rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 1. World International Investment Positions: Bilateral vs. Country-Level Data**

<!-- Start of picture text --> Panel A. Bank Loans and Deposits Panel B. Portfolio Investment<br>40 60<br>35<br>50<br>30<br>40<br>25<br>20 30<br>15<br>20<br>10<br>10<br>5<br>0 0<br>Panel C. Foreign Direct Investment Panel D. International Reserves<br>60 14<br>50 12<br>10<br>40<br>8<br>30<br>6<br>20<br>4<br>10 2<br>0 0<br>Bilateral Country level (based on Lane and Milesi-Ferretti, 2018)<br>USD, trillions USD, trillions<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>USD, trillions USD, trillions<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> Note: This figure compares year-end values of world international investment positions for the bilateral data constructed in the paper and the country-level data constructed by Lane and Milesi-Ferretti (2018). Bilateral data are at the country-pair level and we obtain country-level data by aggregating values for each source country to all destination countries per year. For each year, we keep countries that appear in both samples and aggregate data across all countries. For comparison purposes, we include offshore financial centers (OFCs). For the rest of our analysis, we exclude these countries unless otherwise noted. Because Lane and Milesi-Ferretti's data are not available for 2016-18, for those years we only show the value for our data using the same sample of countries in 2015. Values are in trillions of 2011 U.S. dollars (USD).
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the primary channel for eliminating the balance of payments<sup>and debt problems. Considerable</sup> and recurrent nominal devaluations, macroeconomic<sup>retrenchment, and foreign trade and</sup> institutional reforms were the main vehicles for eliminating<sup>real overvaluation and creating a</sup> structure of incentives consistent with this strategy. Given<sup>the intensity and the nature of tl ese</sup> reforms, in addition to the dominant role of the highly<sup>tr</sup> 4<sup>.dble agricultural sector in the</sup> economies of SSA, agriculture became the main<sup>focus of these programs an the success of the</sup> later depended heavily on the supply response of agriculture.

As can be seen from table (1.2), some real depreciation has<sup>been achieved over 1986-</sup> 89.'5 Also the deteriorating performance of agriculture<sup>have been reversed."' This however,</sup> does not imply that this improvement can be solely or even<sup>mostly attributed to improved</sup> stricture of incentives for agriculture, since other nonprogram<sup>factors such as the weather</sup> conditions may have influenced the outcome. Even if the<sup>structure of incentives was sufficiently</sup> favorable to agriculture, the supply response may be blunted<sup>by other components of the</sup> program, especially the cut in government expenditure and<sup>import compression. The observed</sup> decline in investment and import ratios over the reform<sup>period (table (1.2)) can reduce both</sup> capacity growth as well as capacity utilization'<sup>7</sup> in the economy.

'`1986-89 is argued to be the appropriate period to assess<sup>the effect of structural adjustment</sup> programs on economic performance (see World Bank<sup>(1990)).</sup> "Total agricultural production grew at an average rate<sup>of 4% per annum during 1984-88</sup> (FAO, 1989). Also per capita agricultural output recovcred<sup>from earlier declines and was rising</sup> or stable between 1985 and 1988.

"'This is because imported intermediate goods are imperfect<sup>substitutes to domestically</sup> produced goods in most of the economies of SSA (Ndulu<sup>(1990)).</sup>
and daughters is larger in rural areas, a 50 percent higher intercept for the sons, compared to only a 16 percent higher intercept in the urban areas. Although useful as a benchmark comparable to the existing estimates in the literature, these preliminary estimates are, however, contingent on the maintained assumption that the CEF is linear.

The estimates from the quadratic specification for rural India show that the coefficient of the quadratic term is statistically significant at the 1 percent level, for both the sons and the daughters, providing clear evidence that the null hypothesis of a linear CEF is rejected. The estimated quadratic coefficient is negative, suggesting that both the CEFs are concave, similar to what we observed in urban India (see also the Lowess plots in Figure F2.IR). However, there is an important gender difference between the urban and rural India; the degree of concavity is similar for sons irrespective of location (the estimate of the quadratic coefficient is -0.007 in both rural and urban samples), but the quadratic coefficient is substantially larger in magnitude for daughters in rural areas. In fact, the null hypothesis of equality of quadratic coefficients can be rejected in rural India at the 1 percent level, indicating that the girls face especially strong forces of diminishing returns in the villages.

The estimates of mobility in the bottom panel of Table T2.IR show that the girls face lower mobility, both in terms of absolute and relative measures, when they are born to low educated parents. The expected years of schooling remains consistently lower for girls, even when the father has college education; a girl growing up in rural India can expect a year less schooling on average. For the lowest educated households, the gender gap is almost 2 years of expected schooling. The estimates of relative mobility show that the intergenerational persistence is substantially higher for the daughters born into households with low educated fathers (compare the _IGME_ 0 estimate of 0.52 (sons) with 0.64 (daughters)).<sup>26</sup> The gender difference in _IGME_ , however, becomes negligible for the households with highly educated fathers (college educated). Please see Figure F4.IR.

# **Sources of Gender Bias in Rural India**

The estimates of the parameters of the educational investment in Table T3.IR show that

> 26About 40 percent of fathers in rural India in our data have zero schooling.

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<!-- Start of picture text --> Figure 2b: Job Finding Rates from Unemployment (Trends and Cycle): Mexico an Brazil<br>Mexico Brazil<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>U-S U-I U-S U-I<br>U-F U-F<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>U-S (HP) U-I (HP) U-S (HP) U-I (HP)<br>U-F (HP) U-F (HP)<br>.6 .4<br>.5<br>.3<br>.4<br>.2<br>.3<br>.1<br>.2<br>0<br>.1<br>.2 .4<br>.2<br>0<br>0<br>-.2<br>-.2<br>-.4<br>-.4<br><!-- End of picture text --> Notes: The graph shows the transition rates from unemployment (U=Unemployment) into the three employment sectors (S=Informal Self-employed, I=Informal Salaried, , F=Formal Sector). Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. The bottom panels shows the series logged and de-trended using an HP filter with lambda 1600. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2. Shaded areas indicate recessions.

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car, and some households will change categories, from having at most a motorcycle to acquiring a car for the first time. On average, the predicted probabilities of owning a car increase by 93% compared to a 55% increase in motorcycle ownership. These averages hide important discrepancies between countries. For instance, the increase in motorcycle ownership is larger in Burkina-Faso than in Mongolia, where car ownership is more affected by income increases. Finally, the effect of rising income on bicycle ownership is less clear as some households switch from having a bicycle only to acquiring a motorcycle or a car.

Figure 19: Income changes and vehicle ownership <!-- Start of picture text --> Afghanistan Afghanistan<br>Burkina Faso Burkina Faso<br>Cameroon Cameroon<br>Kyrgyz Republic Kyrgyz Republic<br>Malawi Malawi<br>Mali Mali<br>Mongolia Mongolia<br>Nepal Nepal<br>Nicaragua Nicaragua<br>Niger Niger<br>Nigeria Nigeria<br>Pakistan Pakistan<br>Sierra Leone Sierra Leone<br>Tanzania Tanzania<br>Uganda Uganda<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Predicted probabilities in percent Predicted probabilities in percent<br>No vehicle Bicycle No vehicle Bicycle<br>Motorcycle Car Motorcycle Car<br>(a) Income at current level (b) Income at 2035 level<br><!-- End of picture text -->

**The impact of increased vehicle ownership on carbon emissions** We use country-level time-series to quantify the relation between vehicle ownership and CO2 emissions. Table 7 shows the results of the regression using transport carbon emissions per capita and vehicle ownership variables from the IRF unbalanced panel of data ranging from 2000 to 2017. Column (1) shows that carbon emissions per capita from transport increase by about 35% when the average number of cars per household doubles.<sup>9</sup> Column (2) reports the results for motorcycle ownership. Carbon emissions per capita from transport increase by about 15% if the number of motorcycles per household doubles. This result is in line with the literature reporting lower impact of motorcycle ownership on CO2 emissions (see e.g., Vasic and Weilenmann, 2006).

9The coefficient might be slightly overestimated for our next step as the regression uses average vehicle number per households while our estimates report results for owning at least one vehicle. In richer countries, many households own more than one vehicle. However this concern is valid only for a few countries while our sample covers most countries in the world. Household survey data do not consistently report the total number of vehicles in the household.

36
# **I. Introduction**

Successful exporting countries are often seen as successful economies. The academic literature on the relationship between export success and economic development dates back to the classical economists. More recently, the literature has ascertained that growth accelerations are associated with export expansions (e.g., Hausmann et al. 2005). The microeconomics literature on exporting and firm performance is similarly extensive, and recent literature emanating from Melitz’s (2003) seminal paper on the role of firmproductivity heterogeneity and exporting decisions is already immense. And there is little doubt that exporting is in fact robustly correlated with a variety of intermediate indicators such as prices (unit values) set by exporting firms versus domestic firms. In a recent paper, for example, Hallak and Sivadasan (2009) find that in four countries (India, Chile, Colombia and the United States) exporting firms tend to sell products of higher quality at higher prices, pay higher wages and use capital more intensively than non-exporting firms.

The policy debates about trade and exports are no less thin than the academic literature. Most governments use export promotion policies and have established export promotion agencies, regardless of the level of development or institutional capacities (Lederman, Olarreaga and Payton 2010). The World Bank consistently argues that promoting trade and exports in particular is a recipe for promoting firm and national productivity (e.g., Fajnzylber, Guasch, and López 2009). In the case of Costa Rica, the government incorporated export targets into its National Economic Plan for 2006-2010, thus following in the footsteps of various East Asian economies that used export targets as a means of identifying successful firms in the 1970s and 1980s (Noland and Pack 2003, Pack 1997). Furthermore, export activities are also seen by policymakers as a means to improve the productivity or other outcomes of small and medium enterprises, and thus export promotion policies are often designed to serve these firms rather than large or multinational corporations (Volpe and Carballo 2008).

In this paper we study the role of entrepreneurship or the extensive margin of exports. This entails an examination of the microeconomic structure of Costa Rican export growth during 1997-2007. The empirical analyses rely on customs data compiled by the Costa Rican export promotion agency, PROCOMER. The data includes all firms that exported any good during this time period and provide information on export values, 2
53 117. However, Eckstein made a further study of ejldo efficiency, this time controlling rigorously for scale (Eckstein et al,1978; Table 20 In this report). He surveyed 313 relatively large, Irrigated farms In nine regions of Mexico, ordered by enterprise size: size Is defined In terms of the gross value of output, rather than the area owned or controlled. To begin with, the productivity of all factors increases In stop with the scale of the enterprise, an Interesting finding because It runs counter to much of the empirical evidence about the efficiency attributes of smali-scale agriculture In the developing natlons (Berry & Cline,1979); Mexico's distinctiveness In this respect has been remarked upon by others (Cornia,1985; Johnston,1 983:230-231).

118. Second, within each of the scale categories, comparing output value with Input value, the ejido performs better than the private farm: the gap Is greatest In the case of capital productivity (confirming Interpretations based on the census data) and total factor productivity; the superiority of the ejido sector Is less marked In the areas of land and labour productivity. These results are very Important because they demonstrate that, In Irrigated regions, enterprise scale overshadows tenure category as the determining factor in economic efficiency.

119. The picture that emerges from these data "seems to be strongly contrary to trends In the rain-fed areas, where ejidos show lower efficiency than private farmers of similar size" (Eckstein et al,1978:83-85). The tentative explanation offered Is that, in the irrigation districts, ejidos had equivalent access to credit and Inputs whereas In rainfed areas they had little access to those resources; Johnston (1983:237) concurs In this view. Throughout the period 194070 governments considered large, irrigated farms to be the most appropriate vehicle for rapid agricultural growth (WIonczek,1982); small producers In the rainfed regions were generally starved of resources and, given the specific constralnts
prominently, the Philippines – have enacted policies intended to facilitate and regulate international migration and view such policies as integral components of their overall economic development strategies. If there is a desire to promote migration, it is crucial to understand which barriers are operative and the impact of interventions that are aimed at reducing these barriers. While no study has been completed so far, randomized control trials in Bangladesh and the Philippines are currently underway and seek to shed light on the relative importance of several potential barriers to migration.

Bryan, Chowdhury, and Mobarak (2010) are currently analyzing the results of an ongoing randomized field experiment in the northwestern region of Rangpur in Bangladesh. A relatively impoverished area, Rangpur experiences annual famines that lead to seasonal declines in household income and consumption. A key coping strategy for households in the face of the famine is internal labor migration to other parts of Bangladesh that are less- or unaffected by the famine. The experiment involves 100 Rangpur villages that were randomly allocated to the following experimental conditions: a control group; a treatment group offered information on jobs available, typical wages, and the likelihood of finding migrant work in a set of migration destinations; and other treatment conditions that offered cash or credit to cover the initial fixed costs of migration. The experiment was implemented in 2008 and the endline survey of households in the 100 migrant-origin villages was implemented in 2009. Preliminary results are revealing, suggesting that the information treatments had no effect but the cash and credit interventions had substantial effects on migration both in the year they were offered as well as in the next year’s famine season (when the cash/credit were no longer offered by the research project). Treatments that had effects on migration also led to substantial increases in consumption in migrant households. Should they hold up, the results provide evidence of the importance of credit constraints as a migration barrier, and also – intriguingly – suggest that policies providing a small incentive to migrate in an initial period can have persistent effects in future periods even after the incentives are removed.

A field experiment seeking to shed light on barriers to international migration is being implemented by Beam, McKenzie, and Yang (2010) in Sorsogon province, the Philippines. International labor migration from the Philippines is very large in magnitude: the Philippine Overseas Employment Agency, with which all overseas labor contracts of Filipinos must be 19
<!-- Start of picture text --> Figure 1: Macroeconomic Overview<br><!-- End of picture text --> <!-- Start of picture text --> 1980 1985 1990 1995 2000<br>Year...<br>GDP Growth Inflation<br>Data source: Banco de Mexico<br>Figure 2: External Sector<br>1980 1985 1990 1995 2000<br>Year...<br>Total Trade Exchange Rate<br>Data source: World Bank and Banco de Mexico<br>10 150<br>100<br>5<br>50<br>0 0<br>% Change in GDP −50<br>% Changes in Prices<br>−5<br>−100<br>−10 −150<br>10<br>60<br>8<br>50<br>6<br>40<br>(X+M)/GDP 4<br>Pesos for dollar<br>30 2<br>20 0<br><!-- End of picture text --> During the same period major market-oriented reforms took place. In 1985 Mexico joined the General Agreement on Tariff and Trade (GATT). Between 1985 and 1988, the average tariff dropped from 25 percent to 11 percent and the coverage of import licensing decreased from 92.2 percent to 23.3 percent of total tradable goods. The reduction on trade barriers made Mexican products more competitive in international markets. In Figure 2 we can see that total trade (measured as the sum of exports plus imports as the proportion of GDP) increased from 26 percent in 1985 to 36 percent in 1988. The second major trade reform came in 1994 when the North American Trade Agreement (NAFTA) was enacted. As we mentioned above, in December of that year 5
We are now in a position to provide the poverty rates for the major social groups based on the quinquennial expenditure surveys beginning in 1983. The social groups identified in the surveys are Scheduled Castes (SC), Scheduled Tribes (ST), Other Backward Castes (OBC) and the rest, which we refer to as forward castes (FC). In addition, we define the non-scheduled castes as consisting of the OBC and FC. The NSSO began identifying the OBC beginning in 1999-2000. Since we are excluding this survey due to its lack of comparability with other surveys, the OBC as a separate group begins appearing in our estimates from 2004-05 only.

In Table 2, we provide the poverty rates at the Lakdawala lines in rural and urban areas and the two regions combined at the national level. Four features of this table are worthy of note. First and foremost, the poverty rates have declined between every pair of successive surveys for every single social group in each rural and urban area. Contrary to common claims, growth has been steadily helping the poor from every broad social group rather than leaving the socially disadvantaged behind.

**Table 2** : National rural and urban poverty rates by social groups at Lakdawala lines

|Socialgroup|1983|1987-88|1993-94|2004-05|2009-10|
|---|---|---|---|---|---|
||||**Rural**|||
|ST|64.9|57.8|51.6|47.0|30.5|
|SC|59.0|50.1|48.4|37.2|27.8|
|OBC||||25.9|18.7|
|FC||||17.5|11.6|
|NS|41.0|32.8|31.3|22.8|16.2|
|Allgroups|46.6|38.7|37.0|28.2|20.2|
||||**Urban**|||
|ST|58.3|56.2|46.6|39.0|31.7|
|SC|56.2|54.6|51.2|41.1|31.5|
|OBC||||31.3|25.1|
|FC||||16.2|12.1|
|NS|40.1|36.6|29.6|22.8|18.2|
|Allgroups|42.5|39.4|33.1|26.1|20.7|
||||**Rural + Urban**|||
|ST|64.4|57.6|51.2|46.3|30.7|
|SC|58.5|50.9|48.9|38.0|28.6|
|OBC||||27.1|20.3|
|FC||||17.0|11.8|
|NS|40.8|33.9|30.8|22.8|16.8|
|Allgroups|45.7|38.9|36.0|27.7|20.3|

Second, predictably, the poverty rates in rural India are consistently the highest for the ST followed by the SC, OBC and FC in that order. This pattern also holds in urban areas though with some exceptions. In particular, in some years, the ST poverty 12
consumption for each survey rounds 2011, 2013, and 2015. It shows a clear rightward shift in the distribution of consumption over time. However, this measure does not account for food price inflation over years. To address this, I purge out zone-year fixed effect to account for changes in prices over years in each zone, and plot the results in the right panel. The result shows that there is no clear rightward shift in the distribution of consumption over time. Household total consumption expenditure is composed of food and non-food expenditures (which mainly includes spending on household goods, clothing, and education). On average, non-food expenditures account for only 18.5% of household total expenditures, with significant variation across households.

# **3 Coffee production and marketing in Ethiopia**

Ethiopia is a top coffee producer in Africa and ranked 5th in the world after Brazil, Vietnam, Colombia, and Indonesia in the year 2015/16. Ethiopia produced about 7 million 60-kg bags of coffee in 2015/16 , which is about 9% of the world coffee production. About 95% of Ethiopian coffee is produced by smallholder farmers with about a median land size of one hectare, and average coffee yield of 6.34 quintal per hectare. The remaining 5% is produced by governmentowned farms and large-scale private farms. Over 5 million farm households engage in some level of coffee production in Ethiopia (Central Statistical Agency, 2016), implying that international coffee price changes might have important implications for poverty. Coffee production in Ethiopia is largely concentrated in the southern and southwestern parts of the country, where there is high rainfall and forest cover, the two essential ingredients of _Arabica_ coffee production in Ethiopia.

There are four coffee production systems in Ethiopia, namely: forest, semi-forest, garden, and plantation coffee farms (Tefera and Tefera, 2013). Forest coffee grows under the shades of natural forests with some loose (communal) ownership. Semi-forest coffee is also grown under tree shades maintained by private owners. Garden coffee is grown in the farmers’ gardens and plots of land near their homes, whereas plantation coffee is grown by large-scale commercial farms by applying modern production techniques such as irrigation, and modern inputs.

About 60% of coffee production is exported, and coffee export accounts for about a quarter of the country’s export revenue. Because of the importance of coffee as source foreign exchange, the Ethiopian government tightly monitors the local and export trade through licensing and `worldbank.org/index.php/catalog/2053/download/40407` .

10