rafmacalaba/datause-encoder-data huggingface dataset:
6.9 percent (Figure 3). While unemployment in other Eastern European countries fluctuated as expected with the economic boom of the 2000s and the downturn since 2008, in Romania unemployment responded comparatively little to the prevailing economic conditions. This was particularly beneficial during the crisis. When unemployment increased sharply in other countries, the response by many Romanians was to migrate, with a strong moderating effect on the unemployment rate (Figure 4).

**Figure 1 Estimated Romanian Population**

<!-- Start of picture text --> 22,500<br>22,000<br>21,500<br>21,000<br>20,500<br>20,000<br>19,500<br>19,000<br>18,500<br>18,000<br>17,500<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> _Sources: Eurostat; INSEE; Authors’ estimations_

# **Figure 2 Estimated Romanian Migrants**

<!-- Start of picture text --> 3,000 16<br>14<br>2,500<br>12<br>2,000<br>10<br>1,500 8<br>6<br>1,000<br>4<br>500<br>2<br>0 0<br>2000 2010<br>Number of migrants (000s, lhs) Migrants as % population (rhs)<br><!-- End of picture text --> _Source: World Bank Migration Tables_ 3
distribution but had a negative effect on the upper half, as in the case of men. The contribution of the returns to unobserved skill to explain changes in the upper and lower half of the distribution are shown in Figure 5. As for the case of men, the graphical series underlines the considerable difference in both parts of the distribution.

The fact that composition effects of observed skills played only a small (or partly even negative) role is due to the fact that the within-group variances of experience-education groups (especially educational groups) converged considerably in the 1990s, mitigating the effect of skill-upgrading on the variance. As Tables 6 and 7 show, the variance between 1992 and 2002 increased within each experience group for men and women. Since the size of experience groups does not change drastically (Table 4), the composition effect with respect to these groups is small. On the other hand, the sizes of educational groups change much more over the course of the 1990s (Table 2). Here, the within-educational group variance increases strongest for workers with complete or incomplete primary education. This effect is especially strong within the female workforce. As these groups are shrinking in the population, the composition effects work towards decreasing the variance.

The decomposition shows that 1980s and 1990s, returns to education and to unobserved skills move in tandem. It thus seems plausible that both phenomena might be driven by the same underlying processes in the case of Argentina.

In line with the analysis, the changes observed for Argentina in the 1980s may have occurred via the technology channel. It does not seem likely that a form of ‘negative technological change’ was at work, the explanation may rather lie in the level of technology present, and the supply of and demand for skills: the 1980s were characterized by constant waves of crises and instability. Average GDP growth 1980-1990 was around minus one percent. Over the same period, capital formation was reduced by 50 percent in real terms.<sup>12</sup> There was certainly no positive technology shock during this period – most likely, the technological level was stable, if not decreasing due to lack of replacement and maintenance in times of crises. If, in line with educational upgrading, “technological skills” were improved in the population<sup>13</sup> and the stock of technology to operate deteriorated, we would expect increasing supply of, and falling demand for those skills and consequently falling returns.

> 12 Own calculations based on World Development Indicators.

> 13 Remember that we assume a stable distribution of unobserved skills, conditional on observed skills, in order to interpret changes in the residual as changes in the returns to unobserved skills.

18
# **QUALITATIVE DATA**

The qualitative analytical methodology was designed to inform and elucidate unclear or ambiguous quantitative findings, and to enhance understanding of project impact, the determinants of success or failure, and the channels through which changes in productivity were achieved. The areas of potential impacts we considered to explain possible ways through which the program affected productivity are displayed in Table I.

## [INSERT TABLE I]

The sampling frame for the study consisted of those communities covered in the household surveys that would be used in the quantitative exercises. These communities were selected using a geographic stratification (by district) and selected at random from a list of all communities. Interview instruments were organized ex-ante, following a structure similar to the household survey except using open ended questions. The instruments were designed to capture any possible associations between the project intervention and changes in productivity, and allowed for identification of patterns across respondents on key questions and/or topics. The categories of respondents were designed to ensure a comprehensive representation of key project stakeholders (such as, World Bank project management team, field implementation staff, beneficiaries and non beneficiaries) and to guarantee that a balanced source of information about the project vision, specifics, accomplishments and failures were represented.

The study design used a cause-effect approach to assess the relevance of the predetermined factors hypothesized to influence productivity as compared to other factors outside the project scope. This was done by focusing on changes experienced by beneficiary (treatment) and non-beneficiary (control) participants in the same project intervention areas during a specific period of time. A comparative analysis was done between both groups to provide a more ba11
high informality. As shown in Table 6, a one standard deviation increase in the share of returnees reduces average wages by 13.4 percent for formal firms operating in more informal sectors. Then, when we look at the effects on wages separated by gender (table 7), the estimated impacts are similar between wages of men and women working in firms that operate in informal sectors. Overall, our estimations indicate a reduction in both average salaries of around 15 percent.

Finally, we explore the differences in the effects of returned migrants as a demand shock. Since the economic sector can be obtained at the firm level, we document the demand shock using only firm entry as outcome. The results are presented in Table 8. Columns (1) and (2) present results of regressions using the subsets of firms in sectors that are predominantly formal or informal, respectively. We find no statistically significant effects of returned migrants on the extensive margins of firm growth by sector. However, the estimated coefficient using data of firms in the informal sector suggests that an increase in the share of deportees by a standard deviation can increase the firm’s entry by approximately 10 percentage points, which corresponds to an increase of 15 percent relative to the mean of the dependent variable.

In sum, our results indicate that most of the results are likely driven by the informal labor market. The larger effects among formal firms operating in predominantly informal sectors are consistent with the characteristics of the returned migrants in El Salvador. Considering that at least 71.3 percent of them have no more than primary education, most of them do not speak English, and around 76 percent of them were caught at the border and thus have to pay the cost of trying to get to the United States (Table 2), it is plausible that upon arrival in El Salvador they take lower tier jobs, which are most likely to be available in the informal sector.

# **VII Discussion**

Our results indicate that large deportation inflows have negative effects on the labor outcomes of the formal sector in El Salvador. We document that these effects are predominantly concentrated 18
# Are Automation and Trade Polarizing Developing Country Labor Markets, Too?<sup>_∗_</sup>

William F. Maloney<sup>_†_</sup> Carlos Molina<sup>_‡_</sup> December 16, 2016 Keywords: Labor markets, polarization, globalization, automation, developing countries JEL: J21, O33, F16

> _∗_ Very preliminary, please do not cite without permission. We thank David Autor for helpful discussions.

> _†_ World Bank, Washington, DC 20433, United States. Corresponding author E-mail: `wmaloney@worldbank.org`

> _‡_ Universidad de los Andes, Department of Economics, Bogot ́a, Colombia. E-mail: `ca.molina11@uniandes.edu.co`
<!-- Start of picture text --> B.S<br>636 eere<br>4%<br>296<br>ons<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>—+#— Innovative sources as a share of gross ODA mobilized + bonds<br>—S— Innovative concessional sources as a share of gross ODA mobilized<br>kr Innovative uses as a share of of official flows<br><!-- End of picture text -->
# **Table 1: Main elements of São Paulo State Water Law 7.633/1991 and complementary legislation**

## **_Objective of the State Water Resources Policy:_**

- Reliability of water availability for current and future generations, at the desired level of water quality

**_Principles:_**

- Integrated water management, with the river basin as the planning unit

- Water as a finite and fragile resource

- Water as an economic good

- • Decentralized and participatory management

## **_Organizations:_**

- State Water Resource Council (CRH): deliberative body, with stakeholder participation, in charge of supervising and regulating the State Water Resources Management System

- CORHI: CRH’s Executive secretariat; technical body in charge of elaborating the state water resources plan and promoting institutional integration among all the state water related institutions.

- • State Water Management Agencies: in charge of water use and pollution control and in implementing the Water Resources Management System

- FEHIDRO: São Paulo’s Water Management Fund, supervised by an Advisory Council (COFEHIDRO), to be used mostly for projects and activities approved by the basin committees.

- • River Basin Committees: deliberative stakeholder bodies with decision-making and regulatory powers • River Basin Agencies: their technical and administrative arms

## **_Instruments:_**

- Basin water resources plans / State water resources plan(*)

- Bulk water use permits

- Bulk water charges(*)

- Classification of water bodies according to predominant use and water quality standards

- • State water resource information system(*)

(*) These instruments were first introduced by the State Law. The remaining tools already existed, but either were not functioning as planned or their structure and functioning was modified significantly by the Law.

# **_6.3. Implementing the State water law in the Alto-Tietê Basin._**

The 1991 water law called for the immediate creation of two basin committees, in the Alto-Tietê and Piracicaba-Capivara-Jundiaí hydrographic regions<sup>13</sup> . The Alto Tietê committee, however, was only formally established in November 1994, a result of a deliberate effort by the state _técnicos_ (technical staff) to mobilize municipal government and, especially, civil society.

> 13 Interestingly, what is referred to as the Alto Tietê “basin” does not actually correspond to an entire basin. The state water _técnicos_ that designed the regions understood that management units based only on hydrological factors would be inappropriate for the new model. In 1994, they defined twenty-two management units that mix both hydrographical and socio-economic criteria. The result is that there is much interdependence among basins, including the Alto-Tietê which corresponds to the upper part of the Tietê river basin. The territorial scales in water management in Brazil are discussed at length in FormigaJohnsson, 2004.

17
control communities are immune from contamination. However, given that the PROGRESA program covered the control localities after November 1999, it is also conceivable that households in the control communities might alter their behavior in anticipation of coverage by the program.<sup>25</sup> If that were to be the case then the RDD estimates of program impact may be the ones that are closer to the true impact of the program instead of the experimental estimates used so far as the benchmark of “true” program impact. One way to assess the validity of maintained hypothesis that households in the control localities were unaffected by the presence of the program in neighboring villages is to focus on the control localities and look for possible impacts on households that would be eligible for program benefits in the case their villages were to be covered by the program. If anticipation effects were to contaminate the control communities then one might expect this to occur in the later rounds that are closer to the date of coverage of the control communities by PROGRESA.

Table 6 presents the 2DIF, CSDIF (average and local) and RDD estimates of program impact in the control localities using the eligible households as a treatment group and the noneligible households from the same control localities as a comparison group (groups B and D, respectively). The estimates suggest that the existence of the program in nearby communities did not have any significant effect on the school attendance or work activities of boys and girls from eligible households. Moreover, it is important to note that both the RDD estimates which are local estimates of impact around the threshold as well as the CSDIF and 2DIF estimates, which are average estimates, yield the same general answer. Leaving aside some of the prean estimate of the counterfactual term.

> 25 It has been impossible to establish whether the households in the control localities knew in advance of their eventual coverage by the PROGRESA program. However, Attanasio, Meghir and Santiago(2001) provide evidence that such “announcement” effects induced in the PROGRESA control villages are important.

25
Policy Research Working Paper 7769

# **Abstract**

Considerations of risk and vulnerability are key to understanding the dynamics of poverty in rural Malawi. This study measures vulnerability to consumption shortfalls and analyzes its sources using a two-period panel of 2,789 households, drawn from the 2010 Third Integrated Household Survey and the 2013 Integrated Household Panel Survey. The results show that in 2010 two-fifths of all households had a chance of at least 40 percent of falling below the poverty line in the future. The results show that many households in rural Malawi are vulnerable to poverty, although, as with many other studies of rural areas in other countries, much of the vulnerability is caused by chronic poverty. Nonetheless, risks, particularly rainfall and loss of off-farm employment, are also important in explaining why poor households remain poor, and why some non-poor households are more likely to fall into poverty in the next period. Household wealth and agricultural assets can protect households from falling into poverty and reduce the severity of the fall when shocks occur. However, there is little evidence to suggest that other strategies to reduce vulnerability are effective.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at adelafuente@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **2 Conceptual Framework**

In this section, we provide a concise theoretical framework underlying the trade effects of standards. The aim is to highlight the important theoretical predictions that underpin our empirical analysis and help to make sense of our findings.

The introduction of quality standards imposes additional trade costs on both domestic and foreign firms. These extra costs include enforcement costs, process adaptation costs, and sourcing costs (Ing et al., 2016; De Melo and Shepherd, 2018). Enforcement costs encompass the efforts devoted by firms to show compliance with the standards, for example, the time devoted by staff to process paperwork, and inspections by officials from enforcement agencies. Process adaptation costs relate to capital adjustment costs incurred by firms to meet the required standards. Sourcing costs arise when firms are compelled to transition from low-quality intermediate inputs to high-quality ones to comply with the new standards.

Figure 1: Trade effects of standards in a small open economy <!-- Start of picture text --> P<br>S 1 S 0<br>P 1<br>Standard<br>P 0<br>D 0 D 1<br>Q<br>0<br>x S x S x D x D<br>0 1 0 1<br><!-- End of picture text --> Source: adapted from Swinnen (2016) How do these costs affect trade flows and market structure? For simplicity, let us assume the case of a small open economy that introduces a quality standard. Once the standard gets enforced, it affects the existing market equilibrium through a shift in both the demand and supply curves (Figure 1). The standard will cause a leftward shift of the domestic supply curve from _S_ 0 _− S_ 1. This shift reflects the increased cost of production for producers. However, because the standard addresses information asymmetries between producers and consumers, it will increase demand from _D_ 0 _− D_ 1. Parts of the increased domestic demand are met by imports (i.e., shift from _X_ 0<sup>_S−X_</sup> 0<sup>_D_to</sup><sup>_X_</sup> 1<sup>_S−X_</sup> 1<sup>_D_)fromcountrieswhereproducersmeetthe</sup> standards enforced in the importing country.

4
At the other end of the spectrum is evidence which finds causal impacts between precipitation and agriculture (Auffhammer _et al._ 2006; Rowhani _et al_ . 2011; Fishman 2016; Lesk et al. 2016; Zaveri, Russ and Damania 2018); industry and firm performance (Islam and Hyland 2018); urban growth (Desbureaux and Rodella 2018), and health (Maccini and Yang, 2009; Hyland and Russ, 2018).

Here, we argue that spatial aggregation of weather data at the country level explains why impacts of precipitation on GDP are found to be fragile and inconsistent at the macroeconomic level and yet are found to have powerful economic effects at the level of individual sectors. We demonstrate that highly aggregated statistical models may underestimate the impact of rainfall due to measurement error, since rainfall is spatially heterogenous, and much more so than temperature (Lobell and Asseng, 2017). Aggregation masks heterogeneity of impacts across regions (Barrios _et al._ 2010) -- a problem termed Modifiable Areal Unit Problem, or MAUP in the economic geography literature (Briant et al. 2010). Our results cast light on this issue by suggesting the need for spatial disaggregation to capture local heterogeneity. This remains an area that warrants further research.

Rainfall and water availability exhibit spatial variability – that is considerably higher than that of temperature. Indeed, the wettest areas in Alaska (4,880mL year<sup>-1</sup> ) received 4,830mL more precipitation on average per year between 1990 and 2014 than the Mojave Desert (52mL year<sup>-1</sup> ). Such differences are also observed in the major urban centers of the United States, with a difference of 1,800mL year<sup>-1</sup> near Miami, Florida and 72mL year<sup>-1</sup> in the Coachella Valley, California. And even higher spatial variability between the driest and wettest areas is found in India (10,083mL year<sup>-1</sup> ), Colombia (7,138mL year<sup>-1</sup> ), Peru (6,518mL year<sup>-1</sup> ) or Papua New-Guinea (6,476mL year<sup>-1</sup> ). Temperature also varies within countries (e.g., a 40 oC difference between the coldest parts of Alaska and Miami, Florida) but globally, the within-country coefficient of variation is twice as large for precipitation than it is for temperature. Therefore, more heterogeneity is lost for precipitation than for temperature in studies at the country level, causing important statistical distortions that have direct impacts on the results.

We use recent global sub-national aggregated output data (Kummu, Taka and Guillaume 2018a) derived from Gennaioli et al. (2013) and show a robust impact of rainfall on local GDP growth between 1990 and 2014. Following the literature, we use fixed effects panel regressions to isolate the impact of weather variables from time invariant factors or common time-varying factors that could be correlated with both weather and aggregate economic outputs.

At the smallest spatial resolution available (0.5 degree), we find a concave relationship between precipitation and GDP growth. Rainfall increases total economic productivity at a decreasing rate, until it reaches a peak 3
Table 3: Descriptive Statistics

|||Abidja|n|Gr|and Bas|sam||Sampl|e|
|---|---|---|---|---|---|---|---|---|---|
||Male|Female|Dif|Male|Female|Dif|Male|Female|Dif|
|**PRO-Jeunes Training choice**||||||||||
|Wage-employement track|0.05|0.05|-0.01|0.04|0.04|0.00|0.04|0.05|-0.01|
|Generalist entrepreneurial track|0.20|0.37|-0.17**|0.37|0.60|-0.23|0.23|0.41|-0.18***|
|Vocational training track|0.76|0.58|0.18**|0.59|0.36|0.23|0.73|0.54|0.19***|
|Vocational training in energy|0.50|0.33|0.17***|0.56|0.34|0.23|0.51|0.33|0.18***|
|Vocational training in information and communication<br>technologies (ICT)|0.25|0.22|0.03|0.01|0.00|0.01|0.21|0.18|0.03|
|Vocational training in trade|0.01|0.03|-0.02|0.01|0.03|-0.01|0.01|0.03|-0.02*|
|**Sociodemographics**||||||||||
|Age of the respondent|22.41|22.46|-0.05|21.42|20.85|0.57|22.25|22.19|0.06|
||**_SD_**<br>_3.07_|_3.35_||_2.81_|_2.94_||_3.06_|_3.34_||
|Number of dependent children|0.34|0.67|-0.33***|0.19|0.79|-0.61**|0.31|0.69|-0.38***|
||**_SD_**<br>_0.80_|_0.96_||_0.58_|_1.09_||_0.77_|_0.98_||
|Household is in the top 2 wealth quintile (by cohort)|0.44|0.38|0.06**|0.33|0.32|0.02|0.42|0.37|0.05**|
|**Education and training**||||||||||
|Years of education|10.74|9.74|1**|9.12|8.28|0.84|10.48|9.50|0.98***|
||**_SD_**<br>_3.20_|_3.99_||_3.68_|_3.93_||_3.34_|_4.02_||
|Any training|0.23|0.23|0.00|0.23|0.28|-0.06|0.23|0.24|-0.01|
|Already received training in EICT in the past|0.11|0.09|0.02|0.09|0.05|0.04|0.11|0.09|0.02|
|Want to be trained in energy or ICT|0.75|0.55|0.20**|0.58|0.34|0.24|0.72|0.51|0.21***|
|**Employment and earnings**||||||||||
|Wage-employed in the last 30d|0.68|0.43|0.26**|0.60|0.51|0.09|0.67|0.44|0.23***|
|Self-employed in the last 30d|0.35|0.40|-0.04|0.29|0.47|-0.18|0.34|0.41|-0.07*|
|Had a paid work in the last 30d|0.72|0.53|0.19**|0.65|0.60|0.05|0.71|0.54|0.17***|
|Worked in EICT in the last 30d|0.20|0.05|0.15***|0.13|0.07|0.06|0.19|0.06|0.14***|
|Revenues earned in the last 30d (USD PPP)|290.02|109.27|181***|221.54|118.57|103|279.01|110.84|168***|
||**_SD_** _391.64 _|_184.95_||_320.57 _|_241.02_||_381.84 _|_195.43_||

**Network size and characteristics**