rafmacalaba/datause-encoder-data huggingface dataset:
are not always consistent over short periods of time: a study in rural Colombia reveals that respondents tend to change their answers for survey questions related to the Big Five personality traits and other attitudes when asked two times with a three-week interval (Laajaj and Macours 2018).<sup>8</sup> In addition, using STEP surveys including the Colombia one we use in this paper, Laajaj et al. (2019) show that the scores of the Big Five personality traits based on survey questions do not always measure them as they are supposed to by design and thus cannot be confidently interpreted as measures of them. One of the factors generating the measurement errors could be the limited number of questions on socioemotional skills (24; three in average for each skill measure). That is much lower than usual inventory aiming to capture personality traits and socioemotional skills. The measurement error of skills would produce estimates of β1 and β2 biased towards zero.

# **3.b Structural Estimation**

As an alternative to the reduced-form estimations, we run structural estimations of latent skills based on a measurement system of test scores to solve measurement error and omitted variable bias (Keane and Wolpin 1997; Cameron and Heckman 2001; Heckman, Stixrud, and Urzúa 2006; Sarzosa and Urzúa 2016). These structural estimations give us the level of unobserved heterogeneity linked to latent skills, the distributions of the latent skills, and simulations of how these distributions relate to labor market outcomes.<sup>9</sup> In this setting, the outcomes of interest, Y, are a function of the latent skills and other factors influencing them, as depicted by the following equation: where, θC and θSE are the latent factors that capture the unobserved heterogeneity of cognitive and socioemotional skills; β<sup>Y</sup> , αC, and αSEY are coefficients to estimate; XY is observable controls (e.g. gender, age), and e<sup>Y</sup> is a vector of independently distributed error terms orthogonal to XY, θC, and θSE.

We incorporate the latent factors θC and θSE by treating the available survey measures of cognitive and socioemotional skills only as proxies, or results, of them (Bartholomew, Knott, and Moustaki 2011). Formally, the latent factors are treated as realizations of the following score-production function:

> 8 The test-retest correlation between the fifteen scores of socioemotional skills measured in this study in a threeweek interval is 0.7. The test-retest for the underlying survey questions varies between 0.21 and 0.66. 9 For more details about identification and estimation, see Sarzosa and Urzúa (2016).

10
America (the median ratio of households to dwellings was 0.98), and deficits had been reduced in socialist countries, to a median ratio of 1.10. The reduction in housing deficits was particularly striking between 1960 and 1970 for the three countries other than Poland that had suffered the most serious war-related shortages. Poland had however fdllen to last place among the countries of Table 1 in 1970, with 16 percent more households than dwellings. From 1970 to 1980, housing deficits continued to fall in both Western Europe and the socialist countries, with the median ratio of households to dwellings reaching 0.95 in the West and 1.07 in socialist countries. By 1980, virtual parity between households and dwellings had been achieved by several socialist countries--Czechoslovakia, the German Democratic Republic, and Yugoslavia. In Poland, by contrast, the housing de& it was as high in percentage terms in 1980 as it was in 1970, with 18 percent more households than dwellings.

These housing deficits have proven to be intransigent to considerable efforts by the Poles to reduce them since the early 970s. In 1972, for example, the Sejm (the Polish parliament) announced comprehensive program to expand housing production from an annual level of output of 6.5 dwellings per 1000 people in 1971-1975 to 9 per 1000 people in 1976-1980 and to 11 or more per 1000 people in 1981-1990.

Table 2 indicates actual output, in terms of both dwellings per 1000 people and numbers of dwellings completed annually. During the 1970s, p-oduction goals were met, with output equal to 6.7 dwellings per 1000 people. Ir the late 1970s, production was climbing toward the goal of nine dwellings per 1000 people, and attained a peak of 8.1 dwellings pcr 1000 people during 1978, when numerical output also peaked at 284,000 dwelling units. In the next three years, and mirroring general economic conditions in Poland, output fell by about one-third to a level that had been typical of the 1960s. Output has barely recovered from a nadir of 186,000 dwelling units in 1982 (5.1 per 1000 population).

There are three major s 3yrces of housing in Poland: state, cooperative, and private housing.- Over time the relative importance of supply from each of these sources has varied as a reflection of the state's role in the housing supply system. Before World War II, most housing was supplied privately. After the war, the state began to

- 3/ A fourth sJurce of housing--built by industrial establishments for their employees--was an important source of supply during the 1940s and 1950s. It is still used where housing shortages are critical. We have no separate statistics to identify this source of housing, but it could be as much as 12 percent of the 1978 total housing stock if it is the same as the category "other socialized housing" in Poland's statistical yearbook. Ciechocinska (1987) reported that, as of 1978, 7.7 percent of Warsaw's apartments were owued by enterprises.
Aspect Open (e.g. WMS) Closed (e.g. MOPs) Accuracy of responses = High: Interview format gives opportunity to Medium: Harder to elicit truthful answers if probe and ask for examples. Possible to respondents have preconceptions. Greater risk implement “double blind” method to reduce that respondents might misinterpret questions or preconception bias. rush through the survey. Cost per survey High: High quality trained interviewers needed Low: Initial design and execution costs, but this to run survey. Training includes one week initial fixed cost can be spread over a very large number training and ongoing debriefing and calibration. of respondents, so cost per survey is low. Costs Interviewers’ time primarily spent recruiting can be higher im poorer countries where managers to take part in the survey (rather than enumerators administer surveys onsite because of just running interviews). unreliable mail and e-mail networks. Response rates Medium: Interview is interactive and managers High: Co-operation with a National Statistics more engaged. We obtained an average response Agency can enable the survey to be mandatory. rate of 40%. Given this response rates ofaround 80%. Without such co-operation, response rates will be low. Replicability Medium: Training needed to ensure the surveyis High: Questionnaire essentially the same across delivered in same way. Useful to have some countries and already available pre-tested fromby individuals who have worked in previous survey US Census Bureau. waves as trainers for other surveys foster comparability. Traiming and survey material is available on-line. International High: Multiple countries can be interviewed Medium/High: Easier to implement but there is a comparability from same location. Using bilingual interviewers risk of differential interpretation if this is not means makes it is easier to cross-check carefully translated across languages. responses. Speed of delivery High: Can complete a full survey wave in about Medium: Involves cooperation with national 10 weeks. So including recruitment and set-up statistical agencies, so more planning work in time possible to complete a survey wave from advance. The survey period typically is around3 scratch in about 4 months. months plus 1 to 3 months of data cleaning.
**Table 7: Base model—By size—Fixed effects—regression on firm profitability as dependent variable**

||All firms|Small|Medium|Large|
|---|---|---|---|---|
|**Treatment (ITT)**|-0.00307|0.00117|-0.410|-0.0422|
||(-0.28)|(0.12)|(-0.88)|(-0.53)|
|Reform Period|-0.0318***|-0.0325***|0.0273|-0.000568|
||(-3.40)|(-3.39)|(0.68)|(-0.02)|
|Constant|-0.0183|-0.0150|-1.301|-0.0388|
||(-0.23)|(-0.18)|(-1.17)|(-0.31)|
|Year Dum.|Yes|Yes|Yes|Yes|
|Region Dum.|Yes|Yes|Yes|Yes|
|Sector Dum.|Yes|Yes|Yes|Yes|
|Observations|1 363 022|1 321 871|34 695|7 160|

**t-statistics shown in parentheses, treatment variable bolded**

- ***p<0.1          **p<0.05          ***p<0.01**

**Table 8: 2007 as non-reform year—By model design—Random effects—regression on firm profitability as dependent variable**

||No Dummies|Year Dum.|+Reg. Dum.|+Sect. Dum.|Frm. Skill|Frm. Size|Full Model|
|---|---|---|---|---|---|---|---|
|**Treatment (ITT)**|-0.0112|0.00638|0.00668|0.00705|0.00703|0.00704|0.00702|
||(-1.49)|(0.63)|(0.66)|(0.69)|(0.69)|(0.69)|(0.69)|
|Pre-reform<br>youth share|0.0612***|0.0515***|0.0444***|0.0291***|0.0397***|0.0293***|0.0398***|
||(7.97)|(6.95)|(6.05)|(4.30)|(5.62)|(4.32)|(5.63)|
|Reform Period|-0.00887**|-0.0318***|-0.0319***|-0.0323***|-0.0326***|-0.0323***|-0.0326***|
||(-2.30)|(-3.32)|(-3.33)|(-3.39)|(-3.41)|(-3.39)|(-3.41)|
|Pre-reform<br>Education level|||||-0.0570***||-0.0570***|
||||||(-6.15)||(-6.15)|
|Pre-reform firm<br>size||||||-0.0000202**|-0.0000192**|
|||||||(-2.33)|(-2.20)|
|Constant|0.0413***|0.0486***|0.0761***|0.104***|0.199***|0.104***|0.199***|
||(11.51)|(12.12)|(13.41)|(10.60)|(9.89)|(10.60)|(9.89)|
|Year Dum.|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Region Dum.|No|No|Yes|Yes|Yes|Yes|Yes|
|Sector Dum.|No|No|No|Yes|Yes|Yes|Yes|
|Observations|1 363 022|1 363 022|1 363 022|1 363 022|1 363 022|1 363022|1 363 022|

**t-statistics shown in parentheses, treatment variable bolded**

- ***p<0.1          **p<0.05          ***p<0.01**

22
Given the breadth of damage in local areas, we might expect those with family and social networks extending outside the direct impact zone to have recovered more quickly, since their networks extend to unaffected areas. The evidence available to us on this, while mixed, is not particularly strong. In the April 2008 survey, we asked entrepreneurs whether their relatives were affected by the tsunami. Questions covered parents, siblings, parents- and siblings-in-law, and adult children who were living at the time of the tsunami. We do not find that those with parents or children who were unaffected by the tsunami spent a larger amount on recovering assets in the months following the tsunami, or that they obtained a larger share of the funds they did spend from family members. We do find that entrepreneurs who lived in a different administrative district (DS Division) at age 12 report using family members as a source for a slightly higher portion of their recovery funds (14 percent vs. 9 percent), but the difference is not significant. Those who lived outside the same DS Division at age 12 also report having spent more to replace and repair assets during the first three months, both at the mean ($185 vs. $102, p=0.12) and the median ($40 vs. $33). These data suggest a surprisingly large contribution to the recovery comes from the owners’ savings and loans from family and friends.

Figures 1 and 2 show that enterprises suffering damage had recovered to their pretsunami size within about 15 months of the tsunami. The figures show the value of equipment, tools and vehicles used in the enterprise (Figure 1) and profits (Figure 2) across time, as a percentage of the reported values in the month before the tsunami (November 2004). Mean profits of directly affected firms fell to 65 percent of the pretsunami level in March 2005 before recovering to 107 percent of the pre-tsunami level by March 2006. Capital stock shows a similar pattern. Both figures compare the directly affected enterprises with the unaffected enterprises.<sup>10</sup> Note that while the affected

> 10 We use the set of firms not receiving one of the random grants provided by the project (as discussed below) and reporting profits in all 11 rounds of our panel survey. We trim firms with profits in the top and

12
# I. Introduction

The extent of poverty in developing countries, an.d che choine of policies to ieduce poverty, have long been of interest to scholars, governments, international organizations and many other groups and individuals. The starting point for work in this area is data on poverty in developing countries that is both accurate and up to date. Unfortunately, the data actually available from these countries are often sparse, outdated, of dubious accuracy and not available in a form useful for policy analysis. In recent years several attempts have been made to increase both the quantity and the quality of data from the developing world. Much of tnis data is useful for poverty analysis, and indeed some has been gathered specifically with this purpose in mind.

One of the most ambitious attempts to improve the quality of data collected at the household level from developing countries is the Living Standards Measurement Study (LSMS) program at the World Bank. This program is specifically aimed at improving data collection efforts so .s to better understand the exrent of poverty and the determinants of livings standards. This paper provides a description of LSMS activities since its inception in 1980. It is organized as follows. Section II sketches a brief history of the LSMS project ac the World Bank. The next section presents an overview of the statistical and economic methodology underlying the LSMS data collection efforts. Section IV provides several examples of the use of LSMS data for evaluation of the extent of poverty in developing countries and the analysis of the effect of particular policies on the poor. Section V summarizes the results and concludes the paper.
**Figure 5: Decomposition of Global Poverty Projections**

<!-- Start of picture text --> 10<br>8.51 8.63<br>9<br>0.12<br>8<br>7<br>6<br>5.73 5.73<br>5<br>4<br>2.78<br>3 2.08<br>0.7 0.7 0.7<br>2<br>1 2.08 2.08 2.08 2.08<br>0<br>(i) (ii) (iii) (iv)<br>Scenarios<br>Base Projection Country-specific population growth<br>Country-specific economic growth Inequality-adjusted economic growth<br>2030<br>Projected Global Poverty Rate (%) in<br><!-- End of picture text --> Source: Authors’ estimations using data from PovcalNet (October 1, 2013).

In conclusion, under our benchmark scenario, the global poverty rate in 2030 is projected to be 8.6 percent. In other words, if all developing countries maintain their paces of growth and poverty reduction between 2002 and 2010, the global poverty rate is projected to be 8.6 percent in 2030. This projection is much larger than that of Ravallion (2013). Consideration of country-specific growth patterns is a main source of this difference, but the use of population growth projections by World Development Data Group and within-country inequality adjustments also contribute to the increase in the global poverty projection.

# **VI. Bending the Arc of History**

It is clear that achieving the new target of reducing extreme poverty to 3 percent by 2030 is very challenging if the developing world maintains its current pace of growth and poverty reduction. The analysis above shows that under the benchmark scenario, the global poverty rate is projected to be as high as 8.6 percent in 2030. Therefore, to achieve the new target, it is necessary for developing countries and their development partners to improve the pace of poverty reduction. Is that possible? If so, how can we achieve the target?

There are an infinite number of ways to reduce the global poverty rate and each of them imposes different levels of burden on countries and regions around the world. Reducing poverty rates uniformly for all developing countries is one way, while focusing on a few very poor countries is another. Of course, the implications of these approaches are very different for all countries. This paper looks for a uniform growth target, which is simple, fair, and desirable. It begins by investigating the simple question of whether accelerating all countries’ growth by the same percentage point is enough.

# _Is accelerating growth only enough?_

To achieve the extreme poverty target, it is necessary to either accelerate growth or improve income (or consumption) distribution, or both. Here, we test whether only accelerating growth can achieve the goal.

15
# Table 3.2.

# Turkey: Manufacturing Industry,

# Insured and Employees

# (thousands)

||SSI,|insured|Census,|employees|
|---|---|---|---|---|
|Industry|1980|<br>1963|<br>1980|196|
|Food|159.7|<br>76.3|<br>149.1|69.7|
|Beverages|1lA.|4<br>4.8|<br>12.~|6.1|
|Tobacco|59.2|<br>30.5|<br>52.8|24.7|
|Textiles|163.2|95.5|<br>181.3|109.0|
|Footwear,<br>apparal,<br>text.goods|54.2|<br>5.0|<br>42.7|32.0|
|Wood and cork|25.4|5.5|28.8|16.1|
|Furnitureand fixtures|13.0|2.6|19.2|5.3|
|Paper and paper products|14.8|<br>8.0|<br>19.5|8.2|
|Printing, publishing etc.|19.3|<br>7.1|<br>16.6|8.2|
|Leather and products|6.3|<br>2.2|<br>6.0|4.3|
|Rubber products|12.1|<br>17.0|<br>16.2|6.2|
|Chemicals and chemical products|<br>45.9|<br>13.9|<br>46.2|15.6|
|Petroleum and coal products|7.4|<br>1.6|<br>10.2|2.0|
|Non-metallic products|72.1|<br>21.7|<br>83.8|25.0|
|Basic metal|59.7|<br>10.8|<br>75.6|12.4|
|Metal products, excl. madinery|<br>75.9|17.4|<br>62.4|40.4|
|Machinery, excl. electrical|74.4|<br>12.6|<br>67.4|6.9|
|Electrical macinery, appliance|s,etc.24|.5<br>4.1|<br>37.2|6.8|
|Transportation<br>equipment|95.8|<br>30.5|<br>70.6|26.1|
|Other|27.2|<br>2.6|<br>6.5|5.6|
|Total manufacturing|1,023.6|<br>369.7|1,004.6|430.7|
|of which: Large establishments|<br>n.a.|<br>n.a.|<br>787.0|299.0|
|Small<br>"|n.a.|<br>n.a.|<br>217.6|131.7|

Sources: Statistical Yearbook of Turkeyt several issues.

-57-
9 Table 2-6. Home Country Shares of FDI Flows to Developing Countries, 1975-87 (percent)

|Year|United<br>States|United<br>Kingdom|Fed. Rep.<br>of Germany|France|Japan|Total|
|---|---|---|---|---|---|---|
|1975|63.8|5.8|7.2|2.4|10.9|90.0|
|1976|37.4|10.0|9.2|2.9|22.3|81.9|
|1977|49.4|12.0|8.6|2.7|11.6|84.3|
|1978|48.0|7.0|8.8|3.5|17.3|84.7|
|1979|60.5|5.2|6.2|5.1|10.4|87.3|
|1980|30.9|17.6|14.5|8.3|15.4|86.6|
|1981|38.4|13.8|8.0|6.7|23.3|90.3|
|1982|44.1|10.5|8.0|7.6|18.9|89.1|
|1983|25.6|16.2|9.2|4.4|19.4|74.8|
|1984|39.7|18.9|6.3|2.4|15.6|82.9|
|1985|14.4|32.6|-2.2|9.0|15.5|69.2|
|1986|27.9|17.1|3.7|5.5|27.8|82.0|
|1987|40.3|9.5|3.4|3.5|37.2|93.8|

Note: All percentages are of outflows from OECD countries only, not world totals.

Source: OECD.
|Observations|9,978|2,745|5,199|2,008|
|---|---|---|---|---|
|Adjusted R square|0.304|0.450|0.345|0.288|
|Entertainment|0.519***|0.190|0.298*|0.374***|
||(0.139)|(0.144)|(0.178)|(0.098)|
|Observations|9,850|2,722|5,132|1,971|
|Adjusted R square<br>|0.241|0.336|0.240|0.248|
|Automobiles|0.311***|0.199|0.094|0.307**|
||(0.118)|(0.172)|(0.150)|(0.125)|
|Observations|9,850|2,722|5,132|1,971|
|Adjusted R square|0.241|0.336|0.240|0.248|
|Other vehicles|0.085|-0.129|0.046|-0.030|
||(0.146)|(0.288)|(0.206)|(0.131)|
|Observations|9,940|2,735|5,173|2,007|
|Adjusted R square|0.397|0.416|0.401|0.399|
|Durable goods|0.187|-0.275|-0.019|0.010|
||(0.118)|(0.380)|(0.120)|(0.132)|
|Observations|10,141|2,770|5,270|2,075|
|Adjusted R square|0.435|0.496|0.446|0.412|
|Medical|0.136|0.498*|-0.051|0.181|
||(0.089)|(0.257)|(0.118)|(0.172)|
|Observations|10,045|2,737|5,216|2,066|
|Adjusted R square|0.376|0.367|0.386|0.385|
|Health and fitness|0.183*|0.229*|-0.059|-0.007|
||(0.098)|(0.135)|(0.107)|(0.111)|
|Observations|10,144|2,766|5,276|2,076|
|Adjusted R square|0.309|0.424|0.338|0.294|
|Education|-0.142|-0.410*|-0.160|0.024|
||(0.114)|(0.213)|(0.123)|(0.202)|
|Observations|10,081|2,744|5,235|2,076|
|Adjusted R square|0.221|0.229|0.217|0.275|
|Home repair|-0.124|-0.090|-0.169|-0.325**|
||(0.087)|(0.341)|(0.128)|(0.136)|
|Observations|9,975|2,746|5,196|2,007|
|Adjusted R square|0.452|0.435|0.465|0.451|
|Gifts|-0.062|0.505|-0.014|-0.376**|
||(0.100)|(0.379)|(0.115)|(0.152)|
|Observations|8,279|2,094|4,425|1,738|
|Adjusted R square|0.111|0.133|0.119|0.150|

Note: Alternative extended model, with additional variables of control, including the lagged log household per capita, income per capita lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age squared/100, dependency ratio, and urbanrural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** ·· 42
from _low_ to _high_ . The intuition is that when there are no changes in aggregate shocks ( _D_ 1 = 1 ), wages follow an AR(1) with correlation coecient _ρw_ . When instead there are changes in aggregate conditions ( _D_ 2 = 1 or _D_ 3 = 1 ); wages jump discretely downwards or upwards in the rst period (they overshoot) and adjust gradually afterwards. This rst-period response occurs in models with imperfect labor mobility such as Artuc, Chadhuri and McLaren (2010) and Dix-Carneiro (2014).

Product prices of tradable sectors are determined in international markets. Domestic prices are equal to international prices plus taris. Sectors in which supply is larger than demand are net exporters, whereas sectors in which supply is smaller than demand are net importers. Gross trade ows are not determined. In the non-tradable sector, prices are determined endogenously by the equilibrium of domestic supply and domestic demand.

The previous equilibrium conditions hold for all time periods and all vectors of aggregate state variables. We are also interested in dening a stationary equilibrium, which we will use in simulation exercises to study trade shocks. In a stationary equilibrium, there are rm-specic productivity shocks _Aijt_ and worker-specic utility shocks _εlt_ , but there are no aggregate prot shocks _bjt_ . As a consequence, while we observe uctuations in rm-level labor demand, investment and output, and in worker-level mobility, there are no uctuations at the aggregate level. Labor allocations, aggregate capital, output, wages, prices of non-tradables, and the distribution of rms are time-invariant in a stationary equilibrium.

# 3 Estimation

In this section we discuss the estimation of the model structural paramters. We use two sources of data from Argentina. The rst is the Encuesta Industrial Anual (EIA, Annual Industrial Survey), from INDEC. This is a panel of rms spanning the period 1994 to 2001, with information on investment, disinvestment, employment, revenue, production and materials. The second source of data is the Encuesta Permanente de Hogares (EPH, Permanent Household Survey), a standard labor force survey with a panel component, also from INDEC. It spans the period 1996 to 2006 and has information on sector of employment (or non-employment) and wages. We use the panel component of the survey to compute movements of workers across sectors. Firms and workers are aggregated into 6 sectors: food and beverages; textiles, apparel and leather; nonmetallic minerals; primary metals and fabricated metal products; other manufactures; and non-traded goods. Workers are further grouped into non-employment. We refer the reader to on-line Appendix A for more details 14