rafmacalaba/data-use-mentions-extended huggingface dataset:
2010. Chapter 73 Agricultural Productivity and Economic Growth, in: Handbook of Agricultural Economics. Elsevier, pp. 3825–3866. https://doi.org/10.1016/S1574-0072(09)04073-0

- Gollin, D., Lagakos, D., Waugh, M.E., 2014. Agricultural Productivity Differences across Countries. Am. Econ. Rev. 104, 165–170. https://doi.org/10.1257/aer.104.5.165

- Gollin, D., Lagakos, D., Waugh, M.E., 2013. The agricultural productivity gap. Q. J. Econ. 129, 939–993.

- Gollin, D., Udry, C., 2021. Heterogeneity, Measurement Error, and Misallocation: Evidence from African Agriculture. J. Polit. Econ. 129, 1–80. https://doi.org/10.1086/711369

- Kennedy, G., Ballard, T., Dop, M.C., 2011. Guidelines for measuring household and individual dietary diversity. Food and Agriculture Organization of the United Nations, Rome.

- Little, R.J.A., 1988. Missing-Data Adjustments in Large Surveys. J. Bus. Econ. Stat. 6, 287–296. https://doi.org/10.1080/07350015.1988.10509663

- Ministry of Finance, National Institute of Statistics, 2016. Basic Information Document: 2014 National Survey on Household Living Conditions and Agriculture (ECVM/A-2014).

- Ministry of Rural Development, 2019. Basic Information Document: Enquete Agricole de Conjoncture Intégrée aux conditions de vie des ménages (EAC-I 2017).

- National Bureau of Statistics, 2021a. Basic Information Document: Nigeria General Household Survey– Panel 2018/19.

- National Bureau of Statistics, 2021b. Basic Information Document: National Panel Survey (NPS 20192020), Extended Panel with Sex-Disaggregated Data.

-
**Polk,Panning, and Rosrch Trade Policy**

Under what macroeconomic conditions are industrial growth and financial stability - or disastermost likely in a semi-industrial country?

The question is addressed using an econometric model with the following features. Each firm's net income is a function of macroeconomic variables (such as output demand and interest rates) and firm-specific factors (such as physical asset shocks, currency exposure, and overall indebtedness). Each firm retains some - portion of its income how mi:ch depends on dividend policy and past earnings performance. Retained eamings add to net worth and are distributed among specific assets and liabilities according to the same macroeconomic and firmspecific variables. These incremental additions to assets and liabilities set the stage for the next period's adjustmtnt behavior.

Application of this model to Uruguayan raw data yielded these basic findings:

* Corporate income is very sensitive to output demand and the cost of dollar credit.

* Fluctuations in corporate income have a clear, direct effect on the rate at which net worth expands.

* Firms absorb most short-run fluctuations in net worth by adjusting assets, not debts.

* Corporate demand for peso credit is very unresponsive to the real peso interest rate.

The findings imply that rapid changes in the exchange rate or aggregate demand significantly affect leverage and liquidity in the corporate sector.

This paper is a product of the Trade Policy Division, Country Economics Division. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Carla Cabana, room N8-065, extension 61539.

The PPR Working Paper Series dissby inates the **n a** indgs of work under way in the Bank's Policy, PlWling, and Resek ch Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. T'he findings, interpretations, and conclusions in these papeTs do not necessarily represent official policy of the Bank.

Copyright 0 1988 by the Intemational Banlc for Reconstruction and Developmnenttlle World
# **Appendix A1: Additional analyses**

**Table A1.1.** ITT effects on compost quality.

||Compos<br>|tquality<br>|
|---|---|---|
||(1)|(2)|
|T1 (extension)|0.547**|0.423***|
||(0.216)|(0.126)|
|p-value|0.013|0.001|
|T2 (extension + video)|0.614***|0.443***|
||(0.211)|(0.124)|
|p-value|0.004|0.001|
|Test T1 >= T2 (p-value)|0.393|0.444|
|Endline control mean|4.3|65|
|Additional controls|No|Yes|
|(Pseudo)R-squared|0.030|0.277|
|Observations|1,127|1,127|

_Source:_ Authors’ analyses based on baseline and endline survey data. _Note:_ Subsample of compost producers only. Compost quality is an index ranging from 0 to 9. One-sided equality tests of T1 and T2 are F- tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

35
**Table 4: Testing for an income effect**

||Women no|t yet offcially|promoted|Single|women|
|---|---|---|---|---|---|
||(1)<br>Women’s<br>expenditures|(2)<br>Girls’<br>expenditures<br>(All outcomes|(3)<br>Remittances<br>as % in house|(4)<br>Women’s<br>expenditures<br>hold income)|(5)<br>Remittances|
|selected|3_._59**|0_._92|3_._12*|0_._35|_−_3_._74|
||(1_._42)|(0_._85)|(1_._69)|(2_._09)|(6_._86)|
|N|256|103|255|49|49|
|Nominated mean|5_._49|1_._50|6_._29|11_._58|19_._32|
|Nominated SD|4_._65|1_._92|8_._90|14_._70|20_._45|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Where available, the baseline outcome is included in the amelioration set. The sample in Columns 1-3 is limited to those respondent who had not yet been officially promoted at the time of the household surveys. The sample in Columns
*|||||
|Household size|6.8539|6.8566|0.0027|0.9770|
|Num children 0-7|2.1263|2.0936|-0.0327|0.5062|
|Num children 8-17|2.1323|2.1558|0.0235|0.6716|
|Num adults_>_70|0.0692|0.0706|0.0015|0.8689|
|**Localities characteristics**|||||
|Marginalization index 1995|0.6748|0.5296|-0.1452|0.1352|
|Num. observations|1,663|2,831|||

* Dummy variable

This table tests balance in observable characteristics between the PROSPERA RCT original treatment and control with our sample. Observations are restricted to those households that were found in ENCEL 2017-18 and employed in our main estimations. Balance of variables shown are calculated with the 1997 ENCASEH data. Standard errors clustered at the locality level. Asterisks indicate significance at the ***1%, **5%, and *10% level.

11
# 1 Introduction

The labor force of each industrial country is being shaped by three forces: ageing, ed ~~-~~ ucation and migration. Ageing is a powerful, gradual process that evokes concern but little policy action. Immigration is blamed for the predicament of the low ~~-s~~ killed and is provoking a political backlash. Education is viewed as a panacea to which access is unequally distributed.

Yet one question has not been addressed: what are the relative and combined effects of these three forces on wages across different skill and age groups? This is an especially pertinent question as increased wage inequality across skill and age groups dominates the political and academic debate. This paper addresses the question using data for the OECD countries on the age, education and place of birth of individuals across two decades (Arslan et al., 2015). The impact on wages is derived using a simple model of the economy that allows for imperfect substitutability between the skilled and the unskilled, old and young, and natives and foreign born. The change in the age and skill structure of labor forces emerges as the dominant force influencing relative wage changes, with migration playing a smaller role in most countries.

The database helps us to measure how the education and age structure of the pop ~~-~~ ulations have changed between the years 2000 and 2010. Populations across the OECD have become older and more skilled. Figure 1 shows the changes in the shares of four groups in the native working age population: young low ~~-s~~ killed, young high ~~-s~~ killed, old low ~~-s~~ killed and old high ~~-s~~ killed. The most striking feature is the decline across all coun ~~-~~ tries in the share of the young low ~~-s~~ killed workers (blue bars). This decline is matched by the increase in the share of the skilled, both young (orange)
_Note:_ Squares represent the 5 km by 5 km grids for which the hazard measures are available; the (starred) centroid of the square represents the household or cluster location; shaded squares represent the grids used to spatially aggregate the hazard measure for a 10 km radius (darker shade) and a 20 km radius (lighter shade) around the household’s location.

The results presented in this paper are based on matching a household’s geographic location with a spatially aggregated hazard measure within a 20 km radius. In the absence of any GPS data at the household or cluster level, the hazard measures are aggregated to the geographic or closest administrative level that is coded in the household survey. We chose the level of spatial aggregation so as to provide a roughly consistent area of spatial aggregation for surveys where matching was done using GPS data and administrative data.

Initial analysis suggested that this level of spatial aggregation was appropriate. We compared the sensitivity of the relationship between welfare and hazard measures aggregated using different radii in the case of Malawi, a country that has GPS coordinates for surveyed households and multiple years of data. The statistical significance of results increased for radii of up to 20 km and then started to decrease again with larger radii, suggesting that a radius of 20 km is able to reflect the local shock to a household’s crop yields and is also large enough to capture some localized impacts on prices and wages in the local market area. However, as shown in section 4.2, for the full data set a larger geographic area it is not clear that 20 km works better in every setting, and there is very little difference between the results across aggregating radii.

# 3.5. Using standardized anomalies for analysis

The hazard measures generated are in levels as well as standardized anomalies (z-scores).<sup>13</sup> The latter assume that differences in the level of the hazard from the historic average (i.e., level changes) matter not in
heavily after 2004 independently of EU membership. The small increase after 2007 also reflects weaker economic ties between Germany and Romania.

## [Figure 3 about here.]

It is worth noting that the sharp increase in foreign acquisitions was not likely driven by the lift of restrictions on FDI. OECD data on FDI restrictiveness for Poland show that screening and legal restrictions on FDI in manufacturing sectors had already been greatly removed by the time of accession. Hence, the observed increase in FDI could be attributed to the removal of the uncertainty regarding the future relationship with EU members and the progressive alignment of regulations associated with EU membership.

# **5.3 Differential effects of EU accession on firm performance across sectors**

In this section, we examine how Germany’s initial specialization patterns shaped the overall effects of EU accession on firm performance. These effects capture not only the direct effects of German acquisitions on firm performance, but also horizontal and vertical spillovers from FDI, as well as broader knowledge transfers (which may be unrelated with FDI).

## **5.3.1 Main results**

Table 3 reports the point estimates yielded by estimating equation (4), using Germany’s initial export shares in the sector during 1994-2004 as measures of industrial specialization. The specification in column (1) considers the effects of accession on TFP. The one reported in column (2) considers effects on revenue. Finally, the estimates in columns (3) and (4) estimate effects on employment and exports, respectively. The point estimates reveal that Polish manufacturing firms operating in sectors in which Germany was more specialized at the moment of accession experienced significantly higher TFP growth in the post-2004 period. More specifically, a 10% increase in Germany’s export share is associated to a 0.5% increase in the productivity of Polish firms. The point estimates on revenues and exports are also positive and of slightly larger magnitude. This may reflect the fact that German acquisitions may have targeted Polish firms
# I n s t a l l ( i f not yet i n s t a l l e d ) and load packages r e q u i r e ( ggplot2 ) r e q u i r e ( basedosdados ) r e q u i r e ( dplyr )

# Set user ’ s BigQuery b i l l i n g ID s e t _ b i l l i n g _ i d (" < BILLING −ID > " )

# Define SQL query to e x t r a c t data query <−" SELECT id_municipio , ano , AVG( CASE WHEN modalidade = ’8 ’ OR modalidade = ’10 ’ THEN 1 ELSE 0 END ) AS s h a r e _ d i s c r e t i o n , COUNT( i d _ l i c i t a c a o _ b d ) AS count FROM ‘ basedosdados . world_wb_mides . l i c i t a c a o ‘ GROUP BY id_municipio , ano "

# Execute query and c r e a t e data frame from output data <−r e a d _ s q l ( query )

# Create histogram of share of non−competitive tenders data %>% f i l t e r ( count > 50) %>% ggplot ( ) + geom_histogram ( aes ( x = s h a r e _ d i s c r e t i o n ) , bins = 100 , c o l o r =" black " , f i l l ="#02075 d " ) + xlab ( " Share of non−competitive tenders " ) + t h e m e _ c l a s s i c ( )

43
# **4. Data**

The data we use in this analysis come from three sources. First, we use the SOFES database containing detailed information on all students receiving a credit from SOFES. Second, we use data from a large-scale survey (carried out in 2003) among students and graduates at Mexican private universities who are members of SOFES. Finally, we use data from a household survey on educational attainment and incomes. In this section we describe the data sources. As the Mexican household survey is well-documented, we concentrate on the first two sources.<sup>6</sup>

# _4.1 SOFES database_

The database includes information on the student’s educational attainment (for instance grade point averages at various school levels), socio-economic background (e.g. family income, educational attainment of the father, employment information), study program and grade point average during the last term of the program, and a lot of other information. The important variables of the SOFES database are summarized in Appendix Table 1.<sup>7</sup> The first column refers to Bachelor-students with a SOFES-credit, and the second column refers to Masterstudents with a SOFES-credit. With regard to educational attainment, the SOFES database provides information on the three previously attended schools. In case of a Bachelor-student, this means upper secondary school ( _Bachillerato_ ), lower secondary school ( _Secundaria_ ), and primary school ( _Basica_ ). In case of a Master-student, the last three attended programs are the Bachelor-program, high school, and lower secondary school. This distinction should be kept in mind when interpreting the results. The table shows that Master-students have slightly higher grade point averages at upper secondary school and lower secondary school than Bachelorstudents. In about half of the cases it is reported that the student attended a private school. As most students in primary and secondary education attend public schools, this suggests that attendance of private schools increases the likelihood of attending university (and this is confirmed in the econometric analysis
results in Columns 1 – 3, and results with recipient fixed effects in Columns 4 – 7. All regressions include year fixed effects. A causal interpretation of this result suggests that a policy improvement leads to additional aid. It is important to emphasize that all the regressions control for a one-year lag in the aid level, which means that our coefficient on policy captures _additional_ aid that a recipient receives from a policy improvement. Some regressions include a squared term, which is also statistically significant, implying a non-linear inverted U-shape relationship. The FE results estimate a turning point at a CPIA of 4.4. Only a very few countries in our sample have a CPIA measure that is larger than 4.4. The CPIA at the 95th percentile equals 4.38. Notice also that our fixed effect regression suggests that democratization is associated with more aid. Our democracy measure is taken from Acemoglu, Naidu, Restrepo, and Robinson (forthcoming) and updated in accordance with their methodology. Table 2 repeats the analysis for the years 1985 to 2014 using data from the PennWorld Tables for GDP and population, which cover a longer time-span. We obtain a very similar result.

We consider the results presented in Tables 2 and 3 as evidence that the “contest for aid” works. A recipient improving policies attracts a larger amount of aid. Figure 2 shows the partial point estimate for the CPIA conditional on the level of the CPIA. This figure reveals that there is a statistically significant relationship between the CPIA and the amount of aid given to a recipient for countries that have a CPIA rating below 2.7. Thus, incentives seem to work particularly for countries with low policy levels. Quantitatively, the estimates suggest that a CPIA measure change from 1.5 to 2 for a recipient is associated with about a 13% increase in aid.

With these findings, the question arises whether the “contest for aid” improves policies in recipient countries as our model suggests. Clearly, if improving policies is associated with more aid
.</mark>

- <mark>D'Erasmo, Pablo N., and Hernan J. Moscoso Boedo. 2012. "Financial structure, informality and development."</mark> _<mark>Journal of Monetary Economics</mark>_ <mark>59, no. 3: 286‐302.</mark>

- <mark>Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer. 2013. "The Next Generation of the Penn World Table." Available for download at www.ggdc.net/pwt.</mark>

- <mark>Fields, Gary S. 1975. "Rural‐urban migration, urban unemployment and underemployment, and job‐search activity in LDCs."</mark> _<mark>Journal of development economics</mark>_ <mark>2, no. 2: 165‐187.</mark>

- Friedman, E., Simon Johnson, Daniel Kaufmann, and Pablo Zoido‐Lobatón. 2000. “Dodging the grabbing hand: The determinants of unofficial activity in 69 countries.” _Journal of Public Economics:_ 76(3): 459–493.

- <mark>Fugazza, Marco, and Jean‐Francois Jacques. 2004. "Labor market institutions, taxation and the underground economy."</mark> _<mark>Journal of Public Economics</mark>_ <mark>88, no. 1: 395‐418.</mark>

- <mark>Günther, Isabel, and Andrey Launov. 2012. "Informal employment in developing countries: opportunity or last resort?"</mark> _<mark>Journal of development economics</mark>_ <mark>97, no. 1: 88‐98.</mark>

- <mark>Gupta, Manash Ranjan. 1993. "Rural‐urban migation, informal sector and development policies A theoretical analysis."</
food insecurity have? Third, do basic cross-country patterns in this indicator align with expectations? Because the GWP is conducted by a private organization and its collaborators, much of the description of the formal survey characteristics relies on Gallup materials. We explore correlations between the GWP indicator and non-GWP welfare indicators by conducting a correlation analysis of a cross-section of countries and, in the next section, a multivariate analysis of the full panel dataset.<sup>5</sup>

# **<<B>>** _General characteristics of the Gallup World Poll_

Since 2005–06, the GWP has interviewed households in approximately 150 countries, although not always annually. Most questions are constructed to have yes or no answers to minimize translation errors. In developing countries, all but one of the GWP surveys are conducted face to face (China 2009 is the exception), and most take approximately one hour to complete. The surveys follow a complex design and employ probability-based samples intended to be nationally representative of the entire resident civilian noninstitutionalized population aged 15 years and older. In the first stage of sampling, primary sampling units consisting of clusters of households are stratified by population size, geography, or both, with clustering achieved through one or more stages of sampling. When population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Gallup typically surveys 1,000 individuals in each country, except in larger countries such as India (roughly 6,000), China (4,000), and Russia (3,000). In the second stage, random route procedures are used to select sampled households within a primary sampling unit, and Kish grids are used to select respondents within households. Finally, the data are internally assessed for consistency and validity and then centrally aggregated and cleaned. Data weighting

> 5 Much of what follows is drawn directly from the Gallup _Worldwide Research Methodology_ (Gallup 2010a). The present author purchased country-level data directly from Gallup
(0.177)|(0.185)|
|Observations|7,276|7,276|7,276|7,276|
|R-squared|0.852|0.852|0.852|0.852|
|**Forward links**|||||
|_DVAR_|0.183***|0.191***|-0.0062|0.113**|
||(0.0177)|(0.0200)|(0.111)|(0.0571)|
|_FVADP_|0.232***|0.233***|0.233***|0.233***|
||(0.0258)|(0.0257)|(0.0257)|(0.0257)|
|_DVAR*Quality ISOs_|1.7e-05***<br>(5.3e-06)||||
|_DVAR*R&D intensity_||0.0268**<br>(0.0117)|||
|_DVAR*Technology adoption_|||0.0427**<br>(0.0210)||
|_DVAR*Innovation index_||||0.0280*<br>(0.0143)|
|_Constant_|4.904***|5.070***|4.923***|4.957***|
||(0.170)|(0.182)|(0.176)|(0.179)|
|Observations|7,284|7,284|7,284|7,284|
|R-squared|0.870|0.870|0.870|0.870|

Source: OECD ICIO database. *** p<0.01, ** p<0.05
the U.S. tariffs applied to Latin American goods by 25 percent. Using the results provided in the fi rst column of table 5, we fi nd that the joint political organization of importers and exporters decreases the U.S. tariffs applied to Latin America by 18 percent. Clearly, lobbying is a relevant force in the trade relations between the U.S. and Latin America.<sup>43</sup> Additional Robustness Results The results shown in the last section use data on agriculture and manufacturing to check whether lobbying by U.S. importers and Latin American exporters increases tariff preferences. Most of the literature, however, does not use data on agriculture. Therefore, an interesting exercise is to test whether the results discussed above are sensitive to the exclusion of data on agricultural goods. The methodology employed to obtain the results in table 7 is analogous to that used in table 1. The only difference is that in table 7 the agricultural sector is excluded.<sup>44</sup> The results presented in this table are similar to the ones discussed above. Again, the coefficients of interest have the predicted sign and are signi fi cant at the 1% level. Moreover, the structural parameters a and γ are signi fi cant at the 1% level and have similar values to the ones shown in table 1. Thus, the inclusion of agricultural goods is not essential to the results obtained in this paper.

Mitra, Thomakos and Ulubasoglu (2003) suggest that more disaggregated elasticities should have higher values, and this assertion could imply lower estimates of the parameter a. Since we are fi rst to use elasticities data organized at the HS 6-digit level, this might be behind our results. In table 8 we adopt the same methodology used in table 1, but employ data organized at the ISIC 3-digit level. The method discussed by Dickens (1990) is used to control for heteroscedasticity problems. Most of the coefficients of interest have the predicted sign and are also