rafmacalaba/datause-encoder-data huggingface dataset:
individuals in that group.<sup>31</sup> The poverty gap index is defined as: where k is an individual whose income is below the poverty line, n is the total number of people in the group below the poverty line, _YHk_ is the income of individual k, and _YH*_ is the poverty line.

In practice, of course, whether income or expenditure data should be preferred depends on the scope and quality of the data in the available household survey. In the illustrative simulations reported below, we used instead of an actual household income and expenditure survey a "fictitious" one, that we built as follows. First, we calculated real per capita disposable income and consumption expenditure for each of our six household categories, using the initial values that are provided to solve the model numerically. Second, using a random number generator and a log-normal distribution, we produced a sample of 1,397 observations (corresponding to the total number of workers and capitalists in the initial data set). We thus considered each individual worker and capitalist to represent one household. Third, we used the initial per capita income and consumption data as mean values and imposed a standard error of 0.5 for all household categories, except for skilled workers and capitalists, for which we chose a standard error of 0.35. We set (somewhat arbitrarily) the income poverty line for the rural sector at 0.45 and 0.5175 for the urban sector (or 15% higher than the rural poverty line). For the consumption data, we used initial poverty lines of 0.4 and 0.46 for the rural and urban sectors (again, with the latter being 15% higher than the former). We assume for simplicity that these poverty lines remain constant in real terms for the whole horizon of the simulation period that we consider below (10 periods). Figures 8 and 9 show a log-normal approximation to the initial data on income and consumption that we generated for each of

> **31As** is well known, the headcount index suffers from several limitations (see for instance Blackwood and Lynch (1994) and Ravallion (1994)). In particular, it does not indicate how poor the poor really are-it remains unchanged even if all people with incomes below the poverty line were to experience, say, a 50 percent drop in income. Put differently, when a poor person becomes poorer, the index does not increase. Moreover, it implies that the distribution of income among the poor is homogeneous (it does not distinguish between a poor person who earns one monetary unit less than the poverty line and a poor person who earns 100 monetary units less than the poverty line). But to the extent that the analyst is interested only in the number of poor, the headcount index is a useful measure.

53
of sharp penalty for undernourishment. First, the model described above is consistent with poorer households devoting a larger share of their total expenditure to food. This — — pattern Engel’s Law emerges in empirical work from across the world (e.g., Kaus 2013).

Second, there is an expanding body of evidence demonstrating that households tend to be especially averse to undernourishment. In response to income and price shocks in developing countries, diet quality is much more responsive to income shocks than calorie consumption, which is consistent with households sacrificing primarily non-staple spending in order to avoid hunger (e.g., Block et al. 2004; Brinkman et al. 2009; etc.). Additionally, only richer households reduce overall calorie consumption in response to strong price shocks, while the poorest households are unable to reduce calorie consumption in response to such shocks and tend to reduce expenditure on other non-food goods to avoid hunger (e.g., D’Souza and Jolliffe 2014; etc.).

Third, the addition of a sharp penalty on undernourishment offers an additional way to reconcile why traditional consumption models do a poor job of predicting food access in certain instances. Specifically, in the event of a strong increase in the price of staple grains, traditional models of consumption predict that individuals would pivot away from consumption of staple grains towards other food and non-food items, which are made relatively less expensive. But empirically, the exact opposite happens: when the price of staple grains increases rapidly, households actually reduce the quality of their diet and rely _more_ on staple grains (e.g., D’Souza and Jolliffe 2012; Tandon 2014; Tandon and Landes 2015; etc.). One explanation is that staple grains might be Giffen goods, with the income effect outweighing the substitution effect. However, these patterns are also consistent with a sharp penalty on undernourishment limiting households’ ability to substitute away from staple grains and forcing households to sacrifice more nutritious food items — which represent a more expensive source of calories — and non-food goods.

# **Section 4. Data**

The data for our analysis are taken from the 2018/19 Nigerian Living Standards Sur12
WPS3784

**<u>Table 5. Share of Income from Coffee, by Province</u>**

|**Province**|**Share of Coffee in Total Income (%)**|
|---|---|
|Butare|3.7|
|Byumba|2.5|
|Cyangugu|13.9|
|Gikongoro|5.5|
|Gisenyi|6.0|
|Gitarama|6.7|
|Kibungo|2.6|
|Kibuye|9.0|
|Kigali Ngali|9.2|
|Kigali Town|1.2|
|Ruhengeri|2.4|
|Umutara|0.0|

Source: Rwanda EICV 2001

|**Table 6. Impact of a 50 Percent Red**<br> <br>|**uction of R**|**ural Transpor**<br>|**t Costs on **<br>|**Incomes**<br>||
|---|---|---|---|---|---|
|Effect on:<br>Reduction transport<br>cost|pass-<br>through|Headcount<br>index|Change|Poverty<br>Gap|Change|
|coffee farmers<br>0|0|41.97||0.136||
|coffee farmers<br>50%|1|39.60|-5.65%|0.126|-7.12%|
|coffee farmers<br>50%|0.5|41.31|-1.57%|0.131|-3.72%|
|National<br>0|0|42.92||0.157||
|National<br>50%|1|42.69|-0.53%|0.156|-0.59%|
|National<br>50%|0.5|42.86|-0.15%|0.157|-0.31%|

Source: Computed by authors from EICV data 22
<!-- Start of picture text --> 10<br>Ss<br>§<br>35&°<br>3a<br>o<br>c4<br>5 22 z<55 NNO<br>fo)<br>Apri9— Jul19 Oct19 Jan20 Apr20 Jul20 Oct20. Jan21 Apr21 Jul21 Oct21 Jan22<br>—— Emigrant (leaving) Immigrant (returning)<br>source: CME People of nla<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>2°<br>s&5<br>ag?a<br>xo}<br>@<br>ga4Ff<br>0<br>Apr19, Jul19: Oct19 Jan20 Apr20 Jul20 Oct20) Jan21 Apr21 Jul21 Oct21 Jan22<br>— Return migrant Return migrantto rural Return migrantto urban<br>source: CMI People ot nla<br><!-- End of picture text -->
**Table 10: Change in net revenue per value of livestock owned for each AOGCM climate prediction**

|**Variable**|**Small farm net**<br>**revenue per**<br>**value of animal**<br>**($/$)**|**% of net**<br>**revenue per**<br>**animal**|**Large farm net**<br>**revenue per**<br>**value of animal**<br>**($/$)**|**% of net**<br>**revenue per**<br>**animal**|
|---|---|---|---|---|
|**2020**|||||
|**Change _CCC**|-0.02|-5%|-0.03|-7%|
|**Change _CCSR**|0.00|-1%|-0.03|-7%|
|**Change _PCM**|-0.02|-5%|-0.04|-11%|
|**2060**|||||
|**Change _CCC**|-0.02|-6%|-0.06|-15%|
|**Change _CCSR**|-0.04|-11%|-0.08|-21%|
|**Change _PCM**|-0.02|-6%|-0.05|-13%|
|**2100**|||||
|**Change _CCC**|-0.03|-7%|-0.09|-23%|
|**Change _CCSR**|0.09|24%|0.07|17%|
|**Change _PCM**|-0.02|-4%|-0.07|-17%|

_Note:_ Small farms have a baseline net revenue per dollar of animal owned of $0.38 per farm. Large farms have a baseline net revenue per dollar of animal owned of $0.40 per farm.

35
# **<u>Table 1.8. Medium-Term and Long-Term Income Mobility, RLMS 1994-2015 (percentage)</u>**

||<br>**Unconditional**|**Conditional**|
|---|---|---|
|**Panel A: 1994-2004**|||
|Upward mobility|30.0|34.0|
|Immobility|54.8|54.8|
|Downwardmobility|15.2|39.4|
|**Panel B: 2004-2015**|||
|Upward mobility|54.8|69.6|
|Immobility|37.8|37.8|
|Downward mobility|7.5|15.3|
|**Panel C: 1994-2015**|||
|Upward mobility|66.5|74.8|
|Immobility|28.4|28.4|
|Downwardmobility|5.1|14.4|

**Note** : Estimation results are obtained based on total household income per capita. The vulnerability index is defined as P(Y1<Z1|Z0<Y0<V0) = 0.15 yielding a yearly vulnerability line of 8152 real rubles per capita (in 2011 prices) for period 2004-2015 or about. We use this same vulnerability line for the other periods. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Estimation sample size is 4942 panel individuals from the 5th and 13th rounds of the RLMS, 3719 panel individuals from the 13th and 24th rounds of the RLMS and 2478 panel individuals from the 5th and 24th round of the RLMS.

56
Policy Research Working Paper 7251

# **Abstract**

It is a contentious issue whether large scale mining creates local employment, and the sector has been accused of hurting women’s labor supply and economic opportunities. This paper uses the rapid expansion of mining in Sub-Saharan Africa to analyze local structural shifts. It matches 109 openings and 84 closings of industrial mines to survey data for 800,000 individuals and exploits the spatial-temporal variation. With mine opening, women living within 20 km of a mine switch from self-employment in agriculture to working in services or they leave the work force. Men switch from agriculture to skilled manual labor. Effects are stronger in years of high world prices. Mining creates local boom-bust economies in Africa, with permanent effects on women’s labor market participation.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at anja.tolonen@economics.gu.se.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
consortium Consultative Group to Assist the Poorest, 2008). This hope makes it possible to imagine serving over 1 billion low-income customers, rather than the 133 million counted in 2006 or the 175 million projected for 2015 (Daley-Harris, 2007). Microfinance “has lost its innocence,” a Compartamos-supporter declared. “To mourn this loss of innocence would be wrong...To attract the money they need, [micro-lenders] have to play by the rules of the market. Those rules often have messy results” (von Stauffenberg, 2007).

In the next section, we offer an overview of the economic logic behind microfinance institutions, describe how the movement from socially oriented non-profit microfinance institutions to for-profit microfinance has occurred, and lay out some of the unanswered questions about the role of commercialization in microfinance. We then seek answers to some of these questions by drawing on a data set that includes most of the world’s leading microfinance institutions. The evidence suggests that investors seeking pure profits would have little interest in most of the institutions we see that are now serving poorer customers. This evidence, and other points in our discussion, will suggest that the future of microfinance is unlikely to follow a single path. The clash between the profit-driven Banco Compartamos and the “social business” model of Grameen Bank offers a false choice. Commercial investment is necessary to fund the continued expansion of microfinance, but institutions with strong social missions, many taking advantage of subsidies, remain best placed to reach and serve the poorest customers and some are doing so on a massive scale. The market is a powerful force, but it cannot fill all gaps.

# **The Evolution of Microfinance**

The greatest triumph of microfinance is the demonstration that poor households can be reliable bank customers. The received wisdom at the start of the 1970s held that substantial subsidies were required to run financial institutions serving poor households in low-income countries. Government banks often shouldered the task of serving the poor, usually with a focus on farmers. However, most state-run banks were driven by political imperatives, and so they charged interest rates well below market rates and even then collected loan repayments only halfheartedly. The risks inherent in agricultural lending together with the misaligned incentives led to institutions that were costly, inefficient, and not particularly effective in reaching the poor (for example, Conning and Udry, 2007).

3
# TAXING FOREIGN INCOME IN CAPITAL-IMPORTING COUNTRIES: **THAILADIDS PERSPECTIVE**

# EXECUTIVE SUMHARY

Making tax policy in an increasingly integrated<sup>world economy is a</sup> difficult task. In recent years, a large number of theoretical<sup>and</sup> empirical studies has been undertaken to clarify the<sup>issues involved nd to</sup> develop a framework for policy analysis. This report<sup>has the same basic</sup> objectives. But unlike much of the existing literature,<sup>the focus of this</sup> report is on the questions faced by developing countries<sup>in their capacity</sup> as hosts of foreign investments. The difference<sup>is essentially one of</sup> emphasis and applications, however. The basic analytical methods, as well as broader economic issues, are equally applicable<sup>to industrialized,</sup> capital exporting countries.

The main part of this report is devoted to analyzing<sup>the impact of</sup> taxation on the financing and investment decisions<sup>of multinational</sup> companies. A model is developed to incorporate tax<sup>provisions of both the</sup> host and the home countries. From this effort, new analytical<sup>results are</sup> derived. The second part of this report applies<sup>this new framework to</sup> examine issues of national welfare. As an illustration, a case study based on the tax systems of Thailand and its capital<sup>exporting countries is</sup> carried out. The third part considers the implications of current taxation on the global allocation of resources.

# 1. Impact of Taxation

As with traditional studies of investments, the effects of taxation are measured through the "user cost of capital",<sup>or the gross rate of</sup> return (including depreciation) required of new investments.<sup>An increase</sup>
Table 1 below shows the descriptive statistics for the variables used in the empirical estimations.

**Table 1. Summary Statistics for the variables**

|**Variable**|**Obs**<br>|**Mean**<br>**S**<br>|**td.**<br>**Dev.**<br>|**Min**|**Max**|
|---|---|---|---|---|---|
|privo|173|45.47|42.13|1.33|232.33|
|npl0204|94|9.35|7.58|0.00|30.67|
|sdprivo|168|4.51|7.06|0.00|45.92|
|sdnpl0204|94|2.37|2.65|0.00|15.04|
|T_245|178|4.67|1.14|1.12|6.00|
|legor_uk|203|0.33|0.47|0.00|1.00|
|legor_fr|203|0.43|0.50|0.00|1.00|
|legor_ge|203|0.03|0.18|0.00|1.00|
|legor_so|203|0.17|0.38|0.00|1.00|
|pmindex|102|7.69|1.36|5.00|11.00|
|apmindex|144|7.35|2.24|1.00|12.00|
|creditregindex|89|31.03|10.92|10.00|59.00|
|ospower|143|10.55|2.96|4.00|14.00|
|supower_pc|82|-0.03|0.99|-3.05|1.14|
|pmindex_pc|82|0.07|0.95|-2.95|1.46|
|gdppn02|163|8.50|1.16|6.31|10.98|
|corup0204|140|2.49|1.13|0.00|6.00|
|law0204|140|3.71|1.40|0.78|6.00|

_Source: see Appendix 2 for variable coding and source_ The first set of regressions using OLS are shown in their simplest form in Table 2 below using the main variables of interest, private credit and non-performing loans.

Greater economic transparency is associated with either a larger or better performing (as implied by the non-performing loan ratio) financial sector.

**Table 2. OLS regression results with Transparency index as explanatory variable.**

|**Dependent**|**Coefficient of**|**constant**|**R-squared**|**Number of**|
|---|---|---|---|---|
|**variable**|**T**|||**observations**|
|npl0204|-3.42***|27.39***|0.14|93|
||(-4.53)|(6.8)|||
|Privo|19.44 **|-47.3|0.23|170|
||(6.84)|(-3.91)|||

_(1): numbers in parentheses are t statistics._ _(2): Coefficients with * indicates significance at the .10 level, ** at the .05 level and *** for the .01 level. It is the same for the other regression tables._ 17
over 30 provinces, including rural and urban areas with only 8 percent of the collection points being from large shops. This stands in stark contrast to the 11 cities included in the 2005 round. World Bank (2015, p.3) further states that for India and Indonesia the 2011 ICP “also achieved coverage of both rural and urban areas in their collection of prices.” In the case of Indonesia, price data were collected from 2,645 outlets in rural and urban areas of which only 3 percent were large shops (World Bank, 2014c).<sup>13</sup> While we recognize that any sampling bias in collection of PPP data is a concern, and further work should be done to assess this, we argue there is no valid reason to maintain the same bias adjustments in the 2011 PPPs as used in the 2005 PPPs.

For the purposes of this paper, we remove the urban-rural adjustments for China, India and Indonesia embedded in the data obtained from PovcalNet.<sup>14</sup> When we remove these urban-rural adjustments from the analysis, we refer to this as using the 2011 PPP at “face value”. For the sake of comparison, we also carry out the analysis in some cases with the adjustments left in the data and refer to these results as the 2011 PPP adjusted estimates. Our analysis does not directly assess the relative quality of the 2005 PPP data as compared to the 2011 PPP data, but rather aims to show how the use of the 2011 PPP data affects the global profile of poverty and poverty projections.

> 13 Based on background metadata from World Bank (2014c).

> 14 All of the analysis of the 2011 profile of poverty draws only from published articles and publicly available PovcalNet data. In particular, the underlying data are based on extracting survey means and decile shares (which are publicly available in PovcalNet) to estimate consumption, or in some cases income Lorenz curves. The analysis of the poverty line is based on poverty lines from published data and using the World Development Indicators data to obtain PPP and CPI data. More details on the construction of the data files and on the removal of the urban-bias adjustment are available in Annex 1.

11
**Remark 10** _One possible extension to the set of component distributions proposed above (with different μi but common σ_<sup>2</sup> _) can be obtained by adding components with common zero mean (i.e. μr_ = 0 _) but different variances σr_<sup>2</sup><sup>_._</sup>

# **4 A small simulation study**

This section presents a modest Monte-Carlo simulation experiment. We will focus our attention to the following two questions: (1) How effective is our approach in fitting non-normal error distributions (that are not necessarily normal-mixtures)?, and (2) What are the implications for the estimation of poverty and inequality? Do distinct deviations from normality have the potential to introduce a meaningful bias in estimates of poverty and inequality if this nonnormality is ignored?

We make the following assumptions. The simulated “country” consists of 500 domains (or target areas), which denotes the level at which measures of poverty and inequality will be estimated. Each domain is home to 3000 households. Household per capita incomes will be generated by means of the following model: where _yah_ denotes the log of household income, and where _xah_ represents a single covariate. For the model parameters we consider the values: _var_ [ _ua_ + _εah_ ] = _σu_<sup>2+</sup><sup>_σ_</sup> _ε_<sup>2= 0</sup><sup>_._3,</sup><sup>_β_= 1,</sup><sup>_E_[</sup><sup>_xah_] = 0,</sup> and where _var_ [ _xah_ ] is chosen so that _R_<sup>2</sup> = 0 _._ 4 (which represents a goodness-of-fit that is typical for empirical household income models).

It will be convenient to introduce a parameter that measures the size of the random area effect relative to the total error term. Let us denote this parameter by _ρ_ = _σu_<sup>2</sup><sup>_/_(</sup><sup>_σ_</sup> _u_<sup>2+</sup><sup>_σ_</sup> _ε_<sup>2).We</sup> will be considering both the case of small “area effect” ( _ρ_ = 0 _._ 05) and medium/large “area effect” ( _ρ_ = 0 _._ 25).

The non-normal errors _ua_ and _εah_ will be drawn from a Log-Dagum distribution with probability density function: where _p >_ 0 is the parameter that determines the shape (or skewness) of the distribution. Smaller values of _p_ will result in larger deviations from normality. The remaining two parameters _a_ and _b_ will be fixed by imposing zero mean and setting the variance to _σu_<sup>2or</sup><sup>_σ_</sup> _ε_<sup>2.Note that the</sup> Dagum distribution is not an uncommon choice when modeling income data (see e.g. Kleiber (2007), and the references therein). The covariate _xah_ will be drawn from a normal distribution. Finally, our artificial survey will sample 15 households from each domain.

17
expected value of the VL transfer to household i is equal to the product of the probability of participating and the expected value of the transfer conditional upon participation, Taking the derivative of this expression with respect to the change in the VL grants to the community (G) and applying the chain rule, gives us the following decomposition, <!-- Start of picture text --> ( i)=[ Pr(VL, > 0 ) E(VL,VVL,>)]+[Pr(V O)E(L'I 0)]<br>Extensive Intensive<br>Margin Margin<br><!-- End of picture text --> The object of interest is the change in the probability of participation with a change in the grant to the community. Rearranging terms gives us A common assumption that we make here is that an increase in transfers from the committees is equal to the grant (i.e., that there is no change in the leakage when there is a change in the size of grant), The expected value of transfers for recipients ( _E(VL,_ I _VL, >_ 0) ) and the probability of participation (Pr(VL, > 0)) are estimated using the 1997 ENNIV data. The change in the expected transfer for participants for a change in the grant, is also be estimated using the ENNIV data by regressing per capita VL transfer values among recipient households on district level per capita VL expenditures, correcting for selection into the sample of recipients. Note that since this is estimated on a cross-section, an identifying assumption is that new and old recipients receive similar transfer values.

The estimates give the following, _24_