rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4. Data and Research Design**

# **4.1 Conjoint Experimental Survey**

In order to measure Colombian policy preferences, we conducted a fully randomized choicebased conjoint experiment. Specifically, the conjoint design allows estimation of causal effects of different treatment components simultaneously in a multidimensional manner (Bansak et al. 2021; Hainmueller et al. 2014; Rodon and Sanjaume-Calvet 2020). As noted in Bansak et al. (2021) and Horiuchi, Markovich, and Yamamoto (2021), evaluating several attributes simultaneously helps to obscure the intent of researchers as well as allows respondents to justify their choices along any number of the dimensions. Indeed, evidence shows that fully randomised conjoint designs do mitigate social desirability bias (Dahl 2018; Horiuchi et al. 2021).

We presented respondents with hypothetical pairs of packages comprising policies towards Venezuelans that reflect current Colombian policy as well as options that policymakers in the country could plausibly implement. Each package contained randomly selected levels from within each of the six policy dimensions shown in Table 1. Then, each respondent was asked to choose which of the two policy packages they preferred more, as well as to rate each package on a 1-7 scale, where 1 indicated “absolutely dislike” and 7 indicated “absolutely like.”<sup>9</sup> Each respondent completed five trials (Figure A2 provides an example of the task within the survey interface). In line with good practice, we randomised the order of the dimensions between respondents to reduce ordering effects (where respondents disproportionately focus on items appearing near the beginning of a list), but kept it constant across the five trials to minimize survey fatigue. We also ask a series of questions that allow us to explore the level of concern regarding economic _vs_ humanitarian aspects, as well as respondents’ levels of social contact. <u>Table A2 and Table A3 in the</u> Appendix explain each of these variables in detail.

# **4.2 Survey Sample*
those from Carrel and Hoekstra (2010), we can conclude that it is possible to outweigh the negative effects of misbehaving children by incorporating students with improved or positive behavior in their classrooms. This result particularly contributes to the evidence of optimal class design, indicating that finding the optimal share of bad-to-good peers can maximize the total effect of a program (Krueger, 2003; Lazear, 2001).

In Appendix 4 we present a more complete analysis of the structure and characteristics of these spillover effects, such as optimal combination of treated with high and low violence level, intensity of exposure, and proximity on misbehavior between enrolled and nonenrolled children within classrooms. What do we find?

First, from the results in Table 4 we learn that exposure to a greater share of treated children increases nonenrolled student performance. However, that group of treated classmates can have different levels of violence before the intervention. Therefore, an important missing factor in these results is whether the high- or the low-violence group is causing the main spillover effects. In other

> 21After adding individual controls, estimated coefficients are similar in magnitude and statistical significance except for bad behavior reports, which are no longer statistically significant due to the increase in the standard errors. Despite this, the sign of the effect is negative, indicating that a higher share of treated classmates reduces the effect on bad behavior reports and provides additional evidence of reduction in the formation of violence networks or disruption during classes.

24
a variation of the model specified in equation 1, where we model heterogeneity by GDP and whether a firm is in a heat-sensitive industry. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model for firms not in heat sensitive sectors and columns labeled “high” evaluate the estimated model for firms in heat sensitive sectors within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

40
# **1 Introduction**

How has globalization affected the relative taxation of labor and capital, and why? Has international economic integration eroded the amount of taxes effectively paid by capital owners, shifting tax burdens to workers? If so, which countries have been most affected by this process and through which mechanisms? Answering these questions is critical to better understand the macroeconomic effects and long-run social sustainability of globalization.

To address these questions, this paper builds and analyzes a database of effective tax rates on labor and capital covering more than 150 countries since 1965. Constructed following a common methodology that combines government revenue statistics with national accounts data, these series allow us to study trends in labor and capital taxation comprehensively, globally, and over a long period of time. Our database captures all taxes paid at all levels of government: corporate income taxes, individual income taxes, payroll taxes, property taxes, estate and inheritance taxes, consumption taxes, and other indirect taxes. This makes it possible to estimate total tax wedges, for instance the gap between what it costs to employ a worker and what the worker receives. Because our series are based on national accounts data that are harmonized across countries, they can be used to meaningfully compare effective tax rates internationally and over time. Last, since capital income is always more concentrated than labor income, the relative taxation of the two factors of production is closely linked to the progressivity of the overall tax system. Our database thus provides insights into changes in tax redistribution over the last half-century.

To maximize the time and geographical scope of this database, we conducted a largescale digitization and harmonization of historical data published by national statistical offices, which we combine with existing (but limited in coverage) series published by the United Nations, the OECD, and the IMF. The construction of our effective tax rates proceeds in three steps. Using national accounts data we first compute total labor and capital income in each country. Using government revenue statistics we then classify all government revenue sources into either
***|0.213***||-0.118***|-0.159***|1.909***|
|||_0.000_|_0.000_||_0.000_|_0.000_|_0.000_|
|China IP Growth*Late||0.211***|0.148***||-0.072|-0.100***||
|||_0.000_|_0.000_||_0.000_|_0.000_||
|CRB Commodity Index Growth|||0.077***|||0.034***||
||||_0.000_|||_0.010_||
|Constant|0.031***|-0.034***|-0.018***|0.008***|0.032***|0.039***|-0.227***|
||_0.000_|_0.000_|_0.003_|_0.000_|_0.000_|_0.000_|_0.000_|
|Observations|4360|4360|4360|2661|2661|2661|212|
|R-squared|0.350|0.384|0.388|0.465|0.472|0.473|0.552|
|G7 Growth+G7 Growth*Late|0.935***|0.742***|0.653***|0.739***|0.810***|0.771***||
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_||
|China Growth+China Growth*Late||0.519**
fluctuations in their incomes, as reported in the household surveys. For example, over

**Figure 4: Distribution of Shocks**

<!-- Start of picture text --> Inconx shocks<br>.3 88 [2I.8Rsia<br>'S i i 7.66 .9S 1 .71h 95-96 N9<br>0 so1o N 0 N0 0 0 0 N 0.2 0<br>o 9 ea '' 'la -O So c (%) 0 1 0 N 0 10 I <<br>Size of Shock (%/)<br>Expenditure shocks<br>10.8 -1<br>0 5.9 the mean expndtue ws -. 3 i Ru s i ad 4 1 i Poan. oc inccteorisavdifeen8.2 I5f e tRussia<br>fall.9 o lo t la h l w lol0o i o 4 Poland o lo a0 4 W<br>_i__ Size of Shockicms<br><!-- End of picture text --> Note: The figure shows the distribution of the change in real equivalent income and real equivalent expenditure between the referunec month and the same month one year later. The reference month is the month in which the household was observed in wave 2 of the survey. In Russia, wave 2 was fielded in November and December of 1995 while in Poland wave 2 was fielded between January and December of 1994. Income and expenditure shocks are in deviation of the national mean. The mean income shock was -4.0% in Russia and 1.8% in Ploland while the mean expenditure shock was -8.3% in Russia and 4. 1% in Poland. Since categories have different sizes, these plots do _not_ show distribution functions.

40% of the population in Russia either sees their income increase to more than double, or fall to less than half, while in Poland this fluctuation happens to slightly more than 10% of
design of policies aimed at helping people move out of poverty and to the allocation across ministries or across subnational state or provincial governments of resources derived from national budgets, international donors, or multilateral development banks.

Somewhat paradoxically, it is precisely low-income countries, in which the statistical information needed to measure price variability adequately is rarely available, that are characterized by a

> 1 See BLS Data Viewer (graphic), time series: Milk, fresh, whole, fortified, per gal. (3.8 lit), time period: 2024, series ID: APU0000709112.

> 2 The base period of the index is 1982–84, and the index is not seasonally adjusted. For an overview, see Measuring Price Change in the CPI: Rent and Rental Equivalence (web page), US Bureau of Labor Statistics, Washington, DC, https://www.bls.gov/cpi/factsheets/owners-equivalent-rent-and-rent.htm.

3 This is in addition to any required correction for differences in the prices of consumption goods and services over time (inflation) and differences in household size and composition (adjusted through the use of equivalence scales).

2
MEX 0.6<br>MEX<br>0.55 PAN 0.55<br>PER<br>0.5 PER 0.5<br>0.10 0.20 0.30 0.40 PRY 0.00 0.20 0.40 0.60 0.80 1.00 PRY<br>Productive Informality URY Legal Informality URY<br>c. Comparison with the productivity definition of d. Comparison with the legal definition of<br>informality, country-economic sector level informality, country-economic sector level<br>1 BOL 1 BOL<br>0.9 BRA 0.9 BRA<br>0.8 COL 0.8 COL<br>CRI CRI<br>0.7 0.7<br>DOM DOM<br>0.6 0.6<br>ECU ECU<br>0.5 HND 0.5 HND<br>0.4 MEX 0.4 MEX<br>0.3 PAN 0.3 PER<br>0.2 PER 0.2 PRY<br>0.00 0.20 0.40 0.60 0.80 1.00 PRY 0.00 0.20 0.40 0.60 0.80 1.00 URY<br>Productive Informality URY Legal Informality<br>Job Quality Index Job Quality Index<br>Job Quality Index Job Quality Index<br><!-- End of picture text --> _Source:_ Estimates based on data of SEDLAC (Socio-Economic Database for Latin America and the Caribbean), Center for Distributive, Labor, and Social Studies, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, La Plata, Argentina, and Equity Lab, Team for Statistical Development, World Bank, Washington, DC, https://www.cedlas.econo.unlp.edu.ar/wp/estadisticas/sedlac/.

Similar patterns emerge in
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B1.3. SOSD: Social Sustainability Index B1.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.98*** 0.95*** 0.93***<br>H<br>Kendall tau-b 0.94*** 0.89*** 0.83***<br>Spearman 0.9*** 0.52 0.98***<br>A<br>Kendall tau-b 0.78*** 0.39 0.94***<br>Spearman 0.93*** 0.97*** 0.93***<br>M0<br>Kendall tau-b 0.83*** 0.89*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1 .2<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br>.4<
13|(50)|17.03|(19)|6.10|(14)|8.67|(17)|2.79|3.31|3.60|
|Transport|3.66|(41)|3.21|(17)|1.51|(11)|6.06|(13)|3.04|1.68'|<br>1.60|
|Communication|0.71|(11)|0.39|(5)|||0.99|(6)||||
|Finance|14.52|(46)|17.68|(20)|9.59|(11)|13.92|(15)||0.29|0.07|
|Real estate|4.79|(18)|10.09|<br>(7)|1.99|(3)|1.21|(8)|15.36|28.34|32.60|

_Sources: UN World Investment Directory; China Economic News, No 28, 25th July, 1994. Data on Chinese transportation and communication are combined._ The variations in FDI shares in both the primary and the tertiary sectors are more noticeable. While the world average for FDI stocks in the primary sector (which comprises agriculture, mining and quarrying, and petroleum) is 22 percent, only a little more than 8 percent of the FDI in developed countries goes to this sector; yet more than a third of Asian FDI accumulation is here. On the average, the mining industries attract the most FDI in the primary sector, while agricultural production the least. This trend is especially reflected in the developed economies and the Latin American and the Caribbean countries, where the share of FDI in mining and quarrying is 5.3 and 4.6 times, respectively,
# 1. Introduction

Timely and consistent statistics are essential to inform and monitor economic, environmental, and social development. Yet to be used in decision making, statistics need to be more than of good quality. They need to be timely and trusted. <mark>Trust in official statistics comes, broadly speaking, from two sources (Brackfield, 2011). The statistics themselves must be trustworthy and credible. Next, the institution producing the statistics needs to be trusted. Openness and transparency affect trust in official statistics through both pathways. Transparency allows the public to assess the methods and data used and increases trust in the organization itself. I</mark> n addition to being important for trust in official statistics, statistical transparency also yields an attractive return. Research in middle income contexts demonstrates that the availability of quality, transparent, and timely disseminated macroeconomic and financial data reduces sovereign borrowing costs on international capital markets. Adherence to the Special Data Dissemination Standards (SDDS), for instance, lowers borrowing costs by 50 basis points as it reassures international investors on the reliability and serviceability of a country’s economic and financial data (Cady, 2005).

In this paper, we examine two aspects of statistical quality, microdata collection and access. We focus on microdata for three reasons. They are an important source of data, especially for researchers, who without it often would not have the ability to carry out their work on nationally representative samples. The <mark>demand for readily available microdata can be illustrated with the 2017 Djibouti Household Survey. Its data have been downloaded 2078 times even though the data was only uploaded on the World Bank microdata library in June 2019. After 20 months since the data have been publicly released, Google scholar already gives 290 hits of academic articles that have been prepared using this data set (checked on 23 Feb 2021). The inflow of research with new data strengthens the analytical capacity of the national statistical system and has huge marginal gains especially for lower income countries that are
. Excise tax increases poverty more for men. The poverty reduction effect due to subsidies and transfers is mixed. Spending on PSNP, primary and secondary education, and health reduced poverty more for women than men. There are no gender differences in the poverty-reducing effect of tertiary education.

In what follows, Section 2 discusses the methods focusing on intrahousehold allocation, commitment-to-equity (CEQ) framework and poverty and inequality measures. Section 3 describes the data and assumptions. Section 4 reports the results and discusses gendered-impact differentials. Section 5 draws conclusions.

# **2. Methods**

The analytical approach begins with presenting the intrahousehold allocation mechanism used to identify individual level income. It then summarizes the commitment-to-equity (CEQ) framework used to analyze the incidence, progressivity, and pro-poorness of the fiscal actions as well as their impacts on poverty and inequality at different income concepts. The fiscal actions in this study cover most of the taxes and government spending items (see Annex 1).

## **2.1. Intrahousehold Allocation**

Availability of sex-disaggregated data on income and expenditures is key to examine genderdifferentiated welfare impacts of fiscal policy. When there are inequalities in intrahousehold 4
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# **I. Introduction**

Large-scale infrastructure investment can facilitate structural change of an economy. The literature is generally supportive of this: There is a wide range of direct and indirect effects of large infrastructure investment (e.g., Datta, 2012; Donaldson, 2018; Duranton and Venables, 2018; Ferraz and Coutinho, 2019; Banerjee et al., 2020; IMF 2020; Li et al., 2020). However, the long-term impact remains open to debate when causality is considered (e.g., Jiang et al., 2017; Valila, 2020). One of the most important empirical difficulties comes from potential endogeneity between economic outcomes and infrastructure placement (e.g., Datta, 2012; Banerjee et al., 2020). While infrastructure investment is generally believed to raise the productivity of an economy, public infrastructure is often placed where economic productivity is inherently high. Therefore, regardless of its real impact, positive correlation is likely to be observed between them.

Using micro data from Kazakhstan, the current paper attempts to address the endogeneity issue and examine the effects of large-scale transport infrastructure investment. Kazakhstan is a large landlocked country with a low population density. Transport connectivity has long been an important challenge to the country. In the early 2010s, the government embarked upon a massive infrastructure investment program, Nurly Zhol, to redevelop public infrastructure. In the transport sector, the government doubled road investment in regional corridors connecting major cities to Western Europe and Western China. The paper shows that the improved regional connectivity allowed firms to reduce their inventory, increasing their productivity.

In the literature, there are at least two approaches to deal with the endogeneity problem of infrastructure investment: The first approach is the computational general equilibrium (CGE) model, which captures various demand and supply effects as a system of equations. The CGE approach can analyze causality of different economic activities, including transport infrastructure investment. A number of regional and national CGE models exist (
>Comparability of expenditure and income data.</mark>** <mark>In most developing countries, consumption expenditure is used as a proxy for welfare. Theoretically, consumption is likely to remain more stable over time than income, which can be affected by seasonality and other shocks. Practically, it is extremely difficult to collect accurate income data in developing economies with high informality, a large prevalence of selfemployment or own-account workers, reliance on foreign remittances, in addition to smallholder agricultural production for home use. Income measures are especially problematic due to difficulties in estimating self-employment profits, falsified responses and underestimation (Deaton and Zaidi 2002; Deaton 2019). For these reasons, even though the HIES collects data on income, it is more reliable to continue using consumption as a proxy for income in the REM approach.</mark>

For comparison, household income aggregates were estimated using the data in HIES 2018-19. A household’s total income is given by the sum of all the following sources:

- 1) Work or job-related annual income from main, secondary and other occupations;

- 2) In-kind payments for employment as well as transfers such as pensions;

- 3) Main and secondary income from agriculture (including in-kind transfers) for those in agriculture;

- 4) Unearned income sources including local and international remittances, Zakat/Ushr, inheritances, income from insurance, the Benazir Income Support Programme;

- 5) Income earned (rents or sales) from agricultural and non-agricultural land;

- 6) Rent or sale of animals and equipment; and

- 7) Rent or sale of household enterprise property and equipment.

To enable comparisons with the consumption aggregate, self-reported imputed rents for owneroccupants are included to ensure comparability. Without accounting for these, income falls short of consumption for most households.

Estimates from Pakistan reveal that household incomes are higher than expenditures. This relationship holds across all quintiles in urban Pakistan, with the differences between the two estimates being significant - as highlighted by the non-overlapping error bars in
five, we explore the relationship between these developmental gaps and various factors including access to services, regularization status, and duration of stay in Colombia. The final section, section six, presents concluding remarks, summarizing key findings and reflecting on their implications.

# **II CONTEXT: VENEZUELAN MIGRATION TO COLOMBIA**

# **II.A Venezuelan migration to Colombia**

Over the past decade, more than 7.1 million Venezuelans, roughly 25% of Venezuela’s population, have been forced to flee their country due to economic collapse, political instability, and a dire humanitarian crisis. A majority of these displaced individuals, exceeding 6 million, sought refuge in Latin American countries, with Colombia becoming the primary destination, hosting approximately 2.8 million Venezuelan migrants (Migraci ́on Colombia, 2023).

The geographic distribution of Venezuelan migrants in Colombia, as illustrated by the 2018 population census in Panel (a) of Figure 1, shows that these migrants primarily settled in border areas and Colombia’s major cities such as Bogot ́a, Medell ́ın, C ́ucuta, and Barranquilla. Panel (b) of the same figure outlines the annual influx of Venezuelan migrants to Colombia over the last decade, highlighting a marked increase in arrivals from 2016, with a peak around 2019.

# **II.B Colombia’s policy response**

The Colombian government has adopted a generous approach towards migrants through an unparalleled policy response to the migratory crisis. It has rolled out extensive regularization programs specifically aimed at Venezuelan migrants. These temporary initiatives offer migrants full access to labor markets and ensure their integration into the comprehensive of social and nets available in the This array programs safety country. strategy has not only had a significant impact within Colombia but has also set a precedent for the region, with countries like Chile, the Dominican Republic, Ecuador, and Peru launching 10