rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

A flexible, well-functioning, and entrepreneurial labor market can contribute to economic growth through the efficient allocation of labor and increased competitiveness. A better understanding of the determinants of entrepreneurship – the environment that motivates and supports the creation of self-employment – is essential for understanding the microeconomic foundations of economic growth. Conceptually, the self-employed can be considered as the smallest, but initially most vital unit of entrepreneurial activity. Self-employment was not considered conducive to growth until the 1970s, as it lacked in the emergence of scale economies and the specialization of labor (Blau, 1987). However, this negative trend was overturned in more recent years, and in the transition countries of Eastern and Central Europe, the small and medium enterprise (SME) sector has been the largest creator of new jobs and the vast majority of these new enterprises are small businesses (Ayyagari, Beck and Demirguc-Kunt, 2004; Klapper, Sarria-Allende and Sulla, 2004). Thus, the dynamics of this particular group are of great interest since the literature has indicated that the self-employed have distinct individual and labor market characteristics. In the past few years, there has been a notable amount of interest on issues of labor market mobility and transition into self-employment in developing countries.

Departing from this strand of the literature, we examine the nature of the entrepreneurial decision for the transition to self-employment in Bosnia and Herzegovina (BiH) and its viability, using a rich panel survey for the years 2001-2004. BiH is both a country in transition that emerged from a communist background and post-conflict, following the violent collapse of the former Yugoslavia. An interesting feature of this case study besides the rich dataset is that in the early period of the panel (2000-2001) the country changed its legal framework concerning labor regulation and the business environment. Therefore, BiH offers an interesting setting to examine the dynamism of latent entrepreneurship that was restricted before the market reforms.

The data allow us to directly identify individuals that switched to selfemployment. Compared to studies treating self-employment per se as the dependent variable, the examination of entry into self-employment can provide a better insight into the social origin, financial circumstances, and career choices of new entrepreneurs. Of 1
ways, monetary assistance from OPORTUNIDADES provides a secure income stream that may help alleviate liquidity constraints and grant access to capital, leading to increased investment<sup>6</sup> .

This study uses the randomized experiment from the rural evaluation of the OPORTUNIDADES conditional cash transfer program to estimate the impact of an exogenous increase in unearned income on investment and long-term living standards. We find that beneficiaries invest in production and draft animals, and that previously landless beneficiary households obtain land for agricultural production. Furthermore, there are significant increases in the number of households that operate micro-enterprises. Our estimates indicate that for each peso transferred, beneficiary households consume 88 cents directly, and invest the rest. The aggregate effect of the investments yields a 1.8 cent increase in consumption for each peso of transfers received. These estimates indicate an estimated rate of return on investment of between 15.52% and 17.55%. Through investments in productive activities, beneficiary households increased their consumption by 34% after five and a half years in the program. Because investments are made in productive assets and income-generating activities, the gains in living standards will likely be maintained even after program benefits end.

Our study contributes to the relatively small literature that examines the potential impact of cash transfer programs on long-term living standards. Ravallion and Chen (2005) show that the lion’s share of temporary cash transfers given by an anti-poverty program in China were saved. Sadoulet, de Janvry and Davis (2001) argue that payments from the Mexican agricultural support program PROCAMPO are invested in farm production, and estimate an income multiplier in the range of 1.5 to 2.6. While not related directly to cash transfer programs, studies by Yang (2005) and Woodruff and Zenteno (2001), show that remittances are used as capital to invest in microenterprises in the Philippines and Mexico, respectively.

The paper is organized as follows. Sections 1 and 2 describe the OPORTUNIDADES human development program and review the program’s experimental design and data. In section 3,

> 6 Investment will also depend on expectations about the transfer’s temporal horizon and life-cycle considerations (Modigliani and Brumberg 1954; Friedman 1957 and Hall 1978).

2
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# Evaluating the Asset Based Minimum Tax on Corporations: An Option Pricing Approach

by Antonio Estache and Sweder van Wijnbergen

# Table of Contents

1.

|1.|Introduction|2|
|---|---|---|
|2.|The Design of a Minimum Tax|4|
|3.|A Framework to Assess the Value of Minimum Taxes|6|
||3.1 Introduction<br>3.2 Option Pricing and the Minimum Asset Tax|6<br>8|
|4.|Monte Carlo Evaluation of the Minimum Asset Tax|11|
||4.1 A Simulation Model<br>4.2 Simulating the Incentive Effects of Various Tax<br>Designs<br>4.3 The Lessons for Brazil|12<br>13<br>22|
|5.|Conclusions|26|
|Refe|rences|27|

The authors are indebted to S. Ahmed for providing the data used in this paper. They thank Stijn Claessens, Ricardo Martin, and Jack Mintz for helpful comments and discussions.
- <mark>T</mark> ���� is a binary variable equal to 1 if the mother j of child i spent some years of her early childhood <mark>out and others inside the first and second period of refugees’ presence (was born or less than five years old between 1988-1996 in Figure A2), and 0 otherwise (“control cohort” in Figure A2).</mark>

<mark>We also allow for heterogeneity of mother’s age by the end of the first (1993-1996) refugee influx by constructing a categorical variable ( d� that captures different stages of mothers’ early childhood development with</mark> _<mark>d</mark>_ <mark>=0 if the mother was born before 1988 (base category),</mark> _<mark>d</mark>_ <mark>=1 if she was in utero</mark> _<mark>d</mark>_ <mark>=2 if she was between 0 and 2 years old and</mark> _<mark>d</mark>_ <mark>=3 if she was between 3 and 5 years old by the end of the first refugee influx period (December 1996). Recall that this corresponds to the period before most of the camps (mainly the Rwandese camps) started closing. Heterogeneity of mothers’ age at the time of the crisis is modeled in a separate equation described as follows:</mark> <mark>where T</mark> �� <mark>refers to mothers who were in age 0-5 between 1993 and 1996, d</mark> �� <mark>refers to the mothers’ cohort with</mark> _<mark>s</mark>_ <mark>=1 if the mother was in utero,</mark> _<mark>s</mark>_ <mark>=2 if she was 0 to 2 years old and</mark> _<mark>s</mark>_ <mark>=3 if she was 3 to 5 years old. The analysis is restricted to mothers born after January 1, 1979 (less than 16 years old at the time of the first refugee inflow in 1993) and throughout our analysis our control group consists of mothers born between 1979 and 1988 (more than 5 years of age in 1993).</mark>

# **6.** **<mark>Results</mark>**

## **6.1** **<mark>Means Difference Tests</mark>**

<mark>Before moving forward with the econometric analysis, we provide simple means t-tests on the main outcomes of interest between refugee and non-refugee affected areas before the refugee crisis using the 1991-1992 DHS. Table 1 provides the results splitting the sample into its 1991 and 1992 components to allow for some heterogeneity between the two years covered by the DHS. While it would be plausible to find differences given the different geographical areas considered, we do not find any statistical difference in our main outcomes of interest (HAZ and stunting). This provides a nice benchmark for the econometric analysis.</mark> <mark>Similarly to the geographical comparison, in Table 2 we compare 0-5 and 6-16 y.o. cohorts of children using the 1991-1992 DHS (mothers in 2015/2016). As before, we could expect some differences but the t-tests show no difference in the main outcomes considered (HAZ and stunting). In Table 2, we also</mark> 15
As unit of observation, we use what the UNHCR refers to as the “case”. A case is a group of individuals who register at the UNHCR together with a principal applicant (PA) who takes responsibility for the group. This group may be a family, a household or an extended household. For simplicity and practical purposes, we will consider a case and the PA as a household and its head respectively. The poverty line used is 50 JD/month/person, which is what the UNHCR used in 2014 to select beneficiaries of the cash assistance program. In 2014, this poverty line was higher than the international poverty line and lower than the poverty line used for the Jordanian population. In our case, this poverty line is more relevant than either the national or international poverty line, as it corresponds to what the UNHCR—the UN agency specialized on refugees— considers a sufficient amount to meet basic needs. As for the welfare aggregate, we use the same aggregate used by Verme _et al._ (2016) and Verme and Gigliarano (2019).<sup>8</sup>

# **III.2. Estimation Results**

For the purpose of this paper, the HV data are considered the “survey” data containing information on consumption and the PG registration data are our “census” data containing predictors of consumption but no consumption data. The primary objective of the exercise is, therefore, to test how accurate the estimated poverty figures are using the PG data alone (as both the base and the target survey).

As a first step, we generated two samples by extracting 50% of observations from the HV sample randomly (Sample 1) and using the remaining observations as second sample (Sample 2). We then impute from Sample 1 to Sample 2 to obtain the imputation-based poverty rate in Sample 2, and we compare this imputed poverty rate with the true poverty rate that can be directly calculated from Sample 2 for robustness checks. We also implement this imputation process the

> 8 A full explanation of the consumption aggregate is provided by Verme _et al._ (2016).

10
# **Distance from Rebel Group Headquarters**

We coded the location of the headquarters of the rebel groups participating in the conflicts under study, and calculated the distance from each square to the most proximate rebel group headquarters (we do not know _a priori_ which rebel group or government that will act in a particular square). As the `distance from capital' variable, it was coded as the distance in terms of squares and log-transformed.

# **Border Square**

We coded squares as border squares if a national border runs through it. Such squares belong to more than one country and are not straightforward to code. We coded nationallevel information for border squares according to the following rule: A border square was considered to belong to the country that was most frequent among the eight neighboring squares. In tie cases, we assigned nationality randomly between the tied countries.

# **Interaction country-square population**

This variable was created to test the population settlement pattern hypothesis. It is an interaction between population count at a location (square) as a portion of the country's total population.

# **Road type**

Road type is a variable by ESRI that is available in the Digitial Chart of the World Data. It is a high resolution dataset at 1:1,000,000 scale and consists of arcs which indicate road mass. A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane/divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code 21
[Preferred estimate] The great heterogeneity in the form and function of government banks and the likelihood that there is only one or two (or zero) in any given country renders suspect any econometric regression estimates as were attempted above for commercial banks. Similar to the MOFI estimate, we start with the reported data we have and fill in missing values with data from a previous counting exercise conducted by CGAP presented in CGAP Occasional Paper no. 8. Since this older data is likely to underestimate the true numbers and because there are many countries which likely have government run deposit taking institutions such as national savings banks or postal banks but for which we have no data, we believe our estimates understate the true value.

[High estimate] We don’t make a high estimate (i.e. we assume it’s the same as the preferred estimate) given the likelihood that there is only one or two (or zero) specialized government banks in any given country.

[Low estimate] We use only our reported numbers and leave out the numbers from CGAP Occasional Paper no. 8 on the grounds that in a few cases, they may be double counted as commercial banks, despite their state ownership (we believe this is may have occurred in just a few cases).

# **_4.1.4 Microfinance Institutions_**

For MFIs, we take data from the MIX and use it to supplement our own data for countries which reported nothing. Many MFIs are licensed as a commercial bank, cooperative, or NGO and thus might be counted in the numbers for other categories supplied to us by the regulator so we have removed cooperatives and banks from the MIX data. Since the MIX data are voluntarily reported, and because the data reported to us in our survey covers only 17 countries, we believe these estimates are significantly on the low side. Also, though most of the reported account numbers from the survey data will be from regulated institutions that have a supervisory relationship with the main bank regulator, the MIX includes many institutions which are not supervised or regulated.

# **_4.2 Mapping Bank Accounts around the World_**

19
**Table 5: International Country Risk Guide  Controls**

||1<br>[OLS]|2<br>[OLS]|3<br>[OLS]|4<br>[OLS]|5<br>[OLS]|6<br>[OLS]|
|---|---|---|---|---|---|---|
|Total Outstanding Debt/GDP (initial)|-0.912|-0.763|-0.912|-0.887|-0.908|-0.991|
||[1.80]*|[1.52]|[1.79]*|[1.76]*|[1.80]*|[1.92]*|
|Initial GDP per Capita|-0.109|-0.283|||||
||[0.40]|[1.07]|||||
|Population Growth|-62.641|-63.279|-62.649|-60.607|-66.274|-64.203|
||[2.12]*|[2.18]*|[2.12]*|[1.99]*|[2.16]*|[2.21]*|
|Secondary Schooling|0.477|0.495|0.476|0.432|0.38|0.572|
||[1.35]|[1.38]|[1.36]|[1.13]|[1.08]|[1.56]|
|Trade Openness|1.162|0.936|1.165|1.234|1.369|1.214|
||[1.48]|[1.17]|[1.47]|[1.50]|[1.74]*|[1.52]|
|bureaucratic quality||0.569<br>[1.76]*|||||
|democratic accountability|||-0.007<br>[0.03]||||
|internal conflict||||-0.052|||
|||||[0.34]|||
|external conflicts|||||-0.295<br>[1.73]*||
|ethnic tensions||||||0.202<br>[1.12]|
|Observations|211|211|211|211|211|211|

Robust t statistics in brackets * significant at 10%; * significant at 5%; ** significant at 1% 27
estimated as reflected by the lower standard errors (Table 9).

Finally, and also related to potential issues of measurement error, the results appear to be robust to alternative definitions of the shock that are based on different critical thresholds (i.e. using different standard deviations), different computations of the z-score (using the historical mean and the median) and a continuous treatment (total rainfall recorded in May, 2010). Additionally, results do not change much when we re-estimate the main empirical models drawing from a larger balanced panel of weather stations (covering the period 1990-2011) to allocate households to the treatment and comparison groups based on alternative measures of long-term rainfall trends.<sup>16</sup>

# **5.3 Interpretation**

Overall, the findings of this paper –in line with most of the existing evidence– highlight that the well-being of households is sensitive to the consequences of weather-related disasters. However, the results depart from existing research by documenting that relative to rural households, urban families appear to have carried the heaviest burden of the consequences of the shock –at least in terms of household consumption and poverty. The question is why were urban households disproportionally affected by Agatha? Unfortunately, data limitations make it difficult to empirically disentangle the mechanisms driving the impacts. Yet, in what follows we posit some informed hypotheses about the possible leading channels.

The first observation has to do with the magnitude of the shock itself. Whereas Agatha dropped record levels of rain across several parts of Guatemala, households located in urban areas experienced substantially stronger rainfall shocks compared to rural households. Household weighted mean standardized precipitation anomalies in urban and rural areas attributed to the shock were 3.7 and 3.0 z-scores, respectively. Nonetheless, the average masks large variation in excessive rainfall across areas. Figure 4 plots the density of the z-scores for affected households by area. It reveals that close to 40% of the households in urban centers were hit by rainfall levels that exceeded the historical mean by six or more standard deviations. We ran regressions of the base model (equation 1) on household consumption for the urban sample to spot impact heterogeneity for households that were exposed to rainfall anomalies of

> 16 These results are not shown in the paper but are available from the authors upon request.

19
ipation is multifaceted and diverse across countries. This paper mainly focuses on backward GVC participation measures that have been recently developed in the empirical literature, drawing on newly available international input-output tables, including the EORA database, the Trade in Value Added (TIVA) database and the World Input-Output Database (WIOD).

A country’s backward GVC participation measures the import content of its exports relative to the country’s total gross exports. Those imported inputs are predominantly made up of foreign value added but can also contain domestic value added which has been previously exported. The import content of export measure was ...rst introduced by Hummels et al. (2001) using national input-output tables and was more recently computed by Borin and Mancini (2015) and Wang et al. (2013, 2016) based on international input-output tables. This measure takes into account indirect e¤ects (known as the Leontief inverse matrix) where imported inputs are embodied in domestic output, sometimes at several stages, before being used as inputs for exports.

As a robustness check, we also consider forward GVC participation. A country’s forward GVC participation measures the domestic value added in exports that is used by the country’s bilateral partner countries for export production as percent of the country’s total gross exports. In other words, it captures the portion of domestic value added that is not directly consumed by the bilateral partner (which is the ...nal stage in the value chain). For example, ...nal apparel exports from Bangladesh that are exported to and consumed in the United States (US) would not be accounted for in this measure. The measure was developed in Borin and Mancini (2019) based on a bilateral source-based decomposition of exports.<sup>6</sup> Both backward and forward GVC participation measures are such that their construction ensures that trade ‡ows cross at least two country borders –a key characteristic of a GVC.

The country-level backward and forward GVC participation measures are additionally decomposed into measures for four broad sectors: agriculture, mining, manufacturing, and services. In order to obtain these measures, we split the country-level numerator into these

> 6For a technical discussion of these and other GVC measures, see Aslam et al. (2017) and Borin and Mancini (2015, 2019). Koopman, et al. (2014) propose an alternative forward GVC participation measure: a country’s domestic value added that is used by third countries for export production. We do not use this measure as it double-counts the domestic value added component if it is used in the downstream processes of multiple countries (e.g., petroleum) and thus can counterintuitively exceed 1 as a share of the country’s gross exports.

4
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WTO (WTO, 2010), world’s imports contracted 24 percent in 2009. Thus our results show that trade policy changes at most can explain less than 2 percent of the collapse in world’s import during crisis period.

# 5 Conclusion

The fear that countries may raise tari¤s to protect the domestic market in the wake of the largest global recession since the Great Depression has not materialized. Comparing the published 2008 and 2009 tari¤ schedules of a wide range of countries shows that only a handful of countries have raised their tari¤s in a signi...cant way. These countries include Russia, Malawi, Argentina, Turkey and China. The increase in motor vehicle tari¤s in Russia not only restricted imports, it also caused one of the ...rst reported crisis related demonstrations. For some other countries, such as the US and the EU, most of the policy actions during the crisis period are not about tari¤s but anti-dumping duties. Nevertheless, even after taking anti-dumping duties into account, evidence provided in this paper suggests that the trade impact due to trade policy changes during the crisis period is minimum, and can explain no more than 2 percent of the collapse in world trade.

There are a few reasons why countries have not, so far, used tari¤s as a policy instrument. First, the multitude of multilateral, regional and bilateral trade agreements impose limits on the use of traditional trade policy instruments such as tari¤s. Second, many countries may be more inclined to use non-tari¤ measures such as bail outs and local content requirement to discriminate against imports. Overall, there are as many as 50 countries that have bail outs or state assistance. Some countries, such as the US and China also include local content requirements in their stimulus packages which discriminate against imported products. Third, trade policy generally is a response 15