rafmacalaba/data-use-mentions-extended huggingface dataset:
and the local environment. This new project is also to expand the solid waste collection and disposal service from Cibixiang<sup>12</sup> , the capital town of Eryuan County, where a simple service in garbage collection and disposal has been available even though with a very low management quality, to cover three other small towns (Niujiexiang, Sanyingxiang and Fengyuxiang), the geographical locations of which are given by the dotted blue circles in Figure 1.

# **4. The Survey**

# 4.1 Survey Design and Implementation

In order to estimate the total willingness to pay of the households located in Eryuan for the improved SWM Project, a contingent valuation survey was designed and implemented in the summer of 2007 in the project area. A four stage stratified random sampling approach was used to select a sample of households. At stage 1, according to the distribution of project beneficiaries, a geographical boundary (i.e., towns) was determined so that the sample can cover all the geographical area of the project. At stage 2, the number of households in each town to be surveyed was calculated based on the ratio of sample size to the total number of households in the project area. At stage 3, each town is further divided into a number of communities or villages, and a list of communities is randomly selected to be surveyed, with

> 11 The official exchange rate at the end of 2005 : 1USD=8.19 Yuan.

> 12 Xiang is a constituency under county in China. In this paper, Xiang refers to a town where the Xiang government is located, which is bigger than a village but is smaller than a county town or a small city.

7
persons working in the local unit‖ of respondent‘s main job; in case of employers this of course cannot be considered as a proxy for the total number of employees working for him. Third, ESS data are available not only for all EU countries, but also for Norway, Switzerland, Russia, Ukraine, and Israel (for various data-related reasons our analysis omits Luxembourg, Malta, Turkey, and Croatia).

The contribution of the paper to the literature on informal employment is three-fold. First, we use direct survey evidence (rather than proxies) to provide a multi-country longitudinal analysis of the levels, dynamics and profile of dependent employment without contract, as well as informal self-employment, in Europe. Importance of using direct evidence is highlighted in Henley et al. (2006), who find that ―definitions of informality based on occupation and employer size seem the most arbitrary in practice‖, and in Perry et al. (2007), who report (based on a survey conducted in 9 countries in Latin America) that ―large firms... have a significant number of employees without social security contributions‖. On the other hand, we are able to draw the line between informal and formal self-employment more

> 2 Moreover, in cases when the original questionnaire includes the „no contract‖ option, _Eurostat_ groups these responses together with „temporary‖, making it very difficult to distinguish informally employed from who is legally employed fixed-term workers.

3
Bank’s Pink Sheet, and export weights are from the UN Comtrade database. Countryspecific output gaps are aggregated using real GDP weights at 2010-19 exchange rates and prices.

# ANNEX D Long-term growth expectations

Expectations of output growth over long horizons capture forecasters’ assessment of longterm sustainable growth since they are stripped of unpredictable short-term shocks. Two sources of expectations are used: the International Monetary Fund’s World Economic Outlook (WEO) database, published twice a year, and Consensus Economics, published on a quarterly basis. Since the longest available forecast horizon is 5-years for IMF’s WEO, 5-year-ahead forecasts are selected for both sources for consistency across these two measures. The IMF’s WEO provides five-year-ahead forecasts for up to 173 countries (37 advanced economies, 136 EMDEs) for 1990-2021. Consensus forecasts are available for up to 78 countries (34 advanced economies and 44 EMDEs) for 1990-2022 and the database includes the April vintages.

42
related assistance to mitigate the detrimental effects of trade reforms and to enhance the trading capacity of developing countries. Specifically, in February 2005, G-7 Ministers called on the World Bank and the International Monetary Fund (IMF) to develop proposals for additional assistance to countries to ease adjustment to trade liberalization and to increase their capacity to take advantage of more open markets. Subsequently, in July 2005 Heads of State at the G8 Summit at Gleneagles agreed to increase help to developing countries to building their physical, human and institutional capacity to trade. In December 2005, at the 6<sup>th</sup> Ministerial Conference held in Hong Kong, the Ministerial Declaration endorsed the enhancement of the Integrated Framework and created a new WTO work programme on Aid-for-Trade (Hoekman _et al._ , 2010).

Since 2005 donors and development agencies have increased the overall value of AfT and put in place several mechanisms to channel such aid and to ensure that it alleviates inequality. According to the data reported by the OECD, 25 percent of the official development assistance (ODA) was directed toward AfT in 2008. Also OECD statistics show that in 2009, global AfT commitments reached approximately 40 billion US dollars, a 60% increase from the 2002-05 baseline period. Half of all AfT is provided in grant form, mainly to the poorest developing countries. Disbursements have been growing at a constant growth rate of between 11 and 12% for each year since 2006 – reaching 129 billion US dollars in 2010 – indicating that past commitments are being met (WTO/OECD, 2011). The top three developing regions that received the aid from all donors are Asia, Africa and South America respectively in the past decade (Figure 1)<sup>*</sup> .

> ***** The Aid data set is extracted from OECD.STAT data base. The donors include DAC countries, multilateral agencies, non-DAC countries, G7 countries, DAC-EU members.

**3**
per student spending|34|70|62|74|
|**Secondary (total % change)**|**241**|**121**|**162**|**43**|
|% devoted to access|80|49|25|43|
|% devoted to per student spending|20|51|75|57|
|**Tertiary (total % change)**|**167**|**147**|**118**|**136**|
|% devoted to access|69|64|73|55|
|% devoted to per student spending|31|36|27|45|

_Source:_ World Bank calculations based on UIS and IMF data.

_Note:_ Only countries for which there are data that span across at least four out of the five periods (1998 **–** 2001, 2002 **–** 05, 2006 **–** 09, 2010 **–** 13, and 2014 **–** 17) between 1998 and 2017 are included in the figure. The percentage devoted to access and percentage devoted to per student spending sum to 100 percent of the total percent change over five-year episodes for each level of education.

# _2.5 Summary_

While the overall patterns and trends in public education spending vary widely across income groups and regions, we can draw some general conclusions from our analysis in this section.

- Global averages:

- Average government education spending doubled in real terms between 1998 and 2017, but spending as a share of GDP increased only slightly.

- The composition of public education spending remained relatively stable over the same period.

- Economic growth has been the main source of financing increases in government education spending.

- Differences among regions:

- Real government education spending has increased more than threefold in East Asia and the Pacific since 1999.

- Government education spending
from the population- or subgroup-level joint probability estimates.

# 3.2. Vulnerability

Our analysis of vulnerability and income mobility is based on the definition of a vulnerability line as introduced by Dang and Lanjouw (2017). Below, we summarize this approach and explain how it ties in with the synthetic panel method. Our goal is to estimate 3x3 transition matrices for the income categories poor, vulnerable and economically secure.<sup>14</sup> This study focuses on the lower part of the income distribution, which is reflected in our choice of income categories. We note that once an individual reaches the secure category, there is no further scope for mobility in our setup. That also implies we are not analyzing the likelihood of social mobility in a broader sense, or intergenerational mobility – two relevant topics in the Malaysian context.

We start by defining the category ‘vulnerable’ as those non-poor in period 1 who face a certain probability or higher of falling into poverty in period 2. This conditional probability can be denoted as: PP<sup>VV</sup> 2 1 1 ) =<sup>PP (yyii2≤ zz2</sup> 1<sup>a</sup> 1 ) ,<sup>aa zz1< yyii1≤ vv1 )</sup> ii2 ii1 PP (yy ≤ zz | zz < yy ≤ vv ii1 PP ( zz < yy ≤ vv where 1 stands for the vulnerability line. The latter is derived empirically after setting the vulnerability index , the maximum probability to fall into poverty that we are willing to accept for a household to be vv considered economically secure. For example, we might set the index at a value of 20 percent. This PP<sup>VV</sup> approach contrasts with other approaches in the sense that the vulnerability line is not set arbitrarily, for example at 1.5 times the poverty line, but derived empirically from the vulnerability index. In practice, this requires a recursive algorithm that goes
>1986 est., UNPVSR<br>4/87|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|.gladesh|Mar 81|87120 F|Bank projection from census|Based on U.N. 1984<br>assessment and contra-<br>ceptive prevalence rate|Bank est.|
|rbados|may 80|244 F|Bank projection from<br>official<br>1984 est.|<br>1980-85, U.N. 1988 revision<br>(prelim.);<br>1985-90, Bank|1980-85, U.N. 1988 revision<br>(prelim.);<br>1985-90, Bank|
|||||projection|projection|
|Belgiui|Mar 81|9849J|Eurostat 1987|Eurostat 1987|Eurostat 1987|
|Belize|may80|143 F|UNPVSR<br>7/86 (official<br>est.)|Based on USBOC 1985|Based on USBOC<br>1985|
|Benin|Mar79|3331 F|Bank projection from census|Bank assessment of 1961<br>survey and World Fertility|Bank assessment of Uorld<br>Fertility<br>Survey child|
|||||Survey|survival rates|
|Bermuda|May 80|68 F|UNPVSR<br>1/88 (official<br>est.)|--||
|Bhutan|Dec 69|1035 F|Bank projection from<br>official<br>1986 est.|Based on U.N. 1988 revision<br>(prelim.)|Bank est.|
|BoLivia|Sep 76|4613 F|U.N. 1988 revision (prelim.)|U.N. 1988 revision (preLim.)|U.N. 1988 revision (prelim.)|
|Botswana|Aug 81|941 F|
Labor and Welfare Impacts of a Large-Scale Livelihoods Program: Quasi-Experimental Evidence from India

# **Vivek Pandey, Abhishek Gupta, and Shivani Gupta**<sup>**12**</sup>

**JEL Codes:** D13; D14; G21; I24; J16; J21; Q13; O15; O17

**Keywords** : Female Labor Force Participation; Women's empowerment; Self-Help Groups; Rural credit markets; India.

> 1 Pandey, V: Institute of Rural Management Anand, India (email: Vivek@irma.ac.in). Gupta, A: World Bank, 1818 H Street NW, Washington DC 20433 (e-mail: <u>agupta20@worldbank.org). Gupta, S: World Bank, 1818 H Street NW, Washington DC 20433 (e-</u> mail: <u>shivaniguptaa76@gmail.com)</u>

> 2The authors would like to thank Vinay Kumar Vutukuru, Gayatri Acharya, Madhur Gautam, Ritam Chaurey, Rinku Murgai & Parmesh Shah for their comments.  Financial support from Ministry of Rural Development, Gates Foundation and the JOBS MDTF is gratefully acknowledged as is access to survey and monitoring data from the NRLM.
|-0.661***|0|431,436|
|Asset ownership|-0.010***|-0.053***|0|-0.154***|-0.197***|0|264,680|
|Literacy|-0.012***|-0.085***|0|-0.155***|-0.227***|0|694,463|
|Not employed in the<br>agriculturalsector|-0.009*|-0.079***|0|-0.130***|-0.199***|0|450,521|
|Access to electricity|-0.015***|-0.082***|0|-0.262***|-0.329***|0|697,574|
|Piped drinking<br>water|-0.011***|-0.033***|0.0007|-0.143***|-0.164***|0.0007|595,934|
|Improved sanitation|-0.008***|-0.058***|0|-0.182***|-0.232***|0|592,937|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> <u>Notes: This table shows the results of regressing an indicator variable of being poor on (ranked) residuals of covariates</u> of poverty (see Section 3b in the paper for more details). The residuals are determined by conditioning out household size from the covariates of poverty, including years of schooling, asset ownership/index, literacy, access to electricity
|1.48|
|Machinery services (US$/ha)|49.23|45.30|39.18|42.03|39.76|79.88|
|Daily wage (US$/day)|14.38|15.30|12.50|13.89|16.32|13.89|

_Source:_ Survey of community leaders and local dealers’ adverts, complemented by completed auctions for machinery services on the Prozorro sales platform. Prices are for ammonium nitrate and urea for fertilizer and the cost of harvesting and ploughing/soil preparation for wheat and maize, respectively, for machinery services. Wages are for agricultural labor.

32
# **2.2 Case study area: Somalia**

Somalia is situated in the Horn of Africa with an official population estimated at 12.3 million in 2014, up from the 1975 estimate of 4.1 million with slightly more males (6.2 million) than females (6.1 million) (UNFPA, 2016). In 2012, the first nationwide Population Estimation Survey (PESS) took place. The Somalian government, United Nations Population Fund (UNFPA), and United Nations Development Programme (UNDP) collaborated, prepared, and carried out this survey, aiming to use the PESS as a basis for a census.

The PESS survey in 2014 estimated that 42% of the population was permanently settled in urban areas and 23% in rural areas, while 26% were nomadic people and 9% were internally displaced persons (IDPs) (UNFPA 2014). The Somali population is rapidly increasing with almost 3% population growth per year and a high fertility rate of 6.26 children per woman, which is the fourth highest in the world (Gure et al. 2015).

However, the results of the PESS alone were not suitable for creating a nationally representative sampling frame, as the PESS created EAs in urban areas only. The risks associated with fieldwork and the lack of funding were just two hurdles this approach faced; significantly displaced populations exist in parts of Somalia, without any official population information available. Therefore, the World Bank recognized that re-building Somalia’s statistical infrastructure and capacity was key in supporting resilience efforts and proposed a spatial analysis approach as an innovative way to create a new sampling frame, especially given the barriers in this context (e.g. security risks, lack of funding).

# **2.3 Data sources**

To conduct this work, several data sets have been compiled and combined from various resources (table 1).

Table 1. Data used for Somalia Enumeration Areas.

|**Source**|*
often pay a fixed monthly charge regardless of their actual consumption, long power outages increase their effective electricity tariff. All this suggests that models ignoring the quality of electricity service may underestimate the net benefits associated with reliable electricity supply. A recent World Bank study concluded that reliable supply is the defining issue in achieving the economic benefits of grid‐ connection (Pargal and Banerjee 2014).

The purpose of this paper is to examine the impact of rural electrification on household welfare while paying special attention to how the quality of electricity supply affects the results. Our study takes advantage of newly available panel data from the Indian Human Development Survey (IHDS), which includes a question asking households to estimate the daily average duration of outages. Using hours of

> 2 In the following, we use the terms “access” and “grid‐connection” interchangeably.

> 3 World Bank Enterprise Surveys (http://www.enterprisesurveys.org).

2
Heckman, James J and Rodrigo Pinto. 2015. Econometric mediation analyses: Identifying the sources of treatment effects from experimentally estimated production technologies with unmeasured and mismeasured inputs. _Econometric Reviews_ 34 (1-2): 6-31.

Huber, Martin. 2014. Identifying causal mechanisms (primarily) based on inverse probability weighting. _Journal of Applied Econometrics_ 29 (6): 920-943.

Imai, Kosuke, Dustin Tingley, and Teppei Yamamoto. 2013. Experimental designs for identifying causal mechanisms. _Journal of the Royal Statistical Society: Series A (Statistics in Society)_ 176 (1): 5-51.

Robins, James M and Andrea Rotnitzky. 2001. COMMENTS. _Statistica Sinica_ 920-936.

Rosenbaum, Paul R and Donald B Rubin. 1983. The central role of the propensity score in observational studies for causal effects. _Biometrika_ 70 (1): 41-55.

Sobel, Michael E. 2008. Identification of causal parameters in randomized studies with mediating variables. _Journal of Educational and Behavioral Statistics_ 33 (2): 230-251.

Van der Laan, Mark J and James M Robins. 2003. Unified methods for censored longitudinal data and causality. Springer Science & Business Media.

80
with other parts of the Bank; surprisingly enough IEG does not even share the location with other departments and is currently placed in a different building. It may also be asked whether these evaluations are taken seriously? Again, after analyzing internal reports and discussions, we feel they are. IEG creates a whole range of products that comment on the evolution of World Bank’s projects and these reports are mostly based on the ES/EVM and PPAR data and the project reports behind their construction. Inside the World Bank there is a remarkable attention to the evolution of the ratings, so far that in 1992 after a long-lasting decline in project performance ratings, the Wapenhans Report (Wapenhans 1992) called for immediate changes in the operational structure of the Bank, as it “identified poor project monitoring and supervision, as well as a lack of focus on project outputs and development impact, as contributing to poor project portfolio performance” (Sigurdsson and Schweitzer 1995). Another question which the nature of this data may spark is whether these evaluations are reliable. This may present two types of concern. The first and more general one regards how we define successful a project. It is clearly a micro-definition, which heavily depends on the project; in fact its outcome variable measures the “extent to which the operation’s major relevant objectives were achieved, or are expected to be achieved, efficiently”<sup>19</sup> . Therefore, a project is successful if it reaches the stated objectives. This may lead to think that project results may depend on how carefully their objectives are stated, for example a project may perform exceptionally well given extremely low goals or viceversa. The World Bank addresses this issue substantially in order to avoid such problems, by requiring an intense project review process within sectors, questioning whether the project is sufficiently ambitious, feasible and meets the poverty-fighting objectives guiding the World Bank mission. In order to attain comparability standards several departments are involved in a collective review of each project: the legal Vice
, and _κojg_ are occupation-sector-specific returns to ability.

Services are a CES composite of home and market services _S_ = _{hs, ms}_ , following Ngai and Petrongolo (2017) and Ngai et al. (2024) so that:

> 12Our model and employment data cannot distinguish whether gender differences in utility costs, _λoj_ , stem from intrinsic gender-specific preferences or from restrictive and coercive gender norms. For example, the underrepresentation of women relative to men in engineering jobs could reflect either a genuine preference differential (in the absence of constraints) or societal factors such as stigma, discouragement, or discrimination of women. Given this limitation, we restrict our analysis to a growth accounting exercise and we refrain from any normative welfare analysis, in line with the literature (Hsieh et al., 2019; Chiplunkar and Goldberg, 2024; Ranasinghe, 2024a; Cuberes and Teignier, 2014; Lee, 2024).

> 13In the baseline model, we assume that men and women with the same ability draw _z_ have the same productivity in each occupation-sector pair. Hence, we do not allow for any gender-specific comparative advantage across occupation-sector pairs. We relax this assumption in Section 7.4.

> 14Such income effects have been emphasized in the literature as an important driver of structural transformation, e.g., Herrendorf et al. (2013); Alder et al. (2022); Comin et al. (2021); Conte (2022); Fan et al. (2023).

17
Figure 5. Total Factor Productivity Growth A. Average TFP growth <!-- Start of picture text --> B. Share of economies and GDP with TFP<br>growth below its longer-term average<br><!-- End of picture text --> <!-- Start of picture text --> Percent Percent Share of countries<br>3 100 Share of GDP<br>1998-2007 average 80<br>2 60<br>40<br>1 20<br>0<br>0<br>World AEs EMDEs World AEs EMDEs<br>Sources: Penn World Tables, World Bank.<br>Note: TFP growth stands for total factor productivity growth. This figure refers to potential TFP<br>growth.<br>2003-07 2013-17 2018-27 2003-07 2013-17 2018-27 2003-07 2013-17 2018-27<br>2003-07 2013-17 2018-27 2003-07 2013-17 2018-27 2003-07 2013-17 2018-27 1998-2002 1998-2002 1998-2002<br><!-- End of picture text --> A. GDP-weighted average of total factor productivity growth. Includes 50 EMDEs and 30 advanced economies.

B. Number of economies among 30 advanced economies and 50 EMDEs in which potential total factor productivity growth is lower than its longer-term average (1998-2017).

# Figure 6. Investment Growth A. Investment growth

<!-- Start of picture text --> B. Share of economies with investment<br>growth below its long-term average<br><!-- End of picture text --> <!-- Start of picture text --> Percent Percent Share of countries<br>16 100 Share of GDP<br>2003-07 average<br>1998-2017 average 80<br>12<br>60<br>40<br>8<br>20<br>4 0<br>0<br>World AEs EMDEs World AEs EMDEs<br>2003-07 2013-17 2003-07 2013-17 2003-07 2013-17<br>2010 2012 2014 2016 2018 2010 2012 2014 2016
may be a crude measure since it does not account for differences between cohorts at birth with growth of a cohort over its life cycle. It is obviously of interest to reexamine this relationship using a panel dataset in Africa.<sup>16</sup>

# **7 A Comparison with Survey Data**

Our strategy to measure and quantify the costs of misallocation was taken directly from the literature. In return to relying on strong assumptions, the methodology provides a clear benchmark of efficiency with which to confront the data and is relatively simple to apply. The real limitation is the availability of adequate data sources. In this section we perform the same exercise of measuring and computing the costs of misallocation from an alternative data source, the World Bank’s Enterprise Surveys.

First, we diagnose the differences between the two data sources exploring their implications for the firm size distribution. We show that there are significant deviations in the coverage of the full spectrum of firms in the ES for Ghana, where we find that large firms are over-represented relative to the Census. In Kenya, on the other hand, we find that the two distributions match up relatively closely.

Then, we investigate the properties of the distribution of TFPQ and TFPR and compute counterfactual gains in productivity from the ES, in order to highlight differences with the results from the Census. In this case, we find that accounting for the true distribution of industrial value added share across manufacturing industries is essential for the patterns of misallocation and the aggregate gains in productivity from its reversal. Because the ES does not attempt to ensure representativeness at the 4-digit level in these countries, we use industrial weights from the Census. We find that when doing so, the ES depict a much less distorted economy with significantly lower gains from resource reallocation. We interpret this finding as indicative of the importance of counting with a database that properly accounts for the real distribution of value added shares in the economy.

## **7.1 Firm-Size Distribution: Survey vs Census