rafmacalaba/datause-encoder-data huggingface dataset:
finds that the difference between SSA and non-SSA poverty lines was $0.41 a day (with a standard error of $0.28).<sup>19</sup> Amongst the samples used to calculate the above poverty lines— $1.45 a day at 1993 PPP using the RCS sample versus $1.10 a day using the RDV sample—the share from SSA went from 37.5% (three countries out of eight) to 87.7% (13/15). Assuming that SSA poverty lines are $0.41 a day higher than non-SSA lines, the change in the sample would add about $0.20 per day to the poverty line—closing slightly more than half the gap between the $1.45 a day and $1.10 lines. While the precision of this estimate is clearly rather low, given the sample sizes, but it is at least suggestive that if the RDV sample had better represented African countries then the old international line would have been appreciably higher.

**_Implications for global poverty measures._** The impact of these various data revisions on the global poverty count is theoretically ambiguous. On the one hand, the new PPPs imply that the cost-of-living is higher in developing countries, which will put upward pressure on the poverty count, but (on the other hand) the new PPPs will put downward pressure on the international poverty line. The key factor is whether the PPP revisions tend to be larger in the countries used to set the international poverty line.  RCS find that this is the case. For any fixed set of national poverty lines, the poverty rates in the sub-group of the poorest countries used to set the international line will be unchanged, while the poverty rates in the less poor countries will fall, since they will have relatively higher purchasing power. The Appendix provides a complete analysis, for which the following result emerges as an interesting special case:

_<u>Proposition 1: If the international poverty line is that of the poorest country, which also</u>_ _has the largest upward revision to its PPP, then the aggregate poverty rate will fall._ Intuitively, given that the international line is anchored to the national lines in the poorest countries, the poverty count for those countries is roughly constant, while that for the other (less poor) developing countries tends to fall, since the PPP revisions are smaller for those countries, which translates into a lower local currency equivalent of the international line.

This result is only of expository interest, given that our international poverty line is not in fact for the poorest country but rather for the poorest 15 countries; then there is a potentially confounding effect of the differences amongst those countries. A further confounding effect is

> 19 It made negligible difference if we also allow the slope parameter on consumption to be different for SSA countries and evaluate the mean difference in poverty lines at the median or mean of consumption in the region of common support; this also gave a mean difference of $0.35 (s.e.=0.22) at the median and $0.39 (0.29) at the mean. Nor did adding a squared term in consumption have much effect.

13
<u>Table 4. Oaxaca-Blinder Productivity and Earnings Gap Decompositions</u>

|||Decompo<br>Producti|sition of<br>vityGap|Decompo<br>Earnin|sition of<br>gGap|
|---|---|---|---|---|---|
|A. Mea|n gender differential (log)|||||
|Mea|n productivity of male farmers|9.034|***|||
|Mea|n productivity of female farmers|9.288|***|||
|Mea|n gender differential in productivity|-0.25|4***|||
|Mea|n earnings of male farmers|||9.19|0***|
|Mea|n earnings of female farmers|||8.76|4***|
|Mea|n gender differential in earnings|||0.42|6***|
|B. Aggr|egate decomposition (%)|Endowment|Structural|Endowment|Structural|
||Total|-0.325***|0.072***|0.348***|0.078***|
|C. Deta|iled decomposition (%)|||||
|Fin|Access to finance|0.004***|0.005|0.003***|0.004|
|Inp|Agricultural inputs<sup>a</sup>|0.291***|-0.067|0.292***|-0.027|
|Fla|Unpaid agricultural family workers|0.008**|-0.007|0.008**|-0.007|
|Lan|Log of cultivated area|-0.487***|-0.008|0.185***|-0.011|
|Mec|Mechanization|0.007***|-0.005|0.006***|0.001|
|HHh|Household head|0.022**|0.02|0.022**|0.013|
|NFI|Share of nonfarm labor income|-0.099***|-0.012|-0.098***|-0.011|
|NFn|Share of nonlabor income|0.066***|-0.005|0.065***|-0.004|
|SID|Simpson Index of diversification|0.032***|0.004|0.032***|0.000|
|Sub|Sum of Subsidy|-0.039***|-0.08|-0.037***|-0.072|
||Incidence of crop subsidy|-0.154***|-0.063|-0.159***|-0.054|
||Log of crop subsidy|0.115***|-0.017|0.122***|-0.018*|
|XIN|Export Orientation Index|-0.134***|0.03|-0.135***|0.029|

Source: Authors’ estimation using HIES 2016. Notes: *, **, and *** indicate that the coefficients are significant at the 10, 5 and 1% level, respectively. Negative sign indicates female productivity or earning advantage while positive sign shows male productivity or earning advantage. a Agricultural inputs include seeds, fertilizer, chemicals, hired labor, transport cost and agricultural equipment/rental.

The green and blue bars represent the size and direction of each variables’ contribution to endowment and structural effects respectively while the dots show the combined effect. A negative (positive) bar in Figure 5a and Figure 5b and negative (positive) sign in table 4 indicate that the variables contribute to female (male) productivity or income advantage. For example, the ‘Total’ bar on the far right in Figure 5a represents the gender gap owing to both endowment and structural effects. The negative endowment effect is greater in value than the positive structural effect, indicating female farmers are more productive, unconditionally. Specifically, panel B of Table 4 reports a female productivity advantage of 25.4% (the same as the result of Regression (1) in Table 3) since the endowment effect of 32.5% in favor of women outweighs the structural effect in favor of men (7.2%).

23
negative it suggests the need to examine the performance of health service delivery more carefully.

Potential data and information sources for the indicators outlined above are shown in Table 2. Some of the data and information are readily available and based on administrative data or financing arrangements. Others draw on large surveys, some of which have only been conducted in selected countries (e.g. Quantitative Service Delivery Surveys (QSDS) and Public Expenditure Tracking Surveys (PETS)). Living Standard Measurement Surveys (LSMS), Demographic and Health Surveys (DHS), public expenditure reviews, and other household surveys, exist for more countries but are administered intermittently. Still, such surveys offer a wealth of data and the basis for useful analysis that can shed light on the effects of public investments on health service delivery performance.

The set of indicators discussed in this section are unevenly available and only sometimes enable comparisons across countries, or over time, and even less often across regions and facilities within countries. However, when they do, they provide important insights into governance challenges and performance issues in the health sector. Ideally, data for indicators such as these should be collected on a regular basis and be made publicly available to provide the basis for improved incentives and accountability in health.

The remainder of this paper places each indicator from Table 1 into context describing the underlying governance failure, and presents available evidence on potential solutions to address each of the governance challenges.

12
|Inflation|0.011|0.013|-0.021|0.018|0.679|0.675|1.310**|0.528|
|---|---|---|---|---|---|---|---|---|
||(0.033)|(0.033)|(0.041)|(0.033)|(0.470)|(0.469)|(0.610)|(0.445)|
|GDP growth|-0.134***|-0.133***|-0.133***|-0.129***|1.061***|1.059***|1.438***|0.958**|
||(0.028)|(0.028)|(0.029)|(0.029)|(0.410)|(0.409)|(0.522)|(0.397)|
|GDP per capita|0.003***|0.003***|0.003***|0.003***|-0.003|-0.003|-0.006|-0.001|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.004)|(0.004)|(0.004)|(0.004)|
|Equity|0.008|0.008|||-0.006|-0.008|||
||(0.009)|(0.009)|||(0.131)|(0.131)|||
|Equity* Liabilities over|||||||||
|GDP||-0.112*||||0.676|||
|||(0.059)||||(0.504)|||
|Liquidity|||-0.014**||||0.049||
||||(0.006)||||(0.064)||
|Liquidity* Liabilities<br>over GDP|||-0.105*||||0.866**||
||||(0.057)||||(0.429)||
|Return on assets||||-0.024||||0.856***|
|||||(0.021)||||(0.275)|
|Return on assets*|||||||||
|Liabilities over GDP||||-0.276**||||2.118|
|||||(0.121)||||(1.538)|
|Constant|0.182***|0.181***|0.210***|0.122***|-0.238**|-0.235**|0.301**|0.279***|
||(0.009)|(0.009)|(0.011)|(0.009)|(0.105)|(0.105)|(0.120)|(0.087)|
|N|12832|12832|7890|12839|11154|11154|6835|11159|
|R-sq|0.597|0.597|0.671|0.597|0.044|0.045|0.055|0.048|

Panel B: Big0.1 included

|Big0.1|-0.002|0.000|0.002|-0.000|-0.024*|-0.034|-0.046|-0.022|
|---|---|---|---|---|---|---|---|---|
||(0.001)|(0.004)|(0.004)|(0.001)|(0.015)|(0.031)|(0.031)|(0.016)|

33
Table A3: Food Insecurity Experience Scale (FIES) Questions in Malawi

||Pre-COVID-19 Surveys||COVID-19 Surveys||
|---|---|---|---|---|
|FIES|Question|Recall|Question|Recall|
|FS1|Did you worry that your household would not have<br>enough food?|7 day|You or any other adult in your household were<br>worried about not having enough food to eat be-<br>cause of lack of money or other resources?|30 day|
|FS2|How many days have you or someone in your<br>household had to rely on less preferred and/or less<br>expensive foods?|7 day|You, or any other adult in your household, were<br>unable to eat healthy and nutritious/preferred<br>foods because of a lack of money or other re-<br>sources?|30 day|
|FS3||7 day|You, or any other adult in your household, ate only<br>a few kinds of foods because of a lack of money or|30 day|
||||other resources?||
|FS4<br>40|How many days have you or someone in your<br>household had to reduce number of meals eaten<br>in a day?|7 day|You, or any other adult in your household, had to<br>skip a meal because there was not enough money<br>or other resources to get food?|30 day|
|FS5|How many days have you or someone in your<br>household had to limit portion size at meal-times?|7 day|You, or any other adult in your household, ate less<br>than you thought you should because of a lack of<br>money or other resources?|30 day|
|FS6|How many days have you or someone in your<br>household had to borrow food or rely on help from<br>a friend or relative?|7 day|Your household ran out of food because of a lack<br>of money or other resources?|30 day|
|FS7<br>FS8|How many days have you or someone in your<br>household had to restrict consumption by adults<br>in order for small children to eat?|7 day<br>7 day|You, or any other adult in your household, were<br>hungry but did not eat because there was not<br>enough money or other resources for food?<br>You, or any other adult in your household, went<br>without eating for a whole day because of a lack<br>of money or other resources?|30 day<br>30 day|
# _4.2 The External Sector_

As shown above, the behavior of the price series analyzed differs markedly over the period studied, so it is likely that the relevant world market prices also differ. We therefore use different specifications to estimate equilibrium in the external sector based on equations (2) and (3).

We begin by estimating the long-run relationship for food, equation (3), using the CPI for cereal prices, _pc,_ and the World Bank grain commodity price index _wfp._<sup>17</sup> The choice of cereal prices instead of food prices is made to get a reasonably good match between domestic and world food prices, although the differences between the CPI index for food and cereals are small as evident from Figure 2. The world market prices were converted to local currency using the Birr-Euro exchange rate. We also tested the US dollar, but it was unambiguous that the Euro works better in the models of inflation, both for food and none-food prices. The US dollar is the intervention currency used by the National Bank of Ethiopia, so the official Birr-US$ rate is constant for extended periods. The official parallel exchange rate worked better than the official rate, but it was abolished in early 2008. Moreover, it is not used for most trade.

Figure 6 depicts the log of the three variables for 1999:1-2008:11, where the mean and variance of the series for the exchange rate and world food have been adjusted to highlight the long-run relationship, and Figure 7 shows the relative food price (and the relative price of non-food prices). The three series follow each other over time, and the relative price appears to be a stationary series, although the swings around the mean are very large. The Johansen approach is thus used to test if this is the case, i.e., if _e, wfp_ and _pc,_ are cointegrated with coefficients 1,1,-1. The trace test and the estimated Eigenvalues, reported in Table 2, indicate that there is one cointegrating vector. Moreover, domestic cereal prices seem to be adjusting while the exchange rate and world food prices are weakly exogenous, as shown by the estimates of the α _i_ and their standard errors. Finally, the likelihood ratio test for imposing the restrictions 1, -

> 17 The components of the index are wheat (25%), maize (41%), rice (30%), barley and sorghum (4%). The index does not cover teff, a local grain only produced and consumed in Ethiopia and Eritrea, though major cereals prices closely mirror movements in the teff price. Although it would have been better to have indexes with very similar weights, we preferred to use world grain prices for transparency reasons.

21
**Notes:** The dependent variable is the R&D dummy at firm level. FDI is at sector level. GVC is at firm level. GVC is defined as the average of intermediates import share and the exporting intensity. The latter is defines as the ratio of exports over sales. Ln (years of top mgmt) is the log of years of experience of the top managers. We divide firms into two sectors: manufacturing and services. The standard errors are clustered at the country-sector level. City-year, sector-year and year fixed effects are included. *** p<0.01, ** p<0.05, * p<0.1.

These results suggest that a firm with higher GVC participation is faced with less domestic competition brought about by FDI. With less domestic competition, the firm has less incentive to perform R&D and is likely to have less improvement in its productivity down the line. Note that the firms may be subject to competition in foreign markets, as the coefficients on export intensity and foreign technology adoption are positive and statistically significant. Moreover, it is likely that R&D may be targeted towards improvements in the local markets where the competition is fierce. Interestingly, the share of foreign ownership of the firm is not statistically significant, so local firms are on equal footing. Finally, being a service firm all else equal is associated with less R&D (in the OLS regression), although as explained earlier the sample of services firms in WBES is too small to allow clear conclusions .

# 5.4 Interpretation of results

The finding that higher GVC participation in a sector—which could be considered as its exposure to export markets--reduces the strength of the positive spillover effects of FDI may seem counterintuitive but is a robust result and consistent with related literature. Lu et al. (2014) find that a Chinese firm with higher exporting intensity is less affected by FDI into its industry. We find similar patterns using cross-country firm-level data. The intuition for this would be that to integrate more in the GVC, the sector has already had to improve on various fronts to survive from the fierce competition, so there is less room for improvement once it inserts into the GVC. It is also possible that sectors with higher GVC participation generate less domestic competition since they compete more in the foreign markets. Moreover, sectors with higher GVC participation acquire and sell fewer intermediate inputs and products in the domestic market; it is harder for domestic competitors to imitate, and thus there are fewer FDI spillovers.

The finding that FDI generates domestic competition in the recipient sector, and that firms with higher export intensity engage less in R&D compared to those with low export intensity is similar to the result in Farole et.al. (2018), which finds that GVC participation increases labor demand,
93

# **Appendix 3: Fuel Prices and Pass-Through Coefficients**

Figures A3.1–A3.4 show retail prices in the study countries in July 2012 for gasoline, diesel, kerosene, and LPG, respectively. FOB prices of the fuels in four major refining centers— Northern Europe, Arab Gulf, Singapore, and U.S. Gulf Coast—are noted for comparison. Because of transport, storage, and retailing costs, taxes and other fees, and profit margins at each stage in the supply chain, retail prices are considerably higher than the benchmark FOB prices in deregulated markets. If retail and FOB prices are close, that is likely to signal subsidies. Figure A3.5 plots the ratio of the price of kerosene to that of diesel in countries where information on kerosene prices was available.

Table A3.1 lists pass-through coefficients between January 2009 and July 2012. The calculation methodology and the sources of benchmark FOB prices are the same as those described in Kojima (2012), with one difference. A much more detailed study of the transmission of world oil price increases in 12 major EU countries found that the increases were passed on to consumers within three to five weeks (Meyler 2009), or about a month. These countries can safely be assumed to be passing on prices increases on the global oil market fully to their domestic markets. This study therefore takes reference benchmark prices from the month before―December 2008 and June 2012―as more appropriate measures of gauging the extent of pass through between two time periods. The sources of retail information are the same as those in Kojima (2012) except Uganda, where the price survey results reported by the Uganda Bureau of Statistics were used instead. As mentioned in Kojima (2012), the pass-through coefficients in Uganda are low because the country experienced acute fuel shortages in January 2009, pushing up prices. The limitations of the methodology approached to compute these coefficients are discussed in detail in Kojima (2012, 6–8).

The prices available in Jamaica are ex-refinery inclusive of taxes and exclude distribution margins. The tax-inclusive ex-refinery prices were used for computing the ratio of kerosene to diesel prices in Figure A3.5 and pass-through coefficients for the four fuels in Table A3.1. The fuel prices in Uruguay are ceilings on wholesale prices set by the government and adjusted from time to time. They appear to be close to retail prices, and hence have been retained as retail.
Poverty Alleviation and Interhousehold Transfers: Evidence from BRAC’s Graduation Program in Bangladesh<sup>*</sup> SELIM GULESCI

# **1 Introduction**

In rural parts of developing countries, individuals are faced with substantial risks and often rely on transfers from their social networks for consumption-smoothing (Rosenzweig (1988), Udry (1994)); yet there is limited evidence on how large-scale affect interhousehold transfers. This evidence antipoverty programs paper provides on the causal effect of a large-scale poverty alleviation program on informal transfers within rural Bangladeshi communities. Implemented by the NGO BRAC, “Targeting the Ultra Poor” program (henceforth “the program”) targets the poorest households in rural communities in Bangladesh and provides them with a transfer of productive assets, training and other support services. Bandiera et al. (2017) show that the program leads to long-run improvements in the socio-economic conditions of beneficiary households. Similar programs targeting ultra-poor households have been evaluated in other contexts and, by and large, have found positive effects on socio-economic conditions of the targeted households (Banerjee et al. (2015, 2016, 2018), Blattman et al. (2016), Collins and Ligon (2017)); but to date, we have limited evidence on these proeffects on informal transfers. In this I the randomized roll-out grams’ paper, exploit of the program and panel data covering over 21,000 households over seven years to study the program’s effects on interhousehold transfers.

> *Selim Gulesci is Associate Professor at Trinity College Dublin, his email address is: gulescis@tcd.ie. The author thanks the editor (Eric Edmonds) and three anonymous referees for comments that improved the paper; Oriana Bandiera, Tim Besley, Robin Burgess, Maitreesh Ghatak, Imran Matin, Imran Rasul and Munshi Sulaiman for their support and advice throughout the project; Yann Bramoullé, Greg Fischer, Eliana La Ferrara, Rohini Pande, Jean-Phillipe Platteau for helpful discussions; seminar participants at Aix-Marseille, Bocconi, EBRD, Edinburgh, IFS, George Washington, Koc, LSE-UCL, Namur, Sabanci, Uppsala, Vanderbilt and Wharton for their comments. The usual disclaimer applies. This document is an output from research funding by the UK Department for International Development (DFID) as part of the iiG, a research programme to study how to improve institutions for pro-poor growth in Africa and South-Asia. The views expressed are not necessarily those of DFID. Support from the Bagri Fellowship is gratefully acknowledged. Previous versions of this paper were circulated with the titles “Labor-tying and Poverty in a Rural Economy” and “Poverty and Interlinkage in Rural Labor Markets”.

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