rafmacalaba/data-use-mentions-extended huggingface dataset:
real rural wages. As noted above, an important variable in the set of equations leading to the short-run equilibrium of the NEG model is the concept of "market access" or "market potential." A prefecture with strong market access will, typically, possess pecuniary externalities which will tend to enhance wage levels. Fundamentally, market access is shaped by transport costs. We compute the changes in estimated travel times between each pair of prefectures as the basis for calculating transport cost, and, therefore, market access, changes.

NEN impact evaluation is constrained by necessary trade-offs between geographic specificity and data availability. Since consistent time series data for our impact measures and other relevant variables are not available for a geographically comprehensive sample of prefectures, and because the NEN can be expected to have had substantial spatial general equilibrium effects which might otherwise be difficult to capture, we adopt a counterfactual approach.

> 5 The data set and the construction of our GIS road networks data are discussed in more detail in Appendix A. 6 Our analysis focuses only on mainland China. The only province not included is Tibet. This is because of data constraints.

4
link between urban and rural patterns of welfare as mediated through the labor market, an ILO study based on data from 1964 to 1985 demonstrated that except for intermittent increases, real wages in Nigeria declined sharply between 1973 and 1985 (Jamal and Weeks, 1988). The periodic jumps were observed between 1973 and 1975 and again between 1980 and 1983, the first jump corresponding to the oil price shocks of the early 1970s. Not withstanding these exceptions, real wages in 1985 were a little more than half of the 1973 level. Some data available for 1980 to
# **1 Introduction**

The political and economic crisis in Venezuela has given rise to a massive exodus, estimated at 4.3 million people (as of the end of 2019). Many Venezuelans sought refuge in neighboring countries. It is estimated that 1.5 million Venezuelans had settled in Colombia by the end of 2019, and over 2 million elsewhere in Latin America.

Naturally, Venezuelan migrants have sought employment in the host countries but, because of their recent arrival and their transient status, our knowledge of their working conditions is scant. Our paper aims to fill this gap focusing on Ecuador, where almost 400,000 Venezuelans have settled and many more have transited through the country on the way to other destinations. More specifically, we provide the first analysis of the labor-market conditions of Venezuelan migrants in Ecuador, based on a new nationally representative survey of this population that also includes information on the Ecuadorans living in the same localities. The survey (known by its Spanish acronym EPEC) was promoted by the World Bank and the government of Ecuador and implemented during the summer of 2019. The design and scope of the survey are unique in the context of the countries across Latin America hosting Venezuelan migrants.

By providing detailed information on the skills, employment and wages of Venezuelan migrants, as well as the Ecuador-born population in the same local areas, we are able to analyze the effects of several policies aimed at improving the economic conditions of migrants while alleviating the adjustment costs on the native population and maximizing the net economic gain for the receiving country.

Our analysis delivers several important findings. First, we estimate that the inflows of Venezuelan migrants (between 2016 and the summer of 2019) amounted to 2% of Ecuador’s population, reaching much higher concentrations in several provinces. The data also show that Venezuelan migrants are young (aged 32 on average) and highly educated, compared to Ecuador’s working-age population. They also have very high employment rates, about 17 percentage-points higher than Ecuadorans in the
24 cannot be applied to the European transition at least<sup>20</sup> . This type of consideration implies that when looking at the links between the status of women and fertility, it may not be enough merely to examine the common indicators of status such as education and employment, since the causal factors involved may be quite different, if less easily measurable<sup>11</sup> .

The evidence on fertility and employment is extremely difficult to interpret; while some have taken the lack of an overall strongly consistent negative relationship to argue against the education/employment route (Cleland and Wilson (1987)), other surveys of the literature are more equivocal (Simmons (1985), Youssef (1982), Standing (1976)). In general, negative relationships between fertility and female employment have been observed more in urban employment than in rural employment, more in non-agricultural than in agricultural employment, and more at higher levels of occupational status. Further, a commonly observed differential is that fertility for women who have worked only after marriage is higher than for women who have worked before marriage. In the urban professional sector the incompatibility of wsork with child care is an important factor in tOBy contrast, the contemporary transition in non-European populations would appear at first sight to follow the more classical pattern, fertility decline not starting until morality levels have reached a relatively low level (e.g. Bulatao (1984) pp 18-19). However, mortality data from the developing world is even less complete than fertility data, and so these statements must be treated with caution (Brass (1975) p 50, Mott (1982) pp 7-8).

1 tSee Kreager (1986) and Polgar (1972).
migrated before expect to earn less than the ‘high’ information provided of $5,700. Virtually no one expects to make less than the ‘low’ information provided of $3,000. However, the official figures may not reflect the actual earnings of migrants abroad as it does not include over-time pay, which is often a large share of a migrant worker’s compensation abroad.

In any case, these comparisons, though not perfect, are suggestive of large information gaps between the earnings expectations of the inexperienced potential migrants and the actual earnings they are likely to accrue once abroad. The actual extent of misinformation for inexperienced potential work migrants is likely to be bigger than 26 percent but smaller than that suggested by the comparison with the official figure.

# **Misinformation on expected mortality rate**

Contrary to the popular notion, potential migrants seem to overestimate their mortality rate abroad by a large factor. The average expected two-year mortality rate of inexperienced migrant is 28 per 1000, which is 68 percent higher than the expectations of those who have migrated before. Figure 3 shows that not just the mean, but every quantile of expected mortality rate of inexperienced potential migrants is higher than the corresponding quantile for those who have prior migration experience. For instance, the median expected mortality rate for the experienced is 10 per thousand, whereas it is 5.8 for those who have migrated before. However, these expectations are much higher compared to the actual mortality rate faced by the migrants once abroad. The deaths data from the Foreign Employment Promotion Board, the authoritative data source for mortality of Nepali workers abroad, and migration data from the Census and the Department of Foreign Employment show that the two-year mortality rate of Nepali workers in these destination countries is 1.3 per thousand.<sup>23</sup> Only 3 percent of inexperienced potential migrants and 11 percent of those who have migrated before expect the mortality rate to be lower than what it actually is. The overestimation at the mean is 21
divisions within the household and its members’ labor choices. In addition to the household questionnaire, we collected information at the rural district level on issues affecting the community as a group. This community survey was collected for each rural district, and includes information about social and public infrastructure, incidences of land conflict and the presence of illegal armed groups. It also generated the necessary information for creating a conflict time line.

# **3.2 Colombia: 50 years of conflict**

Since the 20<sup>th</sup> century, Colombia has been confronted by two major internal conflicts. The first, known as _La Violencia_ , started in the middle of the 1940s, and intensified with the assassination of the populist leader Jorge Eliécer Gaitán in 1948. The conflict emerged from the political struggles between the two major, traditional political parties in Colombia, the Liberals and Conservatives. Regional land disputes and power struggles that had been latent for decades combined with political disputes to fuel the conflict in rural areas as well (Oquist 1980). In 1958, Liberals and Conservatives negotiated a power-sharing agreement that paved the way for a peace deal and halted armed confrontations.

The power-sharing agreement effectively eliminated political violence, yet land disputes and regional power struggles remained dormant. By the end of the 1960s, guerrilla movements promoting agrarian land reform emerged in the rural areas of the country. The presence of rebel groups was confined to isolated rural regions of the countries, and actions were occasional and limited to attacking government forces.

The dynamics of the conflict changed dramatically with the appearance of the illegal drug trade at the end of the 1980s. Resources from illicit drug trading provided massive funds with which rebel groups were able to operate. These groups soon began launching attacks on large land-owners and drug barons in order to extract additional resources. Kidnapping, extortions, the taxation of cocaine production, mining and cattle ranching provided additional sources of financing. These attacks and the flow of resources from illegal drug trading contributed to the creation of right-wing
> As expected, employing a lower pass-through rate leads to considerably lower poverty estimates possibly reflecting household capacity to smooth consumption in face of a shock. However, the possibility for households to continue smoothing their consumption over a prolonged conflict spell is dubious. Given the amount of uncertainty around the most appropriate passthorough rate to be used in the case of Syria, we can use the poverty projections based on different values of the pass-through as providing likely lower and upper bounds for the extent of poverty in the country.

> 32 Corresponding estimates obtained using poverty lines in 2011 PPP  ($3.2  and $2.15) are provided in Annex 5. 33 See section 4.1.1.

> 34 Corresponding estimates obtained using poverty lines in 2011 PPP  ($3.2  and $2.15) are provided in Annex 5.
of the electoral census).<sup>14</sup> Table B.4 describes the five presidential elections within the study period, the elected candidate, and the type of election (ordinary or due to an extraordinary event). Appendix B provides detailed information on the outcome construction and data sources. Figures C.2 and C.3 present results for the geographic distribution of election outcomes before and after 2013, showing a sharp nationwide decrease in electoral turnout and opposition support.

One relevant concern is the possibility of data manipulation. To address this, we search for abnormal data patterns consistent with manipulation, following the analysis first proposed by Klimek et al. (2012). The authors propose a test that consists of making two-way scatterplots of election results, illustrating the relationship between electoral turnout and support for the incumbent by municipality, which in our case means support for Ch ́avez and Maduro. Manipulation is evident graphically when there is a clear linear positive trend with concentration along the diagonal, indicating that for each vote added to the electoral turnout, support for the incumbent systematically increases by one vote as well. Elections with no manipulation typically show no obvious correlation between the variables.

The results of this exercise, illustrated in Figure B.2, suggest there was no systematic manipulation before 2018. In fact, the results are consistent with international electoral reports from the Carter Center for 1998–2021 (Carter Center, 2024). However, there seems to be evidence of potential electoral manipulation in 2018.<sup>15</sup> Consequently, we include the 2024 presidential elections in our main specifications in order to validate our results. The opposition directly collected results of the 2024 elections and thus addressed concerns about manipulation by the incumbents.<sup>16</sup> This exercise supports our main findings and we com14We fix the electoral census at the year 2000 to keep the denominator constant in our estimates and to facilitate interpretation of the coefficients. Nevertheless, we also verify that our
# Measuring Urban Economic Density<sup>∗</sup>

J. Vernon Henderson<sup>_†_</sup> Dzhamilya Nigmatulina Sebastian Kriticos Keywords: Economic density, agglomeration, scale economies, city definitions, Africa, economic development, Landscan, land use.

JEL: J24, J31, O15, O55, R1, R3 ∗This work was supported by the World Banks Strategic Research Program funded by the UK Department for International Development and by an Africa Research Program on Spatial Development of Cities at LSE and Oxford which is funded by the Multi Donor Trust Fund on Sustainable Urbanization of the World Bank, and by the UK Department for International Development. We thank Patricia Jones for sharing value added, location, employment and industrial code data for Kampala firms from the Uganda Business Inquiry survey of 2002.

> _†_ **Corresponding author:** _J.V.Henderson@lse.ac.uk_ . All authors are at LSE.
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|TABLE6. Decompos|ition of the Public|-Private Wage|GapBased on|our Simulati|ons|
|---|---|---|---|---|---|
||1993|1998|2002|2006|Change<br>1900–2000s|
|**Real Gap**|0.21|0.21|0.48|0.53|0.30|
|**Simulation 1**||||||
|Price effect|−0.36|−0.23|0.11|0.18|0.44|
|Selection|0.56|0.44|0.37|0.35|−0.14|
|**Simulation 2**||||||
|Price effect|−0.08|−0.01|−0.06|0.03|0.03|
|Selection|0.28|0.22|0.54|0.51|0.27|
|**Average 1–2**||||||
|Price effect|−0.22|−0.12|0.02|0.10|0.23|
|Selection|0.42|0.33|0.45|0.43|0.07|

_Source:_ Author's calculations based on VLSS and VHLSS data.

Compared with the Oaxaca-Blinder method, our model offers a much more comprehensive view of selection on workers' characteristics. Hence, our model strengthens the case against explaining the widening of the public-private sector gap only by selection. The fact that α is significantly inferior to one in the first panel suggests that neither a simple wage equation model nor a fixed effect model (which, de facto, assumes constant returns to the unobserved characteristics across the sectors) could adequately describe
# F. Model selection using the Lasso

Model selection in small area estimation remains an unsettled issue and has traditionally been treated as both art and science. Practice can vary widely. Tzavidis et al (2018), for example, select only 6 covariates present in Mexican census and survey data, based in part on the Akaike Information Criterion (AIC) from a standard OLS model. Most small area applications using ELL, however, use a considerably larger set of variables. Zhao and Lanjouw (2008), for example, note that many successful applications include less than 20 household level variables. However, they also advocate including cluster-level means, which can boost the number of variables significantly past 20 or 30.

In recent years, the least absolute shrinkage and selection operator (LASSO) has become an increasingly popular tool for selecting prediction models. Small area estimation offers a natural application of the “post-lasso” procedure, in which the variables selected via the lasso are used as predictors into the mixed model used to estimate the parameters of the small area estimation model.<sup>19</sup> The post-lasso procedure offers the benefit of a convenient and data-driven approach to model selection that, in its most popular variant, maximizes out-of-sample predictive accuracy, while roughly equalizing in and out of sample R<sup>2</sup> to prevent overfitting the model to the sample. The LASSO framework formalizes and extends the advice in Zhao and Lanjouw (2008) to use out-of-sample cross-validation to ensure that the model is stable and predicts well out of sample. Several varieties of LASSO have been implemented in popular statistical software. We use the plugin lasso estimator, also known as the rigorous lasso, implemented in Stata version 16 (StataCorp, 2019). This variant of lasso is computationally faster and more parsimonious than others that use cross-validation to minimize mean out-of-sample prediction error, and the resulting models maintain strong predictive power.<sup>20</sup>

# G.  Benchmarking

Benchmarking refers to the procedure of forcing
based on hierarchy of knowledge. Peiex is a program from the Brazilian Export Promotion Agency (Apex-Brasil) that focuses on enhancing Brazilian small and medium firms’ (SMEs’) competitiveness by providing coaching and consulting on best management and production practices.<sup>5</sup> This paper is closely related to Bloom et al. (2012), who find significant effects of a management consulting program on firms’ performance in India.<sup>6</sup> We measure the way firms organize knowledge based on occupations that require different levels of skills and competencies, following the conceptual framework in Garicano (2000) and Caliendo and RossiHansberg (2012). Consistent with this framework we show that firms organize knowledge following a hierarchy in a pyramid shape, which is compatible with the hypothesis that production workers learn the standard production problems and managers deal with more exceptional ones. Our analysis uses a unique matched employer-employee firm-level data set covering the full manufacturing sector in Brazil with a panel of about 310,000 firms (an average of 236,645 firms per year), with information on more than 7 million employees, from 2007 to 2010, and more than 5,000 treated firms.

This measure of hierarchy of knowledge takes into account different levels of competencies and skills of workers limited to 4 layers. If a firm already has 2 layers and decides to hire more workers at the same layers, which means same occupations previously available at the firm, we assume that there is no change in the knowledge-based hierarchy of the firm. Hence, this is not about increasing the number of directors or manager positions itself; neither are we assuming that the more managers, the better. Instead, increasing the number of layers in this case means that a firm is incorporating workers with different levels of competency and specialized knowledge in solving different problems at the firm. The idea is that controlling for the number of employees, firms that expand reorganizing knowledge can become more competitive by having lower marginal cost as a function of
Prior to 1984, the measurement errors in the FAI are essentially reproduced in the sectoral data. The only information available for a sectoral breakdown comes from the FAI data. Consequently, there is no possibility to reduce the differences between the data for fixed asset investment and that for GFCF.

Between 1985 and 2002, residential property development became increasingly important. So, using an estimate of residential GFCF rather than residential FAI results in the sectoral estimates of GFCF moving slightly closer to the aggregate GFCF data.

Between 2003 and 2015, the aggregate FAI data become essentially useless as a measure of gross fixed capital formation, but the estimated sector GFCF tracks aggregate GFCF quite well. On average, the yearly estimates of sectoral GFCF are less than 2% different from the aggregate GFCF data. Over the whole period, the cumulated sectoral GFCF is practically equal to the cumulated aggregate GFCF series – the difference being just 0.02%. However, in the two most recent years (2014 and 2015), the difference has become much larger: rising to 10% for reasons that cannot be determined. However, this difference will have little impact on the estimates of the capital stock.

Overall, between 1985 (when the reliability of investment data improved) and 2015, the annual sectoral estimates of GFCF differ from the aggregate estimate of GFCF by just 1.7%, though the standard deviation of the difference is 4.7%. Thus, the methodology for estimating sectoral GFCF gives a good basis for evaluating the development of the sectoral capital stock over the medium term but does not accurately reflect the annual year-to-year changes in GFCF and the capital stock.

In order to remove both the year-to-year differences, a uniform scaling factor has been applied to the sectoral estimates to ensure that they sum to the aggregate GFCF data.

# **_Measuring GDP by sector_**

The above estimates of investment and capital stock have to be put into the context of the valueadded that is generated by the capital stock. The sectoral classification used here
12 one must in addition allow for the sampling design and structure of the survey into which _s_ consumption is being predicted. Hence, an additional sampling component (  _t_  1<sup></sup> _t_  1) must be added to the three error components arising from the standard SAE poverty mapping application. The size of the sampling error will depend on the design and sample size of the household surveys being analyzed.

It is important to note that alongside the error components described above, prediction error will also depend on underlying assumptions, notably that the relationship between consumption and covariates remains stable over time, and that covariates are defined identically across surveys.

# **_Minimizing prediction error through astute predictors and estimation techniques_**

As has been illustrated in the Great Indian Poverty Debate and shown empirically by Mathiassen (2009), one clear task facing the analyst is to select model specifications that keep model error to a minimum.<sup>8</sup> In addition, success of the procedure will depend on selection of surveys that maintain their survey and questionnaire design over time. The Demographic and Health Surveys provide one good example, exploited earlier by Stifel and Christiaensen (2007). Two further considerations are important: 1) the sensitivity of the poverty predictor to both upward and downward changes in income among those who are poor and those who are vulnerable to becoming poor, and 2) the likely stability of the relationship between the predictor and consumption over time.

> 8 Predicting poverty for rural and urban areas in one region in Mozambique, based on a small set of poverty predictors from the labor survey and an estimated poverty prediction model from an earlier household budget survey, Mathiassen (2009) finds that about 80 percent of the variance in the prediction error of the future poverty headcount could be attributed to model error.  About 20 percent was due to sampling error and only 1 percent due to the idiosyncratic error.