rafmacalaba/datause-encoder-data huggingface dataset:
12 from five African countries form the primary information base for this study.<sup>2</sup> They have been stratified to be representative for rural and urban areas. The surveys gather a wide range of socioeconomic information on households and the communities of which they are a part, with detailed attention to their sources of income and geo-referenced, plot-level information on their agricultural activities and crops grown. Most importantly for this study, they also include for all uncultivated plots detailed information about the type of crop (including tree crops), the harvest, and expenses incurred. For fallow or uncultivated plots, farmers were explicitly asked whether they contained trees. In countries where two seasons of agricultural data were collected (Malawi, Tanzania and Uganda), the average presence of trees across both seasons was taken. Across these countries a total of 12,500 rural households (and 30,000 plots) were surveyed and through application of survey sampling weights a representative portrait of on-farm tree prevalence and their economic contribution to rural household incomes was obtained.

In the absence of a standard classification of crops as trees, potential trees were first identified from the LSMS-ISA agricultural crop production data, following the biological convention that to qualify as a tree a plant must be a woody perennial with a trunk or elongated stem that supports branches and leaves. With the help of several experts this list was subsequently divided into five subcategories: (1) fruit trees (e.g. mango, orange, etc.); (2) tree cash crops (e.g. coffee, tea, etc.); (3) timber and fuelwood trees (e.g. Mahogany, bamboo, etc.); (4) plant/herb/grass/roots (e.g. maize, banana, etc.); and (5) unidentified (e.g. voandzou, wechino, etc.) (Table A.1 includes a detailed list of all the crops considered as trees and their further classification across these five subcategories). As expected, most of the listed crops fell in the plant/herb/grass/roots category (across countries on average about 60%).

Only the first three subcategories are considered here. While they contain all three perennials (with substantial lags between planting and harvesting which distinguishes them from other crops), they are nonetheless still quite distinct in their biological and economic features and support systems. Unlike fruit and timber trees, cash crops have been extensively studied in the development literature, for example, but not in forestry, and they are usually politically important and part of well-organized and integrated cooperatives and value chains. Unlike timber trees, fruit trees yield an annual return. This dramatically changes the parameters of the investment decision. For these reasons, we explore the three tree

> 2 For details, see Appendix A and www.worldbank.org/lsms . Niger was excluded because, unlike in the other LSMS-ISA countries, total income from trees or tree products was not recorded, only sales.

4
_Appendix Table 2:_ _<u>Characteristic balance at baseline: nonbeneficiary households</u>_

|Baseline survey variables|Control<br>Mean|Difference<br>(Treated-<br>Control)|Standardized<br>Mean<br>Difference|p-value<br>|
|---|---|---|---|---|
|LN Proxy Means Test Score|9.871|0.008|0.026|0.703|
|Household composition:|||||
|Household size|4.240|-0.146|0.108|0.149|
|Children 5 years old and below|0.566|-0.015|0.032|0.702|
|Children between 6 and 14 years old|0.952|-0.037|0.051|0.547|
|Primary occupation: Farming and<br>|0.312|-0.000||0.990|
|livestock|||0.007||
|Educational attainment of the household he|ad:||||
|No grade completed|0.028|0.001|-0.005|0.899|
|Some elementary school|0.230|0.029|-0.083|0.320|
|Completed elementary school|0.171|-0.008|-0.004|0.751|
|Some high school|0.135|-0.001|0.016|0.944|
|<br>High school graduate|0.195|-0.018|0.059|0.402|
|Some college|0.122|-0.002|0.012|0.923|
|College graduate|0.121|-0.001|0.018|0.951|
|School Attendance:|||||
|Children between 6 to 11 years|0.947|-0.008|0.041|0.689|
|Children between 12 to 14 years|0.873|-0.009|-0.010|0.804|
|Housing Amenities:|||||
|Strong roof materials|0.662|-0.046|0.125|0.160|
|Strong wall materials|0.552|-0.039|0.111|0.217|
|Light roof materials|0.232|0.027|-0.097|0.352|
|Light wall materials|0.220|0.047*|-0.145|0.066|
|Owns a house and lot|0.537|-0.030|0.085|0.369|
|House has toilet|0.177|-0.036|0.080|0.219|
|Shares a water source|0.173|0.001|-0.007|0.962|
|Household Assets:|||||
|Electricity in house|0.797|0.001|0.036|0.989|
|Owns a television|0.570|-0.017|0.061|0.687|
|Owns a video recorder|0.400|-0.088**|0.213|0.014|
|Owns a Stereo/CD player|0.349|-0.052|0.114|0.110|
|Owns a refrigerator|0.312|-0.012|0.052|0.682|
|Has a telephone/cellphone|0.273|-0.054|0.133|0.104|
|Owns a motorcycle|0.173|-0.042*|0.130|0.068|

Note: *** p<0.01, ** p<0.05, * p<0.1.

Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 130 villages. Source: 2008 Household Assessment Form 51
55 realistic cost parameters the increase in enforcement by imposing restrictions on the use of bonds actually improves matters.

The benefits from regulations may be substantial even when inflation tax is relatively unimportant in terms of revenues. For example, when the inflation tax is equivalent to a 4% consumption tax and most agents spend 2% of their income on tax evasion, the benefit from regulation is close to 2% of GDP but the revenues from the inflation tax are small. This type of example can also explain why a steady state inflation of say 16% is considered a disaster while a consumption tax of 4% (that is equivalent to an inflation of 12% when the length of the period is a quarter) seems acceptable. To understand the difference in the attitude towards the two seemingly similar taxes, we may want to think of the case in which labor supply is inelastic. Under the consumption tax regime agents do not spend resources on tax evasion and get services or lump sum payments from the government in an amount that is equal to the tax they pay. The inflation tax regime is akin to a situation in which the consumption tax is not perfectly enforced and as a result agents spend resources on tax evasion and get a relatively small transfer payment or services.

In our model the welfare cost of inflation is equal to the area under the demand for money curve only in the special case in which there are no direct taxes and no proportional intermediation costs. To estimate the welfare cost and the gains from regulation, we use the 2011 income distribution in the US. We focus on the range of the fixed cost  that leads to the use of both assets (bonds and money) when the interest rate is between 1.5% and 8.5%. The choice   0.001 is in the middle of this range.

> When   0.001 and the nominal interest is 1.5% per period (or 4.5% annual rate if the length of the period is 3 months), the welfare cost of inflation is 0.28% if the fixed cost is the only cost of using bonds, it is 0.71% if we add a proportional cost of 1.5% of the assets intermediated and it is 1.57% if we add income tax at the flat rate of 20%.
# **Collateral Registries for Movable Assets:**

**Does Their Introduction Spur Firms’ Access to Bank Finance?**<sup>∗</sup>

Inessa Love, María Soledad Martínez Pería and Sandeep Singh

**Keywords:** movable collateral, access to bank finance

**JEL:** K20, G21, G30

# **Board:** FSE

> ∗ Inessa Love is an Associate Professor at the University of Hawaii; Maria Soledad Martinez Peria is the Research Manager of the Finance and Private Sector Development Group of the Development Research Department of the World Bank, and Sandeep Singh is a Consultant at the World Bank. We are grateful to Mehnaz Safavian, Jose María Garrido, Santiago Croci and other participants at the World Bank Global Financial Development Report Seminar Series for comments and suggestions. We also received valuable inputs from Alejandro Alvarez de la Campa. The views expressed in this paper are those of the authors and do not represent the views of the World Bank, its Excecutive Directors or the countries they represent. Corresponding author: Maria Soledad Martinez Peria, The World Bank, 1818 H St. MSN MC 3-307, N.W., Washington, D.C. 20433. mmartinezperia@worldbank.org

<mark>1</mark>
Through this approach we are able to measure and quantify a number of the key economic effects of high-skilled emigration. We estimate that the best and the brightest stand to gain $40,000-75,000<sup>5</sup> per year from emigrating from these five countries. This gain to the migrants swamps by an order of magnitude any of the other measured impacts: annual remittances of $2,000-7,000, trade and foreign direct investment effects which are infrequent and at most of similar gross value to remittances, and annual fiscal impacts which are at most $1,000 for Tonga and Micronesia, $6,000 for Ghana, $10,000 for New Zealand and $17,000 for Papua New Guinea. We also find migration to lead to large increases in human capital of the migrants; little evidence of net knowledge transfers to home governments or business, but significant provision of knowledge about study and work opportunities abroad by highly skilled emigrants. Our assessment of the likely size of possible negative externalities from the absence of these individuals suggests that such externalities are small relative to the magnitude of the benefits from emigration.

The remainder of the paper is structured as follows: Section 2 discusses our unique survey and the incidence of high-skilled emigration seen among the best and brightest, Section 3 estimates the impact of migration on the incomes and human capital of the highly-skilled, and Section 4 attempts to measure the value of impacts on trade, foreign direct investment, and fiscal balance, as well as to provide evidence on the extent of various knowledge transfers. Section 5 concludes.

# **2. Surveying the Best and Brightest**

The small existing microeconomic empirical literature on the brain drain has generally focused on individuals from a selected profession. For example, Hunter et al. (2009) consider Nobel Prize winners and highly-cited scientists, Ben-David (2007) Israeli economists, Clemens and Pettersson (2008) African health professionals, Commander et al. (2004) doctors in the United Kingdom, and Constant and D’Agosto (2008) Italian researchers and scientists abroad. However, in addition to these studies lacking detailed micro-data on the interactions migrants have with their home countries, there are several concerns with such occupation-specific studies when it comes to looking at the consequences of high-skilled migration. First, the initial decision to

> 5 All values are expressed in current United States dollars as of January 2010.

4
# **The Improved Biomass Stove Saves Wood, But How Often Do People Use It?  Evidence from a Randomized Treatment Trial in Ethiopia**

Abebe D. Beyene<sup>1</sup> Randall Bluffstone<sup>4</sup> Zenebe Gebreegziabher<sup>1,2</sup> Peter Martinsson<sup>5</sup> Alemu Mekonnen<sup>3,1</sup> Ferdinand Vieider<sup>6</sup> Keywords: improved biomass cookstoves, RCT, fuelwood, indoor air pollution, Ethiopia. _JEL_ classification: _C93, D12, O13, Q41, Q56_

1. Environmental Economics Policy Forum for Ethiopia/Ethiopian Development Research Institute, Ethiopia

2. Mekele University, Ethiopia 3. Addis Ababa University, Ethiopia 4. Portland State University, USA 5. University of Gothenburg, Sweden 6. Risk & Development Unit, WZB Berlin Social Science Center, Germany
- [3] Barro, R.J. and J.W. Lee (2000): International Data on Educational Attainment: Updates and Implications, CID Working Papers 42, Center for International Development (Harvard University).

- [4] Bauer, T.K. and A. Kunze (2004): The demand for high-skilled workers and immigration policy, Brussels Economic Review, Forthcoming.

- [5] Bhagwati, J.N. and K. Hamada (1974): The brain drain, international integration of markets for professionals and unemployment: a theoretical analysis, Journal of Development Economics, 1, 1: 19-42.

- [6] Beine, M., F. Docquier and H. Rapoport (2001): Brain drain and economic growth: theory and evidence, Journal of Development Economics, 64, 1: 275289.

- [7] Beine, M., F. Docquier and H. Rapoport (2003): Brain drain and growth in LDCs: winners and losers, IZA Discussion paper, IZA Bonn.

- [8] Carrington, W.J. and E. Detragiache (1998): How big is the brain drain?, IMF Working paper WP/98/102.

- [9] Carrington, W.J. and E. Detragiache (1999): How extensive is the brain drain, Finance and Development, June: 46-49.

- [10] Cinar, D. and F. Docquier (2004): , Brussels Economic Review (special issue on skilled migration), forthcoming.

- [11] Commander, S., M. Kangasniemi and L.A. Winters (2004): The brain drain: a review of theory and facts, Brussels Economic Review, forthcoming.

- [12] Domingues Dos Santos, M. and F. Postel-Vinay (2003): Migration as a source of growth: the perspective of a developing country, Journal of Population Economics, 16, 1: 161-75.

- [13] Dustmann, C. and O. Kirchkamp (2002): The optimal migration duration and activity choice after remigration, Journal of Development Economics, 67, 2: 35172.

- [14] Faini, R. and A. Venturini (1993): Italian migrations: the pre-war period, in Hatton and J. Williamson (eds), International Migration and World Development, 1850-1939, London: Routledge.

- [15] Freeman, R.B. (1993): Immigration from poor to wealthy countries. Experience to the United States, European Economic Review, 37: 443-451.

- [16] Funkhouser, E. (1995): Remittances from international migration: a comparison of El Salvador and Nicaragua, Review of Economics and Statistics, 77, 1: 137-46.

35
2015; Dix-Carneiro, 2014). An important nding of our paper is that capital adjustment, though costly and imperfect, prevents this overshooting of real wages. To illustrate this more clearly, we compare the behavior of our model, with both capital and labor imperfections, with a model that features imperfectly mobile labor but xed capitalthe most common environment in the literature. Following a full tari cut in textiles, we solve the model assuming a constant capital stock in all sectors during the transition and we compute the evolution of wages and employment.

Figure 2 plots the responses of the real wage in the textile sector. The solid line depicts real wage dynamics for the benchmark model with imperfect mobility of both labor and capital (from Figure 1, panel b, and Table 4). The dashed line depicts the dynamic responses of real wages for the xed-capital model. Comparing these two models, there are striking dierences. In both models, because capital and employment are predetermined, the real wage declines on impact in Year 1. However, while real wages continue to decline in the benchmark model, they increase in the xed-capital model. This is the typical overshooting in xed- _K_ models. As explained, the tari shock creates wage dierences that induce workers to move out of sector 2. When rms can adjust the capital stock in response to a negative tari shock, the capital stock shrinks. As a result, labor productivity further declines and, in fact, this eect dominates the increase in the marginal product of labor created by the outow of workers. In the end, real wages continue to drop. By contrast, when the capital stock is xed, labor productivity increases in the sector as workers are displaced during the transition and, as a result, real wages recover. This mechanism is mild in our simulation for textiles (but it might not be in other cases). To see this, note that the real wage in the new steady state of the xed- _K_ model is 13.93 percent lower. Compared to the initial decline of 15.04 percent, this implies a recovery of roughly 1 percentage point. Moreover, the transition is much shorter, 3-4 years only.<sup>16</sup> It is important to note that whether wage overshooting occurs or not depends on the interaction between the capital adjustment costs relative to the labor adjustment costs. This is because the labor market response depends on the speed and magnitude of disinvestment relative to the speed and magnitude of the outow of workers. If, in a given sector, a trade shock can trigger a suciently large exodus of workers, then the longer run recovery of real wages that is distinct to recent frictional models of trade such as Artuc, Chaudhuri and McLaren (2010) or Artuc, Lederman and Porto (2015) could be preserved. To explore this, we simulate a model with reduced _C_ in which we set the labor

> 16The role of capital adjustment in impeding the overshooting of real wages is also present in the other sectors. However, these eects are very small in our current setting and are thus not shown to ease the exposition.

22
concept. An aggregate index is then constructed at the country level, usually after re-scaling or ranking the component series.<sup>2</sup> Neither the menu of the primary series nor the aggregation function is pre-determined from theory and practice, but are ―moving parts‖ of the index—key decision variables that the analyst is free to choose, largely unconstrained by economic or other theories intended to inform measurement practice.

Borrowing from web jargon, the data going into this second type of index can be called a ―mashup.‖ In web applications one need not aggregate the data into a composite index; often users look instead for patterns in the data. When a composite index is formed from the mashup, I will call it a ―mashup index.‖ This is defined as a composite index for which the producer is only constrained by the availability of data in choosing what variables to include and their weights.

To illustrate the distinction, consider two stylized examples of composite indices, both formed from the data on household assets and consumer durables found in the _<u>Demographic and Health Surveys</u>_ (DHS). For index A the variables and their weights are set by the analyst, who has some concept of ―economic welfare‖ in mind, and thinks this is related to certain variables in the DHS, which are aggregated based on the analyst‘s judgments. For index B, the variables and weights are instead based on a regression model calibrated to another survey data set for which a comprehensive measure of consumption (though still containing measurement errors) could be derived. The model is calibrated to common variables in the expenditure survey and the DHS, and the regression model is used to predict wealth in the DHS. A is a mashup index, B is not.

The country rankings implied by mashup indices often attract media attention. People are naturally keen to see where their country stands. However, the details of how the composite index was formed—the variables and weights—rarely get the same scrutiny. Typically the (often web-based) publications do not comply with prevailing scholarly standards for documenting and defending a new measure. No doubt many users think the index has some scientific status.

Just as it is recognized that there can be gains from bringing together data and functionality from different sources in creating a web-application hybrid, there can be gains in

> 2 A common re-scaling method is to normalize the indicator _x_ to be in the (0,1) interval by taking the transformation ( _x_ -min( _x_ ))/(max( _x_ )-min( _x_ )) where min( _x_ ) is the lowest value of _x_ in the data and max( _x_ ) is the highest value, and then add up the re-scaled indicators. The most common ranking method is to rank countries by each indicator _x_ and then derive an overall ranking according to the (weighted) aggregate of the rakings across components (a version of the voting method called the Borda rule).

3
areas. So even if raising income or its monthly share is not the most direct route, there must be other routes through which SP1 might have helped Thai households smooth their consumption by reducing monthly variations in per capita income. In order to test other possible routes other than income through which government programs help, we also examine at the coping strategies households adopt during an economic crisis other than participation in a government welfare or transfer program. As described earlier, the NSO in Thailand collected information about the strategies households adopted during an economic crisis in its 2008, 2009 and 2010 SES surveys. The household responses are categorized in two ways: What would they do in an economic crisis, and what do they expect the government to do during the crisis? The responses are presented in Table 2. We find that the most frequent coping strategy households say they would adopt during an economic crisis was reducing household spending (more than 85 percent of Thai households report they would reduce spending during a crisis). Households might also adopt some other strategies: 9 percent of households say they would tap into savings, and 8 percent report that they would seek part-time employment if hit by a crisis. Interestingly, when borrowing is a way to meet the gap between expected income and consumption, about half of Thai people do not like to be in debt when a crisis hits. On the other hand, households are found to repay debt at a higher percentage rate: For example, in urban Thailand, some 41 percent repaid debt in 2008 and 46 percent in 2009, and 44 percent in 2010 (Table 3). The loan repayment rate is higher in rural areas than urban areas.

We therefore examine the effects of the stimulus on savings, borrowing, and debt repayment by households in the community as possible coping strategies. Did households draw down on their savings, or change their borrowing patterns to raise or maintain consumption? Table 6 presents the results, again in a dynamic panel framework estimated by GMM. We find a negative but not significant effect of the stimulus on savings in urban areas, as well as a negative effect on borrowing. We also find that participation in the government stimulus helps increase the probability of debt repayment with the effect being a little more pronounced in urban than rural areas Our analysis 25