rafmacalaba/datause-encoder-data huggingface dataset:
number of tests also exist to support the purported validity and strength of the instruments adopted. In this paper, we start by trying to make a convincing argument for the use of certain variables available to us from either the ALSMS05 or the 2001 Population Census, and then select the most appropriate instruments through a number of diagnostic tests.

One of the main features characterizing migration, and in particular the choice of destination, was the knowledge of the language of the destination country. Knowledge of either Greek or Italian at the onset of the migration flow in 1990 by a household member, besides making the destination country more attractive by lowering the costs of assimilation, may also reflect affinity in culture and mentality, as well as geographical vicinity. This familiarity with the language spoken in the host country has been noted to be an important factor in determining the direction of migration in Albania (IOM, 2005). As a result, migrants from the South and Southeast areas of Albania still comprise the majority of migrants to Greece. These areas are closer to Greece, and Greek is widely spoken there due to presence of Greek minorities. On the other hand, migrants from the Coastal and Central areas comprise the majority of the migrants to Italy. As with Greek, a central characteristic of these areas is the familiarity with the Italian language, mainly due to the popularity of Italian television channels prior to and following the fall of communism. Thus, knowledge of languages by the migrant or other household members in 1990 is considered a good predictor of their migration decision. Additionally, the fact that exposure to these languages was almost uniformly dependent on location and cultural background, and was mainly induced by exogenous factors such as the presence of Greek minorities and Italian channels and not a reflection of differences in education or skills, suggests that knowledge of languages in 1990 may be considered exogenous to our outcome variables. Thus, dummy variables indicating knowledge of Greek or Italian language by the migrant or anyone in the migrant’s household in 1990 is proposed as an instrument in the analysis.

The use of migration networks to identify migration has been widely used in the literature (McKenzie, 2005; Woodruff and Zenteno, 2001; McCarthy et al., 2006; Rozelle et al., 1999, Taylor et al, 2003). Previous migration, and the resulting formation of social networks abroad, is assumed to promote further migration by lowering the costs, both 10
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issue, however, is confined to non-treaty countries, including the United States.

4. Many countries, such as Canada and recently the United States, limit the amount of interest to be deducted by multinational subsidiaries from taxes owing to the host country. In Canada, a "thin capitalization" rule limits "non-arm's length" interest deductions by foreign companies to a certain percentage of assets held by the company. New U.S. rules limit interest deductions of foreign multinationals to a percentage of U.S. taxable income. Thailand currently has no such rules, however. Given the low withholding taxes on certain forms of remitted income, new rules that would reduce the amounts of interest deductions are desirable.

5. The Thai government could consider a special tax or adjustments in withholding taxes that would reduce the incentive for multinationals to use transfer pricing. Transfer pricing arises from understating or overstating commodity prices to shift taxable income from one jurisdiction to another. In the case of Thailand, transfer prices are used to reduce Thai taxes when the Thai statutory tax rate is greater than the tax rate applied abroad. A justification given for using import duties in Thailand has been to reduce the incentive for companies to overstate imported prices. Import duties, however, are not an appropriate instrument for eliminating transfer pricing. Instead, the Thai government could impose special provisions to bring Thai company tax rates in line with trading partners.

6. Data on accounting statements of Thai and foreign companies provide considerable information on company taxes paid to the Thai government. A striking feature is that a large number of companies do not pay any company taxes, particular those companies not listed on the
|**Country**|**Total dispatched or**<br>**purchased(GWh)**|**Sales (GWh)**|**Sales (kWh per**<br>**person)**|**T&D losses**<br>**(GWh)**|**T&D losses (% of GWh**<br>**dispatched orpurchased)**|
|---|---|---|---|---|---|
|Nigeria_2014|30,715|18,819|105|11,896|39%|
|Rwanda_2013|517|382|32|135|26%|
|São Tomé and||||||
|Príncipe_2014|90|51|258|38|43%|
|Senegal_2013|3,038|2,507|177|531|17%|
|Seychelles_2014|357|313|3,415|45|12%|
|Sierra Leone_2012|195|120|20|75|39%|
|Somalia_2011|310|288|29|22|7%|
|South Africa_2014|238,201|216,274|4,005|21,927|9%|
|South Sudan_2013|99|70|6|29|29%|
|Sudan_2014|11,359|9,709|250|1,649|15%|
|Swaziland_2014|1,209|1,074|848|135|11%|
|Tanzania_2015|9,434|7,754|147|1,680|18%|
|Togo_2013|1,091|784|115|307|28%|
|Uganda_2014|2,894|2,277|59|617|21%|
|Zambia_2014|13,812|12,104|806|1,708|12%|
|Zimbabwe_2012|10,079|8,534|622|1,545|15%|

_Sources_ : Utility annual reports and other documents _Notes_ : The year following each country name is the reference year for countries included in the QFD analysis, or the latest year with available data for countries not included in the QFD analysis. — = not available.

94
the potential distributional effects of tax reforms in Pakistan, by linking the output of a Computational General Equilibrium (CGE) model to a nationally representative household survey (the Pakistan Standard of Living Measurement Survey, PSLM). A top-down approach is utilized, in which the macro and sectoral outputs of the CGE model are used as inputs into micro-simulations.<sup>4</sup> A particular characteristic of the CGE model is that it incorporates tax evasion as an endogenous outcome of optimizing behavior by the corporate sector. Given Pakistan’s relatively low rate of overall tax collection, as well as high statutory tax rates, it is important to be able to include the impact of evasion.

Our paper will be structured as follows. The next section will be a review of the relevant literature on tax evasion in the context of general equilibrium models, while Section III will give the intuition behind the treatment of tax evasion in our model. Section IV will develop the dynamic computational general equilibrium (CGE) model that we use to analyze tax changes at the macro level. The next section will give the numerical results of the simulations of alternative tax policies in the CGE model. Section VI will discuss the micro simulation model that links the CGE macro results to poverty analysis, and will present the poverty outcomes of alternative tax regimes. The final section will be a conclusion.

# **II. Literature Review**

A number of studies consider the sustainability of poverty reduction in Pakistan. Thus, the World Bank report from 2013 analyzes the trends of poverty reduction by regions in Pakistan. It concludes that, for the period 2001-2008, Pakistan had a positive performance in linking economic growth with poverty reduction. The study addresses both urban and rural

> 4 This is a commonly used approach in trying to analyze the impact of fiscal policy such as taxes and tariffs on heterogeneous consumer groups (See Feltenstein, Lopes, Porras-Mendoza, and Wallace, 2014 for a survey).

3
highlighted in the Johannesburg World Summit on Sustainable Development (Modi and others 2005; DFID 2002).

The ways in which energy contributes to welfare have been well documented. For example, the burning of biomass energy such as fuelwood, dung or crop residue in conventional ways often contributes to indoor air pollution and is thus a health hazard (World Bank 2002a, 2002b).<sup>2</sup> The use of more modern fuels such as LPG can alleviate this problem. Collection of fuelwood also takes a lot of time in rural areas and keeps people (particularly school-going children or women) away from other productive pursuits (Saghir 2004; Barnes and Toman 2006); so ways to reduce traditional biomass energy consumption can lead to saving time and better opportunities. The use of electricity in the evening extends work and study hours and contributes to productivity and educational achievements (Brodman 1982; Cabraal and Barnes 2006; Roddis 2000; Saunders and others 1975; Wasserman and Davenport 1983; World Bank 2002c; Unnayan Shamannay. 1996).

Based on these benefits, international donors have been promoting rural energy development as part of the goals towards achieving MDGs and in general for rural development (WHO 2006; United Nations 2005). This paper intends to deepen our understanding of energy as one of the factors underpinning both economic development and poverty reduction through the study of the relationship between energy and poverty in Bangladesh.

In our empirical analysis of a comprehensive survey representative of rural Bangladesh we find that energy poverty is pervasive in rural Bangladesh in spite of the government’s efforts to promote better forms of rural energy. However, there are ways to alleviate energy poverty. Both traditional and modern ways of using energy contribute to the alleviation of energy poverty. In particular, the use of electricity significantly improves household income. This study finds that

> 2 Burning biomass also adds carbon to the atmosphere, contributing to Green House Gases trapping heat in the atmosphere.  So an inefficient burning of biomass fuels contributes to climate change.

3
**TABLE 6.4**

|_Sml_<br>u|(Food Jnduay)<br>nions-<br>Depn<br>(Val<br>|: IV E_mits<br>dent Vuiable<br>ues**in**parthee<br>|of Augmnted<br>- _LAg_of Cota<br> <br>are sadard<br>|Cobb-DougsS<br> <br>**Price alu**<br>erors)<br>B|Pro&I'on<br>**e d**<br>VlA|dd|
|---|---|---|---|---|---|---|
||Base<br>in<br>|on Value-A<br>Domestic Pric<br>|dded<br>es<br>|ase<br>in<br>|on aue-<br>World Prices<br>|e<br> <br>|
||1|2|3|4|5|6|
|Intercept<br>(% Senegalese Private)|0.367<br>(1.049)|-0.743<br>(0.954)|0.031<br>(0912)|2.442<br>(1.756)|1.527<br>(1.791)|1.229<br>(1.510)|
|Ownership:|||||||
|% Senegalese State|1.165'<br>(0.682)|1.055<br>(0.682)|0.844<br>(0-593)|1252<br>(0.617)|1.759'<br>(0-530)|1.006'<br>(0.530)<br>|
|% French|0.261<br>(0330)|.0246<br>(0.256)|-0.102<br>(0.233)|0.034<br>(0388)|-0312<br>(03m0)|_-0267_<br>(0279)|
|% Syro-Lebanese|0.068<br>(0394)|-0.152<br>(0.227)|-0202<br>(0211)|0.136<br>(0.456)|0.160<br>(0.275)|0.019<br>(0253)|
|% Other Foreign|0.661'<br>(0390)|0.402<br>(0275)|0383<br>(0254)|0.470<br>(0.461)|0524<br>(0379)|0289<br>(0336)|
|Trade Variables:|||||||
|Effective Rate of<br>Protection|0.09?<br>(0.048)|0.148!<br>(0.044)|0.115<br>(0.040)|-0353*<br>(0.063)|**_0248e_**<br>(0.064)|**_.0298_**<br>(0.054)<br>(Ctie|

<
as measured by life expectancy for a cross section of countries. More recent data confirm his finding of a concave relationship between health status and income (see Figure 2) and show that this relationship is becoming stronger over time.

**Figure 2. The Preston Curve, 2001**

<!-- Start of picture text --> 85<br>80<br>75<br>70<br>65<br>60<br>0 10,000 20,000 30,000 40,000 50,000<br>GDP per capita (USD)<br>Life expectancy at birth<br><!-- End of picture text --> _Source_ : World Bank, World Development Indicators.

8
# **Annex 2:  An Empirical Analysis of Sources of Volatility in Nigeria over 1970-2005.**

## **_Testing the VE versus the DO Hypothesis_**

This annex provides an empirical test that will help single out the main source of volatility in Nigeria for the past 36 years. We have identified two different explanations for high expenditure volatility in Nigeria: _“voracity effect”_ (VE), and Debt Overhang (DO).

VE is defined as an institutional inability to reconcile competing claims for oil money, resulting in a marginal propensity to spend out of rising oil wealth larger than one, but a propensity to adjust in years of falling income that is smaller than one, and thus certainly smaller than the coefficient in "good" years. DO hypothesis generates the exact opposite prediction, an adjustment coefficient in years of falling revenue that exceeds the coefficient in years of rising revenue.

To test the VE versus the DO hypothesis, we run a regression of government spending (also its components – current and capital spending) on oil-related fiscal revenues over the period 1970 – 2005. All data are presented in constant dollars. We first show the results for regressions with symmetric adjustment during “good” and “bad” years (defined as years with rising and years with falling oil revenues, respectively). Regression results are shown in Table A.1.

**Table A.1. Government Spending and Oil Revenues for 1970-2005, levels in constant $**

||**EQ.1**<br>TotalGovernmentExpenditure|**EQ. 2**<br>CurrentExpenditure|**EQ. 3**<br>Capital Expenditure|
|---|---|---|---|
||1.45|0.87|0.52|
|Oil Revenue|(9.87)|(10.29)|(6.36)|
||1.76|1.10|0.26|
|Constant|(0.83)|(0.90)|(0.22)|
|No. of Obs.|36|36|36|
|R-sq. overall|0.73|0.75|0.53|
|DW|1.13|1.46|0.73|

_Note: 1. Data source: IMF World Economic Outlook database. 2. t-statistics in parentheses._ All three regression equations, for total, current and capital expenditure show a very strong relation between expenditure and revenues from oil, as is to be expected. But there is also a serial correlation pattern in the errors (see Table A.2). The DW coefficient is too low in all three regressions, and the Breusch-Godfrey Serial Correlation LM test rejects the H0 hypothesis of no-serial correlation (except for the second regression equation, for current expenditure). Analysis of the error terms furthermore suggests a before and after 1984 difference. The Chow Breakpoint test for 1984 strongly rejects the H0 of no structural break in 1984 in all three equations.

28
Turning to the de jure measures of labor market regulation, Figures 2, 3 and 4 present prior published indicators of policy: the statutory minimum wage (as a share of average per-worker value added); the OECD’s employment protection legislation (EPL) index, as expanded by that organization in partnership with IADB to non-member countries of OECD in Latin America and the Caribbean; and ILO’s EPLEX measure of employment protection. Mainly as a validation exercise, the figures benchmark these variables by countries’ level of development.

**Figure 2. Statutory Minimum Wage (Ratio to Average Worker Value Added) and Level of Development**

<!-- Start of picture text --> HND<br>HTI<br>NIC<br>GTMBLZ PRI<br>BOL PRY ECU CRI ARG<br>JAM DMAPERGRDDOMCOL PANATGKNA<br>BRA<br>VCT BHS<br>VENURY<br>GUY SLV<br>MEX<br>6 8 10 12<br>Log of GDP PC PPP<br>Log MW to VA per worker Log MW to VA per worker<br>Fitted values Fitted values<br>Source: Authors using WDI Jobs Database<br>1<br>0<br>-1<br>-2<br>Log MW to VA per worker<br>-3<br>-4<br><!-- End of picture text --> The level of countries’ statutory minimum wages, normalized by average value added per worker, has a downward slope when plotted against income per capita. This is to be expected given the positive correlation between value added per worker and gross domestic product per capita. Capturing workers’ output in the minimum wage variable also helps to contextualize countries where the distortions to the market-price of labor from a high statutory minimum wage are minimized by higher worker productivity. Although the slope of the curve fitted for countries in Latin America and the Caribbean is slightly steeper than that for the full sample, this difference is not statistically significant.

12
_<u>Table 13: Poverty and inequality in the Doha scenario, by sector</u>_

||2001 levels<br>Al|2001-15<br>changes<br>l households|% of BaU<br>change<br>|2001 levels<br>Non-agri|2001-15<br>changes<br>cultural hou|% of BaU<br>change<br>seholds|2001 levels<br>Agricu|2001-15<br>changes<br>ltural house|% of BaU<br>change<br>holds|
|---|---|---|---|---|---|---|---|---|---|
|PC income|314.9|1.5|101.5|351.9|1.3|102.1|148.3|2.4|101.3|
|Gini|58.6|-0.2|194.4|57.1|0.5|81.8|56.6|-0.8|111.5|
|P0|23.6|-5.8|103.4|18.6|-3.3|106.5|46.2|-14.0|101.5|
|P1|9.6|-3.1|102.7|7.1|-1.6|104.6|21.0|-8.2|102.1|
|P2|5.3|-1.9|102.5|3.7|-0.9|104.3|12.3|-5.3|102.0|
|Population %|100.0|||81.8|3.2|98.3|18.2|-3.2|98.3|
|Contr. to P0||||64.4|8.6|96.0|35.6|-8.6|96.0|

Source: Authors’ calculations.

Given its larger price and quantities shocks, the full liberalization scenario yields more significant poverty changes, as shown in Table 14. In contrast to the Doha scenario, agricultural households gain considerably from full liberalization and their headcount index is reduced by almost 1.5 percentage points. This sector specific income gains more than compensate the further (albeit small) reduction of agricultural out-migration.

For non-agricultural households, the full liberalization scenario improves the income distribution, the Gini increases by only 72 per cent of the increase recorded in the BaU. Growth is only slightly higher for this group of households but, as shown above, minor distributional shifts accompanied by slightly higher growth can result in significant poverty reduction.

_<u>Table 14: Poverty and inequality in the Full scenario, by sector</u>_

||2001 levels<br>Al|2001-15<br>changes<br>l households|% of BaU<br>change<br>|2001 levels<br>Non-agri|2001-15<br>changes<br>cultural hou|% of BaU<br>change<br>seholds|2001 levels<br>Agricu|2001-15<br>changes<br>ltural house|% of BaU<br>change<br>holds|
|---|---|---|---|---|---|---|---|---|---|
|PC income|314.9|1.6|106.4|351.9|1.3|106.8|148.3|2.6|109.8|
|Gini|58.6|-0.3|312.2|57.1|0.5|72.0|56.6|-0.9|117.0|
|P0|23.6|-6.1|109.2|18.6|-3.6|116.3|46.2|-14.9|108.0|
|P1|9.6|-3.2|108.2|7.1|-1.8|113.7|21.0|-8.6|107.4|
|P2|5.3|-1.9|107.8|3.7|-1.0|113.0|12.3|-5.6|107.2|
|Population %|100.0|||81.8|3.1|93.0|18.2|-3.1|93.0|
|Contr. to P0||||64.4|8.4|96.0|35.6|-7.6|96.0|

Source: Authors’ calculations.

Trade shocks simultaneously increase agricultural incomes and reduce inter-sectoral migration and how these two contrasting forces affect poverty outcome depends on the income levels (and therefore on the socio-economic characteristics) of those who decide to stay instead of moving. The next set of tables sheds some light on this issue.

Table 15 shows the poverty levels and changes under the BaU and the trade scenarios for agricultural households who _remained_ in agriculture. First consider the BaU case. Having identified those households that will not move, it is possible to calculate the headcount 44
Berg, Claudia and M. Shahe Emran (2011), “Does Microfinance Help the Ultrapoor Cope with Seasonal Shocks? Evidence from Seasonal Famine (Monga) in Bangladesh”, Working paper, George Washington University.

Blundell, R and Monica Costa Dias (2009), “Alternative Approaches to Evaluation in Empirical Microeconomics”, Journal of Human Resources, Summer 2009, Vol. 44, No.3.

Bose , Pinaki (1988) “Formal-Informal sector interactions in rural credit markets” _Journal of Development Economics_ , Vol. 56, pp. 265-280.

Busso, M., DiNardo J., McCrary, J. (2011), “New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators”, Working Paper, University of California, Berkeley.

Ebbes, Peter, Michel Wedel, Ulf Böckenholt (2009) “Frugal IV alternatives to Identify the Parameter for an Endogenous Regressor”, _Journal of Applied Econometrics_ , 24: pp. 446–468 Emran, M. Shahe and Zaoyang Hou (2013), “Access to Markets and Rural Poverty: Evidence from Household Consumption in China”, _Review of Economics and Statistics,_ May, 2013.

Emran, M. Shahe and Forhad Shilpi (2012) “The Extent of the Market and Stages of Agricultural Specialization” _Canadian Journal of Economics_ , August, 2012.

Emran, M. Shahe and Yan Sun (2011) “Magical Transition? Intergenerational Educational and Occupational Mobility in Rural China: 1988-2002”, working paper, GWU and IFPRI.

Emran, M. Shahe, Virginia Robano, and Stephen C. Smith (forthcoming) “Assessing the Frontiers of Ultra-Poverty Reduction: Evidence from CFPR/TUP, An Innovative Program in Bangladesh _”, Economic Development and Cultural Change._ Farré, Lídia, Roger Klein, and Francis Vella (2013) “A parametric control function approach to estimating the returns to schooling in the absence of exclusion restrictions: an application to the NLSY”, _Empirical Economics_ , 43 (1), 2013.

Farre, Lidia, Roger Klein and Francis Vella (2012), “Does Increasing Parents' Schooling Raise the Schooling of the Next Generation? Evidence based on Conditional Second Moments”, _Oxford Bulletins of Economics and Statistics,_ (2012), vol 74(5), p.p. 676-690 Ghosh, Parikshit, Dilip Mookherjee, and Debraj Ray (1999) “Credit Rationing in Developing Countries: An Overview of the Theory”, in D. Mookherjee and D. Ray Ed. Readings in Development Economics.

Gilchrist, Simon and Egon Zakrajˇsek (2012), “The Impact of the Federal Reserve’s Large-Scale Asset Purchase Programs on Default Risk”, Working Paper, Boston University.

Hartarska, V., Shen, X., and R. Mersland, (2013) “Scale Economies and Elasticities of Substitution in Microfinance Institutions,” _Journal of Banking and Finance,_ 37(1): 118-131.

Heckman, James, Hidehiko Ichimura, and Petra Todd (1998) “Matching as an Econometric Evaluations Estimator”, _Review of Economic Studies_ , vol. 65, No. 2.

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The study then used the estimated impacts of the shocks and the historical and spatial distribution of shocks to calculate how many children are vulnerable to malnutrition under different circumstances. We estimate that around a third of the children in our sample face a 50% risk of falling into malnutrition in the near future, partly as a consequence of exposure to weather shocks.

# **REFERENCES**

Anttila-Hughes, Jesse and Solomon Hsiang. 2013. “Destruction, Disinvestment, and Death: Economic and Human Losses Following Environmental Disaster.” UC, Berkeley.

Alderman, H., J. Hoddinott, and B. Kinsey. 2006. Long term consequences of early childhood malnutrition." Oxford Economic Papers, 58(3): 450-474.Morduch, 1990; Almond D. Is the 1918 influenza pandemic over? Long-term effects of in-utero influenza exposure in the post-1940 U.S. population. Journal of Political Economy 2006; 114: 672-712. doi: 10.1086/507154 Chaudhuri, S. 2001. “Assessing vulnerability to poverty: concepts, empirical methods and illustrative examples.” Discussion paper series, Department of Economics, Columbia University.

Chen, Xi. 2014. “Fetus, fasting, and festival: the persistent effects of in-utero social shocks.” International Journal of Health Policy Management, 3(4), 165–169 Christiaensen and Boisvert. 2000. “On Measuring Household Food Vulnerability: Case Evidence From Northern Mali.” Working Papers 127676, Cornell University, Department of Applied Economics and Management.

Christiaensen, Luc J. and Kalanidhi Subbarao, 2005. "Towards an Understanding of Household Vulnerability in Rural Kenya," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 14(4), pages 520-558, December.

Dang, Hai-Anh and Peter Lanjouw. 2014. "Welfare dynamics measurement: two definitions of a vulnerability line and their empirical application," Policy Research Working Paper Series 6944, The World Bank.

Dercon, Stefan and Pramila Krishnan, 2003. "Risk Sharing and Public Transfers," Economic Journal, Royal Economic Society, vol. 113(486), pages C86-C94, March.

Dercon, S., and L. Christiaensen. 2011. “Consumption risk, technology adoption and poverty traps: evidence from Ethiopia." Journal of Development Economics, 96(2): 159-173.

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