rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5865

# **Abstract**

This paper examines the role of social safety-net programs in Bangladesh run by the government and nongovernmental organizations to mitigate seasonal deprivation in the country’s highly vulnerable northwest region. Specifically, the paper explores whether social safety nets are limited to averting seasonal deprivation or can also address seasonality of income and employment more generally. Using a recent survey from the greater Rangpur (northwest) region, the paper finds that social safety nets have a positive effect on mitigating both seasonal and non-seasonal food deprivation. The results are robust, owing to the recent expanded coverage of social safety-net programs run by nongovernmental organizations active in the region. But given the annual recurrence of _monga_ (seasonal food insecurity) in the northwest region owing to agricultural seasonality and an overwhelming dependence on agriculture for livelihoods, social safety nets are not a reliable tool for _monga_ eradication. Programs are also needed to promote the income and productivity of the poor through diversification of income and employment.

This paper is a product of the Agriculture and Rural Development 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 author may be contacted at skhandker @worldbank.org.

_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
> <sup>22</sup>

To ensure that party stronghold effects are not simply reflective of non-competitive elections, we control for the closeness of electoral races in constituencies, _MarginVictory_ .<sup>20</sup> This is the average margin of victory in constituency _d_ of the winning candidate (over the runner-up candidate, under a simple plurality electoral law in single-member constituencies) over the three elections of 1996, 1998, and 1999.<sup>21</sup> Competitiveness of districts may also be influenced by a unique institutional arrangement in India of political affirmative action for disadvantaged groups. This requires that in approximately 20 percent of constituencies (108 of 483 in our sample) parties must nominate candidates from constitutionally scheduled castes and tribes. The last electoral delimitation law of 1977, which was fully implemented by 1982, determined which constituencies would be reserved (Pande, 2003).<sup>22</sup> To control for the impact of this affirmative action policy on legislator incentives to distribute pork, we include an indicator variable for whether a constituency is reserved for candidates belonging to the scheduled castes and tribes: _Reserved_ equals 1 if constituency _d_ is so reserved, and 0 otherwise.

The unobserved error in our specification consists of a constituency-specific term,  _d_ , a state-specific term,  _s_ , and noise,  _ds_ . We are relying on the variable _1993-99Spending_ 20 Candidates and parties are expected to target closely-contested districts with greater resources and effort (Dixit and Londregan, 1996; Lindbeck and Weibull, 1987; and Snyder, 1989). Consequently, all studies of legislator responsiveness take into account some measure of the electoral competitiveness of a district at the time a policy decision is made. However, the evidence that governments target spending to those districts where the vote was closest is mixed. For example, looking at Indian state governments, although Cole (2001) finds evidence governments target spending to close districts, Bardhan and Mookherjee (2005) find marginality is a weak predictor of service allocation by the state ruling party in the Indian state of West Bengal.

21 We test robustness of estimates to calculating the average over different periods of elections, including just the most recent election of 1999. There is no difference in the sign or significance of any of the estimated coefficients relating to changes in how the average margin of victory is calculated.

22 Reservations were introduced substantially before the initiation of the MPLADS program, and before most of the parties we consider in this study were even established.
<!-- Start of picture text --> Greece Argentina<br>10<br>5<br>06<br>10<br>06 07<br>07<br>5 11<br>0 14 17 18 19 08<br>161508 131715<br>0<br>13 12<br>14 1 918186<br>10 09<br>-5<br>12 -5 09<br>11<br>11<br>20<br>20<br>-10<br>-10<br>-10 -5 0 5 -10 -5 0 5 10<br>Reported % Chg. Real GDP Reported % Chg. Real GDP<br>Greece Argentina<br>Reported Composite Difference Reported Composite Difference<br>2011 -10.70 -7.95 -2.75<br>* -6.80 -3.90<br>2018 -2.51 -1.55 -0.86<br>* -1.15 -1.36<br>Composite Estimate Composite Estimate<br><!-- End of picture text --> Figure 11: Financial Distress.

# **6.2 More Nuanced Cases**

We now consider instances of GDP errors for countries within grades A and B, which are less obvious, and yet still measurable.

**Financial Distress.** Figure 11 presents the example of financial distress in Greece and Argentina, which consequently were are at the top of the list among grade B countries in Table 7. First is the Greek government debt crisis, which reached a crescendo in 2011-2012 and amounted to an overall reduction in GDP by 25% over 2008-2013 (Gourinchas et al., 2017). We find that the officially reported change in economic activity of -10.70% in 2011 was in reality closer to -7.95%, and even more moderate, -6.80%, if Greece had been treated as a grade C country. One possible explanation could be related to high levels of informality. In particular, a greater fraction of output comes from the self-employed in Greece relative to other nations, making tax evasion an imporant concern (Alstadsæter et al., 2019).

The 2018 Argentine monetary crisis, however, tells a different story. In this case, we find very modest differences between reported changes in GDP, and the composite estimate. This is the case regardless of how we treat the weight on GDP and despite the fact that tax evasion is also a problem in Argentina.

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component analysis. MLE estimates reject the null hypothesis that a single factor model is as good as a no factor model. They also reject the null hypothesis that a single factor model is as good as a many factor model. In fact, the chi-square for a 2-factor model (5205) is larger than that for a single-factor model (5023), which suggests that the uniqueness component associated with PS and VA should not be ignored. Further, the difference between these two chi-square values, at 6 degrees of freedom, is itself significant.

# Path Analysis:

If the single factor measurement model is correct, the predicted correlation between variables i and j should equal the product of the factor loading (path coefficient or correlation in this case) between variable i and the factor k and between variable j and factor k. We can use a chi-square statistic to test if the observed correlation equals the correlation that would be predicted if the model were true. The results appear in Table 3. There is very little difference between the predicted and actual values. Since 2 χ<sup>2</sup> = Σ (Predicted – Actual)i / Predictedi, i = 1,.....,n pairs, χ<sup>2</sup> = .0019, which is clearly not significant at n-1=14 degrees of freedom. We fail to reject the null hypothesis that the observed correlations differ from those predicted by the measurement model. Overall, this suggests strong evidence in favor of the argument that the WGI variables do not measure distinct concepts. On the other hand, the pairs that contribute most to this small χ<sup>2</sup> are those that involve VA and PS.

# Confirmatory Factor Analysis:

Confirmatory Factor Analysis is similar to the informal path analysis model, but it uses MLE procedures to compare observed and predicted values, and explicitly allows for estimates of error. In CFA, each of the 6 WGI variables is regarded as a standardized endogenous variable written as a linear function of a latent exogenous variable and an error term. The error terms are independent of each other, and independent of the latent factor. Because the manifest variables are standardized, the variance of the path coefficient plus the error variance must sum to one. The MLE statistics provide estimates 12
On the other hand, spending on health projects comes close to tracking the number of poor people by governorate, at least in the governorates with the highest incidence of poverty. It is clear from Figure 3 that despite SFD claims that its programs are allocated largely based on the incidence of poverty at the governorate level, all of them fall short of allocating spending (at the governorate level) in proportion to the number of poor.

<!-- Start of picture text --> 100<br>90<br>Poverty<br>80<br>70<br>Community<br>Development<br>60<br>50<br>40<br>Education<br>30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Cumulative % of population (by governorate, from poorest to least poor)<br>100<br>90<br>Poverty<br>80<br>70<br>Public Works<br>60<br>Health<br>50<br>40<br>30<br>Microcredit<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Cumulative % of population (by governorate, from poorest to least poor)<br>Cumulative % of poverty, etc<br>Cumulative % of poverty, etc<br><!-- End of picture text -->

**Figure 3. The incidence of poverty, and of SFD spending, at the governorate level** _Source:_ Authors’ calculations based on HIECS and community survey 2004/2005.

Even if SFD programs are not allocated to governorates in proportion to poverty, they could be well targeted to the poor _at the village or ward level_ – for instance, by going disproportionately to poor villages, even in rich governorates. With the exception of microcredit, we do not have household-level _Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 30 of 36_
Table 7: Impact of the Intervention on Test Scores (Heterogeneity by gender)

||(1)<br>Math Score|(2)<br>English Score|(3)<br>Total Score|
|---|---|---|---|
|Treatment|14_._361_∗_|10_._796|12_._670_∗_|
||(7_._620)|(8_._244)|(7_._520)|
|Female|_−_9_._922|1_._668|_−_3_._772|
||(6_._093)|(6_._010)|(5_._606)|
|Heterogeneous Treatment Impact|_−_0_._900|_−_1_._166|_−_1_._316|
||(8_._322)|(8_._304)|(7_._719)|
|Constant|500_._340_∗∗∗_|495_._023_∗∗∗_|497_._635_∗∗∗_|
||(5_._129)|(5_._556)|(5_._023)|
|Observations|4363|4363|4363|
|R-sqr|0_._004|0_._002|0_._003|

_Notes:_ This table looks at the heterogeneous impact of the school leadership on student level Test scores (columns 1-3) with respect to student gender. The heterogeneous treatment impact is the estimated coefficient for the value of the treatment effect interacted with the variable of interest. The outcome variables are: Math Knowledge Score (column 1); English Knowledge Score (column 2); Total Knowledge Score (column 3). Analytical sample restricted to MLSS schools observed in baseline and endline. All regressions are adjusted by the value of the outcome variable observed at MLSS baseline. Robust standard errors (clustered at the school level) are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

Table 8: Impact of the Intervention on Test Scores (Heterogeneity by student age at baseline)

||(1)<br>Math Score|(2)<br>English Score|(3)<br>Total Score|
|---|---|---|---|
|Treatment|13_._355_∗∗_|9_._174|11_._226_∗_|
||(6_._652)|(6_._860)|(6_._396)|
|Below Median BL Student age|64_._247_∗∗∗_|72_._024_∗∗∗_|67_._681_∗∗∗_|
||(6_._300)|(7_._138)|(6_._123)|
|Heterogeneous Treatment Impact|_−_1_._390|0_._911|_−_0_._343|
||(8_._830)|(10_._129)|(8_._743)|
|Constant|477_._676_∗∗∗_|476_._505_∗∗∗_|477_._379_∗∗∗_|
||(4_._319)|(4_._644)|(4_._229)|
|Observations|4183|4183|4183|
|R-sqr|0_._055|0_._070|0_._073|

_Notes:_ This table looks at the heterogeneous impact of the school leadership on student level Test scores (columns 1-3) with respect to student age at baseline. The heterogeneous treatment impact is the estimated coefficient for the value of the treatment effect interacted with the variable of interest. The outcome variables are: Math Knowledge Score (column 1); English Knowledge Score (column 2); Total Knowledge Score (column 3). Analytical sample restricted to MLSS schools observed in baseline and endline. All regressions are adjusted by the value of the outcome variable observed at MLSS baseline. Robust standard errors (clustered at the school level) are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

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SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE high as those with larger systems (above 500 MW). South Sudan’s production is well below the minimum efficient scale size of around 200 MW.

**Figure 17. The costs of producing power in South Sudan are extremely high**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Total costs (US cents per kWh) Mali South Sudan Niger Congo Benin North Sudan Cape Verde Cameroon Rwanda Burkina Faso Madagascar Kenya Tanzania Chad Ghana Senegal Namibia Cote d'Ivoire Lesotho Mozambique Uganda Zimbabwe Nigeria Malawi Ethiopia DRC Zambia South Africa Botswana<br><!-- End of picture text --> _Source:_ Briceño-Garmendia and Shkaratan 2010.

Another explanation for high costs lies in the high price of diesel in the country, reflecting the high cost of road transportation (table 14). It is striking that though Sudan and South Sudan are both oilendowed, they pay steep prices for diesel relative to other countries in Africa. Further, unlike in the National Electricity Corporation in Sudan that has received fuel subsidies for power production from the government, such subsidies have not been provided to South Sudan’s power utility, SSEC. Since the referendum, challenges for power production have been compounded due to even higher fuel prices attributed to inadequate fuel supplies coming into South Sudan from Sudan (Sudan Tribune 2011).

**Table 14. High prices of diesel in all parts of Sudan (cents per liter)**

||1991|1993|1995|1998|2000|2002|2004|2006|2008|
|---|---|---|---|---|---|---|---|---|---|
|Ethiopia|14|19|24|25|27|32|42|62|89|
|All parts of Sudan|6|58|25|26|24|24|29|49|125|
|Uganda|55|71|85|68|75|70|88|101|122|

Source: GTZ.

Inefficiencies due to underpricing of power, distributional losses, and lack of adequate revenue collection also add significant costs. In 2006 the tariffs levied recovered only a little over 60 percent of the total costs of power production. For every kilowatt-hour produced, the electric utility lost around $0.14 due to the underpricing of power. The net drain due to underpricing of power was around $2.7 million per year. Additionally, losses in transmission and distribution have been estimated at 50 percent of total electricity produced, five times higher than good-practice standards and double what is observed in other parts of Africa in general. These represent monetary losses of up to $3.3 million a year. Further, despite enormous subsidies to end users implicit in non-cost-recovery tariffs, over 60 percent of electric bills go unpaid, adding a further $2.7 million to the inefficiency price tag. Overall, hidden costs for the SSEC are equivalent to around $9 million a year, or around 188 percent of the SSEC’s revenues—system losses amount to 70 percent of revenues, poor collection of bills amount to another 60 percent, and 31
Table 5: Comparison of various methods with Näıve as the benchmark

|Mdl|5 year fore|cast horizon<br>|10 year for|ecast horizon<br>|
|---|---|---|---|---|
|oe|Median<br>% Better|% signifcant<br>(_p_= 0_._05)|Median<br>% Better|% signifcant<br>(_p_= 0_._05)|
|Lowest sMAPE|77%|85.0%|74%|67.5%|
|VAR3 / VEC3|19%|83.3%|16%|68.3%|
|VAR2 / VEC2|7%|96.5%|11%|72.5%|
|GARCH|37%|84.6%|10%|69.3%|
|ARIMA|13%|83.9%|-8%|70.1%|
|HOLT-WINTERS|-2%|86.0%|-11%|78.1%|
|UCM-RWD|-9%|86.8%|-22%|82.5%|
|UCM-LLTM|-10%|88.4%|-23%|82.5%|
|UCM-RWC|-40%|94.8%|-58%|87.9%|

horizon. And the difference between forecast errors is statistically significant (assuming 5 percent level) for 85 percent of countries over the 5 year forecast horizon and for 67.5 percent of countries over the 10-year forecast horizon. As regards specific forecasting methods, VAR/VEC and GARCH methods yield more accurate forecasts than the Näıve model over both 5- and 10-year forecast horizons, with median accuracy improvement ranging between 7 and 37 percent. To the contrary, the Holt-Winters method and Unobserved Components Models yield less accurate forecasts over both 5- and 10-year forecast horizons, with median accuracy decline ranging between 2 and 58 percent. Finally, the ARIMA model produces more accurate forecasts than the Näıve model over the 5-year forecast horizon, with median accuracy improvement of 13 percent. However, the ARIMA model yields less accurate forecasts than the Näıve model over 10year forecast horizon, with median accuracy decline of 8 percent. Regardless of the direction of forecast error differences, they are mostly statistically significant across all methods, ranging between 83.3 to 96.5 percent of countries over the 5-year forecast horizon, and between 68.3 and 87.9 percent of countries over the 10-year forecast horizon.

Table 6 compares the accuracy of forecasting methods relative to Näıve2 model, which assumes that electricity demand grows at the same rate as GDP. The results are qualitatively similar to those reported in Table 5, and the quantitative improvements over forecasts of Näıve2 model are even more pronounced. The median sMAPE forecast error of the Näıve2 model is 184% percent higher than forecast error of the best performing model over the 5-year forecast hori12
50 TAILE 9A Gains of Debtors sith Counterfattual **Torn** of TraCe (Sas, export and import prices relative to the manufacturing unit value **as** in 1969.731 Situlated since 1978S 1982 1984 1986

|Teros of Trade<br>CFNetworth<br>gain|(in<br>current dollars)|||
|---|---|---|---|
|<br>Africa|<br>($27,0791|1140,135)|**(146,454)**|
|Asia|<br>(146,493)|<br>t174,1911)|<br>1101,5121|
|India<br>|$20,551<br>*|o34,082<br>|<br>149,919|
|EHENA|6,109|$116,65|S17,374|
|LAC|(134,3111|14z,19)1|(127,0O4)|
|Ln* Income|*12,621<br>|124,660|134,510|
|middle<br>Income|IS114,4011|($165,7301<br>|<br>(1172,1571|
|Non oil Exporting|*101,453|$177,254|8270,067|
|OilExporters<br>|($215,65I4|($342,94)<br>|(1$442,224)<br>|
|Hnufactuerrs|895,257|t171,819|$261,513|
|Primary|S1),S16|$30,096|**143,064**|
|ilCs|(167,770)|(897,772)|($94,7361|
|HicsOil<br>Comtries|1$126,477)|(1194,2071|(1237,423)|
|HicsNon<br>Oil<br>Countrie<br>|<br>158,706|<br>S96,435|<br>$142,637<br>|
|SkCountries|(121,662)|(11461l)|122,3SS|
|Non SAL Cotmtries|<br>($90,118)|<br>($126,409)|<br>($160,046)|
|CF Networth<br>gain GDP%h|are<br>**(as**percent<br>ofSOP)|||
|Africa|-15.5?|-24.52|-35.3l|
|Asia<br>|-15n.7<br>|-24.21|-33.12|
|India|11.1I|17.62|21.32|
|ENENA|2.12|6.12|11.12|
|LAC|-4.8U|-6.02|-4.11|
|Lov Incou<br>|10.52|21.22|27.11|
|Middle Incone<br> <br>|-6.4|-13.02|-13.2t|
|NonOil<br>Exporting|12.11|23.02|30.6?|
|OilExporters|-41.9?|-63.3?|-105.22|
|Nanufacturers<br>|14.72<br>|30.4:<br>|38.12<br>|
|Priarvy|6.0|9.3?|13.32|
|HICS<br>|-7.62|-12.3?|-11.9?|
|Hics Oil Countries|-37.12|-60.2S|-100.86|
|Hics Non Oil Couwtries|<br>10.62|20.42|25.5?|
|SAL Countries<br>|-1.32<br>|-1.3?<br>|2.0?<br>|
|Non SAL Countries|-20.72|-46.2n|-53.42|
|CF Nletorth gain**DetSha**|**re(as**<br>percent of total debt)|||
|<br>Africa|<br> <br>-44.21|-56.42|-50.i2|
|Asia<br>|-37.02<br>|-50.9?<br>|-57.42<br>|
|India|80.22|109.0S|120.13|
|EnIIIDI|471|123?|210?|
|<br>LAC|.<br>-10.62|.<br>-12.02|.<br>-6.9?|
|Lo Intome|21.51|33.2?|41.1?|
|Kiddl Intoe|-19.7?|-25.4?|-22.9n|
|Non oil Exporting|27.0?|42.11|55.02|
|11 Exporters|-104.52|-147.n|-168.9?|
|<br>Manufacturwrs|36.7n|59.S|76.32|
|Priwy|10.I2|15.22|18.3?|
|NiCs|-17.5t|-22.7?|-20.02|
|Nice OilCountries|-91.12|-122.0?|-141.22|
|Hics NonoilCoustrsn<br>SAL Contries|23.7n<br>-432|3S.5<br>-26|_4.n_<br>34?|
|<br>NoSAL Cmntries|.<br>-60.02|.<br>-U4.3?|.<br>-93.02|

Note: Asia eicldes India and China
‘commercial’, education and health, which are taken up _seriatim_ . The first lends itself much more readily to estimation than the other two; but even in the commercial sphere, there are considerable uncertainties. Indeed, the empirical basis for estimating the main effects leaves much to be desired. The only available primary sources that provide reasonably reliable evidence are field-notes on a tour of Assam, Himachal Pradesh and Orissa (Bell, 2009a), a special re-survey of 240 households drawn from 30 villages in Orissa’s uplands (Bell and van Dillen, 2012), and materials on agricultural marketing provided by Grahame Dixie.<sup>2</sup> A useful secondary source is Khandker et al. (2009), which yields estimates of certain effects of rural roads in Bangladesh. The secondary literature is otherwise disappointingly thin for present purposes. In keeping with the aim of developing a robust estimate of the Project’s social profitability, we adopt some extreme alternatives, as near polar as possible.

# **4.1 Commercial**

The household is endowed with labor, land and other resources. As a consuming unit, it has preferences over goods and leisure. Faced with the prices **p** ruling in the marketplaces in which it trades, it draws up a production plan **y** and a consumption plan **x** . The difference **y** _−_ **x** is the vector of its net exports, and its total outlay on transportation is **c** _· |_ **y** _−_ **x** _|_ , where the vector of associated unit costs of transporting goods to and fro is **c** . If there is complete market integration, the farmgate price of a good that is exported (imported) will be _qi ≡ pi − ci_ ( _qi ≡ pi_ + _ci_ ). An all-weather feeder road is now built, which reduces the vector of unit transport costs from **c**<sup>1</sup> to **c**<sup>2</sup> , where the superscripts 1 and 2 denote the village environment without and with the road, respectively.<sup>3</sup> Leaving aside transactions in the spheres of education and health, what value does the household place on this change in its environment?

At one extreme, let the household’s resources be fully employed, the opportunity cost of leisure being the going wage rate _w_ . If the household sticks with its original plan ( **y**<sup>1</sup> _,_ **x**<sup>1</sup> ), which remains feasible after the road has been provided, and there is full market integration, its (full) income will increase by the amount of the savings in transportation costs. If, however, the new prices and opportunities induce it to make any adjustment to that plan, then it must do strictly better, though the resulting whole gain in (full) income cannot exceed the total savings at the net export vector with the

> 2Private communication.

> 3The provision of an all-weather road may sharpen competition among traders who buy at the farmgate, and so improve the farmer’s net price independently of any reductions in **c** . It is also possible that increasing the number of all-weather roads will affect **p** . See Section 6.

10