rafmacalaba/data-use-mentions-extended huggingface dataset:
variable approach similar to Acemoglu and Restrepo (2017) and Dauth et al. (2017).

# **4.1 Instrumental Variable**

This approach instruments _ETRi,t_ with robots’ adoption in the same industry in other countries. The idea underlying the instrument is that the use of industrial robots is induced by improvements in technology (or a reduction of their price) increasing their profitability for adopters. As such, these are trends that are largely unrelated to the specific market conditions prevailing in Indonesian industries. Similarly to Acemoglu and Restrepo (2017), we focus on countries that are ahead Indonesia in terms of robots’ adoption. To that end we use data from OECD countries, and we first match IFR data with 2-digit industry employment figures from the OECD Structural Analysis Database (STAN).<sup>27</sup> Then for each 2-digit industry-year pairs we compute the number of imported robots per thousand workers averaged across OECD countries. To construct the instruments at various levels of aggregation, we simply replace the density of robot imports from Indonesia with this OECD average for each industry-year pair.

If technological trends drove robot adoption, we would expect that the industries with higher exposure of robots should be broadly the same across countries, even for countries with different levels of economic development. The strong correlation between 2007-2015 changes in _ETRi,t_ between Indonesia and OECD countries is consistent with the notion that Indonesia’s automation across industries is driven by technological factors (Figure 5).<sup>28</sup> An endogeneity concern relates to the possible relation between robots’ adoption,

> 27 We are not able to construct employment for Wood and furniture, and Installation and repairs industries. Those are available in IFR data but not in STAN, which does not report a sufficiently disaggregated breakdown of employment for these 2 industries. As a result, the number of available 2-digit industries drops to twelve.

> 28As the change for Motor vehicles and Plastic and rubbers is much larger than for the other
GDP growth, effectively limiting the disproportionate influence of very large swings in quarterly growth.

For the level of economic development, we rely on the World Development Indicators (WDI) database from the World Bank and use purchasing power parity-adjusted GDP per capita in constant 2017 international dollars. For the income status of countries, we use the IMF’s definition of advanced economies (AEs), emerging markets and middle-income countries (EMs), and low-income developing countries (LIDCs) to divide countries into mutually exclusive groups. For the main analysis, we combine the last two into a single group: emerging market and developing economies (EMDEs).

For the shares of agriculture, manufacturing, and services in each economy, we also rely on the WDI database.

# _Statistical Capacity and Voice and Accountability_

We use the World Bank’s Statistical Capacity Indicator, which measures a nation’s ability to collect, analyze, and disseminate high-quality data about its population and economy. It is a composite score assessing the capacity of a country’s statistical system based on the methodologies and data used, as well as the periodicity and timeliness of data releases. It uses publicly available information and country inputs to score countries against 25 criteria.

From the World Bank’s Worldwide Governance Indicators, we obtain a measure for voice and accountability. It captures perceptions about citizens’ ability to select their government, the extent of freedom of expression and association, as well as the presence of a free media.

# _Cloud Cover, Subnational Population, and Spatial Aggregation_

The Climatic Research Unit at the University of East Anglia provides monthly highresolution gridded data sets on cloud cover (Harris, Osborn, Jones, and Lister, 2020). For the analysis of elasticities, we take the area-weighted average of cloud cover (CRU TS version 4.03) within each country and across time between 1901-2018.

For our subnational analysis, we use population data from the fourth version of the Gridded Population of the World from the Socioeconomic Data and Applications Center.

Throughout
# **Table A2: Impacts of mental health on labor outcomes – Coefficient estimate of IVLogit-FE model**

|Dependent variable:|Employment<br>status|Labor force<br>participation|Permanent<br>job|Skilled<br>occupation|Work in<br>agriculture|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Standardized mental health scores|-0.765***|-0.542***|-0.185|-0.680***|0.746***|
||(0.108)|(0.079)|(0.120)|(0.143)|(0.112)|
|_First stage of 2SLS (dependent variable_|_is standardize_|_d mental health_|_scores)_|||
|Trauma experience*Year after arrival|-0.039***|-0.023***|-0.039***|-0.039***|-0.039***|
||(0.009)|(0.006)|(0.009)|(0.009)|(0.009)|
|Kleibergen-Paap F stat.|17.692|35.644|17.692|17.692|17.692|
|Dep. Mean|0.455|0.418|0.163|0.135|0.746|
|Other controls|Yes|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|Yes|
|Observations|3,687|7,
# **4 Results**

Table 3 presents results from the discrete choice experiment estimated using conditional logistic regression. Policymakers preferred programs with larger estimated treatment effects or programs that came recommended by a local expert, as well as those that had an impact evaluation from their country (Column 2). Policy practitioners preferred programs with larger, more precisely estimated impact evaluation results as well as results from the same country as the target program and results from RCTs (Column 3). They also preferred programs that came recommended by a local expert.<sup>10</sup> Table 4 translates these results into estimates of how much policymakers and policy practitioners would be willing to pay, in terms of estimated impact, for programs with these different attributes. Policymakers would prefer a program recommended by a local expert even if it had an approximately 5.0 percentage point lower estimated impact on enrollment rates (Column 2), which is very large relative to the range of effects that programs typically have on enrollment rates.

Table A1 further breaks down responses across the World Bank and IDB preworkshop samples and tests whether policymakers and policy practitioners put statistically significantly different weights on different attributes. Results appear largely comparable across these sub-samples, though policymakers put relatively less weight on large or precise estimates and do not emphasize programs with impact evaluations done in the same country as much as policy practitioners do.<sup>11</sup> To further explore the importance placed on precise estimates, we construct an indicator for whether the result shown was significant. While this variable was not explicitly shown to participants, it could be discerned from the provided estimated impact and confidence interval. When we include this variable in the regressions, the

> 10Note that in order to avoid framing a program as _not_ recommended, whether the program was recommended was displayed outside the table that contained study attributes; this also effectively clustered the study attributes together and kept the non-study attribute separate, to make the distinction between study-related information and non-study-related information easier for participants to
77|33|
|TFP|1.94|2.05|2.12|1.74|1.88|2.01|1.77|
|||**Manuf**|**acture of A**|**dvanced Product**|**s**|||
|Number of frms|2,059|590|444|146|1,469|602|867|
|_% of sample_|100%|24.5%|73.2%|26.4%|75.5%|33.2%|66.8%|
|Age (years)|17|23|24|20|15|19|13|
|Size (nb. of employees)|58|141|167|66|31|50|22|
|TFP|1.69|1.85|1.77|2.05|1.64|1.90|1.51|

Notes: Authors’ calculations based on the World Bank Enterprise Surveys. Descriptive statistics computed using survey weights, except for the number of firms. Sample restricted to firms that reported a positive or null number to question d3c _”What percentage of this establishment’s sales were direct exports?”_ and to those that replied yes or no to question d13 _”Were any of the material inputs or supplies purchased [...] imported directly?”_ of the Manufacturing Module. Included firms are those located in the 48 developing countries retained in the empirical analysis (see Section 2.2 and Table A1 in the Appendix). _Age_ is the number of years since the firm began operations; _Size_ is the number of permanent, full-time employees; _TFP_ stands for total factor productivity.

The explanatory variable of interest is captured by the interquartile range of the average number of days to clear imported material inputs or supplies through Customs and other border
within and across budget categories. The templates, which are provided in the Appendix Tables A1 and A2, were in turn circulated among World Bank staff to facilitate their intelligence gathering from their NSO counterparts. The information that was received from the regional poverty economists was based on Appendix Table A1, with Table A2 serving as a reference point for the types of expenditures that could be included under the aggregate implementation cost categories in Table A1.

The final database of country-specific implementation costs per household (in USD 2014 prices) is presented in Table 1, based on the inputs received as of June 2016, spanning 18 countries from all six regions. Of the 18 countries, three are from South Asia, six from sub-Saharan Africa, two from Middle East and North Africa, five from Latin America and the Caribbean and one country each from Europe and Central Asia, and East Asia and Pacific. While nine out of 18 countries are 8
As in a majority of countries, the unemployment rate is negatively related to the level of education of individuals (see Appendix Table 4). On average, members of the labor force with higher education face an approximately 2 times lower unemployment risk than their counterparts with upper secondary education. This degree of difference in unemployment rates amongst education groups is similar to that found in Lithuania, but somewhat lower than those measured for other Central European countries (see Appendix Figure 5).

**Ethnic considerations.** Unemployment is higher amongst non-Latvians than among ethnic Latvians. Until 2001, the unemployment rates were about 1.8 times larger for ethnic minorities in comparison with Latvians. In 2002, however, this ratio fell to 1.5 as a result of a decline in unemployment among nonLatvians. Evidence from the 2000 Population Census (see Appendix Table 4) suggests that the ethnic gap in unemployment rates is caused mainly, but not entirely, by a lack of Latvian language skills. Another factor which contributes to the difference in unemployment rates between Latvians and ethnic minorities is that the Latgale region, with the highest unemployment rates in the country, is predominantly populated by non-Latvians.

**Figure 12.  Unemployment Rates by Ethnicity: Ethnic Minorities vs. Majority Population. Estonia and Latvia, 1997-2002**

<!-- Start of picture text --> 25 2.0<br>1.8<br>Estonians<br>20 1.6<br>1.4 Non-Estonians<br>15 1.2 Latvians<br>1.0 Non-Latvians<br>10 0.8 EE ratio<br>0.6<br>LV ratio<br>5 0.4<br>0.2<br>0 0.0<br>1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> _Source:_ Calculation based on LFS data.

**Unemployment duration.** As in other transition
>2</sup> decreases considerably which indicates that the covariates are associated to a lesser extent with the difference in values between CC and ML yields.

# **4.2.4 Application on the nationally representative survey**

To assess the external validity of our findings thus far, we replicate the analysis with data from the Enquˆete de Conjoncture Int ́egr ́ee (EACI 2017)<sup>16</sup> a nationally-representative multi-topic household survey, spanning both rural and urban areas of Mali and with detail questionnaire modules on smallholder agricultural activities. The households were interviewed by semi-resident enumerators in three visits, following a fieldwork protocol that was similar to that of ERIVaS. All major crops, including sorghum, were subject to crop cutting during the EACI fieldwork, and the size of the randomly-placed crop cut sub-plot on sorghum plots specifically was 5m x 5m (opposed to 8m x 8m in ERIVaS). One of the objectives of EACI is to derive national- and regional-level estimates of total crop production and yields. On yield measurement, plots, rather than households, are the primarily units of analysis, with direct implications on the sampling of plots for crop cutting purposes. Specifically, in each sampled EACI enumeration area, a complete listing of plots was conducted, and the listed plots were organized into separate subsets in accordance with the cultivated crop (or crop combinations, in the case of intercropped plots). A third of plots were then selected at random from each subset. This means that households with more plots had a greater probability of having a plot selected for crop cutting. In the end, we have farmer self-reported production data for about 24,000 plots of which 7,800 were subject

> 16The EACI 2017 is also the Mali LSMS-ISA 2017. It was built upon the Agricultural Conjuncture Survey which is implemented yearly by the Statistical Unit of the Ministry of Agriculture. For the 2017 edition, living conditions modules were added on a subsample as part of the Mali LSMS-ISA project.

26
|
||(0.174)|(0.242)|(0.212)|(0.168)|(0.183)|
|p-value UCT vs. CCT|0.115|0.006|0.025|0.068|0.078|
|p-value Treatment|0.114|0.002|0.075|0.184|0.186|
|Sample Size|507|506|506|506|506|
|<br>Control Structure:||||||
|Maternal selection controls + propensity weight||X|X|X|X|
|Father selection controls|||X|X|X|
|Cubic in child age in months||||X|X|
|Maternal age in years, age interactions|||||X|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. The height-for-age z-score is calculated using the 2006 WHO child growth standards. Specification (1) controls for the gender of the child. Specification (2) adds selection weights and controls directly for maternal baseline characteristics ( stratum indicators, household asset index, highest grade attended, and an indicator for never had sex). Specification (3) adds controls for paternal attributes (highest education level, religion, ethnicity, main activity, and likely HIV status). Specification (4) adds a linear, quadratic, and cubic in child age. Specification (5) adds maternal age and maternal age interacted with the other baseline covariates. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.

49
pixel</mark> _<mark>i</mark>_ <mark>within a certain country (e.g. Country X) except the crop value for that pixel.</mark> _<mark>AgGDP</mark>_ i <mark>is the agricultural GDP allocated to pixel</mark> _<mark>i</mark>_ <mark>in Country X while</mark> _<mark>TotalAgGDP</mark>_ <mark>is the total agricultural GDP for that country. Therefore:</mark> <mark>Let</mark> _<mark>PreAlloc</mark> i_ <mark>be the prior allocation of AgGDP share from our best guess. One possibility for the first approximation is from pixel level components of AgGDP where we have gridded data sets:</mark> PreAgGDP� �Crop� �Livestock� �Forestry� �Hunting� �Fishing� <mark>As we have the major components of AgGDP, and the sum of these component should be close to the official value. At least, we make sure that the official AgGDP value (</mark> _<mark>TotalAgGDP)</mark>_ <mark>should be no less than the sum of all five components of agricultural GDP. Therefore, we first sum them up</mark> 7 We select GHS Settlement grid as an example of a geospatial definition of urban population to exclude from the model. The definition of urban is an active topic of research (e.g. Dijkstra and Poelmann 2014; Roberts et al. 2017; Angel et al. 2018; Bosker et al. 2018).

4
<!-- Start of picture text --> Figure 1.5 Costa Rica: Foreign Direct Investment Inflows<br>1970-2003<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>FTZ Law<br>FTZ Law Reform<br>Export Promotion Import Liberalization<br> US$ millions<br>Capital Account Liberalization Intel’s Arrival<br>1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Source: Prepared by the authors based on figures from Banco Central de Costa Rica.

Complementing the previous paragraphs, it should be pointed out that, thanks to the liberalization process, Costa Rica’s economy has succeeded in reducing its extremely high dependence on traditional activities, such as coffee, bananas, sugar and beef, for the generation of foreign exchange earnings. In fact, as shown in Figure 1.6, owing to the economic liberalization exports of non-traditional products have substantially increased their relative importance in total exports, from 38.6% in 1982 to 64.9% in 1992 and 87% in 2003. Such increased diversification of exports currently provides Costa Rica with greater economic stability and a more stable source of foreign exchange, thus avoiding the recurrent problems of the past, when a good economic performance was dependent on whether the price of coffee increased as a consequence of external impacts (for example, sleet in Brazil). Moreover, we might wonder about what the condition of Costa Rica’s economy would be, given the current crisis of the coffee sector, if this major diversification of exports had not taken place.

<!-- Start of picture text --> Figure 1.6 Costa Rica: Relative importance of<br>
# **III.** Examples

The previous section has shown that under certain assumptions we can anticipate how measured poverty will change if different definitions of consumption are used. To check these predictions and also to ascertain by _how much_ measured poverty changes, we consider two empirical examples, drawing on household survey data from Ecuador and Pakistan.

The Ecuador household survey _(Encuesta Sobre Las Condiciones de Vida)_ is a nationally representative household survey modelled on the World Bank's Living Standard Measurement Surveys. It was fielded in Ecuador during the period June-September, 1994. Over 4,500 households were surveyed in total and after cleaning and data consistency checks, information for 4,391 households is available for analysis. Hentschel and Lanjouw (1996) describe in detail the construction of the Ecuador consumption aggregates. The Pakistan survey _(Pakistan Integrated Household Survey)_ is also nationally representative and based on the LSMS model. It was fielded over the course of the whole year in 1991. In total, information on 4,673 households is available for analysis. The construction of the consumption aggregates has been described in Howes, Gazdar and Zaidi (1994).

Tables 2 and 3 provide calculations of poverty in Ecuador based on the "traditional" and "austere" poverty lines in turn. In each table three common summary indicators of poverty are applied: the headcount index and two indicators from the FGT class, with parameter values of 1 and 2. From Table 2 it appears that, if the poverty line is set using the "traditional" approach, the headcount ratio is robust to alternative definitions of consumption (the differences are not statistically significant). This accords with the theoretical prediction. On the other hand, there is also no significant change in the FGT indicators. On the basis of the arguments above we might have expected them to increase. Turning to Table 3, where the "austere" poverty line is set, the incidence of poverty falls as predicted: from 50% when only food expenditures
you felt down, depressed, or hopeless?” (iii) ”Over the last 2 weeks, how often have you had poor appetite or overeating?” (iv) ”Over the last 2 weeks, how often have you had thoughts that you would be better off dead or of hurting yourself in some way?” and (v) ”Did you seek mental health assistance during the last three months? (1=Yes or 0=No)” The Mental Health index is constructed by calculated the average of the five outcome variables explained above. The average is calculated just with the information available for each individual, it excluded from the average the missing answers.

- Labor Market Access: we measured the labor market access dimension using the following variables: (i) Employed is an indicator equal to 1 if the respondent reported to have worked for remuneration for at least one hour in the last 7 days, (ii) Weekly Hours Worked: is the total number of reported hours the respondent spent working in the last 7 days, and (iii) Monthly Wage: is the total amount in somoni that the respondent earned from salary or self-employed profits after taxes in the last 30 days.

The Labor Market Access Index is constructed using the methodology of Kling, Liebman and Katz (2007) with the variables explained above. It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again.

- Income and Consumption: we measured the income and consumption dimension using the answer to the following questions: (i) ”Approximately, what was the total household income in the last month? (amount in Somoni)”, and (ii) ”Approximately, how much did your household spend in the last month in total? (amount in Somoni)”. With these two questions, we created the Income and Consumption Index using the methodology of Kling, Liebman and Katz (2007). It implies i) standardizing the variables, ii) averaging
10 An additional factor contributes to depress even further the tax base of the pension system. There is increasing evidence that, even in the formal sector, a substantial amount of underreporting of productive activities and sales takes place. A March 1996 survey indicates that 39 percent of sales of all enterprises (48 percent for private enterprises) remain hidden in Ukraine (see Kaufman, 1996). This phenomenon is neither new nor specific to Ukraine. Personal informal incomes and outlays were extremely widespread in the FSU: 75 percent or more of households derived over 5 percent of their income from informal sources and 40 percent derived over one fourth in the late 1970s in Ukraine ( see Grossman, 1989). The phenomenon is also observed in many transition economies (see for example, Arvay and Vertes 1995 for the case of Hungary). Its relative importance however, may have increased over recent years.

Overall, the decline in real wages, the fall in formal employment and underreporting of productive activities in the formal sector have contributed to a decline in the reported wage bill and thus, to an erosion of the tax base for the social security system. By 1993, the share of wages (net of taxes) in GDP had fallen to 23 percent, down from about 45 percent in 1991 (see **Table 4)** As a result, while until 1993 payroll contributions were sufficient to cover the expenditures of the Pension Fund, spending climbed much faster than contributions in 1993 leading to a transfer from the central budget equal to about 2.6 percent of GDP to cover the deficit (see **Table 5).**

# 3.3 The Government's Response

Faced with resource limitations, the Government was unable to maintain a significant budget transfer to the Fund after 1993. Thus it opted for _shrinking benefits while attempting to preserve a minimum standard of living for low-income pensioners._ This was achieved by narrowing the range of payments and reducing the overall benefit level. As the majority of pensions (standard
some types of entrepreneurial activity, conditional on the household already

> 25We use a household-matched nationally representative sample from the 2006 Family Income and Expenditure Survey (FIES) and Labor Force Survey (LFS). 2006 is the most recent matched FIES-LFS microdata publicly-available from the National Statistical Office at the time of writing. We inflate all peso figures from 2006 to 2010 using the Consumer Price Index.

24