rafmacalaba/datause-encoder-data huggingface dataset:
depreciation, are presented below. Very strong convergence was achieved in 28 iterations. The value of the log-likelihood is 198.18. High and low frequency cycles (common to all endogenous variables in the system) were estimated, with periods 1.55 and 2.92 years, respectively. For the first difference of log real MI and GDP, estimatin results are satisfactory, in terms of the normality and heteroskedasticity of the residuals.

**Venezuela: Combined Model for First Differences of Logs of Real Money Balances, Real GDP and Depreciation Rate** **_Key Summary Statistics (T=34)_**

|Statistic|ALog<br>**RealMI**|ALog<br>**Real**<br>**GDP**|ALog<br>**Depr.**|
|---|---|---|---|
|Normality(Bowman-Shenton):n_-_ X22|1.25|0.31|3.87|
|Skewness:_s-_ **Xi2**|0.32|0.05|3.34|
|Kurtosis:_k_|0.93|0.25|0.52|
|Heteroskedasticity:H(I0)_- F1o_lO|0.30|0.20|0.09|
|Autocorrelation (upIto12U"order)|16.17|12.57|23.70|
|(Box-Ljung):Q(12,65)- _X62_||||
|Autocorrelation (first order)|2.28|1.83|3.05|
|GoodnessofFit(w.r.t.seasonal mean)|0.60|0.54|0.70|
|GoodnessofFit(ordinaryR<sup>2</sup>)|0.79|0.73|0.19|
|Akaike**Information**Criterion|-3.68|-5.55|-1.23|
|Bayes Information Criterion|-2.20|-4.07|0.25|

The skewness of the distribution of the residuals of the equation corresponding to the first difference of log of the rate of depreciation, is somewhat higher than has been the case with most of the other models; however, at standard 5 percent critical values it is still consistent with the null of normality of residuals. At conventional critical values, there is nevertheless evidence of (higher-order) residual autocorrelation, except for the equation associated with the change in log real GDP. Goodness of fit measures, including with respect to seasonal means, are otherwise satisfactory.

Money Demand in Venezuela, Mario A. Cuevas 22
|**Table A30: U**|**.S. Department of State Trafficking in People Report (TPR)**|
|---|---|
|**Data Provider**|United States Department of State|
|**Description**|Foreign affairs department of the United States Government.|
|**Website**|www.state.gov|
|**Data Source**|Trafficking in People Report|
|**Type**|Expert assessments|
|**Respondents**|United States embassy staff worldwide subject to centralized review|
|**Frequency**|Annual since 2001|
|**Coverage**|Global country sample|
|**Public Access**|Yes|
|**Description**|This report scores countries on a four-point scale based on the extent of government<br>efforts to combat "severe trafficking in persons" defined as (a) sex trafficking in<br>which a commercial sex act is induced by force, fraud, or coercion, or in which the<br>person induced to perform such act has not attained 18 years of age; or (b) the<br>recruitment, harboring, transportation, provision, or obtaining of a person for labor<br>or services, through the use of force, fraud or coercion for the purpose of subjection<br>to involuntary servitude, peonage, debt bondage, or slavery. Note that the reports<br>refer to data from the previous year: we therefore lag the data from this source by<br>one year and that for 2008 we used same data as previous year.|

|**Voice and Accountability**|2008|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|---|
|NA|..|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||||
|Trafficking in People|X|X|X|X|X|X|X|X|||
|**Control of Corruption**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Country coverage**|**153**|**153**|**151**|**149**|**142**|**131**|**116**|**82**|**..**|**..**|
|**Year of Publication**|**2008**|**2008**|**2007**|**2006**|**2005**|**2004**|**2003**|**2001**|**..**|**..**|

69
syste <mark>m dataset for</mark> the time period January 2008 to December 200 <mark>9.</mark><sup>84</sup> We follow Bessembinder et al. (2009) by eliminating cancelled, corrected, and commission trades from the data. We merge the remaining bond trades with the Fixed Income Security Database (FISD), which contains information regarding each bond’s rating, time to maturity, coupon payments, and other characteristics. Our sample includes all U.S. corporate bonds listed in <mark>FISD t</mark> hat satisfy a set of selection criteria commonly used in the corporate bond literature.<sup>85</sup> We remove all bonds with equity or derivative features (i.e., callable, puttable, and convertible bonds), bonds with warrants, bonds with floating interest rates, and bonds with any credit enhancements. We also eliminate all bonds that have less than one year to maturity or have been issued less than a year ago, and require all bonds to have an assigned rating and SIC code. As we are interested in industrial firms, we remove all financial firms from the sample (SIC codes 6000-6999). So as to not bias our analyses, we also remove from the sample Ford, General Motors and Chrysler. To be able compute daily bond returns, we also require bonds to have traded on two consecutive days over the sample period. Our final sample consists of 508 bonds from 269 firms. � Bond returns are calculated as: where _Pt_ is the clean price of the bond, _AIt_ is the accrued interest over one period, and _Ct_ is the coupon payment whenever it is paid (in which case _AIt_ = 0). To remove potential data entry errors, we follow Bessembinder et al. (2006) by requiring that the absolute value of the return be less than 30%. As there are multiple bonds for each firm, company-level returns are computed by value-weighting individual bond returns using their market values.

To identify firms subject to collective bargaining, we go through 10-K reports filed with the U.S. Securities and Exchange Commission by the 269 firms in our

> 84  TRACE reports prices for roughly 95% of all corporate bond transactions.  The only transaction that are not reported are those that take place in exchanges.

> 85 _See,_ for instance, Duffee (1999), Collin-Dufresne, Goldstein, and Martin (2001) and Avramov et al. (2006).

23
SPLIT DECISIONS

# 1. Introduction

Overseas workers are expected to send home over US$600 billion per year by 2014, double the amount just 8 years before (Ratha and Silwal 2012). This has raised interest in how international migration might finance investment in developing areas.

But migration affects more than just incomes. Another important effect is that overseas jobs, unlike many local jobs, often disrupt and split households for extended periods. Substantial literatures have separately studied the effects of new income and changes in household composition. These two effects are bundled in labor migration, but research has only begun to accurately measure the net effects or gauge the relative importance of each mechanism. Existing efforts face major empirical challenges in accounting for migrant self-selection.

We present estimates of the effects of temporary overseas work in a setting that allows unusually reliable causal identification. We study a sample of the households of 25,320 workers in the Philippines who applied to high-wage temporary jobs in Korea between 2005 and 2007. Each applicant was required to pass a basic test in the Korean language, and all those who failed were unable to take the job. In 2010 we surveyed the households of those who barely passed for comparison to the households of those who barely failed. This regression discontinuity design allows uncommonly reliable attribution of differences in those households to the effects of overseas work. Our purpose-built survey allows nonexperimental tests of impact mechanisms.

We contribute to the literature in three principal ways. First, we offer well-identified estimates of the reduced-form effect of large increases in income for one member of a low-income household bundled with the change in household composition that accompanies overseas work. Each of these bundled effects, separately, is the subject of an active literature. Second, we explore theoretically and empirically the relative importance of these mechanisms for the effect of overseas work. Third, we offer evidence on how the subjects of this natural experiment differ from representative Filipinos, in both observed and unobserved traits. We explore unobserved differences by comparing our 1
change scenarios predicted by Atmospheric-Oceanic Global Circulation Models (AOGCM’s). We rely on a broad set of scenarios that predict a wide range of outcomes, consistent with the expectations in the most recent IPCC report (Houghton et al. 2001). Specifically, we use the A1 scenarios from the following models: CCC (Boer et al. 2000), CCSR (Emori et al. 1999), and PCM (Washington et al. 2000). In each case, a countryspecific forecast is generated by weighting each model grid zone by its population.

Table 6 summarizes the climate scenarios of the three climate models. The Latin American mean temperature and rainfall predicted for the year 2020, 2060, and 2100 are presented. The models provide a range of predictions: PCM predicts a 1.9 ° C increase, CCSR predicts a 3.3 ° C increase, and CCC predicts a 5 ° C increase in average temperature in 2100. The temperature projections of all the models steadily increase over time. The models also provide a range of rainfall predictions. PCM predicts a general increase in rainfall, whereas CCSR and CCC predict a reduction by 2100. The rainfall predictions of the models do not steadily increase but rather have a noisy pattern over time. The climate change predictions, however, are country-specific. For example, even though the mean rainfall for Latin America might increase (decrease), some countries will nonetheless experience a reduction (increase) in rainfall.

For each climate scenario and each time period, we add the climate model’s predicted change to the baseline temperature in each district. We then multiply the climate models predicted percentage increase in precipitation times the baseline precipitation in each district or province. This gives us a new climate for every district in Latin America. We then compute the land value per hectare (or net revenue) of the current climate and each new climate. Subtracting the future land value (net revenue) 15
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increases in the stock of skills and wage premium characterize carbon reducing technology as being skill-biased in our model.

Table 8. Marginal effects of an increase in carbon price

||Mil|Elasticities:<br>endogen<br>(effect on em<br>effec|marginal effect<br>ous variable per<br>issions is negati<br>t on the variable|expressed as a pe<br>percent change i<br>ve; negative elas<br>is positive & vic|rcent change in<br>n emissions<br>ticity implies the<br>e versa)<br>L diff|
|---|---|---|---|---|---|
||argna<br>effect on<br>carbon|||Marginal<br>product of|og erence<br>in wages<br>between high|
|Country|emissions|Output|Skills|skills|and low wealth|
|Average of countries|-0.031***|0.0846*|-0.1383***|-0.1383***|-0.0129***|
||(0.0163)|(0.0396)|(0.0642)|(0.0646)|(0.0036)|
|Belgium|-0.0285***<br>(0.0157)|0.0802*<br>(0.0401)|-0.1021***<br>(0.0114)|-0.1021***<br>(0.0114)|-0.0199***<br>(0.0042)|
|Czech Republic|-0.0734***|0.0822*|-0.1166***|-0.1166***|-0.0104***|
||(0.0369)|(0.0392)|(0.0047)|(0.0047)|(0.0039)|
|Denmark|-0.0366***<br>(0.0188)|0.0826*<br>(0.0399)|-0.1044***<br>(0.0094)|-0.1044***<br>(0.0094)|-0.0126***<br>(0.0037)|
|Spain|-0.0122***|0.0919***|-0.1481***|-0.1481***|-0.0171***|
||(0.0051)|(0.0386)|(0.0053)|(0.0053)|(0.0037)|
|Estonia|-0.1038***|0.0821*|-0.0909***|-0.0909***|0.0008|
||(0.0533)|(0.0401)|(0.0075)|(0.0075)|(0.0027)|
|Finland|-0.0427***|0.0706*|-0.1396***|-0.1396***|0.0026|
||(0.0317)|(0.039)|(0.0043)|(0.0042)|(0.0023)|
|France|<br>-0.0084***|<br>0.0861***|<br>-0.1529***|<br>-0.153***|<br>-0.0209***|
||(0.0041)|(0.0388)|(0.0096)|(0.0095)|(0.0048)|
|United Kingdom|<br>-0.0144***|<br>0.0913*|<br>-0.0368***|<br>-0.0368***|<br>-0.0093***|
||(0.0069)|(0.0442)|(0.0171)|(0.0171)|(0.003)|
|Greece|-0.0066**|0.0924***|-0.4788***|-0.4792***|-0.026***|
||(0.0047)|(0.0359)|(0.9597)|(0.9656)|(0.0062)|
|Italy|<br>-0.0111***|<br>0.0926***|<br>-0.1985***|<br>-0.1985***|<br>-0.0255***|
||(0.0043)|(0.0374)|(0.0238)|(0.0238)|(0.0044)|
|Netherlands|-0.0278***|0.0743*|-0.1044***|-0.1044***|-0.0066**|
||(0.0183)|(0.0402)|(0.0107)|(0.0107)|(0.0034)|
|Norway|-0.0204***|0.0745*|-0.1609***|-0.1609***|0.0095***|
||(0.0138)|(0.0388)|(0.0082)|(0.0082)|(0.0029)|
|Poland|-0.0449***|0.09***|-0.0751***|-0.0752***|-0.0198***|
||(0.0197)|(0.0406)|(0.0096)|(0.0096)|(0.0048)|
|Slovak Republic|<br>-0.012***|<br>0.0818*|<br>-0.0944***|<br>-0.0945***|<br>-0.0264***|
||(0.0062)|(0.0399)|(0.0064)|(0.0064)|(0.0051)|
|Slovenia|-0.0221***|0.0964***|-0.0702***|-0.0702***|-0.0122***|
||(0.0088)|(0.041)|(0.0126)|(0.0126)|(0.0038)|

24
# **Figure 1: Bangladesh, India, and low-income Sub-Saharan Africa are the source of 45 percent of South-South migrants**

- A. Share of South-South migrants by LMI country source and destination in 2007 (percent of total South-South migrant stock)

<!-- Start of picture text --> Bangladesh From LMIs<br>Brazil<br>To LMIs<br>China<br>Central Asia<br>India<br>Indonesia<br>Low-Income Sub-Saharan Africa<br>Mexico<br>Middle-Income Middle East and North Africa<br>Other Former Soviet Union and Other Eastern Europe<br>Other Latin America and Caribbean<br>Other Low-Income South and East Asia<br>Other Middle-Income Asia<br>Other Middle-Income Sub-Saharan Africa<br>Pakistan<br>South Africa<br>Turkey<br>0 5 10 15 20 25<br><!-- End of picture text -->

- B. Share of migrants to/from LMI in total emigration and immigration (percent)

<!-- Start of picture text --> LMI shares in immigration LMI shares in emigration<br>Bangladesh<br>Brazil<br>China<br>Central Asia<br>India<br>Indonesia<br>Low-Income Sub-Saharan Africa<br>Mexico<br>Middle-Income Middle East and North Africa<br>Other Former Soviet Union and Other Eastern Europe<br>Other Latin America and Caribbean<br>Other Low-Income South and East Asia<br>Other Middle-Income Asia<br>Other Middle-Income Sub-Saharan Africa<br>Pakistan<br>South Africa<br>Turkey<br>0 20 40 60 80 100<br><!-- End of picture text --> Note: There were 15.7 million migrants from low and middle-income countries residing in other low and middleincome countries in 2007. The values presented are shares of this total. Source: Data from Walmsley et al. (2013) 10
<!-- Start of picture text --> Figure 5a: Concentration of Direct Transfers Figure 5b: Concentration of Direct Transfers<br>by Income Groups, Men by Income Groups, Women<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Non PSNP transfer Market income Non PSNP transfer<br>PSNP transfer Direct transfer PSNP transfer Direct transfer<br>Population share Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of direct transfers Cumulative proportion of direct transfers<br><!-- End of picture text -->

# _Indirect Taxes and Subsidies_

Most of the assignable goods that are identified in this study are subject to VAT and excise taxes. There is a substantial difference in the total value of annual consumption of these items by gender. The total value of annual consumption by men is birr 1,595 and by women birr 917 (Table 1). However, the effective tax rate, 19.4 percent, is the same for both genders (Table A1.2). In addition, the richest individuals (top 20 percent) spent more on assignable goods than the poorest (bottom 20 percent); the value of assignable goods, on average, is 35 percent of the non-assignable goods for the richest but only 7 percent for the poorest (Table 1).

**Table 1: Distribution of Assignable and Non-assignable Goods by Gender and Income**

||**Assignable**|**Non-assignable**|**Share**<sup>*****</sup>|
|---|---|---|---|
|National|1,247.5|10,811.8|14.9|
|Female|917.5|10,088.6|12.1|
|Male|1,595.2|11,573.7|17.9|
|Poorest|185.3|2,964.3|7.2|
|Poor|371.2|5,486.7|8.3|
|Middle|631.1|8,106.3|10|
|Rich|1,165.1|11,891.6|14.2|
|Richest|3,885.7|25,616.9|34.8|

_Source_ : Authors’ calculations; ESS 2018/19 data. _Note:_<sup>*</sup> Share refers to the average of the ratio of assignable to nonassignable goods.

13
expropriation? Given the profound implications for firm productivity and innovations, further research should address the impacts and the deterrninants of regulation in developing countries. To accomplish this research agenda it is important to gather panel data on, for example, pricing behavior, service coverage and quality, profitability, market structure, rent distribution (i.e., were employment and wages affected by regulatory changes), and dynamic efficiency (measured by the rate and direction of innovation and productivity).

World Bank surveys probably have focused more on regulation than any other theme, although the variation in coverage is vast across surveys. Those that cover regulation thoroughly _(the Bosnia Survey, the World Business Environment Survey, The Emergence of Private Sector: Hungary,_ and the RPED) ask questions about: the waiting period for goods to arrive, the level of government to deal with in regulation, the main problems in dealing with govermnent agencies, the frequency with which firms are required to meet with government officials, the costs of facilitators necessary for dealing with the government, the burdens of licensing requirements, costs of obtaining licenses and permits, various types of taxes, tax treatments for profits reinvested in company, and incentives for investment in new machinery and equipment.

A general problem with current Bank surveys on regulation, however, is that they focus on the barriers that regulations impose on business. They tend to ask firms "how severe" certain regulations are to firrn operations. It is true that burdensome and often unnecessary regulations are common. However, many regulations are important to the functioning of the economy (e.g., environmental regulations that force firms to internalize all costs of their production). In those cases, the regulation will be an obstacle from the firm's perspective, but efficient from the perspective of the entire economy. Surveyors need to think carefully about how to uncover the true costs of regulation, since estimates will be biased upwards by simply asking the firm whether a regulation is an obstacle.

# **State-Owned Enterprises (SOEs)**

Developing countries claim a higher share of SOEs in their economies than do industrialized countries (Haggarty and Shirley, 1997). While SOEs resemble private firms in many respects, surveys of SOEs must ask some different questions. These differences reflect the different objectives of SOE management and objectives. SOE principals are typically bureaucrats or 17
- 10 -

available at a 2.5 minute resolution, approximately 5km at the equator. Other global population data sets, such as WorldPop, may have higher resolution estimates. The GPW has estimates for both 2005 and 2010, which are needed to match our SPAM panel data.

Land data also come from the SPAM. It provides an estimate of land area used for each crop at each location. Cultivated land is divided into two types: rain-fed ( _R_ ) and irrigated ( _I_ ). Most of the agricultural land is currently rain-fed with little irrigation applied in Ethiopia. For each location, land areas are aggregated across crops.

_F_ represents the quantity of plant nutrients applied to all crops produced at each location. This is calculated based on land area (ha) where fertilizer is used and the national average fertilizer consumption (kg per ha). The size of land areas with fertilizer applied is derived from SPAM, in which land areas under high-input and irrigated production systems are available. The average fertilizer consumption is calculated based on Ethiopia’s Agricultural Sample Survey for 2012/13. Our fertilizer variable is the sum of DAP and Urea, although the actual combination may vary across fertilizers ( **Table 3** ).

**<u>Table 3. Average fertilizer use</u>**

|<br>Crop|<br>kg/ha|Crop|kg/ha|
|---|---|---|---|
|Wheat|111.9|Chickpea|26.5|
|Maize|100.3|Lentil|59.6|
|Barley|75.7|Other pulses|48.0|
|Small millet|74.3|Soybean|52.4|
|Sorghum|41.7|Groundnut|63.7|
|Other cereals|89.9|Rapeseed|121.2|
|Potato|82.7|Sesame seed|44.3|
|Sweet potato|23.4|Other oil crops|34.8|
|Other roots|0.3|Vegetables|100.0|
|Bean|64.3|||

Note that only land areas with fertilizer applied are considered. Source: Authors’ estimates based on Ethiopia Agricultural Sample Survey for 2012/13.

The transport connectivity is measured by the unit transport costs from each location to either a nearest large market, which is defined by a city with more than 200,000 population (denoted by _TC1_ ), or the Port of Djibouti, a major regional gateway port for Ethiopia