , 7, 195-225.

Dollar, D. T. Kleineberg, A. Kraay. 2013. “Growth Still is Good for the Poor.” _Policy Research Working Paper_ 6568. World Bank, Washington, DC.

Dercon, S. and N. Lea. 2012. “The Prospects of the Poor: The Future Geography of Poverty and its Implications for DFID.” DFID. London.

Dhongde, S. and C. Minoiu. 2013. “Global Poverty Estimates: A Sensitivity Analysis.” _World Development_ 40(3): 1-13.

Edward, P. and A. Sumner. 2013. “The Future of Global Poverty in a Multi-Speed World – New Estimates of Scale and Location, 2010-2030.” _Centre for Global Development Working Paper_ 327 Karver, J. C. Kenny, and A. Sumner. 2012. “MDGs 2.0: What Goals, Targets, and Timeframe?” _Centre for Global Development Working Paper_ 297.

Klasen, S. and M. Misselhorn. 2006. “Determinants of the Growth Semi-Elasticity of Poverty Reduction.” Proceedings of the German Development Economics Conference, Berlin 2006, _Research Committee Development Economics_ , No. 15.

Lanjouw, P., S. Chen, and A. Kraay. 2013. “The World Bank’s Poverty Target of 3% in 2030. Assessing the Realism of this Goal and Exploring Some Country-Level Implications” World Bank, Washington, DC. Mimeo.

Planning Commission, Government of India. 2013. Press Note on Poverty Estimates, 2011-12.

Ravallion, M. 2012. “Benchmarking Global Poverty Reduction.” _Policy Research Working Paper_ 6205. World Bank, Washington, DC.

Ravallion, M. 2013. “How Long Will It Take to Lift One Billion People Out of Poverty?” _World Bank Research Observer_ .

Ravallion, M. and S. Chen. 2012. “Monitoring Inequality.” Blog Post on Let’s Talk Development. World Bank. http://blogs.worldbank.org/developmenttalk/monitoring-inequality.

23
**Table 4: Reasons given for not obtaining title or registration (in percentages), total and by sub-sample of producers**

||_Plo_|_tsthathavenotit_|_le_|||
|---|---|---|---|---|---|
||Total Sample|Established<br>Producers|Landscarce<br>Households|Titlingprog.<br>Participants|Landbuyers|
|Don'tknowhowtoget|0.92(%)|0.66(%)|--|0.56(%)|1.87(%)|
|Notworththeeffort|90.37(%)|91.41(%)|92.59(%)|93.85(%)|84.58(%)|
|Worthitbut lacktheresources/time|7.21(%)|6.17(%)|3.7(%)|5.03(%)|1.68(%)|
|Otherreasons|1.60(%)|1.76(%)|3.70(%)|0.56(%)|1.87(%)|
|No.ofObservations(plots)|874|454|27|179|214|
||_Plotst _|_hatare_**_not_**_regist_|_ered_|||
||TotalSample|Established<br>Producers|Landscarce<br>households|Titlingprog.<br>Participants|Landbuyers|
|Don'tknowhowto get|7.34(%)|4.93(%)|7.69(%)|13.61(%)|7.05(%)|
|Not worththeeffort|5.29(%)|5.21(%)|7.69(%)|4.08(%)|6.41(%)|
|Lack theresources/time|69.45(%)|73.43(%)|84.61(%)|74.15(%)|54.48(%)|
|Other (incl.notitle)|17.92(%)|16.44(%)|--|8.16(%)|32.05(%)|
|No.ofObservations(plots)|681|365|13|147|156|
|Source:<br>Owncomputation from2000 W|orldBank/Universi|tyofWisconsinsur|vey.|||

26
Table 3. Determinants of migration ‡ows : <u>IV estimation</u>

||(1)<br>Total|(2)<br>Total|(3)<br>Total|
|---|---|---|---|
|Lagged diasp|0.761|0.766|0.758|
||(10.92)***|(11.09)***|(10.86)***|
|Col links|-0.051|-0.064|-0.045|
||(0.26)|(0.32)|(0.23)|
|linguage|0.234|0.228|0.236|
||(2.27)**|(2.22)**|(2.29)**|
|Log(dist)|-0.259|-0.253|-0.262|
||(2.84)***|(2.78)***|(2.86)***|
|Schengen|0.160|0.161|0.160|
||(1.11)|(1.11)|(1.11)|
|Constant|2.365|2.306|2.392|
||(2.69)***|(2.64)***|(2.72)***|
|Orig Dum|Yes|Yes|Yes|
|Dest Dum|Yes|Yes|Yes|
|Method|IV|IV|IV|
|F-stat First stage|27.51|26.15|27.29|
|Hansen J-test (p-value)|0.128|0.0640|0.101|
|R2|0.883|0.882|0.883|
|Observations|3486|3486|3486|

Absolute values of robust t statistics in parentheses

* signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1%

Instrument sets for Mij in all columns include a dummy for bilateral guest worker agreement and a proxy for diaspora size in 1960. In column (1) , the proxy is computed as ln(popi � immstj=distij) � Conf 1i;In column (2) , the proxy is computed as ln(popi � immstj=distij) � Conf 2i;in column (3), the proxy is computed as ln(popi � immstj=distij) � (conf 1i + Conf 2i):

# 5.2 Impact on Selection

The next question is on the determinants of the selection and the human capital (educational) composition of migrants and the speci...c role of diasporas in this process. Columns (1) to (4) in Table 4 report the results of the estimation of equations (12) and (13) for the skill ratio whereas columns (5) and (6) are estimated for the change in the skill ratio. Columns (1) and (2) are obtained using OLS whereas results in columns (3) to (6) are obtained with the Heckman two-step procedure without instruments.

26
The value of the log-likelihood is 218.30. Common cycles were estimated with periods 1.36 and 2.99 years. Estimation results for all equations are generally satisfactory in terms of residual normality and heteroskedasticity. There is nevertheless some evidence of (higher-order) residual autocorrelation. Goodness of fit measures are also satisfactory, indicating ihat this model, just as the models before, represents an improvement over randorn walk with drift models. Finally, we note that as in the other specifications based on the structural time series approach with fixed seasonality, the estimated variance of the slope disturbances is non-zero, suggesting that the estimated trends are 1(2), instead of I(1), as we would have expected based on the results of preliminary (conventional) unit root tests.

Please refer to Annex V for a discussion of the preferred model, which features non-stationary seasonal components with seasonal cointegration, using currency depreciation as the opportunity cost measure, following the structural time series approach.

# **Estimation results follovwing the** VECM **approach**

A VECM model taking log of real MI, log of real GDP and the inflation rate as endogenous variable was estimated. Intercepts (but no trends) are included in the cointegrating relation as vell as in the short-run model. The signs of the estimated longrun parameters are consistent with economic theory.

|**Venezuela:VECMforLogReal Money**<br>**Log RealGDPandInflation**<br>**_LagStructure:1,2_**|**Balances,**||
|---|---|---|
|**Slatistic**<br>**LogReal**<br>**Ml**|**LogReal**<br>**GDP**|**Inflation.**|
|Coefficientincointegratingequation<br>1|0.26|-0.68|
|GoodnessofFit(ordinaryR<sup>2</sup>)<br>0.68|0.63|0.19|
|AkaikeInformation Criterion<br>-1.88|-3.80|-3.48|
|Schwarz Criterion<br>-1.52|-3.43|-3.11|

Money Demand in Venezuela, Mario A. Cuevas 18
**5 Estimation Strategy**

In order to calculate the rawh.1 1<sup>s.tiiindex</sup><sup>**G**for each3-digitindustryandyearwe</sup> regate the plant-level employment data to the 3-digit industry and state level in each year weighting **by** sampling weights. To obtain the **EG** index for each 3-digit industry and year using Equation **1,** we make use also of a Herfindahl index for each industry and year calculated using plant-level data on market shares." Table 2 shows that the mean of the **EG** index rose from **1980** to **1990** and then declined in **1999.** Thus Indian manufacturing ;1.-. i.-'t1 during the 1980s and dispersed during the 1990s. Appendix Tables B.2 and B.3 present the ten most and least - 1.-. n.-.t1 industries in **1980** and the evolution of their **EG** indices over the next **19** years.

Our main specification modifies that used **by** Rosenthal and Strange (2001) based on two important considerations. First, the spatial distribution of manufacturing industry is a slow moving process. This is bound to be the case although both the raw **-** 1.-.fn.-:.-jij index **G** as well as the **EG** index are expected to respond fairly quickly to changes in their determinants. That is because these indices change not only due to entry/ exit of plants, but also due to changes in employment **by** existing plants. However, it does take time for existing plants to change location or for new plants to emerge in response to changes in policies or industry characteristics. Hence, we allow all our independent variables to affect 1-ii.-.1j with a lag. Second, spatial concentration is likely to be persistent: industries that were more concentrated in the past may tend to be systematically more concentrated in the present and in the future. We account for a very general form of serial correlation in concentration **by** clustering the standard errors at the 3-digit industry level. The main specification we

> 25The **ASI** data do not allow us to identify geographic units smaller than states: the data includes a **numeric** district variable but no label to go with it. Hence it is not possible to identify which district corresponds to a particular value of the district variable. However, we were able to identify districts using matching techniques to create a pseudo-panel of plants and presuming that a given plant does not change location over time. Once districts can be identified and followed over time we are able to calculate the **EG** index using the district as the geographic unit. For most industries we find that the values of the **EG** index calculated at the district level tend to be smaller compared to those of the **EG** index calculated at the state level. This is consistent with the findings in other studies **1** i and Mayer (2004)). These results can be obtained from the authors upon request.

**19**
_Appendix Table 4: Characteristic balance at baseline: nonbeneficiary households in below_ _<u>median saturated areas</u>_

|Baseline survey variables|Control<br>Mean|Difference<br>(Treated-<br>Control)|Standardized<br>Mean<br>Difference|p-value|
|---|---|---|---|---|
|LN Proxy Means Test Score|9.911|0.023|0.082|0.416|
|Household composition:|||||
|Household size|4.348|0.064|-0.002|0.586|
|Children 5 years old and below|0.620|0.026|-0.005|0.621|
|Children between 6 and 14 years old<br>|0.994|0.023|0.004|0.781|
|Primary occupation: Farming and<br>livestock|0.269|0.039|-0.094|0.357|
|Educational attainment of household head:|||||
|No grade completed|0.025|-0.007|0.065|0.556|
|Some elementary school|0.229|0.010|-0.063|0.780|
|Completed elementary school|0.150|0.008|-0.103|0.813|
|Some high school|0.143|0.004|0.030|0.874|
|High school graduate|0.182|-0.002|0.025|0.932|
|Some college|0.134|-0.008|0.041|0.785|
|College graduate|0.140|-0.004|0.069|0.892|
|School Attendance:|||||
|Children between 6 to 11 years|0.956|-0.020|0.110|0.466|
|Children between 12 to 14 years|0.889|0.007|-0.048|0.877|
|Housing Amenities:|||||
|Strong roof materials|0.699|0.019|0.086|0.595|
|Strong wall materials|0.601|0.021|0.078|0.514|
|Light roof materials|0.241|-0.041|-0.061|0.202|
|Light wall materials|0.225|0.000|-0.148|0.994|
|Owns a house and lot|0.642|-0.064|0.196|0.113|
|House has no toilet|0.155|-0.063|0.125|0.110|
|Shares a water source|0.158|-0.034|0.056|0.302|
|Household Assets:|||||
|Electricity in house|0.842|-0.007|0.107|0.874|
|Owns a television|0.576|0.016|0.027|0.762|
|Owns a video recorder|0.408|-0.063|0.194|0.135|
|Owns a Stereo/CD player|0.335|-0.029|0.085|0.436|
|Owns a refrigerator|0.250|0.046|-0.024|0.186|
|Has a telephone/cellphone|0.269|-0.014|0.071|0.740|
|Owns a motorcycle|0.152|-0.039|0.116|0.357|

*** p<0.01, ** p<0.05, * p<0.1.

Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 65 villages. Source: 2008 Household Assessment Form 53