rafmacalaba/datause-encoder-data huggingface dataset:
census data from 2000 simply because the latter census allows an open-ended race definition resulting in a greater number of racial groups.

There is clearly something unsatisfactory about this. ELMO proposes a relatively minor adaptation of the conventional procedure to produce an alternative statistic that overcomes some of these issues. Suppose that a given population is divided into two groups. ELMO compares the extent of between-group group inequality against a new benchmark, namely the extent to which these two groups are completely “separate” from each other in income terms; i.e., whether the richest person of the poorer group is poorer than the poorest person in the richer group. The standard decomposition procedure is entirely silent on this question. Yet, from the perspective of assessing the importance of group differences, the ELMO index is arguably quite relevant. If two population groups divide the income distribution into two entirely non-overlapping partitions, but there exists a high level of inequality within each of the two groups, overall between-group inequality - conventionally calculated – would be relatively low. Yet a fairly strong statement about the contribution of groups to inequality would remain unstated; namely, that the two groups “stand apart” in income terms – they are somehow economically “excluded” from one another. Elbers et al (2008) illustrate this point with reference to South Africa. They show that when inequality is decomposed by racial group defined in terms of a “white/non-white” classification, the conventional decomposition suggests that only about 27% of inequality is attributable to between-group differences. Their alternative statistic, on the other hand, shows that two groups are 80% of the way towards a completely partitioned South African income distribution.

ELMO is assessing the extent to which inequality is derived from group-based differences by comparing it against the benchmark of what the maximum amount of groupbased inequality could be given the size and composition of the actual groups in the 4
# **Abstract**

Rural poverty remains a crucial part of the poverty picture in Argentina. This paper used a rural dataset collected by the World Bank in 2003. Findings show that extreme income poverty in rural areas reached 39 percent of the people or 200,000–250,000 indigent families. These families tend to: be large, and young, and to escape from poverty as they mature and children leave the household (life cycle); live largely in dispersed areas where basic service provision is often weak and delivery is difficult (in particular school attendance beyond 11 years of age falls off very rapidly compared to grouped rural or urban areas); and be more likely to be small landholders than landless laborers. The structure of poverty in rural Argentina shows that: larger households are poorer than smaller households, female headed households are poorer than male headed households, young households/household heads are poorer than older households/household heads, the poor tend to work more in the informal sector, and a greater share of those engaged in agriculture are poor. However, poverty is by no means strictly an agricultural problem. Furthermore, the deepest poverty is among the poorly educated and young household heads with children. Without interventions to improve their opportunities and assets, their plight is likely to worsen.

2
# **A.4 Tables: Alternate Matching**

Table 11: Productivity Growth over Time from Export Entry: Matching Results

||Total Productivity<br><br>|Imports<br>|R & D<br>|
|---|---|---|---|
||�<br>_αχχit_ + �<br>_ωit_|�<br>_αχχit_|�<br>_ωit_|
|t+1|0.050*|0.002|0.048*|
||(0.027)|(0.004)|(0.027)|
|t+2|0.063**|0.007|0.056*|
||(0.028)|(0.005)|(0.029)|
|t+3|0.051|0.012***|0.039|
||(0.035)|(0.004)|(0.034)|
|t+4|0.070**|0.017***|0.053|
||(0.035)|(0.005)|(0.033)|
|t+5|0.081**|0.018***|0.063*|
||(0.039)|(0.005)|(0.036)|
|t+6|0.094**|0.018***|0.076*|
||(0.041)|(0.005)|(0.040)|

Notes: Nearest Neighbor matching using five neighbors with replacement. The number of treated is 348. Block Bootstrap Standard errors given in parentheses (200 replications). *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Table 12: Productivity Growth from Export Entry (All export entrants): Matching Results

||t+1|t+2|t+3|t+4|t+5|t+6|
|---|---|---|---|---|---|---|
|Total Productivity|0.063***|0.077***|0.084***|0.087***|0.107***|0.094***|
|(�<br>_αχχit_+ �<br>_ωit_)|(0.024)|(0.023)|(0.031)|(0.032)|(0.032)|(0.036)|
|Imports|0.008***|0.013***|0.013***|0.018***|0.016***|0.015***|
|(�<br>_αχχit_)|(0.003)|(0.004)|(0.004)|(0.004)|(0.005)|(0.005)|
|R & D<br>|0.055**|0.064***|0.070**|0.069**|0.092***|0.080**|
|(<sup>�</sup><br>_ωit_)|(0.024)|(0.024)|(0.031)|(0.032)|(0.032)|(0.038)|

Notes: Nearest Neighbor matching using five neighbors with replacement. The number of treated varies by year (544 to 364). Block Bootstrap Standard errors given in parentheses (200 replications). *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 37
|1993|33,081 - -|7,224 - -|
|---|---|---|
|1997|39,714 1,701 4.28|7,699 1,304 16.94|
|2000|54,991 2,835 5.16|10,435 2,022 19.38|
|2007|73,016 6,352 8.70|13,536 3,787 27.98|

Table 2 shows the gender composition of these migration flows: almost two thirds of those recorded as migrants in 2007 are men, but women are more than twice as likely as men to migrate internationally, and this is particularly true for mothers who leave their family behind. On the other hand women, especially those who leave their children behind, tend to stay away for a period of time that is significantly shorter than that of men.<sup>12</sup>

**Table 2.** Migrants by Gender. IFLS 2007.

||Number|% of total<br>migrants|% international|Migration spell<br>(months)|
|---|---|---|---|---|
|Migrants|6,352|-|11.04|62.89|
|Men|4,046|63.70|7.74|64.83|
|without children|3,255|51.24|7.74|64.10|
|with children along|493|7.76|2.44|78.23|
|withchildren left behind|298|4.69|16.55|51.23|
|Women|2,306|36.30|17.00|59.48|
|without children|1,892|29.79|13.27|59.76|
|with children along|222|3.49|10.36|76,43|
|withchildren left behind|192|3.02|60.32|37.48|

To identify households who have chosen the different migration portfolios described in section I, I check, for every child under the age of 18, whether his father or mother migrated and the other parent is in the household. Table 3 shows the partition of households with children in the 2007 IFLS sample: the vast majority of them, over 95% chose a no migration portfolio, while 1.6% chose the female migration portfolio and 2.7% the male migration portfolio. A negligible share, about 0.4% chose a portfolio in which both parents migrated leaving their children behind with other relatives.

**Table 3.** Migration Portfolio Choices of Households with Children. IFLS 2007.

||Mother migrates|Motherstays|
|---|---|---|
|Father migrates|40|258|
|Fatherstays|152|9186|

Table 4 provides a comparison between households that have chosen different migration portfolios: column (1) reports the characteristics of the households in which none of the parents migrated, column (2) refers to households in which either the mother or the father

> 12 Table S.1 in the supplemental appendix reports descriptive statistics of the characteristics of migrants by gender, comparing those who have no children with those who migrate with their families and those who leave their family behind. While there are sizable differences between migrants who have children and those who don’t, parents who bring their children along when they migrate do not appear to be significantly different from those who leave them behind, except for the fact that those who bring their children along are generally more educated.

12
observations with 6,745 firms surveyed at least once in the panel (Table 1). On average firms have been surveyed around twice from 2006 to 2012.

The number of exporting firms in the industrial survey ranges from 10 to 16% of all firms in the sample. The proportion of exporting firms increases slightly from 2006 to 2012 (Table 1).<sup>2</sup> Movements in and out of export markets are quite large. Table 1 shows the dynamics of entry and exit into the export market by tracking firms that exported the year before and ceased to export the following year and vice-versa (firms that exported but were not exporting the year before). Between 15% and 20% of exporters cease to export the following year while a slightly lower number of exporters starts exporting. These figures are likely to be underestimated because larger firms are overrepresented in the matched data set. Computing entry and exit rates for the entire universe of Georgian exporters from the full custom data gives rates close to 50%. This is high if compared with other countries as Albania (35%), Bulgaria (40%), Mexico (35%) and Peru (35%).<sup>3</sup> The data also show that number of HS 6-digit products exported increases substantially between 2006 and 2012 as does the number of destinations.

**Table 1: Georgian Firms by Export Condition**

|Year|Firms|Exporting<br>firms (%)|Exit<br>from<br>exporting|New<br>exporters|Total<br>Exported<br>products<br>(HS6)|Total<br>Exporting<br>destinations|
|---|---|---|---|---|---|---|
|2006|2,117|234 (11.1)|45||265|50|
|2007|1,581|166 (10.5)|29|30|286|55|
|2008|1,606|168 (10.5)|25|29|317|51|
|2009|2,415|237 (9.8)|46|28|397|70|
|2010|2,220|211 (9.5)|34|37|479|69|
|2011|1,848|292 (15.8)|46|36|665|76|
|2012|2,029|255 (12.6)||42|588|71|

Source: Authors’ Calculations based on Geostat – Industrial Survey Exporters are larger both in terms of turnover and employment. In 2006, exporters’ turnover and employment were four times bigger than those of non-exporters. The difference increases over the years with exporters having on average five times the turnover and employment of nonexporting firms in 2012. This shows that Georgian exporters grew faster than domestic oriented firms. Exporters are more capital intensive, more productive, and show a higher share of foreign ownership than firms oriented to the domestic market. Total factor productivity (TFP), an indicator of firms’ efficiency that measures how much output a firm can produce with a given amount of inputs, is greater for exporters than for non-exporters (see the Appendix for a discussion of

> 2 In 2012 the proportion of exporters fell with respect to 2011, this may be due to the recession in the Euro area which is the second most important destination for Georgian exports after the ECA region. In fact, cumulated exports towards Europe declined between 2011 and 2012 and it is likely that firms exporting toward Europe experienced problems in 2012.

> 3 Entry and exit rates for comparator countries are obtained from the Exporter Dynamics Database (EDD).

5
mitigated the effects of negative shocks on physical development in early childhood. Our results are consistent with these papers, suggesting that CCT programs do provide a safety net against income losses for poor households. A distinctive feature of our work is that we consider income losses that are likely to be permanent and that are therefore even more difficult to insure against than transitory reductions in income.

# **<<A>>** _<mark>II. DATA</mark>_

In this section, we discuss the data used in the paper and present key descriptive statistics relating to our sample.

# **<<B>>** _<mark>Background</mark>_

We use three years of panel data from a survey of households and individuals in rural Colombia. These data were collected to evaluate the large-scale welfare program _Familias en Acción_ , which has been in place in several rural areas of Colombia since 2002 and has since expanded to cover urban areas. The program aims to alleviate poverty by fostering human capital accumulation among the poorest households through conditional subsidies for investments in education, nutrition, and health.

The first wave of data collection for the evaluation of the program occurred in 2002, when approximately 11,500 households were interviewed. We refer to this as the baseline survey. A year later, after the program began, a second wave of data was collected, and a third wave was collected in 2006. We refer to these as the first and second follow-up surveys, respectively. In this paper, we estimate the effects of the father’s permanent absence on children’s outcomes.<sup>3</sup> The socioeconomic data are rich and reflect face-to-face interviews that lasted 3.5 hours on average.

8
The current issue is not entirely new in the history of agricultural development. First, the British enclosure movement during its industrial revolution played a role in consolidating farm land to thus suppressing wages and releasing labor to non-agricultural industries. This example shows that labor cost and supply in the labor market could be critically related to landownership and land consolidations in rural areas at that time. Second, as Hayashi and Prescott (2006) recently illustrated in a calibration model, the family system governing land inheritance (maintaining small farms) could have reduced the supply of labor to non-agricultural sectors, which thus reduced the potential of industrial growth in prewar Japan.

Third, land reforms in developing countries have typically aimed to create small farmers by redistributing land own from landlords to tenants. Successful land reforms contributed to promoting equity as well as efficiency in agriculture but could impose a historical constraint on production efficiency when labor shortage becomes serious. While small farmers find it difficult to maintain familylabor intensive production, it is politically challenging to re-redistribute land to enlarge operational land sizes unless land markets including rental arrangements work perfectly.

The paper is organized as follows. Section 2 describes empirical strategy. Section 3 describes the panel data collected in 7 provinces in Indonesia, with two rounds in 2007 and 2010. The empirical findings are summarized in Section 4, followed by concluding remarks. The Appendix sketches a simple model that helps us to interpret the empirical findings.

# **2. Empirical Strategy**

Theoretical predictions on land transaction, machine investment and farm profit are examined with farm panel data, described in the next section. Though the theory in the Appendix has a rich set of structural relationships, I take the initial approach using reduced forms in this paper. A change in real wage is the key factor assumed to impact two behavioral outcomes: land transactions and machine investment.

5
function’s estimating sample to include only plants which invest. On average in a sample year, 45% of Chilean manufacturing plants do not invest and these plants tend to be smaller than those that do invest, which might introduce a different source of bias to the production function coefficients, plant TFP levels and growth.<sup>30</sup> Even so, we pursue the Olley and Pakes (1996) estimation, obtain residual plant TFP levels, and use the corresponding plant TFP growth as the dependent variable in Column 9 of Panel A.<sup>31</sup> The results show a positive and significant effect of the services FDI linkage on TFP growth smaller than those estimated in Tables 1-2. Next, we compute plant TFP growth using an index number approach where the contribution of input growth to output growth is given by the share of each input in total sales, an approach which is advocated by Van Biesebroeck (2007) and is described in the Appendix. The coefficient in Column 10 of Panel A shows a positive and significant effect of the services FDI linkage of a larger magnitude than those in the other columns and tables. Lastly, we estimate a production function for each 2-digit industry following the technique proposed by Ackerberg et al. (2007) which addresses the potential problems of collinearity from which the Levinsohn and Petrin (2003) technique may suffer.<sup>32</sup> Column 11 of Panel A presents the results from using the plant TFP growth measures based on the corresponding residual TFP levels as the dependent variable. Again, we find a positive and significant effect of the services FDI linkage on TFP growth.

Sixth, we examine whether our results are driven by any particular industry. Panel B of Table 4 shows the results from estimating our preferred specification using output

> 30 The average total employment for plants in the full sample considered in Tables 1 and 2 is 71 workers while that average for plants entering the Olley and Pakes (1996) estimation is 95 workers.

> 31 Note that we compute TFP growth rates (based on TFP levels) for _all_ plants - including those that do not invest - following Olley and Pakes (1996).

> 32 The Olley and Pakes (1996) and Ackerberg et al. (2007) coefficients are available upon request.

26
**w**

Table 3. **Estlnated** Impact of the Uruguay Round on Regional Groups of Low and Mfddle Income Countries' NTM Coverage Ratlos.

|Product Group (SITC)|East<br>Asia|Eastern<br>Europe|Latin<br>America|Middle East<br>and Nornh<br>Africa|Middle<br>Income<br>Europe|South<br>Asia|Sub-Saharan<br>Africa|All<br>LMIC<br>Countries|
|---|---|---|---|---|---|---|---|---|
||||199|2 Value of OECD|<br>Imports ($milli|on)|||
|ALL GOODS|197,262.4|67,123.3|118,140.1|38,364.7|36,008.8|32,772.1|28,905.3|519,576.6|
|All Non-Oil Goods|181,651.1|50,174.1|98,297.6|12,877.4|35,2f J.0|23,586.4|14,359.7|416,206.3|
|Ores and Metals|7,028.9|9,512.6|13,189.4|940.7|1,905.6|1,171.9|2,426.7|36,175.9|
|Chemicals|4,825.9|4,813.6|4,492.0|672.0|1,106.3|247.0|282.2|16,439.0|
|Other Manufactures|144,765.1|29,238.8|48,716.7|7,573.8|26,518.7|17,173.0|3831.3|277817.4|
||||<br>Ike-Urug|<br>uay Round NTM T|<br>rade Coverage|<br>Ratio(%)|,|,|
|ALL GOODS|18.5|25.6|11.7|9.1|31.1|36.7|16.2|18.0|
|All Non-Oil Goods|19.5|19.8|11.2|7.8|32.3|37.8|15.5|18.4|
|Ores and Metals|9.4|19.3|9.6|19.6|12.2|1.5|6.1|10.0|
|Chemicals|3.3|4.8|3.5|7.0|1.1|1.8|0.2|3.4|
|Other Manufactures|21.9|19.1|10.4|3.4|41.7|48.9|10.5|21.6|
|||Estimated P|ost-Uruguay Rou|nd NTM Trade Co|verage Ratio_(|!)- Constant|Trade Values||
|ALL GOODS|3.6|14.0|3.9|6.!|2.1|5.5|9.5|_5.5_|
|All Non-Oil Goods|3.5|4.1|1.6|3.6|0.7|_5.8_|_5.1_|3.8|
|Ores and Metals|9.4|19.3|9.6|19.6|12.2|1.5|6.1|10.0|
|Chemicals|3.3|4.8|3.5|7.0|1.1|1.8|0.2|3.4|
|Other Manufactures|3.9|2.7|_<br>2.7|2.2|0.8|7.2<br>||<br>4.5<br>||<br>4.5|
|||EFtimated P|ast-Uruguay Rou|nd NTM Trade Co|verage Ratios(|<br>%)- Increased|<br> <br>Trade Values||
|ALL GOODS|2.6|11.8|3.4|5.8|1.1|3.5|8.5|4.2|
|All Non-Oil Goods|2.5|1.8|1,2|3.4|0.3|3.6|4.3|2.8|
|Ores and Metals|_9A._|19.3|9.6|19.6|12.2|1.5|6.1|10.0|
|Chemicals|3.3|4.8|3.5|7.0|1.1|1.8|0.2|3.4|
|Odher Manufactures|2.8|2.1|2.3|2.1|054.0||4.0|3.0|

Note: The trade values and NTM coverage ratios shown in this table are for the OECD countries listed in the notes to Table 1.

Source: World Bank-UNCTAD SMART Database.
33 2012). The regulator announced that this new regulation was intended to “rid the power sector of corruption and protect electricity consumers from fraud and extortion often committed against them by electricity companies.” As one potential indication of progress in this area, the percentage of households reporting informal payments declined from those in the 2010 survey by 3.8 percentage points nationally, 2.0 percentage points in urban areas, and 5.8 percentage points in rural areas. The decline was primarily among lower‐decile households, while the top decile statistics remained largely the same.

Figure 8: Percentage of households who paid a bribe to get grid connection <!-- Start of picture text --> 60<br>40<br>20<br>0<br>Malawi urban Malawi rural Malawi total Nigeria urban Nigeria rural Nigeria total<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data.

In Senegal, 86 percent of households said that corruption had increased over time. However, only 1 percent of those connected to the grid said that they had been asked by utility officials to pay bribes. The question asked in Senegal is not comparable to that in Malawi and Nigeria. It is possible that this more positive outcome in Senegal was due in part to the fact that the question concerned only the past 12 months, and if bribes are associated primarily with the initial connection, those households that had obtained grid connection more than 12 months earlier would not have reported bribe payments.

# Community versus Household Access

In Malawi, Niger, and Nigeria, the survey asked whether their communities had electricity.<sup>11</sup> The data can be used to examine electrified communities and compute the percentage of households that have chosen to connect to the grid. In urban Nigeria, 92 percent of all households living in electrified communities had connected to the grid, whereas less than half of such urban households in Niger and Malawi had grid electricity. In rural areas, the percentages were much lower in the latter two countries, as shown in Figure 9. Predictably, the percentage increased with quintile, but remained one‐third or lower in rural Malawi and Niger.

Figure 10 examines households without grid connection. Most urban households without grid connection in Malawi and Niger lived in electrified neighborhoods, whereas a lower percentage in urban Nigeria lived in neighborhoods connected to the grid, pointing to lack of community electrification as an important barrier. Most rural households without grid connection lived in communities without grid electricity.

> 11 In Malawi and Nigeria, the survey asked each household, whereas in Niger the survey asked this question in the community questionnaire. In Ghana the survey also asked in the community questionnaire, but the data were not available to the World Bank. In Senegal the survey asked if the community had been newly electrified over the previous five years, but that would not identify all households living in communities with electricity.