rafmacalaba/datause-encoder-data huggingface dataset:
households to consume more food items than they desire, or those food items may go to waste if they are not easily storable.

Recognizing that the effectiveness and efficiency of cash largely depend on contextspecific factors like the availability of financial service providers, functioning markets, and security, humanitarian actors have committed to increasing the use of cash (instead of, or in addition to, in-kind) whenever feasible (UNOCHA 2017). Reasons for cash preference include ethical motivations, as it is generally believed to respect beneficiary preference, empowering them to address their own needs by affording them more choice in local markets. On the other hand, governments may prefer in-kind transfers under the assumption that households may be short-sighted and spend money for immediate gratification (Hanna and Karlan 2017), or they may not take into account social benefits when making individual decisions. Nonetheless, donors and practitioners continue to call for additional evidence from the humanitarian space on how the benefits and impacts of cash-based transfers compare to in-kind, with an eye to outcomes regarding risk and cost-effectiveness (HLPHF 2016).

In this section, we examine impact evaluation studies that compared the relative effectiveness of alternative transfer modalities according to different dimensions of outcomes. A summary of the impacts is presented in **Table 6** , which displays the most effective transfer modality according to different outcome dimensions.

# **Basic Needs Outcomes**

We found six studies that report the most effective modality in terms of basic needs outcomes, such as food security, food expenditure, non-food expenditure, and coping strategies.

**Food security** . Even though food security is one of the most explored outcomes in humanitarian settings, the debate over the most effective modality for improving food security outcomes remains inconclusive. When comparing different modalities of transfers, the data show that, on average, cash and in-kind transfers are similarly effective in improving overall food security. Certain differences between cash and in-kind transfers are not very significant and depend on the indicator used to measure them. For instance, one of the most used indicators is food consumption. Hidrobo et al. (2014) used a randomized controlled trial to assess the impacts of cash (CCT), food vouchers, and food transfers in a refugee camp in Ecuador, and they find that

30
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3 3 3
- 37-

Table 1: Selected 1982 Household and Individual Characteri,itcs

||Nonmigrants|Internal<br>migrants|Mexico-to-U.S.<br>migrants|
|---|---|---|---|
|Household characteristics||||
|Adilt family size<br>(13 years or older)|7.84|8.47|9.11|
|Landholdings (in hectares)|4.75|6.48|7.14|
|Percentage with family<br>contacts (sibling, parent,<br>sibling of parent) with<br>internal migrant<br>destinations|0.71|0.80|0.57|
|Percentage with family<br>contacts (sibling, parent,<br>sibling of parent) at<br>U.S. destinations|0.50|0.44|0.89|
|Wealth (total 1982 U.S.<br>dollar value of land,<br>anima_.s,<br>and machinery,<br>in thousands)|2.11|2.47|3.47|
|Individual characteristics||||
|Sex (male=1.0)|0.43|0.49|0.63|
|Age|33.40|28.48|28.70|
|Years of schooling<br>completed|3.93|6.50|4.06|
|Years of internal<br>migration experience|0.32|4.75|0.79|
|Years of U.S. migration<br>experience|0.76|0.74|4.91|
|Sample size|273|80|70|
Table 14: Impact of 3G/4G access on food consumption by group (fixed effects models)

||Female|Headed|Consu|mption|Re|gion|
|---|---|---|---|---|---|---|
||No|Yes|Low|High|South|North|
|_Access to_:|||||||
|Medium or strong 3G/4G|0.007|-0.000|0.042|-0.033|0.043|0.004|
||(0.027)|(0.042)|(0.031)|(0.032)|(0.032)|(0.033)|
|Medium or strong 3G/4G|0.070**|0.019|0.102***|0.061|0.073**|0.044|
|for one year|(0.027)|(0.041)|(0.031)|(0.037)|(0.032)|(0.033)|
|<br>Medium or strong 3G/4G|<br>0.090***|<br>-0.005|<br>0.172***|<br>0.105***|<br>0.056*|<br>0.054*|
|for two years or more|(0.027)|(0.040)|(0.030)|(0.032)|(0.032)|(0.032)|
|Medium or strong 3G/4G|0.092***|0.009|0.208***|0.104***|0.019|0.034|
|for three years or more|(0.028)|(0.041)|(0.033)|(0.031)|(0.031)|(0.033)|
|# of observations|10,772|2,959|7,246|6,297|6,747|6,985|
||Age House|hold Head|Localit|yType|Edu|cation|
|||50 or|||Primary|Less than|
||Under 50|older|Rural|Urban|<br>or more|primary|
|_Access to_:|||||||
|Medium or strong 3G/4G|-0.021|0.035|0.009|0.026|-0.006|0.036|
||(0.033)|(0.029)|(0.029)|(0.048)|(0.029)|(0.052)|
|Medium or strong 3G/4G|0.061*|0.056*|0.077**|0.021|0.080***|0.034|
|<br>for one year|(0.034)|(0.031)|(0.032)|(0.044)|(0.030)|(0.048)|
|Medium or strong 3G/4G|0.090***|0.052*|0.129***|-0.044|0.090***|0.014|
|for two years or more|(0.030)|(0.031)|(0.033)|(0.042)|(0.028)|(0.051)|
|Medium or strong 3G/4G|0.081***|0.076**|0.186***|-0.111**|0.078***|0.092*|
|for three years or more|(0.031)|(0.034)|(0.032)|(0.043)|(0.030)|(0.053)|
|# of observations|6,738|6,574|9,442|4,290|8,645|2,076|
||Liter|acy|Children|under 3|||
||Illiterate|Literate|No|Yes|||
|_Access to_:|||||||
|Medium or strong 3G/4G|0.031|-0.015|0.003|0.008|||
||(0.041)|(0.032)|(0.030)|(0.032)|||
|Medium or strong 3G/4G|0.043|0.066**|0.056*|0.063**|||
|<br>for one year|(0.042)|(0.031)|(0.031)|(0.031)|||
|Medium or strong 3G/4G|0.100**|0.056*|0.038|0.107***|||
|<br>for two years or more|(0.040)|(0.031)|(0.030)|(0.030)|||
|<br>Medium or strong 3G/4G|<br>0.161***|<br>0.055*|<br>0.069**|<br>0.092***|||
|<br>for three years or more|(0.043)|(0.032)|(0.031)|(0.032)|||
|# of observations|4,976|8,587|6,423|7,309|||

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. *** p<0.01, ** p<0.05, * p<0.1.

23
|**Table 2: Monetary Survey ‐ Impact of Fina**|**ncial Liberali**<br>**2019/20**|**zation Refo**<br>**2020/21**|**rms**<br>**2021/22**|**2022/23**|**2023/24**|
|---|---|---|---|---|---|
||||**30-Jun**<br>**Projection**|||
|**Foreign Assets (Net)**|**8,355.8**|**23,609.8**|**48,204.6**|**84,806.1**|**135,981.4**|
|National Bank|6,220.1|17,875.2|36,907.6|65,436.7|105,501.5|
|Assets|12,214.7|30,943.4|58,279.5|96,512.9|147,874.9|
|Liabilities|5,994.6|13,068.2|21,371.9|31,076.2|42,373.3|
|Commercial Banks|2,135.7|5,734.6|11,297.1|19,369.4|30,479.9|
|**Domestic Credit**|**-29,319.0**|**-75,207.1**|**-142,826.7**|**-238,031.1**|**-367,056.0**|
|**Claims on Gov't**|0.0|-10,000.0|-20,000.0|-30,000.0|-40,000.0|
|**National Bank**|-10,000.0|-30,000.0|-60,000.0|-100,000.0|-150,000.0|
|Government Borrowing|-10,000.0|-30,000.0|-60,000.0|-100,000.0|-150,000.0|
|Direce advances|-10,000.0|-30,000.0|-60,000.0|-100,000.0|-150,000.0|
|Gov't bonds|0.0|0.0|0.0|0.0|0.0|
|Government Deposit|0.0|0.0|0.0|0.0|0.0|
|**Commercial Banks**|10,000.0|20,000.0|40,000.0|70,000.0|110,000.0|
|Claims on Gov't|10,000.0|30,000.0|60,000.0|100,000.0|150,000.0|
|Treasury bills|10,000.0|30,000.0|60,000.0|100,000.0|150,000.0|
|Bonds|0.0|0.0|0.0|0.0|0.0|
|Government Deposit|0.0|10,000.0|20,000.0|30,000.0|40,000.0|
|**Claims on Other Sectors**|-29,319.0|-65,207.1|-122,826.7|-208,031.1|-327,056.0|
|**National Bank 2/**|0.0|-10,000.0|-20,000.0|-30,000.0|-40,000.0|
|**Commercial Banks**|-29,319.0|-55,207.1|-102,826.7|-178,031.1|-287,056.0|
|By Clients||||||
|I. Public Sector (SOEs)|-32,337.5|-66,465.9|-130,711.5|-235,353.4|-392,689.2|
|II. Private Sector|3,018.5|11,258.8|27,884.8|57,322.3|105,633.2|
|**Broad Money (M2)**|**-20,963.2**|**-51,597.3**|**-94,622.1**|**-153,225.0**|**-231,074.6**|
|Currency Outside Banks|-14,374.4|-33,198.1|-59,409.3|-94,915.7|-141,947.1|
|Deposits|-6,588.8|-18,399.2|-35,212.8|-58,309.3|-89,127.5|
|**Other Items (Net)**|**0.0**|**0.0**|**0.0**|**0.0**|**0.0**|
|**Memo:**||||||
|**Exchange rate (end)**|1.7339|3.7798|6.1816|8.9884|12.2560|
|<br>**Net Domestic Assets of the Banking System**|-29,319.0|-75,207.1|-142,826.7|-238,031.1|-367,056.0|
|**Imports of Goods and Services (Mlns of Birr)**|33,301.6|84,568.6|151,445.2|237,681.6|347,848.5|
|**Gross Official Reserve (Months of Import)**|0.1|0.2|0.3|0.5|0.6|
|**Reserve Money (M0)**|-4,222.8|-9,817.1|-17,043.9|-26,190.0|-37,566.1|
|Components: Currency in Circulation|-3,664.9|-8,259.2|-14,062.4|-21,252.8|-30,019.4|
|<br>CBs Reserve with NBE|-557.9|-1,557.9|-2,981.6|-4,937.2|-7,546.7|
|Determinants: Net Foreign Assets of NBE|6,220.1|17,875.2|36,907.6|65,436.7|105,501.5|
|<br>Net Domestic Credit of NBE|-10,000.0|-40,000.0|-80,000.0|-130,000.0|-190,000.0|
|Net Claims on Gov't|-10,000.0|-30,000.0|-60,000.0|-100,000.0|-150,000.0|
|DBE Priority Sector Credit|0.0|-10,000.0|-20,000.0|-30,000.0|-40,000.0|
|Other Items Net of NBE|442.9|-12,307.7|-26,048.5|-38,373.3|-46,932.4|
|**Money Multiplier (M2/M0)**|0.0|0.0|0.0|0.0|0.0|
|**Source: Authors' calculation**||||||

41
**Table 2: Previous studies of differential progress by wealth toward the health MDGs**

||Moser, Leon and Gwatkin<br>(2005)|Suzuki, Sharan and Bos (2012)|Victora et al. (2012)|
|---|---|---|---|
|Time period|1990-2001|1995-2007|1990-2007|
|No. countries|22|40|35|
|Data source|Demographic and Health<br>Survey (DHS)|Demographic and Health Survey<br>(DHS), Multiple Indicator Cluster<br>Survey (MICS)|Demographic and<br>Health Survey (DHS)|
|No. surveys per<br>country|2|2|2|
|Official indicators|Under-five mortality|Prevalence of underweight children<br>under five years of age<br>Under-five mortality rate<br>Infant mortality rate<br>Measles immunization among children<br>under one<br>Proportion of births attended by<br>skilled health personnel|Skilled birth attendant<br>Measles immunization|
|Additional core<br>intermediate<br>monitoring indicators|None|Use of insecticide-treated bednets by<br>under-five children<br>Proportion of infants under six months<br>who are exclusively breastfed<br>Proportion of children with diarrhea in<br>the past two weeks who received oral<br>rehydration therapy (ORT)<br>Proportion of children with fever in the<br>past two weeks who received an<br>appropriate anti-malarial (in<br>malarious areas)<br>Contraceptive prevalence rate|Use of insecticide-<br>treated bednets by<br>under-five children|
|Stratifying variable|Wealth index|Wealth index|Wealth index|
|Summary statistic|Changes in ratio of the richest<br>20 percent to the poorest 20<br>percent|Changes in ratio of the richest 20<br>percent to the poorest 20 percent|Changes in<br>concentration index|
|Findings|An approximately equal<br>number of countries with<br>widening and narrowing<br>inequalities|Pro-poor progress on underweight<br>Pro-rich progress on under-five<br>mortality<br>Pro-poor progress on most intervention<br>indicators except skilled birth<br>attendance|Inequalities have been<br>falling over time|

Source: Authors’ compilation based on listed studies.

21
# **Firm-Level Decomposition of Energy Consumption in Turkish Manufacturing Industry**<sup>**#**</sup>

**Sebnem Sahin**<sup></sup>

**JEL Classifications:** O13, O14, Q55 **Keywords:** Energy intensity; Turkish manufacturing. **Sectors:** Energy, Environment

> # I acknowledge the generous financial support from the World Bank research support budget. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank. I thank an anonymous reviewer for very useful comments on an earlier draft of the paper.

>  Senior Environmental Economist at the World Bank. Author can be contacted at ssahin@worldbank.org.
years of retrospective livestock history to examine wealth dynamics in southern Ethiopia, finding some support for a poverty trap in livestock wealth. However, panel data on assets is rare in developing countries, especially outside of an agricultural context, and even when available, is likely to suffer from the same measurement error and attrition issues as data on income.

This paper proposes a dynamic pseudo-panel approach to the estimation of nonlinear income dynamics and uses this method to test for the presence of poverty traps in Mexican income and expenditure.<sup>5</sup> The use of pseudo-panels can greatly mitigate the three major data issues facing the empirical study of poverty traps: a lack of long panels, attrition, and measurement error. A pseudo-panel tracks cohorts of individuals over repeated cross-sectional surveys (Deaton, 1985). Since such surveys are often available over longer time-periods than genuine panels, this approach can allow for the study of longer-term dynamics than is usually possible with panels. The Mexican Urban Labor Force Survey (ENEU) used here is a quarterly rotating panel available from 19872001. This enables us to use 58 quarters of data in the pseudo-panel estimation, compared to panels of only 5 quarters.

Non-random attrition is much less of a problem in pseudo-panels than in panels since a new sample of households is drawn in each period. In Antman and McKenzie (2005), we show that construction of a pseudo-panel eliminates the measurement error bias from dynamic linear models. This bias is found to be large: panel data estimation would suggest that one-third of the gap in income between two randomly selected households would close within one quarter, while pseudo-panel analysis shows only 1.2 percent of this gap would be eliminated. This paper extends the study of pseudo-panels to the non-linear dynamic models needed for the study of poverty traps. We employ the functional form of Lokshin and Ravallion (2004), in modelling income as a polynomial of lagged income.

> 5 Only limited data on asset ownership, in the form of housing infrastructure, is available in our survey and so we do not examine asset dynamics here. Nevertheless, the methods proposed here could also be used to form pseudo-panel estimates of asset dynamics where such data are available.

4
4.4.1 Basic characteristics In Timor-Leste, cultural values in family and social life are influenced by catholic beliefs. As in most countries in the world, women have traditionally the primary responsibility for housework and the upbringing of children. Men have assumed the main responsibility in resource allocation and investment decisions both in the household and in the community. In the bivariate setting, being male is associated with slightly higher welfare. However, once controlled for basic characteristics, the correlation reverses and the coefficient is insignificant. Welfare is positively associated with age for the bulk of the age range. It peaks at the age of 67, at the 97th percentile of the age distribution. °2 One in six women aged 15 years and older are widows. Elderly women are widows primarily due to higher female than male life expectancy years versus 54 years), whereas many younger women have lost their husbands as a result of the conflict with Indonesia. Widowhood can affect the physical safety, identity and mobility of women and children, their access to basic goods and services necessary for survival, and their rights to inheritance, land and property. The regression confirms that widows and widowers have lower welfare than either single or married persons. °° Larger households benefit from economies of scales. Holding per capita levels of economic variables constant, larger household size is associated with higher subjective welfare. Replacing a prime-age adult with children or elderly tends to reduce welfare. Education is not just critical for productive employment and economic growth, but also instrumental for accessing and disseminating information and participating in community life. More than one in two adults have no schooling whatsoever, and only one in seven have more than lower secondary education. Education is positively associated with welfare. About one in 14 individuals aged 15 or older have at least one parent who went to school.*4 Parental education has a positive and significant impact on welfare.

82 This contrasts to evidence from developed countries of a U-shaped relationship between happiness and age (Blanchflower and Oswald (2000)).

33 Using British household data from 1991 to 1997, Clark and Oswald (2002) find that widowhood brings a degree of unhappiness that would take on average an extra 170,000 pounds per annum to offset. 34 Educational attainments of the parent generation is abysmally low, a legacy of the Portuguese colonial period up to 1975. The percentage of individuals without primary education rises from 61 percent for those aged 33 to 42 years, to 81 percent for those aged 43 to 52 years, and to 92 percent for those 53 years or older.
One potential explanation for this is that information and quality concerns may not be the binding constraint for many firms in the sample, and instead issues like a lack of funding to pay for these services may be more binding. However, we are unable to reject a lack of heterogeneity in treatment effects when we interact the information treatment with whether or not they said knowledge was an issue at baseline (p=0.853), that it was difficult to assess the quality of providers (p=0.833), and that a lack of funding was the constraint (p=0.920). We also find no evidence of heterogeneity by the predicted likelihood of using these services at endline, using the endogenous stratification approach of Abadie et al. (2018). Our statistical power is low for examining treatment effect heterogeneity, and it may still be the case that there is a subset of firms for which purely providing the business services marketplace would be enough to get them to use this market, but this is not the case for the majority of firms.

Instead, we think two other explanations are likely. The first is that there are other information and knowledge frictions that simply providing access to the marketplace does not correct. Surveys of management practices find that many badly managed firms do not know that they are badly managed (Bloom et al. 2013, Iacovone et al., forthcoming). A consequence is that firms may not know that they need to improve. Then even if they know they need to improve, the information they may be unsure about is whether the business service providers can provide the right expertise and advice for their specific needs. Even knowing that providers have offered quality services to other firms may not be enough to resolve this uncertainty, and as a result, firms may only be able to learn the value of using professional business services through learning-by-doing. Evidence for this is seen in the fact that many of the firms in the experiment of Anderson and McKenzie (2020) do go back to the market for services after having been subsidized to try it out.

A second explanation is that firms face multiple constraints to growth, and alleviating this information friction alone is not enough. If firms also have their financial constraints alleviated and also experience demand shocks that offer a pathway for business growth, they may be more willing to go to the market to seek professional advice in hiring workers to help this growth. As noted, we do not find any significant treatment heterogeneity, but are underpowered for detecting whether there are impacts for subsets of firms with more finance and demand.

15
men must command at least 27% (the GSS impact on teacher wages) higher wages then women. If they didn't, then private schools could setup in villages without a GSS by hiring (local/non-local) men rather than women as teachers. Andrabi et al. (2008) show that men (with the same observed characteristics) indeed earn 33 percent more than women, suggesting that men do not oer a viable teaching alternative.

Second, the entrepreneur could try to setup a school with a larger initial class-size in order to pay for the greater cost of hiring a male teacher. However, this has the trade-o of lowering quality. A natural constraint here is that student performance in the private schools must exceed that in the (free) public schools. Andrabi et al. (forthcoming) uses GMM methods together with children who switch school types to show that the yearly value-added of private schooling is around 0.25 standard-deviations. Although the estimates from the experimental literature on class-size reductions vary somewhat, a number of studies suggest gains of 0.2 to 0.3 standard deviations due to a reduction of four to ten students (Angrist and Lavy 1999, Krueger 1999, Muralidharan and Sundararaman forthcoming). Given median wages and school fees in Punjab, to generate enough revenue to cover the 33 percent higher wages of a male teacher, the school would need a class size that is seven children more than the median private school. This suggests that the quality drop from the increase in class size required to hire male teachers would almost entirely oset the private school advantage in these villages and hence not be viable. In other words, parents would choose public schools instead if the private school had a larger class size.

Third, the entrepreneur could set fees 33 percent higher than the current levels. Using data from Pakistan, Carnero et al. (2010) structurally estimate the elasticity of private school market shares to fees and nd that a 1 percent increase in prices reduces the market share per private school by 1.2 percent. Given this high price elasticity, private schools would therefore not be able to increase prots by raising fees, ruling out this arbitrage opportunity, as well.

28
SAR, and FSU. With these three regional dummy variables included, aid has a negative coefficient, but it is no longer significant.

Figure 4. Aid and Growth in the 1990s (no regional dummies) <!-- Start of picture text --> coef = -.24370803, se = .11956833, t = -2.04<br>.116168 HTI<br>CHN<br>KNA<br>VNM IRNLBN GUY GNQ<br>DOM<br>ARGTWN<br>IND SYR IRLKOR<br>NGABGDNPLPAKETHIDNPHLCHLYEMKENCRILKATHAPERMYSGHAMDGMARECUROMCOLPANTUNURYGTMZWEBRABENTURJAMTCDPRYEGYPOLUGASLVBWAPRTNZLZAFMEXMUSTTOHUNNERVENDZAFJICMRGINBFAGRCLSONLDGBRESPCIVNORSWGERBDITGONAMFINCZAUTAUSSVNDNKMLIPNGFRASVKBELCANBRBBLZCHE E HKGBOLISLHNDLUXJPNUSABLRITASENVCTJORGMBGABISRTZACOGMWICOMRWAMOZSYC MRT ZMB CPV GNB<br>LVA NIC<br>CAF<br>UKR<br>-.092758 ZAR<br>-.036761 .112145<br>e( aidgdp | X )<br>e( growth9099 | X)<br><!-- End of picture text -->

The second panel in Table 2 shows the same specifications, but now initial income, institutions, and aid are all treated as endogenous. There are good reasons to treat each of the right-hand-side variables as endogenous. In the case of aid, it is very possible that the negative correlation between aid and growth reflects the endogenous response of aid donors to countries hit by unexpected shocks or crises. Without instrumental variables, growth regressions involving aid are suspect. In the case of initial per capita GDP, measurement error would introduce a negative correlation between initial income and subsequent growth. In the case of the institutions variable, we are not too worried about reverse causation from fast growth to good institutions in a ten-year period. However, all of the institutional quality variables are essentially subjective, and there is a danger of a “halo effect” in which fast-growing countries are rated to have good 14