rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2.5:  Poverty rates in Paraíba by education level**

<!-- Start of picture text --> 6 0<br>5 0<br>4 0<br>3 0<br>2 0<br>Y e a rs o f S c h o o lin g : n o e d u c a tio n o r < 1<br>Y e a rs o f S c h o o lin g : 1 to 4<br>Y e a rs o f S c h o o lin g : 4 to 8<br>1 0 Y e a rs o f S c h o o lin g : 8 to 1 2<br>Y e a rs o f S c h o o lin g : m o re th a n 1 2<br>0<br>1 9 9 5 1 9 9 7 1 9 9 8 1 9 9 9<br>Source: calculations based on PNAD 1995-99.<br><!-- End of picture text --> The younger the household head, the poorer the household. Data reveal that 63 percent of the households headed by a person younger than age 25 are extremely poor in Paraíba. This number may be overstated because no adjustment has been made for the fact that young children, which these households are likely to have, do not need to consume 2,288 calories, as do adults. However, what is worrying is that they are getting worse off. The poverty rate for these households rose 14 percentage points during 1981–99. This is not as bad as the Northeast as a whole, where 66.1 percent of the households headed by a person younger than age 25 are extremely poor, which is a 22-percentage-point increase during the same period.

In Paraíba, only 20 percent of those older than age 65 are below the indigent poverty line—a decline of 15 percentage points since 1993. Additionally, the latter group has the highest average income of any age group, which may be explained in part by pension reforms. The P0 of the population groups aged 25 to 44 and 45 to 64 fell by 15 and 23 percentage points, respectively, during 1981–99. In 1999, 47 percent of the younger group were extremely poor, but 37 percent of the older group. Thus the younger the head of household, the more likely it is to be poor. This life-cycle profile of poverty illustrates that many households are born poor (due again to inadequate assets), with some escaping poverty as they accumulate more assets or as their household size shrinks. It illustrates the need to raise the entry income of the poor through increased labor demand growth and better education opportunities.

The age of household members also affects household poverty. The more young children there are, the poorer the household, although this may be partly explained that Brazil does not adjust for the fact that it takes less income to feed young children or that there are economies of scale for larger households. Among households with children under age 5, 11
12 where xj* and X.* represent world exports of product j and total world exports _exclusive of the intratrade_ of the RTA member countries. Regional trade is excluded in order to more accurately reflect the capacity of Mercosur members to compete evenly in markets where discriminatory trade arrangements do not provide an "unnatural" edge.<sup>8</sup> As such, direct comparisons of the two indices provide an indication of the extent to which Mercosur distorted exports from patterns consistent with comparative advantage. Although they do not measure import diversion directly, they provide closely related information by allowing one to infer whether the additional trade generated by Mercosur was primarily in products in which Mercosur countries had low enough costs to be competitive in third markets. If not, they suggest that the additional trade within Mercosur could have been replaced by more efficient outside suppliers.<sup>9</sup> This study will test the use of these indices in this context using actual trade data.

Before proceeding, it should be noted that there are additional empirical procedures that can provide useful supplementary information concerning changes in the composition and direction of trade that accompanied the formation of Mercosur. Specifically, Table 4 lists the 30 three-digit SITC processed products that met two separate criteria: (i) they recorded at least one-quarter of a million dollars in intratrade in 1988 (this lower limit was set to prevent the tests being biased by marginal products, while 1988 - was selected as a period prior to the implementation - and likely the anticipation of major preferences); and (ii) that registered the highest 1988-94 compound annual growth rates in trade among Mercosur 8RCA indices are generally only computed for processed goods or manufactures because trade in agricultural products is distorted by export incentives and trade barriers which are likely to obscure whether a country has a real comparative advantage, or disadvantage, in these products. The present analysis does not attempt to derive revealed comparative advantage indices for agricultural products and other primary commodities. As such, the present analysis is based on 128 three-digit SITC products that include all manufactured goods as well as a number of processed foodstuffs and processed raw materials. Equation (3) is a modified version of the normal RCA formula in that intraMercosur trade is excluded to prevent preferential trade among members from obscuring the true comparative advantage of Mercosur members.

9The issue is essentially whether RTAs foster "high cost" imports at the expense of "low cost' ones. The traditional calculations of trade diversion based on import data infer this from the displacement of imports from nonpartners by those from partners, implicitly comparing partner and non-partner costs by their relative competitiveness in the pre-RTA regional market. The supplementary view developed here makes inferences about 'high" and 'low" costs by implicitly comparing the relative competitiveness of partner and non-partner goods in world markets.
Table 14. Estimates of the Elasticdty of Revenue with Respect to Volume for a 20% Increase in Cotton Producer Price.

|'Base,<br>PargMeter<br>I/S<br>aL<br>AR<br>S2 sicviy to Price E**a**sticity of<br>D maan =/<br> <br> <br>|
|---|
|0.00<br>0.91<br>0.5<br>_0.35_<br>08<br>0302<br>1<br>1<br>3<br>|
|<br> <br> <br> <br> <br> <br> <br>Sensitvity to Income Elatiity<br>_of_<br>Demand<br>.2..|
|11 r**i**e<br>laso y<br>_f_Dnad<br>-0.00 In.om<br>41.9ewofDaan3<br>0.5;<br>Growt Rste<br>0in<br>0l<br>GD0=29pa<br>0.00<br>1.00<br>~~~<br>089<br>085<br>083<br>080<br>078<br>076<br>086.0]<br>K<br>Sea!Iitiviy to Ineonto GroWth R.Ate<br>_41:_<br>0.00<br>0.9<br>05<br>09<br>8<br>8<br>8<br>.<br>07<br>7<br>8<br>.~~~~~~~~~~~..<br>...|

Source: IECff, World Bank. 1/ Price Elasticity of Damand = -0.2; income Elasticity of Demand 0.5; Growth Rate in Rcal GDP =2% p.a. 2/ Price Elasticity of Damand = -0. 1; Other Parametem as in Base Simulation. 3/ Income Elasticity of Demand =0.25; Other Parmeter as in Base Simulation. 4/ Income Growth Rate = 1% ; Other Parmun as in Base Simulation.

27
Figure 6: Abstract & Manual Tasks <!-- Start of picture text --> (A) Income Share (B) Income Share Relative to 1988<br>Clerical & Administrative Production Clerical & Administrative Production<br>Management & Professional Service Management & Professional Service<br>1980q1 1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1 1980q1 1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1<br>Quarter Quarter<br>1.4<br>25<br>1.2<br>20<br>15 1<br>Labor Share (%) 10 Index: 1988=1 .8<br>5 .6<br>0 .4<br><!-- End of picture text --> _Notes:_ Panel A plots unadjusted income shares of four major occupation groups as reflected in the CPS MORG. These shares do not add up to 100% as earnings reflected in the CPS MORG do not properly measure benefits as well as proprietors’ income. Panel B imposes the normalization 1988q1=1 to illustrate the clear divide between routine and non-routine tasks. The dashed vertical line indicates 1988q1.

lar occupations lost about 40% , administrative/clerical occupations lost a little more than 20% in their income share. This makes clear that, while traditional blue collar occupations are by far the dominant driver of the decline in the routine labor income share, administrative/clerical jobs still explain at least a quarter of the decline in the routine share since 1988—clearly a non-negligible portion. Moreover, our estimates of the non-routine wage premium based on equation (1) control for industry fixed effects, suggesting that the premium for non-routine work captures a withinindustry feature.

Third, it is possible that international trade has played a key role in shaping the allocation of labor across routine and non-routine occupations (Autor, Dorn and Hanson, 2013; Elsby et al., 2013). Specifically, if most of the output produced by non-routine labor in recent years is in fact exported—such as management and other business consulting services—and if routine-intensive products are now primarily imported, then the true driver of our measured income shares would in fact be international trade and not necessarily ICT.

To evaluate this hypothesis we calculate the share of labor expenditures that is embodied in (net) exported goods and services. Define _sR,i,t_ and _sNR ,i,t_ as the routine and non-routine labor shares in industry _i_ , respectively. Further define _NX i,t_ the total expenditure on net exports in 14
6 Changes in employment have been accompanied by rather marked changes in the structure of production. Manufacturing output decreased by around 25 percent during 1990-93 recession, and increased by 12 percent in the 1992-93 recovery (overall, it decreased by around 25 percent in the period 1990-93). Once again, however, the data on one and two-digit industries suggest something more than a purely cyclical phenomenon. For example, in 1993, i.e. the second year of recovery, real output decreased in as many as eight out of twenty two-digit industries (i.e., coal, non-ferrous metallurgy, engineering, precision instruments, electronics, glass, clothing and leather). As a result, the shares of metallurgy and electro-engineering in total production fell, respectively, from around 20 and 25 percent in 1990, to 10 and 20 percent in 1993. On the other hand, the share of Food Processing rose from 15 to more than 25 percent of total manufacturing output.<sup>5</sup> Secondly, in parallel with the changes in the structure of supply, the ownership structure in industry has been sharply modified. The private sector in 1995 accounted for more than half of economy-wide GDP and employment, and more than one third of industrial output. Most new private firms are typically small. However, in sectors such as trade and construction, the private sector accounted in 1993 for more than half of production even among medium and large enterprises. This is partly the result of privatization of previous state-owned enterprises, but mainly of creation of new firms by private entrepreneurs.<sup>6</sup> Private sector growth is significant in manufacturing as well. In the last quarter of 1993, private enterprises accounted for 40-45 percent of production in Light Industry and Wood and Paper, and 25-30 percent of production in Electro-Engineering, Chemicals and Minerals. Furthermore, since these data refer to medium and large enterprises, they _5_ An interesting exercise is the analysis of the correlation between industry-specific rates of growth in 1990-91 and 1992-93. If the correlation index were close to one, it would indicate that the industries where output decreased more during the recession are also those where it increased more during the recovery - that is, it would be evidence of cyclical behavior. On the other hand, the lack of correlation would suggest the absence of cyclical factors. Consistent with our previous observations, the correlation index computed with data on 2-digit manufacturing industries turns out to be very close to zero (precisely, it is equal to .01).

6 Although by the end of 1993 about one fourth of all state enterprises had been privatized, their combined share in industrial output wvas only about 4 percent. See Chmiel (1995).
in eligible areas and advertised the program through campaigns. Interested households had to go to these offices and provide the information to determine eligibility, a costly process that resulted in much lower take-up rates and led to significant self-selection in program participation among potential beneficiary households. Administrative data suggests that, initially, only about 50 percent of eligible households were enrolled in the program.<sup>6</sup>

# **3. Data**

The main data source of this paper is the Mexican Family Life Survey (MxFLS), a longitudinal survey that collects a rich set of information on demographic and socioeconomic characteristics of individuals, households, and communities. At baseline, the sample is representative at the national, rural-urban and regional level. The first wave (MxFLS1) was conducted in 2002 and interviewed 35,677 individuals in 8,440 households. These households reside in a total of 150 communities located across 16 different states.<sup>7</sup> The second wave (MxFLS2) was conducted during 2005-2006 and achieved a 90 percent re-contact rate at the household level. This wave consists of 36,946 individuals in 8,434 households, who due to migration decisions are located across 247 localities in 21 states throughout Mexico. A third wave (MxFLS3) spanned through 2009-2013. Due to the timing in the roll-out of Oportunidades, this paper draws on data collected in the first two rounds of the survey.

The two key pieces of information in our analysis are: height – our outcome of interest, and the timing in the roll-out of the program in MxFLS localities. With respect to height, MxFLS collects

> 6 More specifically, households interested in the program had to go to the registration offices on specific dates and answer an inclusion questionnaire. With that information households were immediately classified as qualified for the program or not. If qualified, they had to answer a second questionnaire and were visited later in their dwellings to confirm their eligibility status.

> 7 Mexico is divided into 31 states and the Federal District.

8
**Table 18: Effective annual interest rate (%) of the partner organizations (POs) of PKSF**

|**Loan type**|**Highest rate**|**Lowest rate**|**Average rate**|
|---|---|---|---|
|**Rural microcredit (regular)**|35.75|28.11|32.05|
|**Microenterprise (weekly payment)**|34.67|28.39|31.59|
|**Microenterprise (monthly payment)**|30.39|25.30|26.65|

Note: The effective rate calculation takes into account all fees and additional charges paid by the borrowers, and thus, it correctly reflects the cost of borrowing. Source: Faruqee and Khalily (2011)

**Table 19: Effective interest rates of 50 selected MFIs (%)**

|**Loan type**|**Category**|**Number**|**Range of effective**|
|---|---|---|---|
|||**of MFIs**|**Interest rate**|
|**Rural microcredit**|I|29|28-85|
||II|9|31-124|
||III|4|32-63|
||IV|3|65-72|
||I|22|22-65|
|**Micro Enterprise (weekly**|II|3|34-50|
|**payment)**|III|1|37|
||IV|3|38-93|
|**Micro Enterprise (monthly**|I|19|21-57|
|**payment)**|II|1|45|

Note: Categories are defined as follows: I. Top MFIs based on loan outstanding; II. MFIs not financed by PKSF; III. MFIs financed by bank and member savings; IV. Small MFIs with loan outstanding Tk. 4-7 million Source: Faruqee and Khalily (2011) 49
resident deaths, this finding might be explained by the fact that co-resident own children and young adult deaths are associated with larger responses, and these categories of adults are mostly not spouses of the elderly respondent. In the case of non-co-resident deaths, we cannot compare the extent of the association for spouses versus non-spouses, since as mentioned above the variable for spouses is dropped in the regression.

The results in Table 4a and 4b for PHHM deaths hold if the deaths of children living outside are excluded. Together, the results of Table 4b suggest that support to the elderly from co-resident adults may be in the form of farm labor, and young adults and children of the elderly respondents may provide this support more intensively. On the other hand, non-co-resident support may be more intensively provided by older adults, whose deaths cause a greater increase in labor hours for elderly than non-co-resident young adults.

# **4.2. Impact of adult death on labor hours over time**

Since the survey data also includes the year of death for previous household members who died, we can look at the dynamics of the impact of experiencing a primeage death in the household over time. We divide the time span of the panel into three periods: 1991 to 1995, 1996 to 1999, and 2000 to 2004. We construct six dummy death variables, three corresponding to the deaths over time of co-resident PHHM adults, and three corresponding to the deaths over time of non-co-resident adults. These are not mutually exclusive categories, since the older adult may have had multiple death shocks across periods, and for co-resident as well as non-co-resident adult relatives. We also include the death of a child living elsewhere. Since we do not know the time of these non-co-resident children’s deaths (denoted in the model below as CLE, or child living elsewhere), we cannot create the same, time-specific indicators as described above.

We denote co-resident PHHM deaths over time with “co” in the subscript, and non-co-resident deaths with “non-co” in the subscript. The new model we estimate is the following: 19
<!-- Start of picture text --> Capital Distortion vs Productivity Output Distortion vs Productivity<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Log TFPQ (Percentile) Log TFPQ (Percentile)<br>Cote d’Ivoire Ethiopia Cote d’Ivoire Ethiopia<br>Ghana Kenya Ghana Kenya<br>.5 2<br>1<br>0<br>0<br>Capital Distortion−.5 Output Distortion<br>−1<br>−1<br>−2<br><!-- End of picture text --> Figure 5: Distortions vs. Productivity

# **6.4 Productivity and Distortions Over the Life Cycle**

Our analysis so far has focused on measuring the static effect of resource misallocation, but distortions are likely to also have important dynamic implications through the effect that greater misallocation has on firms’ incentives to invest in technological upgrading. As already mentioned, the fact that more productive firms are “taxed” more could discourage firms from investing in productivity enhancing technologies, and as a result generate slower life cycle productivity growth, which in turn leads to slower employment growth.

Hsieh and Klenow (2014) document a notable difference in the post-entry dynamics of firm performance between developing and advanced economies. Using comprehensive manufacturing census data, they find that while firms in the US grow by a factor of eight by the age of 40, Mexican firms grow by a factor of two and such growth is much slower in India. The authors attempt to rationalize the flatter growth of productivity over firms’ life-cycle in developing countries through an age-dependent component in the distribution of distortions across firms. Indeed, they find that firms get progressively more taxed as they age, and show quantitatively through a model of innovation that this age-dependent component of distortions undermines productivity growth. Furthermore, they show that the dynamic response in the underlying distribution of physical productivity magnifies the losses from misallocation that result from a static analysis.

19
**Table 4:  Outlier observations removed**

|Outliers||
|---|---|
|(z>5)|FUAs|
|_Td_<br>0|1,761|
|_rens_<br>1|35|
|In each case<br>2|2|
|,<br>3|1|
|Total|1,799|

Model estimation yields t-statistics for results classification by degree of significance. We group urban areas into four categories by significance level: p>.05 (no significant change at 95% confidence); p≤.05 (95% confidence); p≤.01 (99% confidence) and p≤.001 (99.9% confidence).

Figure 2 illustrates this classification for selected urban areas with negative and positive trends at different levels of significance for the period 2014 – 2021.<sup>13</sup> Each graph includes the regression line (equivalent to model (6)) through the observations. The graphs for the non-significant group have been chosen for FUAs with t-statistics near 1.0. The illustrations highlight the need for statistical analysis, given the role of random variation in satellite-based measurements. Of course, the same is true for ground-based measurements (e.g. Chakraborty et al. 2008).

# **5.4 Tracking Results: FUA Trends, 2014 - 2021**

Table 5 summarizes our results for trend equation (6). Our sample comprises 1,799 urban areas with 30 degrees of freedom or more during the period 2014 - 2021.<sup>14</sup> Of these, 272 have significant decreases in local concentration anomalies and 108 have significant increases. Overall, 380 of 1,799 cities (21.1%) have significant changes during the 8-year period. Table 6 distributes the changes across regions, showing Asia with a disproportionate share of increases and the other regions with disproportionate shares of decreases. Figure 3 maps the same information, showing that within Asia, substantial portions of the decreases and increases are accounted for by India and China, respectively.

> 13 Scaling on the y-axis varies across cases because observations vary over different ranges.

> 14 FUAs in the database have populations of 50,000 or more.

13
||increases in remittances are associated with increased expenditure<br>on education and health.|
|---|---|
|Mountford (1997), Stark et al. (1997)|Skilled migration could induce human capital investment in the<br>home country|
|Rapoport (2016)|The role of diaspora networks in enhancing cross-border flows of<br>goods, capital, and knowledge, eventually contributing to efficient<br>specialization, investment, and productivity growth in the migrants’<br>home-countries|
|Stillman et al. (2014)|Migration improves migrants’ subjective mental health and income<br>adequacy, reduces feelings of happiness and social respect, and does<br>not alter self-rated overall welfare.|

However, there are clearly also some costs associated with high rates of emigration. At the household level, the absence of household members is intrinsically difficult in many cases and can also lead to lower labor force participation and a concentration of household responsibilities on members who stay behind. The results of this study suggest that this tendency is indeed present in Uzbekistan as well (i.e. household members that remain in Uzbekistan tend to work slightly less than those that do not have migrant members), but that on net, households with migrants are substantially better off in monetary terms nonetheless.

At the aggregate level, migration leads to the loss of human capital, and when this group is particularly highly skilled, a situation commonly referred to as “brain drain” can develop. In this scenario, particularly educated people are more likely to leave the country, creating a deficit of highly skilled workers. However, this is a less relevant concern in Uzbekistan – as is shown in section III of this report, most international migrants are younger, and have terminal degrees in either vocational or secondary education. These groups are strongly over-represented in migrant flows in comparison to the general population. In contrast, tertiary educated people are underrepresented in current migrant flows.

The following sections of this paper proceed as follows. Section II includes an overview of the data used, section III provides an overview of aggregate migration patterns for Uzbekistan, demographic and related information on migrants, and details on the profile of sending households. Section IV investigates the drivers of migration decisions, focusing mostly on the panel survey data which allow analysis of the patterns of departing/current/returning migrants over time. Section V provides analysis of household well-being, including remittances and monetary measures, as well as subjective measures. Section VI concludes.

4