rafmacalaba/datause-encoder-data huggingface dataset:
and trends may also affect family income, the cost of education, and the demand for migrant labor, and we control for these effects using province-year dummy variables, p∙Tt.

The ability of individual middle school graduates, νi, is unobserved but important for high school enrollment decisions, and reflects the education productivity parameter, _ψt_ , in equation (1). In particular, students must test into high schools and it is likely that examinations are more competitive where the local supply of spaces in high school is more constrained. In models that include household information, parents’ years of schooling proxy for inherited ability and the quality of parent-child interactions, although other dimensions will remain unobserved. In order to identify the effect of migrant opportunity on enrollment decisions, instruments for the migrant share of the village labor force must be plausibly unrelated to unobserved individual ability.

The value of Eihjt is determined by whether a child _i_ , who is estimated to complete middle school in year _t_ , decides to enroll in high school in year _t_ + 1. Assigning the value of Eihjt requires making two assumptions about the timing of school enrollment and years of primary school. Although the RCRE supplementary survey provides us with an individual’s age and years of schooling completed by 2004, we do not know the precise age at which each individual started school, but evidence from the 1990 China population census and other surveys makes it clear that the modal age for rural children to start school during this period was seven. We make a second assumption regarding the age at which children normally make the decision to enroll in high school. This age varies depending on whether children are in villages with five- or six-year elementary schools, and whether children are deciding to enter high school after 8 or 9 years of schooling, respectively. These differences are straightforward to determine from the RCRE data, and stable over the years that older children and young adults in our sample were making high school enrollment decisions. We provide evidence from the population census and other surveys on both of these assumptions in Appendix A.1.

A final concern involves repeated years of schooling or skipped grades. Although the supplemental survey did not ask explicitly about repeats or skips, the protocol required respondents to report years of schooling completed and the common interpretation is to answer in terms of the level of schooling completed. Examination of surveys that explicitly ask about repeats suggests that they are not common. The China Center for Agricultural Policy survey discussed in Appendix A.1, for example, finds that only six percent of rural students repeat a 10
using data for four African countries (Togo, Lesotho, Burkina Faso, and Ghana), find that a CWIQ survey, which is similar to our short questionnaire, generates lower LFP estimates than a more detailed survey. However, since the surveys they compare are implemented two years apart, their results are only indicative.

The right-hand-side panel of Table 5 reports the OLS results for the natural log of weekly hours of work in the child’s main job conditional on working. The weekly hours of work are significantly higher for boys in the short questionnaire; they are also higher for girls, but the difference is not significant. This is consistent with the hypothesis that in the short questionnaire marginal jobs are disproportionately more likely to be forgotten (or not considered as jobs worth reporting) than jobs with greater weekly hours in comparison with jobs reported in the detailed questionnaire.

Next we estimate a multinomial logit to investigate how the survey assignment affects the allocation across three categories (“outcomes”): agriculture and other sectors, domestic work, and the omitted category “no work.”<sup>14</sup> The results in Table 6a present the marginal effects, while Table 6b presents the predicted probabilities estimated at the mean value of the covariates for the three outcomes and the two experiments. For the pooled sample (Table 6a, panel A), using a short module produces lower participation in agriculture and other non-agricultural sectors with respect to “no work” than a detailed questionnaire produces, with a larger effect for girls. Both girls and boys are more likely to be classified as working in domestic work than identified as not working when given a short module. This effect is also larger for girls than boys. The proxy module is not associated with significant changes in sector classification.

The multivariate analysis confirms that the largest difference between the short and the detailed modules is in the allocation of children across the two categories “domestic work” and “no work” (both considered as not in employment, based on the ILO definition). Although the detailed module captures higher participation in employment, the largest and most significant switch is from domestic work (in the short module) to agriculture or no work (in the detailed module). However, the type of

> 14 We merged “other sectors” with the agricultural sector because there are few observations in the nonagricultural sectors. Alternative categorizations do not change the results.

17
all 18 years of data biases the estimates upward somewhat, but when including industryregion linear trends we plausibly recover the true causal effect.

Panel B presents these same regressions using regions (instead of industry-regions) as units of observations. Using this much broader definition of a local labor market results in far larger estimates of the positive impact of immigration on native employment (including region and year fixed effects), see Panel B.1. Once we also control for region-specific linear trends, see Panel B.2, the estimated impact of immigration on employment in the region is in line with our main results, a point estimate of 0.56. Our interpretation of these findings is that at the region-level (unlike the industry-region) there are very pronounced long-term trends in employment. Hence, in the baseline specification our instrument, in particular since it is constructed in-sample but likely even if were based on a pre-period, is not valid and our estimate upward biased. However, once we include region-specific trends (which would not be possible with the typical shift-share instrument) we are able to recover a plausibly unbiased IV estimate.

# 4.4.3 Varying Assumptions About Labor Supply Elasticities

A concern for our identification strategy is that we do not obtain precise or significant estimates of the effect of immigration on native wages in a local labor market (relative to other industry-regions). We have argued that this suggests native labor is highly mobile across industry-regions and consequently immigration results in changes in employment not wages. However, we can not exclude the possibility, and indeed it is likely, that labor supply across industry-regions (φn) is less than perfectly elastic.<sup>34</sup>

> 34We will continue to assume that native labor supply to an industry-region is non-negative. Unlike aggregate labor supply, which may be backward bending, it is hard to imagine that less people are willing

38
Sánchez, F., and Núñez, J. (2003). A dynamic analysis of human capital, female work-participation, returns to education and changes in household structure in urban Colombia, 1976-1998. _Colombian Economic Journal_ , Vol. 1, No.1, pp. 110-149.

Santamaría, M., and Rojas, N. (2001) La participación laboral: ¿qué ha pasado y qué podemos esperar? _Planeación y Desarrollo_ , Vol. 32, No. 1, pp. 5-34.

Shami, A.H.M.S., Islam, T., and Rayhan, I. (2019). "Investigating the Macroeconomic Factors That Determine a Female Worker to Participate in the Labor Force: Evidence from the South Asian Countries," _Journal of Business_ , Vol. 4, No. 2, pp. 12-18.

Smith G. (2014), Long-Term Trends in Female and Male Involvement in Crime, in R. Gartner and B. McCarhty (eds.). _The Oxford Handbook of Gender, Sex, and Crime_ , pp. 139–57. Oxford University Press.

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Sun, A. and Zhao, Y. (2016). Divorce, Abortion and Children's Sex Ratio: The Impact of Divorce Reform in China. Journal of Development Economics, Vol. 120, pp. 53-69. <u>https://doi.org/10.1016/j.jdeveco.2015.11.006</u> Tenjo, J.; Álvarez, O.; and Jiménez, M. C. (2016). “Diferencias en las tasas de desempleo por género”, en L. E. Arango, F. Castellani y E. Lora (eds.), _Desempleo femenino en Colombia_ , Bogotá: Banco Interamericano de Desarrollo y Banco de la República Tenjo, J., and Ribero, R. (1998). Participación, desempleo y mercados laborales en Colombia. Documento No. 081, _Archivos de Macroeconomía_ , Departamento Nacional de Planeación, Bogotá.

Tribín, A.M. "Paramilitaries and Electoral Support" Peace Economics, _Peace Science and Public Policy_ , vol. 21, no. 2, 2015, pp. 191-216. https://doi.org/10.1515/peps-2014-0050 Valdés, T. and Gomáriz, E. (1993). Mujeres latinoamericanas en cifras: Colombia. Instituto de la Mujer, Ministerio de Asuntos Sociales de España y Facultad Latinoamericana de Ciencias Sociales (FLACSO), Santiago de Chile.

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34
tant for their location decisions (see Munshi, 2003; Patel and Vella, 2007; Beine, Docquier and Ozden, 2011; for recent work on immigration networks). Such immigrants may be systematically different from those whose locations decisions are determined primarily by demand shocks, and we would be estimating a local average treatment effect (which may or may not be the same as the average treatment effect for all immigrants).

# 4.3 Results

We present our OLS estimates in Table 2; our instrumental variable and parameter estimates, as well as the implied country-level wage effects, are in Table 3. Extensive robustness checks are presented in Section 4.4, below. In Panel A we have the estimates of the industry-region-year level effect of immigration on native employment (equation 8). Panel B presents the effect of an immigrant induced change in native employment on native wages (equation 10). Panel C shows the impact of immigration on immigrant wages (equation 9). Each column of the table presents estimates for different sets of fixed effects. In all specifications standard errors are clustered by industry-region-year (rst).<sup>28</sup> OLS estimates show that native outcomes, both employment and wages, are uncorrelated with immigration flows. Immigrant wages are negatively correlated with immigration flows; however, this correlation is not statistically significant when including our full set of The instrumental variable estimates show that immigration, on average, increases the

> 28 Conditional on the included fixed effects the serial correlation within an industry-region is negative, hence we do not cluster on industry-region. In Section 4.4 we report standard errors clustered by industryregion.

> The first-stage differs across specifications: in equation (8) an industry-region is left unweighted, while the observations are individual native workers and the immigrant workers in equations (9) and (10), respectively. As a result the F-statistics of the first-stages differ.

30
Despite the possible gains in wages for wage earners, we note a decrease in the regionally deflated total household expenditure (column 5), and a decrease in per capita expenditure on food and nonfood items (column 4). The increase in wages but decrease in total expenditure can possibly be explained by rising prices and wages in mining communities, where everyone has to pay the higher prices but only some (those who earn wages), benefit from a rise in wage rate.

Columns 6 through 9 of table 12 look at nondeflated expenditure measures for food, housing, health and education, and household energy.<sup>17</sup> We confirm that total household expenditure on food decreases (compared with the per capita deflated measure in column (4)), but find that households spend more money on housing, transport, and communication, and household energy, such as electricity and gas. The electricity and gas expenditure is only for those who have any positive expenditure on these, and we saw earlier that electricity access changes with the mine. This confirms that, among those who spend anything on electricity, they spend more on it in mining communities.

**Table 12 Using GLSS: Household income and expenditure**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||ln|ln|ln|ln||househol|d level ln e|xpenditure||
||wages<br>all|wages<br>women|wages<br>men|pc<br>exp.|total<br>exp.|<br>food|<br>housing|health<br>education|hh<br>energy|
|active*mine|0.520**|0.694***|0.391|-0.178*|-0.126|-0.069|0.316**|-0.168|0.297**|
||(0.226)|(0.241)|(0.238)|(0.093)|(0.089)|(0.095)|(0.139)|(0.199)|(0.119)|
|Observations|6,226|2,914|3,312|7,522|7,522|7,396|7,420|6,541|4,752|
|R-squared|0.121|0.128|0.118|0.959|0.964|0.963|0.933|0.837|0.950|
|_controls_||||||||||
|individual|Y|Y|Y|||||||
|hh head||||Y|Y|Y|Y|Y|Y|
|hh size|||||Y|Y|Y|Y|Y|
|district fe|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|year fe|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|deflated|N|N|N|Y|Y|N|N|N|N|
|mean (ln)|15.30|15.29|15.31|13.04|14.19|13.42|10.88|10.74|9.52|

_Note:_ (1) Annual wages and salaries for individuals in all ages (nondeflated).

(2) Annual wages and salaries for women in all ages (nondeflated).

(3) Annual wages and salaries for men in all ages (nondeflated).

(4) Real per capita annual food and nonfood expenditure (regionally deflated).

17 Additional results for recreation and transport and communication are available upon request. The expenditure on the three measures increased in mining communities.

32
EAs devote to control CFs. Reassuringly, we nd that control and treatment CFs received equal amounts and types of attention from their EAs in the year after the training (Table A.3).

# 3.4 Data

We conducted two follow-up surveys after the rst training and demonstration season (October 2010 to April 2011). A 2012 (midline) round and a 2013 (endline) round form a panel of households and CFs in the study area.<sup>17</sup> We randomly sampled 18 non-CF households in each community from a full listing performed by our enumerators ahead of the survey. At both midline and endline, households were visited twice: pre- and postharvest. This allows us to observe SLM practices when they are most visible, just after planting (preharvest, from February to April), and to record production data after harvest (from mid-May on). Hence, our eldwork ran from February to April and May to August in 2012 and 2013.

Midline and endline surveys gathered longitudinal CF and household information on the two main agricultural seasons that followed the rst demonstration season. Our eldwork included ve survey instruments: a household questionnaire, a household agricultural production questionnaire, a CF questionnaire, an EA questionnaire, and a community questionnaire. The household survey was also administered to all 200 CF households, in addition to the specic CF survey. These surveys provide household demographics, SLM knowledge for the two main agricultural producers in the household, individual and plot-level SLM adoption, and production information for approximately 3,600 non-CF households in 200 communities. Since the plot roster identies the adult in charge of making agricultural decisions for each cultivated plot, we obtain individual measures of knowledge and adoption for a sample of 5,884 and 5,071 individuals at midline and endline, respectively.

A rapid baseline survey was administered to all CFs in August 2010, before the district-level randomization. This provided data to perform balance tests on the success of the randomization, using the pre-intervention characteristics of CFs by treatment status. Figure 2 illustrates the timing of the surveys and CF trainings over the course of the four-year study.

> 17Operational constraints precluded us from conducting a household survey at baseline.

12
statistics of the Penn World Table. Correcting for PPP bias raises global inequality by at least onequarter. Almås and Johnsen (2012) apply the same method to China but add a time dimension to uncover spatial bias in real growth rates. China’s CPI seems too low in rural areas and too high in urban areas; the Engel curve deflator shows a 44% rise in the rural cost-of-living from 1995 to 2002 and no change in the urban cost-of-living, versus CPI increases of 8% and 11%.<sup>3</sup> Correcting this bias raises the rural cost-of-living from 60% of the urban level in 1995 to 87% by 2002, and one-half of apparent poverty reduction in rural China disappears.<sup>4</sup> Likewise, an Engel curve deflator for India gives a fall in rural poverty of just 5% between 2005 and 2010, versus a 20% fall at official poverty lines that are allegedly time-space consistent (Almås, Krelsrud, and Somanathan 2013). Remarkable gaps occur in the records for some states; for example, the official lines show that the poverty rate fell in rural West Bengal from 38% to 29% while the Engel curve deflator has Bengalese poverty _rising_ from 67% to 70%.

The record of recent progress in poverty reduction for poor countries may need to be revised if these results based on Engel curve deflators are correct. But it is hard to know how credible these findings are since China and India lack spatially disaggregated price surveys, preventing comparison of Engel curve deflators with multilateral price indexes.<sup>5</sup> In this paper we

> 3. Chamon and Filho (2014) use Urban Household Survey data for ten Chinese provinces and estimate an upward bias in the CPI of about one percentage point per year over 1993–2005 but do not consider any spatial deflation.

> 4. Specifically, for the $1 a day poverty line, the fall in the rural poverty gap between 1995 and 2002 is -0.67 using the CPI deflator but only -0.32 using the food Engel curve deflator.

> 5. Household expenditure surveys in both countries allow unit values to be constructed (mainly for foods), but these are best treated as a proxy for quality rather than for price (Gibson 2013). Unit values have been widely used in India to calculate spatial multilateral price indexes for urban and rural sectors and states, most recently by Deaton and Dupriez (2011) and Majumder, Ray, and Sinha (2012). The most widely used spatial deflator for China is from Brandt and Holz (2006), who used provincial CPI data from 1990 to price national rural and urban expenditure baskets (containing 40–60 items; with 40% of the rural basket using urban prices since rural prices were missing). The annual rate of change in the CPI for each province was then used by Brandt and Holz to extend from the base year back to 1984 and forward to 2004, which likely causes time-space inconsistencies, as demonstrated for the example of Russia by Gluschenko (2006).

3
Policy Research Working Paper 6726

# **Abstract**

While self-assessments of welfare have become popular for measuring poverty and estimating welfare effects, the methods can be deceptive given systematic heterogeneity in respondents’ scales. Little is known about this problem. This study uses specially-designed surveys in three countries, Tajikistan, Guatemala, and Tanzania, to study scale heterogeneity. Respondents were asked to score stylized vignettes, as well as their own household. Diverse scales are in evidence, casting considerable doubt on the meaning of widely-used summary measures such as subjective poverty rates. Nonetheless, under the identifying assumptions of the study, only small biases are induced in the coefficients on widely-used regressors for subjective poverty and welfare.

This paper is a product of the Poverty and Inequality 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 authors may be contacted at kbeegle@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
( ∆ log( _IFirmd_ )93 − 03 ), and between 1983 and 1993 ( ∆ log(IFirmd)83 − 93 ), and the growth of firms in services between 1993 and 2003 ( ∆ log( _SFirmd_ )93 − 03 ) and between 1983 and 1993 ( ∆ log(SFirmd)83 − 93 ). We estimate equation 1 using district-level employment figures that we construct from firm-level data.

Table 3 reports the regression results for equation 1, separately for light and heavy manufacturing, at the national level, and separately for the north and the south. Overall, the association between changes in employment in both heavy and light manufacturing and changes in employment in mining is positive. The elasticity of heavy manufacturing employment with respect to mining employment (.31) is almost 50 percent larger than the elasticity of light manufacturing employment with respect to mining employment (.21). The difference in the results between the north and the south supports a stronger production network between heavy manufacturing and mining in the south than in the north. The aggregate employment effect at the region level, however, is stronger for light manufacturing than heavy manufacturing in the south. Lastly, industrial development in the previous decades appears to play a less significant role compared to the growth in mining employment in explaining employment growth in manufacturing between 2003 and 2013.

Identification of intersectoral linkages in equation 1 relies only on geographic proximity to a mining firm. As such, it is not sensitive to the time lag between the establishment of a mining firm and a manufacturing firm. In our next model, production networks are identified jointly by the location of a mining firm and the year in which it was established, as follows: _NewFirmds_ , _t_ is an indicator variable, which takes the value of one, if at least one new firm enters in sector _s_ , in district _d_ , and in year _t_ , and zero otherwise. β 1 and β 2 denote the immediate and lagged (by one year) effect of the number of new mining firms established in district _d_ on the probability of having a new firm in sector _s_ and district _d_ . To estimate equation 2, we use a district-year panel comprising 119 districts and 24 years (1990–2013) based on the location and the year of establishment of a firm, using 2014 IBES data. Since employment and other firm characteristics are available only for the census year, we use the number of firms to measure the outcome variable. For the same reason, the explanatory 14