rafmacalaba/datause-encoder-data huggingface dataset:
means that the higher credit level for students above the threshold increases their GPA by 0.234 points (on a 10 point scale). This corresponds to a 3 % improvement in grades obtained, which is a relatively large effect. However, statistical significance is lost when educational background controls are included. No systematic treatment effects were found for Master-students (cf. Table 3B).

|**Table 3B: Im**|**pact of SO**<br>|**FES on GPA, M**<br>|**aster students,**<br>|**SOFES data**|||
|---|---|---|---|---|---|---|
||N|<RD|>RD||OLS||
|DS=0.08|(1)|(2)|(3)|(4)|(5)|(6)|
|±0.01|86|8.638|8.491|-0.147<br>(0.163)|-0.122<br>(0.174)|-0.156<br>(0.163)|
|±0.02|154|8.592|8.603|0.011<br>(0.114)|0.001<br>(0.113)|0.023<br>(0.102)|
|±0.03|211|8.607|8.621|0.014<br>(0.096)|-0.031<br>(0.099)|-0.025<br>(0.085)|
|±0.04|297|8.559|8.634|0.075<br>(0.079)|0.036<br>(0.082)|-0.011<br>(0.070)|
|DS=0.15|||||||
|±0.01|65|8.538|8.622|0.085|-0.108|-0.078|
|||||(0.255)|(0.257)|(0.238)|
|±0.02|86|8.547|8.435|-0.112|-0.183|-0.115|
|||||(0.184)|(0.188)|(0.151)|
|±0.03|130|8.496|8.465|-0.032<br>(0.122)|-0.094<br>(0.126)|-0.036<br>(0.106)|
|±0.04|173|8.544|8.438|-0.106<br>(0.109)|-0.164<br>(0.113)|-0.050<br>(0.094)|
|DS=0.30|||||||
|±0.02|34|8.640|8.578|-0.062<br>(0.241)|-0.167<br>(0.266)|-0.234<br>(0.309)|
|±0.03|43|8.700|8.708|0.008<br>(0.204)|0.032<br>(0.213)|-0.058<br>(0.232)|
|±0.04|48|8.714|8.693|-0.022<br>(0.203)|-0.068<br>(0.206)|-0.094<br>(0.207)|
|_Controls_|||||||
|Socio-ec.<br>background||||No|Yes|Yes|
|Educational<br>background||||No|No|Yes|

Note: Standard errors are between brackets. * indicates significance at the 10%-level, ** indicates significance at the 5%-level, and *** indicates significance at the 1%-level. Controls denoted by “socioeconomic background” include the tuition fee level, a dummy indicating whether a student receives a grant or not, family income, the socio-economic stratification index, a dummy indicating the student’s gender, the student’s age, and a dummy indicating whether the student’s family owns a real estate property. Controls denoted by “educational background” include the student’s grade point average at the highest three completed education levels, dummies indicating the type of school attended (public or private), the term of the program in which the student is enrolled, and dummies indicating the study area. DS stands for discontinuity sample and equals the cut-off points for the Economic Need Factor (ENF) where the loan <u>amount increases by additional 10% of tuition.</u> A comment is in order. Students in the control group receive a lower loan and have a lower ENF than students in the treated group. This could induce so-called dynamic selection bias when loans matter for the 21
**Table A14: Impacts of mental health on labor outcomes – Past-employment experience**

|Dependent variable:|Employm|ent status|Weeklylabor inco|me ($1,000AUD)|
|---|---|---|---|---|
||No work<br>experience|Have work<br>experience|No work<br>experience|Have work<br>experience|
||(1)|(2)|(3)|(4)|
|Standardized mental health<br>scores|-0.027*|-0.078***|-0.131**|-0.293***|
||(0.014)|(0.011)|(0.062)|(0.083)|
|_First stage of 2SLS_|||||
|Trauma experience*Year<br>after arrival|-0.044**<br>(0.017)|-0.040***<br>(0.011)|-0.050**<br>(0.022)|-0.046***<br>(0.013)|
|Kleibergen-Paap F stat.|15.150|17.151|15.670|16.416|
|AR 95-CIs|[-0.117, -0.008]|[-0.195, -0.010]|[-0.237, -0.074]|[-0.663, -0.169]|
|Dep. Mean|0.455|0.455|0.716|0.716|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|1,238|2,429|1,238|2,429|
|Numberof individuals|684|915|684|915|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Columns (1)-(2) and IV-FE model in Columns (3)-(4). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6- item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.
# **Energy Poverty in Rural and Urban India: Are the Energy Poor Also Income Poor?**

## **Introduction**

The relationship between energy and poverty has preoccupied development specialists for decades. Running modern economies without energy is impossible; thus, it has been argued that energy use, particularly such modern energy<sup>2</sup> as electricity, is related in some way to economic development (Besant-Jones 2006; UNDP 2005). However, the concern is whether the provision of energy services leads to economic development or whether economic development leads to expanding demand for energy. The conventional wisdom is that energy is a necessary but not a sufficient condition for development. But this begs the policy question as to whether the lack of energy, especially modern energy, is a cause of poverty.

The linkage between modern energy access and welfare is well documented. For example, the burning of biomass energy (e.g., 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>3</sup> The use of more modern fuels, such as LPG, can alleviate this problem. In rural areas, the collection of fuelwood, often performed by women and school-going children, takes time away from other productive pursuits (Saghir 2004; Barnes and Toman 2006). Thus, replacing traditional ways of consuming biomass with more efficient energy-consumption methods or consumption of modern energy can lead to time savings and better opportunities. Use of electricity in the evenings can

> 2 The term modern energy connotes those types of energy used in more developed societies, compared to traditional energy types still prevalent in developing countries that have probably been used for thousands of years.  In general, liquid fuels, such as kerosene and LPG, along with electricity, are considered modern forms of energy; while traditional fuels generally consist of biomass fuels (wood, agricultural residue, and dung), which are used in traditional stoves.

> 3 The inefficient burning of biomass fuels also adds carbon to the atmosphere, contributing to the heat-trapping effect of greenhouse gases (GHGs) and thus to climate change.

2
# **1 Introduction**

In most of the developing world, wealth is unequally spread geographically and increases with city size (Ferre et al. 2012; Young, 2013; Gollin et al., 2021). This suggests migration as an important avenue for growth and poverty reduction, affecting both those who move (McKenzie et al. 2010, Beegle et al. 2011, Bryan et al. 2014) and those who remain behind (Giles and Yoo 2007, Kinnan et al. 2018, De Weerdt and Hirvonen 2016, Morten 2019). Moves to big cities are, on average, much more lucrative than moves to small towns. But survey data show that few people make it to the cities and many more migrants end up with more modest income gains realized through moves to smaller towns (Christiaensen et al., 2019). A key question is therefore how migrants choose their destinations: why do migrants move to seemingly suboptimal locations?

One consideration is that there are perhaps a number of nonmonetary amenities, such as health, public goods, crime or pollution that serve to compensate for the lower consumption levels (Dustmann and Okatenko, 2014). Gollin et al. (2021) argue against this possibility by showing that in 20 developing countries and across a range of non-monetary amenities the urban gradient remains clearly visible: practically every amenity they can measure seems to improve with population density. The authors interpret this as evidence in favor of a friction model in which people’s mobility is somehow restricted, which allows the spatial disequilibrium to persist. Support for this view from our study setting in Tanzania comes from Ingelaere et al. (2018) who show that there is a widespread desire to move out of the rural areas and into larger urban agglomerations, especially by younger people. But despite their acute awareness of the spatial differences in living standards, for the average young person living in a remote village, moving is also considered a daunting, sometimes impossible, endeavor.

Much of the migration literature over the past couple of decades has further emphasized the importance of selection, with the younger, better educated and richer found more likely to move. They are often better equipped to overcome migration costs and could stand to benefit more from the employment opportunities for the skilled that urban areas offer (Young, 2013; Lucas 2015; Hamory, 2020). As those categories are also smaller in number, this could help 1
**Figure 1: Reported incidence of being engaged in or a victim of criminal activity**

<!-- Start of picture text --> Panel A: Any crime ‐ activity/victim, %<br>70.0<br>60.0 57.3<br>53.9<br>45.6 47.7<br>50.0<br>40.0 36.7<br>30.0<br>20.5<br>20.0<br>10.0<br>0.0<br>Total Males Females<br>Activity Victim<br><!-- End of picture text --> Source: Authors’ calculations.

**Figure 2: Reported incidence of being involved in a bad/criminal activity or being a victim of it, by type of activity**

<!-- Start of picture text --> Panel A: Stealing ‐ activity/victim, % Panel C: Alchol‐related (fights) ‐ activity/victim, %<br>45.0 45.0<br>40.0 40.0<br>37.0 38.3 34.7<br>35.0 35.0<br>28.1 30.8<br>30.0 30.0<br>24.2 24.6<br>25.0 25.0<br>20.1<br>20.0 20.0<br>16.6 13.5<br>15.0 15.0<br>10.0 10.0<br>5.6<br>5.0 3.0 5.0<br>0.0 0.0<br>Total Males Females Total Males Females<br>Activity Victim Activity Victim<br>Panel B: Assault ‐ activity/victim, % Panel D: Trespassing ‐‐ activity/victim, %<br>45.0 15.0<br>39.1 13.9 14.1<br>40.0<br>34.7 12.5<br>35.0 11.6<br>31.1 9.6<br>30.0 10.0<br>25. 9 26.7<br>25.0 7.1<br>7.5<br>20.0<br>16.6<br>15.0 5.0<br>10.0<br>2.5 1.9<br>5.0<br>0.0 0.0<br>Total Males Females Total Males Females<br>Activity Victim Activity Victim<br><!-- End of picture text --> Source: Authors’ calculations.

19
Figure 39: Yield and Input Use for the Four Major Crops, 2014/15 <!-- Start of picture text --> (a) Sorghum (b) Millet<br>4 2.5<br>Input not used Input used Input not used Input used<br>3 2<br>1.5<br>2<br>1<br>1<br>0.5<br>0 0<br>(c) Sesame (d) Groundnut<br>2 6<br>Input not used Input used Input not used Input Used<br>5<br>1.5<br>4<br>1<br>3<br>0.5 2<br>0 1<br>0<br>Yield (100 kg/acre) Yield (100 kg/acre)<br>Yield (100 kg/acre) Yield (100 kg/acre)<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

Crop Sales The proportion of sorghum yields to the crop sold in market exhibits a positive relationship, while millet yields do not show any such relationship (Figure 40). This helps explain why a negative relationship between yields and poverty exists for sorghum but not for millet. Increasing yields of sorghum are associated with improved welfare conditions, and this is in turn correlates with the ability to sell more produce in the market. For millet, due to its subsistent nature of cultivation for households in rural Sudan, increasing yields are not associated with increased opportunities to sell surplus produce. Rather, these households produce efficiently but do not sell much produce in the market, precisely because of its need for household consumption.

41
## 2.6 Summary of Theory Developments.

A summary of the main thrusts of the theory can be put as follows. The older theory o¤ered two stories: Trade a¤ects inequality either by a¤ecting the skill premium (in the StolperSamuelson theorem), or by a¤ecting industry premia (in the speci...c-factors model). It was hard to rationalize how trade could raise inequality everywhere in the world at the same time, or inequality within any group of similarly-skilled workers all doing the same job. It was also hard to see how North-North trade could a¤ect inequality at all. But now, we have stories that predict rising skill premia across countries as a result of North-South trade in tasks, and even as a result of North-North trade in goods due to R&D e¤ects or the skill bias of the transport sector. We have high-dimensional models that go beyond the skill premium to analyze the e¤ect of trade on the ‘middle class,’and distinguish between wage inequality and inequality in lifetime consumption through explicitly dynamic models of labor adjustment. We are also able to analyze the e¤ects of trade on inequality among observationally identical workers doing the same job in the same industry, through heterogeneous-...rms models or implicit contracts models. This rich set of stories helps in describing the e¤ects of trade on income distribution in the real world.

# 3 Empirical Work.

An immense empirical literature exists on the possible linkages between trade and inequality. Most of this literature predates the ‘new’trade models and focuses on testing the implications of the Heckscher-Ohlin framework for trade-inequality linkages. A number of literature surveys also review this work. See for example, Feenstra and Hanson (2001), and Goldberg and Pavcnik (2004, 2007). As in Section 2, we begin in Section 3 with only a brief review of the empirical literature associated with the ‘older’trade models, and then move on to review empirical papers that test the newer theories linking trade to inequality described in Section 28
# 2. Overview of current policy and institutional framework: Opportunities and challenges

## 2.1. Stagnation and uncertainty after years of export-led growth

For Lesotho, a small landlocked country with limited natural resource reserves, trade and global integration are essential for economic development. Over the past two decades, Lesotho has taken advantage of many of the opportunities afforded by globalization. The country’s export-led growth model relied in large part on the country’s preferential market access to the United States through the African Growth and Opportunities Act (AGOA). This access enabled Lesotho to attract large volumes of foreign direct investment (FDI) in the textile and apparel sector. As shown in Figure 1, both gross domestic product (GDP) per capita and exports began to increase rapidly following the country’s AGOA eligibility in 2000 and throughout the first decade of the 21<sup>st</sup> century, before peaking in 2011 and declining during the past four years.

**_Figure 1: Lesotho’s exports (left axis) and GDP per capita (right axis), 1997-2015_**

<!-- Start of picture text --> 1,400,000 1,600<br>Apr 2001: Lesotho<br>1,200,000 Oct 2000: becomes eligible for 1,400<br>Lesotho AGOA wearing apparel<br>1,000,000 becomes provisions 1,200<br>eligible for<br>1,000<br>800,000 AGOA<br>preferences 800<br>600,000<br>600<br>400,000<br>400<br>200,000 200<br>0 0<br>Exports of goods and services ('000, BoP current US$) GDP per capita (current US$)<br><!-- End of picture text --> Source: World Bank World Development Indicators However, over a decade of export-led growth has resulted in only limited poverty reduction, while inequality has worsened (World Bank 2017).<sup>3</sup> Moreover, the dynamism of Lesotho’s export sectors has in recent years stagnated and contributed to a significant worsening of its macroeconomic situation. GDP growth has remained at or below 5% since 2008. In terms of sectoral contribution, services have made a far more significant impact on growth than other sectors and following the discovery of significant diamond

> 3 While Lesotho has seen some poverty reduction since the start of the century, there is little reliable data on the magnitude of this decline, and it is assumed to have been rather modest. The headcount poverty rate ($1.90/ day PPP) fell from 61.3% in 2002 to 59.7 in 2011 and estimates suggest that 56.3% were in poverty in 2016. According to the most recent World Bank Macro Poverty Outlook (World Bank 2017), the slow pace of poverty reduction is likely to continue in coming years due to fiscal consolidation, declining remittances and droughts.

4
23

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Browning, M and V. Lechene (2001), “Caring and Sharing: Tests Between Alternative Models of Intra-household Allocation”, Discussion paper **01-07** , _Department of Economics Oxford University and Institute of Economics University of Copenhagen_ .

Burgess, R. and J. Zhuang (2003), “Modernisation and Son Preference”, _STICERD_ , Development Economics Discussion Paper No. 29, _London School of Economics_ .

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Clark, A. (2000), “Son Preference and Sex Composition of Children: Evidence From India”, _Demography_ , **3(1)** : 95-108.

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Deaton, A. (1987), “The allocation of goods within the household: adults, children, and gender”, _Living Standard Measurement Unit, The World Bank_ ; Research Program in Development Studies, Priceton University.

Deaton, A. (1989), “Looking for boys-girls discrimination in household expenditure data”, _World Bank Economic Review_ , 3: 1-15.

Deaton, A. and J. Muellbauer (1986), “On Measuring Child Costs: With Application to Poor Countries”, _Journal of Political Economy_ , **94(4)** : 720-744.

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The Box-Jenkins methodology (Box and Jenkins, 1976) is applied for determining the components of the ARIMA process; i.e. we test different ARIMA(p,d,q) models with p and q to be smaller or equal 4 (due to the limited amount of data) and estimate φ and θ using Maximum likelihood techniques and the Akaike Information Criterion (AIC) as well as diagnostic checks to detect a suitable model. The data requirements were set thus that at least 5 observed data points are needed for projections into the future. This is the smallest number of observations which are needed to estimate ARIMA(4,1,4) models (however, the majority of the sample (greater 90 percent) has at least 10 data points). Furthermore, all models are tested to be stationary (usually d=1 suffices to assure a stationary process) and all series are demeaned. To include uncertainty in the projections, also 95 percent confidence forecasts were calculated and analyzed.

Forecasts into the future are performed with the selected models and then compared to the observed variables. Increases or decreases of GDP in future years are measured as a percentage increase or decrease to baseline GDP (i.e., baseline =100) which is defined to be GDP a year before the disaster event.<sup>5</sup> Furthermore, the differences between observed values and projected ones are calculated and called Diff(t), which indicates the percentage difference between the observed and projected value of GDP in year t. We focus on projections with a medium term perspective (up to 5 years into the future). This limitation is due to important data constraints for the ARIMA models within the sample and increasingly large uncertainties beyond the medium-term time horizon.

# **3.2 Data used**

Our sample consists of 225 large natural disaster events during 1960-2005. The sample is based on information from two databases and was compiled by Okuyama (2009) with the threshold for a large event defined arbitrarily to a loss exceeding 1 percent of GDP.<sup>6</sup> One database is the open-source EMDAT disaster database (CRED, 2008) maintained by the Centre for Research on the Epidemiology of Disasters at the Université Catholique de Louvain. EMDAT currently lists information on people killed, made homeless, affected

> 5 To decrease variance a logarithmic transformation of GDP was performed at the beginning.

> 6 In order to define the “event set” the threshold of stock losses is set as a share (1%) of flow effects (GDP). While it would have been more systematic to define an asset threshold, yet we responded to the larger intuitive appeal of using GDP as a denominator, and the fact that this threshold was also used by another paper in the EDRR working paper series which we wanted to be in line with.

14
are virtually unchanged, giving some assurance that the relationship between aid inflows and the REER is not being driven by the 1986 crisis.<sup>18</sup> Lastly, we estimated the specification incorporating the parallel exchange rate into the REER measure. The signs of the coefficients do not change and all three variables remain highly significant. However, the coefficients on the terms of trade and the trade liberalization variables decline somewhat.

# **iii) Elasticity estimates**

The results from the FM-OLS (with a structural break) estimation are preferred when interpreting the elasticities given by the long-run parameter estimates (Column 5). Given the uncertainty surrounding the correct incorporation of the parallel nominal exchange rate in the REER measure we present the elasticities as ranges. On the basis of these results, the long-run relationship between Tanzania’s REER and its terms of trade, net aid inflows and trade liberalization can be identified as follows:

- The terms of trade parameter estimates suggests that a 1 percent increase in Tanzania’s terms of trade leads to an approximately 0.3 to 0.8 percent increase in the REER. This positive relationship suggests that the income effect of terms of trade increase dominates the substitution effect.

- The parameter estimate on the aid variable indicates that 1 percent increase in aid inflows (as a share of GDP) is associated with a 0.3 to 0.5 percent depreciation of the equilibrium REER.

- The parameter estimate on the openness proxy capturing trade liberalization suggests that an improvement in trade liberalization captured by a 1 percent increase in Tanzania’s trade to GDP ratio will result in a 0.6 to 1.0 percent depreciation of the equilibrium REER.

To be confident in the interpretation of the long-run elasticities it is important that the parameter estimates are stable over the sample period. We test for parameter stability using the _Lc_ and _MeanF_ tests proposed by Hansen (1992). Both tests have the same null hypothesis of parameter stability. The results indicate that we can not reject the null hypothesis of parameter stability over the sample period at all conventional significance levels.

# **iv) Possible explanations for the Aid-REER relationship**

There is a growing body of theoretical research that shows an REER depreciation in the presence of increasing aid inflows. A number of models focus on supply-side impacts of aid inflows. They suggest that aid inflows may induce a rapid supply-side

> 18 As an additional check, we re-ran a similar parsimonious specification on a quarterly dataset beginning in the first quarter of 1998 and the aid inflows REER relationship remained unchanged. Although, the aid inflow variable and the trade liberalization variable are defined differently than in the annual dataset, so the results are not directly comparable.

17