rafmacalaba/data-use-mentions-extended huggingface dataset:
and economic data). An appealing feature of the APC ME method is that it overcomes the potential arbitrariness of identification restrictions.<sup>5</sup> We limit our analysis to workers ages 20–65.

# **5.2 Analysis of long-term trends in job tenure**

The APC decomposition focuses on the evolution of job tenure over time and age cohorts. But the characteristics of the members of cohorts could change over time. For example, individuals within a group may change their family or fertility status, type of employment, occupation, industry, and/or mobility preferences. If such characteristics affect job tenure, compositional changes will partly explain the cohort effects estimated by the APC approach.

This section discusses our empirical strategy for an individual-level analysis of the evolution of job tenure conditional on workers’ and employers’ characteristics that can change over time. The primary objective of this analysis is to understand whether changes in individual and employment characteristics of the labor force might explain long-term trends in job tenure. The persistence of a trend in tenure after controlling for these characteristics would imply that other factors, probably operating at the economy-wide level, may drive the evolution of job tenure.

We use a binary probit model to estimate equation (2) separately for the three durations of tenure.<sup>7</sup> Consistent with previous studies, we estimate the probability of being on the job for less than 1

> 5 The least squares estimator proposed by <u>Deaton and Paxson (1994)</u> remains popular and widely used in economic analysis, despite its deficiencies.

> 6 <u>Bratberg, Salvanes, and Vaage (2010)</u> use a similar methodology to analyze changes in job stability in Norway.

> 7 The duration of tenure is derived differently in the data from the countries in our sample. The majority of surveys asked a question about the number of years in the current job (as discussed in the Data section). The
>25<br>4,000<br>20<br>3,000 15<br>2,000 10<br>1,000 5<br>0 0<br> 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Exports Imports Average tariff<br>Intel’s Arrival<br>Export Promotion<br>Import Liberalization FTZ Law Reform<br>Percentage<br> US$ millions<br>Capital Account Liberalization<br><!-- End of picture text --> Source: Prepared by the authors based on figures from Banco Central de Costa Rica.

It is worth noting that in 1984, along with economic liberalization, Costa Rica’s export products began enjoying free access to the US market (zero tariffs on almost all manufactured and agricultural products), thanks to the Caribbean Basin Initiative (CBI). Not surprisingly, therefore, the US market has grown in terms of its relative importance

> 2 Applying Lerner’s theorem (1936), Monge (1994) estimated that 0.66 out of each percentage point of protection granted to Costa Rica’s manufacturing sector during the import-substitution period was transferred as an implicit tax to the country’s export efforts.

> 3 See Corrales and Monge (1990), Lizano (1992), Céspedes and Jiménez (1997), and Monge and Lizano (1997).

6
governance and opinions on current events. Observations are also spatially identified at the municipality and again we focus on variation in the Venezuelan share of the population at the province level.

_Gallup World Poll (GWP)_ is a nationally representative opinion survey and has been conducted annually since 2006 in a wide range of countries around the world. The sample collected in Peru is a repeated cross-section of about approximately 1,000 observations each year. For our analysis, we use data from 2013 to 2020. The survey questions are centered around politics, governance and opinions on current events. We make use of several opinion indices provided by Gallup that measure individual opinions on various domains. Observations are spatially identified at the region level for Peru, which is our level of analysis in this case (there are 25 regions in Peru).

_PTP_ We measure the location of Venezuelan immigrants on a monthly basis from January 2015 to December 2020 using administrative data on the district Venezuelan immigrants register at with the Peruvian authorities to obtain access to social services. There are strong incentives to register as this is also a prerequisite for applying to obtain the PTP. This data only records monthly gross arrivals so we do not know the outflows of Venezuelans to other locations within Peru or out of the country entirely. However, in ENPOVE, 84% of Venezuelan immigrants in Peru report having lived in the same district during their entire time since arriving in the country. The data shows the arrival of 511,223 Venezuelans as of December 2020, which, while somewhat lower than estimates of the actual number of Venezuelans living in Peru, is quite substantial.

We also use data from the _National Census 2007 and 2017_ . We use the 2017 Census data to measure the share of workers in the formal and informal sector in each centro poblado as well as the total local population in each centro poblado, province and region. We use the 2007 data to construct both of our instruments discussed in more detail below as well as to create additional controls for the local economic
functional latrines on premises. According to the 2008 WHO guidelines, HCFs must have at least 4 functional, _Table 3:HCFs with limited/ basic WASH, by category_ improved sanitation facilities on premises.  Less than 2 percent of the community clinics met this WHO _WASH Category_ Freq. % criterion. Eighty‐five percent of the community clinics _Basic Water_ 4,201 35.33 have adequate handwashing facility with soap and _Limited Sanitation ≥ 1 latrines_ 8,352 70.24 water. However, it is unclear to what extent the _Limited Sanitation ≥ latrines_ 1,950 16.4 handwashing facilities were affected by the lack of _Limited Sanitation ≥ 4 latrines_ 181 1.52 functional water supply. _Limited Handwashing_ 10 190 85.7

|_WASH Category_|Freq.|%|
|---|---|---|
|_Basic Water_|4,201|35.33|
|_Limited Sanitation ≥ 1 latrines_|8,352|70.24|
|_Limited Sanitation ≥ latrines_|1,950|16.4|
|_Limited Sanitation ≥ 4 latrines_|181|1.52|
|_Limited Handwashing_|10,190|85.7|

**The CBHC survey revealed that a major challenge is**

**that many community clinics did not have any type of WASH facility within premises;** 2,280 community clinics (19 percent) did not have any functional water, sanitation or hand‐washing facility.<sup>6</sup>

**Of the 11,891 CCs surveyed, 3,660 community clinics reached the ‘limited’ JMP standard detailed in Figure 1.** In other words, only about 31 percent of the community clinics had at least a functional improved water source (handpump or piped), at least one functional latrine (type unknown), and a handwashing station with water and soap. If we set a stricter standard of at least two functioning latrines (one for each gender) as suggested
2. Estimating trade and employment elasticities within sectors

# 2.1 Do trade-employment elasticities differ for exports and imports?

Analysis for a sample of 48 OECD and non-OECD countries covering 45 sectors for the period 19952018 finds that export-employment elasticities within sectors are positive.<sup>11</sup> Specifically, a 10 percent increase in exports is associated with a 3.1 percent increase in employment (Figure 1, left panel).<sup>12</sup> On the downside, the potential for exports to contribute to job growth within sectors in the country sample was driven by the period before the global financial crisis (i.e., 1995-2006), while estimates for the more recent period (i.e., 2007-2018) are either insignificant or the chosen instruments for the trade variable not valid.<sup>13</sup> A possible explanation may be a growing capital intensity of export production, including the use of laborsubstituting technologies, during the more recent period (Hallward-Driemeier and Nayyar 2017).

**Figure 1: Trade-employment (left) and trade-income elasticities (right), by type of trade**

<!-- Start of picture text --> 0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>Exports Intermediate Final imports Exports Intermediate Final imports<br>imports imports<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE and TiVA data, covering 48 countries (see Appendix 2) and 45 sectors for the period 1995-2018. Note: Trade-employment (trade-income, resp.) elasticities at the country-sector level have been
Policy Research Working Paper 6019

# **Abstract**

The 2005 Paris Declaration on Aid Effectiveness sets targets for increased use by donors of recipient country systems for managing aid. The target is premised on a view that country systems are strengthened when donors trust recipients to manage aid funds, but undermined when donors manage aid through their own separate parallel systems. This study provides an analytical framework for understanding donors’ decisions to trust or bypass country systems. Empirical tests are conducted using data from three OECD-DAC surveys designed to monitor progress toward Paris Declaration goals.

Tests show that a donor’s use of the recipient country’s systems is positively related to: (1) the donor’s share of aid provided to the recipient (a proxy for the donor’s reputational stake in the country’s development); (2) perceptions of corruption in the recipient country (a proxy for the trustworthiness or quality of the country’s systems); and (3) public support for aid in the donor country (a proxy for the donor’s risk tolerance). Findings are robust to corrections for potential sample selection, omitted variables or endogeneity bias.

This paper is a product of the Human Development and Public Services Team, Development Research 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 author may be contacted at sknack@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
biases resulting from country level confounders correlated with both water and sanitation and child mortality we also control for survey fixed effects.<sup>13</sup> In a second step, we use the estimated relative mortality reductions to compute the predicted absolute mortality reductions per 1000 live births which would be generated by improving water and sanitation infrastructure under two scenarios: In the first scenario, we assume that eliminating the lack of access to improved water and sanitation is the policy target. Under this “low cost” scenario, we assume that any household currently using surface or unprotected water sources would be upgraded to an improved water source, and any household using open defecation or only unimproved sanitation technologies would get an improved latrine. The second scenario reflects a more ambitious program aiming at upgrading water and sanitation access to the highest possible standard. In this scenario, any household that does not already have access to privately piped water and a toilet connected to a septic tank or sewage system will be provided with such access.

The main question we want to address in this paper is the potential contribution of water and sanitation improvements towards achieving MDG 4. MDG 4 aims at reducing child mortality by two thirds in each country between 1990 and 2015. Based on the 1990 child mortality levels (World Population Prospects, United Nations Population Division), we first assign an under-5 mortality target for 2015 to each country. In a second step, we look at the latest mortality numbers and evaluate the potential contribution of investment in water and sanitation infrastructure towards achieving MDG 4. To do so, we compare the predicted absolute mortality reduction under scenarios 1 and 2 to the observed gap between the countryspecific 2015 target and under-5 mortality as estimated in the most recent DHS surveys. The expected contribution of water and sanitation under each scenario for each country is then given by: where _CM DHS_ is under-5 child mortality in the most recent DHS survey, _CM cf_ is the predicted (counterfactual) child mortality rate, and _CM MDG_ is the country-specific child mortality rate
2019.<sup>6</sup> The survey collected information on household and dwelling characteristics, educational attainment, asset ownership, and access to services. It forms the basis for constructing a household consumption aggregate and estimating the country's official poverty rate. The survey is representative at the regional (14 regions) and the national level. It contains information on 7,156 households (66,120 individuals) distributed across rural (55 percent) and urban areas (45 percent).

For the population census, the analysis relies on the 2013 Population and Housing Census.<sup>7</sup> The census questionnaire includes information on household characteristics and living conditions, but does not cover household expenditures, which are necessary for the calculation of poverty. Appendix A, table A.1 compares the variables available in the census and the household survey. The comparison is critical because a fundamental assumption of small area estimation is that the survey used to model welfare is representative of the entire population. Consequently, the objective is to ensure that the data in both the survey and census bear a close resemblance. The next subsections explain vulnerability estimation in the context of small area estimation techniques. The paper then describes how this compares with the Günther and Harttgen (2009) method, which relies solely on survey data.

# Estimation of vulnerability: Adding vulnerability to the poverty map

The first step is to estimate a function of the household income-generating function in the survey data. The regression uses a multilevel model because this allows the decomposition of the unexplained variance of the dependent variable—expenditure per capita in the case here—into a household (idiosyncratic) and a community (covariate) component. To illustrate the estimation formally, suppose represents households, while represents communes; and every household h is nested h= 1, . . . ; II withing a community . Equation 1 summarizes the model for the log of household per capita expenditure cc= 1, . . . ; CC l ) as a
<!-- Start of picture text --> .22<br>.2<br>.18<br>.16<br>.14<br>.12<br>.1<br>.08<br>.06<br>.04<br>.02<br>0<br>−.02<br>Educational attainment (years)<br>1+ 2+ 3+ 4+ 5+ 6+ 7+ 8+ 9+ 10+ 11+ 12+ 13+ 14+<br>Difference−in−Differences<br><!-- End of picture text -->

**Figure 7: Difference-in-Differences: Cumulative distribution of maternal education.** Notes: Figures based on 2,118 observations in ECED. These are point estimates of the difference-in-differences coefficient in a sequence linear probability models. All models control for (ln) of school-aged population in maternal district of birth around 1973 (interacted with ”treated cohort“).
|1.00|
|Dwelling contract: pay rent or lease [=1]|5,592|0.98|0.13|0.00|1.00|
|Dwelling contract: consent with the owner [=1]|5,592|0.01|0.10|0.00|1.00|
|Dwelling: individual apartment or house [=1]|5,592|0.92|0.27|0.00|1.00|
|Dwelling: shared apartment or house [=1]|5,592|0.08|0.27|0.00|1.00|
|<br>Dwelling: temporary shelter, shack or tent [=1]|5,592|0.00|0.04|0.00|1.00|
|<br>Income from wage labor (Somoni)|5,592|29,628|73,163|0.00|1,555,000|
|<br>Income from self-employment (Somoni)|5,592|16,779|47,019|0.00|800,000|
|<br>Income from livestock or crops (Somoni)|5,592|1,593|16,205|0.00|300,000|
|Income from handcrafted products (Somoni)|5,592|317|2,452|0.00|60,000|
|<br>Income from remittances (Somoni)|5,592|2,711|13,513|0.00|350,000|
|Income from a loan (Somoni)|5,592|20,353|516,832|0.00|20,000,000|
|Number of refrigerators in the HH.|5,592|1.06|0.57|0.00|
design. –_ Field experiments in Gujarat were conducted in 2007, the year after the baseline survey described above. Unlike Andhra Pradesh, where interventions were implemented through household visits, in Gujarat, SEWA used several techniques to market rainfall insurance, such as flyers, videos, and discount coupons. We randomly varied the content of each of these three marketing methods at the household level.

> 11 This two-tiered assignment structure was implemented to measure possible spillovers of trust within the village. It also helped reduce the demands on BASIX staff time.

> 12 ICRISAT employees recorded the degree to which the BASIX LSA followed the instructions. Instructions were followed exactly in 56% of cases. For the remainder, 25% did not show up or stayed at the house for too short a time. The remaining 19% stayed for the duration of the visit. In private conversations after the sales period, BASIX LSAs had no recollection of which individuals they had endorsed and whether they had purchased insurance. 13 Based on time use surveys reported by the insurance educator team, this education was presented rather briefly (an additional two minutes relative to a standard household visit).

11
different compared to including manufacturing as a downstream sector only. Surprisingly, for manufacturing firms entry barriers are the main regulatory barrier for better productivity performance. From a wider perspective this suggests that the relative importance between conduct and entry barriers is moving along shifts that the domestic economy makes into services.

The rest of this paper is organized as follows. The next section discusses the data and gives an overview of the various productivity measures, liberalization reform indicators and input‐output coefficients used for this paper. Section 3 presents the estimation strategy, and Section 4 reviews the estimation results. Finally, the last section concludes by interpreting the results in a wider European context.

# **2 Data**

As in all previous papers which are very close to our line of research, one needs to have three types of data in order to perform our empirical analysis, namely services reform indexes, TFP performance at the firm level, and finally input‐output coefficients measuring the extent to which downstream (services and manufacturing) industries use services as inputs. These latter coefficients will be interacted with the reform indexes so as to have a “weighted” score of services reform.

# **2.1 Services Reform**

The EU has a long history regarding its integration process of services. Next to goods, capital and skills, the EU strives for the free flow of services across member states’ borders which was initially defined in the Treaty of Rome in 1957. Together this objective of free factor movement is more commonly referred to as the “four freedoms” or, in other words, the EU’s internal market. Although legislation in the areas of goods and capital has progressed over the years, reforms in services regulation appear to be difficult. For instance, the recent passing of the Services in the Internal Market Directive (2006/123/EC) in 2006 did not happen without fierce public opposition and was eventually significantly watered down by the European Parliament with many sectors such as broadcasting, audio‐visuals and
of matching all observations with each other within each country-type panel maximizes our potential power in estimation, but we also conduct analyses of

> 13 Global Database of Shared Prosperity, circa 2013-2018, available at <u>http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity (accessed May 8,</u> 2021).

> 14 See Yang and Nguyen (2021) for a description of the data and methodology. Access to the data is available at: <u>https://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity.</u>

> 15 When a household survey spans more than one calendar year, we construct a weighted national accounts aggregate where the weights correspond to the distribution of months of survey fieldwork across the years. This is consistent with the practice of dealing with surveys in PovcalNet that run over multiple years, as described in Chen and Ravallion (2010).

9
a shock (loss of crop or livestock) at baseline (when the child was in utero or recently born). Given the richness of the YL data, there are several potential exogenous shocks that can serve as instruments for maternal depression or additional controls that can be included in our model. Since the IV estimator is biased when the number of instruments is large, we use IV-LASSO to select the instruments that we include in our model. This method helps to also avoid overfitting the model or omitted variables bias by selecting the vector of control variables that fits better to the data. We also strengthen the robustness of the analysis by considering variations of the indicator used for maternal depression, exploring heterogeneous effects of household characteristics, such as mother’s education, household wealth, and the presence of a male partner and some of his characteristics.

3
unobservables is arguably less problematic for the distribution of refugees reported in the census of April 1923. At that time, refugees had been in Greece for less than 6 months (since the fire of Smyrna in September 1922), and still hoped to return to their homeland in Turkey. Upon arrival, the main worry of refugees, who were in a state of “utter destitution”, was to find relief and shelter where they could and less so to seek places with better economic opportunities (Kontogiorgi, 2006, pp. 88). The first place of settlement in 1923 was often temporary, as more than one third of refugees relocated to a different settlement between 1923 and 1928. As Figure A.5 shows, the spatial distribution of refugees in 1923 is correlated with but far from identical to the one in 1928.

# **5 Results**

## **5.1 Social integration of refugees**

**Closing the gap** I first examine the educational outcomes of refugees. The Panel A of Table 1 shows that, among adult refugee men in the 1928 census (15-65 years old in 1923), 66% are literate, while it is the case for the 75% of native men.<sup>21</sup> This initial negative gap disappears for refugees arrived in Greece at a younger age, i.e., below 15. The 1,270 refugee men sampled in the 2001 census and born between 1908 and 1922 are as likely as natives (born in the same period) to be literate (93%) and to have completed primary school (70%).<sup>22</sup> Differently from men, adult refugee women in 1923 had a literacy rate similar to native women (around 35%). When displaced as children, they became later in life more educated than native women. As for the second-generation refugees born in Greece, the Panel C of Table 1 shows that, among adult men,