rafmacalaba/data-use-mentions-extended huggingface dataset:
function of household characteristics, such as asset ownership, housing quality, education levels, etc., that produces a score used to rank households by their estimated level of welfare, when verifiable consumption or income data are not readily available and costly to obtain. Various methods exist for identifying and aggregating household characteristics to produce a PMT score (Coady, Grosh, and Hoddinott 2002; Chen and Schreiner 2009). However, it has become common to use statistical, regression-based methods to identify weights in the PMT from regression coefficients of household consumption or income using household survey data (Brown, Ravallion, and Van de Walle 2018). The PMT weights are then applied to information collected from program applicants to construct a household specific PMT score, which is an out-of-sample prediction of consumption expenditure or income, depending on the welfare variable the National Statistics Office measures. The resulting PMT score is thus an estimate of the household welfare variable, which national poverty and welfare measures are typically based. The PMT score is then used to determine household eligibility for social programs based on a predetermined cutoff (i.e., the PMT score cutoff, similar to a poverty line) and may also be used to determine the level of benefit.

While universal social protection programs that cover the entire population may be desired, they are often not feasible given limited budgets, particularly in developing countries. The use of a PMT to target social programs has been shown to improve targeting incidence and reduce the inclusion of people unintended for public program support, improving program efficiency (Grosh, 1994). Furthermore, because the PMT ranks households by their estimated welfare level, the same tool can be used for targeting various welfare programs. A common targeting approach for the various social programs, based on a single PMT score, can improve consistency across programs, reduce overlap and duplication of benefits, and promote future harmonization of public programs (Sebastian et al. 2018). Due to these benefits, PMTs are now widely used in administering various social programs in
TBT and SPS measures that are adopted by other WTO members (indirect notifications). Both sources of information have pros and cons. Direct and indirect notifications are more consistently available for a longer time period relative to data collected by international organizations. However, direct notifications are often incomplete, as they are self-reported by governments that do not necessarily fully abide to their notification requirements. Moreover, this information is not coded and is not readily available for researchers.

> 5 The data are available through the World Integrated Trade Solution (WITS) at: <u>http://wits.worldbank.org/</u>

> 6 The TTB database is available at: http://go.worldbank.org/W5AGKE6DH0. 7 An example of a non-government institution collecting data on NTMs is The Global Trade Alert (GTA), which was set up by the Centre for Economic Policy Research (CEPR) to monitor the protectionist response of governments in the aftermath of the global financial crisis.
incentives.<sup>5</sup> But the empirical evidence on decentralization is limited, focusing primarily on de-layering in large publicly traded US firms (Rajan and Wulf, 2006).

Fourth, the paper contributes to the literature on Information Technology (IT) and productivity. A growing body of work has examined the relationship between technology and productivity, emphasizing both the direct productivity impact of IT and also its complementarity with modern management and organizational practices (e.g. Bresnahan et al. 2002 and Bartel et al. 2007). But again the evidence has focused on survey data rather than experimental data. Our experimental evidence suggests one route for computers to affect productivity is by facilitating

> 4 See, for example, the surveys in Delery and Doty (1996) and Bloom and Van Reenen (2010).

> 5 See the recent reviews in Garicano and Van Zandt (2010), Mookherjee (2010) and Gibbons and Roberts (2010).

3
<!-- Start of picture text --> EXPORT PRODUCTION ij<br>SjEXPORTij SiPRODUCTIONij<br>IESij= ---------------- IPSij =---------------------<br>Si EXPORT ij Si PRODUCTION;;<br>SiSj EXPORT ij SiSjPRODUCTION i<br><!-- End of picture text --> Data sources and classifications Export, production and R&D data for 1973-'90 are taken from the OECD STAN/ANBERD data base (OECD, 1992), which reports variables in national currencies and current prices. These have been transformed them in dollar real values at 1985 prices, using the GDP price indices of the six countries and 1985 PPP US dollars.

Patent data are taken from the US Patent and Trademark Office data base and represent the number of patents granted by the USPTO according to the inventor countries. These are fractional counts: patents are originally classified by technological groups and then attributed to the industrial sectors which use these technologies. If one invention has more then one industrial sector of destination, only a fraction of this will be assigned to the sectors (e.g. if one patent has three sectors of use, each of these sectors will present one third of patent counts). The sectors examined are the following: Textiles (ISIC 3200; SIC 22); Chemical Products excluding Drugs (ISIC 351, 352 except 3522; SIC 281, 284-287, 289); Mechanical Equipment including Computers (ISIC 3820; SIC 35); Electrical & Electronics (ISIC 3830; SIC 36, 3825); Motor Vehicles (ISIC 3843; SIC 371); Aerospace (ISIC 3845) 36
bins, half of the village clusters were randomly selected to enter a “gender parity lottery”.<sup>5</sup> Among these, half of the village clusters (74 clusters and all 325 villages in those clusters) were selected to be free to choose the gender composition of their management committees. In the other clusters (75 village clusters, 337 villages), the program was implemented as normal; i.e. with an obligatory equal number of men and women in the committee. This design feature allows us to learn about the impact of having women in leadership positions.

The short-term impact of the program was assessed by Humphreys et al. (2019), who collected data between October 2010 and October 2011; after the onset of village-level projects, but well before the end of the village cluster-level projects. Appendix C illustrates the timing of the village and village cluster phases of the program, and the short-run data collection. This study leverages the same design as Humphreys et al. (2019) but returns to the villages in 2015, eight years after program onset and three years after all program activities have concluded.

# **Outcomes, Data and Empirical Strategy**

This study focusses on eight outcome families: service provision, health, education, economic welfare, women’s empowerment, governance, intra-village cohesion, and inter-village cohesion.<sup>6</sup> We collected data between June and December 2015, targeting 781 villages in the provinces of Haut Katanga, South Kivu and Tanganyika.<sup>7</sup> Data were collected from four sources. In each village, surveys were conducted with the village chief and a randomly selected adult from five randomly selected households per village. In addition, as part of the household survey, in each household with children of school-going age (between 6 and 11 years old), one child was randomly selected for a brief exam. Finally, enumerators visited the village’s primary school and health facility.
**c) Destination** : Destination countries in our cross-country raw dataset follow UN’s guidelines (International Merchandise Trade Measures: Concepts and Definitions, p. 60) which recommend that countries of origin record their destinations as defined by the destination countries themselves. For instance, Bermuda (UK); Hong Kong SAR, China; and Macau (China) are all considered distinct destinations although they are not independent countries. Therefore, each country has a potential set of 247 destinations as of end-2011.<sup>11</sup>

The first cleaning operation applied to the country of destination variable relates to the use of special trade regimes by some countries –Bulgaria, Costa Rica, Colombia, Ecuador, Egypt, Jordan, Kuwait, Lebanon, Morocco, Peru, Turkey, Yemen.<sup>12</sup> Customs in these countries record the sales from inland to their own free zones/customs warehouses as exports which results in a larger set of potential destinations. For example, in Colombia’s dataset, the Free Zone “Zona Franca Bogota” appears as a separate destination. Since there is a lack of uniformity across countries in the definition of the special trade regime, we do not consider this type of transactions as exports and hence we drop the observations related to sales to free zones from our dataset.<sup>13</sup> This operation had a minor effect on total export volumes as sales to free zones/customs warehouses are negligible in most cases.<sup>14</sup> The second operation applied to the country of destination variable accounts for the changes in name that some statistical territories have undergone over time due to spatial

> 11 See http://unstats.un.org/unsd/methods/m49/m49alpha.htm for the current potential set of destinations.

> 12 The “special trade regime” considers transactions where the goods are sold from the domestic territory _only_ to both third countries and free zones/customs warehouses
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using electricity or LPG for major household activities, such as heating and cooking. This is expected to

> 14 There is some difference between the LSMS data and people’s perception about the energy alternative. While the LSMS data indicate that some amount of household expenditure was spent for LPG and firewood, people perceive that they are largely dependent on electricity for energy. This is partly because there is a considerable difference in living conditions between urban and rural areas. In rural areas, firewood is often an alternative energy source. According to the 2005 LSMS, some 70 percent of rural households used some firewood for heating or lighting, while only 30 percent of urban residents used.

> 15 According to the 2005 LSMS, only about 3 percent of households answered that an alternative energy source for lighting was power generator. On the other hand, about 80 percent of firms in the country have their own backup generators, which are estimated to have generated some 30 percent of firm electricity consumption in 2006 (Iimi, 2010).
where _i_ represents household; _α_ and _ɛ_ represent constant and error terms, respectively; and D1, D2, R1 and R2 are identifiers representing a household’s assignment to diary-visit, diary-phone, 6-month recall, and 12-month recall, respectively. D1 is the comparison category in Equations 1 and 2, and crop cutting is the comparison category in Equation 3. _C_ is a vector of household attributes that is included with the intention of capturing any remaining unobserved heterogeneity that may be correlated with these controls and that may jointly determine both the dependent variables and household survey treatment assignment.P29F<sup>30,</sup> 30F<sup>31</sup> As described in Section 3, annual cassava production (kilograms) and cassava yield (kilograms/hectare) are the dependent variables of interest. To limit the influence of potential outliers that were not deemed agronomically plausible upon extensive review of the data, both production and yield estimates were winsorized at the 95<sup>th</sup> percentile. The sample includes 1,218 households for Equations 1 and 2. To estimate Equation 3, we append the household-level crop cutting-based yield estimates available for 1,123 households onto the data set used for the estimation of Equations 1 and 2. As such, the standard errors are clustered at the household-level for Equation 3, and at the EA-level for Equations 1 and 2. The results from each regression are coupled with the full spectrum of tests of equality of coefficients for complete inter-arm comparisons.

# **5. Results**

Panels A, B, and C of Table 6 report the results from the estimations of Equations 1, 2 and 3, respectively.P31F<sup>32</sup> If true cassava production is assumed to be underestimated even by diary-keeping due to imperfect compliance, we find that compared to D1, the annual household cassava production was 295 kilograms higher, on average, under D2, corresponding to 28 percent of the 30 The control variables include: the
market are present in the general labor market as well. Thus, to understand the challenges in the youth labor market, one must first know whether these challenges are youth-specific. Understanding these questions can better guide the policy makers to allocate limited resources to youth-specific interventions or to interventions with a much broader target.

The objectives of this paper are to analyze the patterns and the challenges of youth employment in Pakistan and to determine whether these challenges are youth-specific. This paper uses the 2005/2006 Labor Force Survey (LFS), and the analysis covers various dimensions in the labor market, including determinants of unemployment, determinants of working in the formal sector, rate of return on education, and determinants of working hours.

I find that many of the challenges to youth employment in Pakistan are not youth-specific. This suggests that policies should emphasize a broader labor market reform, even in the context of tackling youth employment issues. Still, some challenges are youth-specific, such as the high unemployment rate and insufficient returns to better-educated youth. For these challenges, more youth-specific interventions are needed.

The paper is organized as follows. Section 2 describes the data and the key variables used in the paper. Section 3 presents youth labor market trends and compares the key dimensions of youth employment and adult employment. Section 4 analyzes the determinants of youth activities, and Section 5 analyzes the difference between youth and adults on characteristics associated with being unemployed and working in the formal sector. Section 6 presents the empirical results on rate of return on education for both youth and adults, and Section 7 explores the determinants of total hours worked. Section 8 concludes.

3
**Table 8: Survey data weighted changes in wages by sector and education level after a 10% change in the world price of sugar, %**<sup>_a_</sup>

||Low-Educated<sup>_b_</sup>|Average-Educated<sup>_c_</sup>|High-Educated<sup>_d_</sup>|
|---|---|---|---|
|Sugar growing|2.05|2.73|3.41|
|Sugar processing|3.06|3.54|4.39|
|Other agriculture|0.56|1.12|1.95|
|Other industry|0.62|1.12|1.86|
|Services|-0.46|0.08|0.82|

> _a_ Obtained using the estimates provided in Tables 5 and 6 and taking averages over group of individuals (using sample weights).

> _b_ Low-educated workers are defined as those with less than 5 years of education.

> _c_ Average-educated workers are defined as those with more than 4 years of education, but less than 9.

> _d_ High-educated workers are defined as those with more than 8 years of education.

38
corruption is combatted|(0.01)|(0)|
|**Number of obser**|**vations**|45881|2885|
|**Log-likelihood**||-966870.93|-61074.971|
|**LR test of model**|**vs. Saturated**|32107.30***|5222.04***|

The robustness analysis aims to assess the sensitivity of results to changes in parameters. We consider four scenarios: i) changes in the cross-dimensional threshold, ii) changes in the definition of indicators, iii) changes in weights, and iv) changes in the number of indicators per dimension, and analyze the changes using dominance analysis and rank correlation.

# **Scenario 1: Changes in the cross-dimensional threshold**

Figure 7 reports the incidence, intensity, and social sustainability rates for each possible cutoff point in Peru and South Africa. As expected, incidence rates decrease, intensity rates increase, and the Social Sustainability Index decreases as cutoff points become more stringent. That is, the higher is the number of indicators required to identify a person as experiencing multiple social gaps, the lower the incidence, while its intensity will increase. The robustness analysis also shows that such changes are not linear. In Peru, incidence declines slowly for cutoff points lower than 40 percent, declines more markedly up to a 60 percent cutoff point, and then stabilizes regardless of increases in cutoff points (Figure 7, panel A). Intensity in Peru also follows a nonlinear pattern. It increases slowly up to the 45 percent cutoff point to then increase markedly across the rest of the cutoff points. The resulting Social Sustainability Index rates in Peru follow a nonlinear pattern as 21
br>tors.|Statistics<br>egistrar of C<br>modern enter|Institute<br>ompanies<br>prise sec-|_Census_ of all registered frms.|2003-2012|Raw<br>6,746|data:|
|Ethiopia|Central<br>(CSA):<br>Scale<br>Electrici|Statistical<br>Large<br>and<br>Manufacturi<br>ty Industries|Agency<br>Medium<br>ng<br>and<br> Survey|_Census_<br>of<br>frms<br>employing<br>more than 10 workers.|2011|Raw<br>1,936|data:|
||Central|Statistical|Agency|Survey of frms employing less|2011|Raw|data:|
||(CSA): <br>turing In|Small-Scale <br>dustries Sur|Manufac-<br>vey|than 10 workers and use power-<br>driven machinery.||3,882||
|Ghana|Ghanaia<br>(GSS).<br>Census|n<br>Statistica<br>National<br>|l<br>Service<br>Industrial|Census of more than 10 work-<br>ers and representative sample<br>of establishments engaging less<br>than 10 workers.|2003|Raw<br>3,302|data:|
|||||||Continued|on next page|

34
auspices of the Reconstruction and Development Program (RDP) and its successor, Breaking New Ground (BNG), the State constructed over a million fully serviced houses across South Africa. Following a standard format with a 40m<sup>2</sup> dwelling on a serviced residential land parcel averaging 160m<sup>2</sup> in size, the roll-out of so-called “RDP houses” greatly expanded the opportunity for backyarding throughout urban areas.

# 2.3. Landlord and tenant incentives

The improvement in living conditions enjoyed by the RDP household was rarely accompanied by an improvement in its economic prospects. In fact, the peripheral location and dormitory nature of the sprawling RDP housing settlements often resulted in poor job accessibility. In response, housing recipients became landlords by erecting and then renting out informal dwellings in their backyards. In doing so, they successfully exploited one of the few resources at their disposal: space (Govender, Barnes, and Pieper 2011).

Why do poor people live in backyard dwellings, dependent on landlords and liable for rent, rather than moving to an informal settlement and experiencing an independent and rent-free lifestyle? The principal reasons appear to include better access to services, better locations, a reduced threat of eviction, and greater personal safety (Tshangana, 2014, Lemanski, 2009). For poor households dependent on irregular and informal employment, backyard dwellings offer a degree of locational flexibility in response to economic opportunities not available with static residence in a peripheral, dormitory RDP settlement (Lemanski, 2009). Such locational flexibility is particularly attractive to newly urbanized residents seeking job opportunities (Lemanski, 2009). Perhaps counter-intuitively, both official statistics and case studies confirm that the economic and educational profiles of backyard dwellers are superior to those of their landlords (Govender et al., 2011). In addition, 2011 Census data analysed by Rabe (2017) show that backyard dwellings in Cape Town are more likely to contain a single person, and less likely to contain more than four persons,
# **Data**

19 advanced economies are included in the sample. Country coverage is defined by data availability, especially data on debt structure.<sup>5</sup> Macro variables over 2018-22, such as b�, s�, g�, and π� are drawn from the IMF WEO Projection (April 2018) to construct the baseline scenarios. π� is the GDP deflator so that the nominal GDP growth can be decomposed into g� and π�. Share of short-term and medium and long-term debt at the start of the simulation comes from SNA data on short/long debt ratio (assumed to be constant). The rollover ratio that controls the trajectory of the medium-to-long-term debt comes from the April 2018 Fiscal Monitor for 2018-20. For 202122, the rollover ratio is assumed to be identical to 2020.

We consider a temporary, 1 percentage point shock to inflation in 2018 only, to be comparable with the impulse response function estimated in section IV.<sup>6</sup>

# **B.   Responses to temporary inflation shock**

# **Benchmark case**

Results are shown in Figure 1. It shows the percentage deviations of the debt-to-GDP ratio from the baseline levels in response to a temporary 1 percentage point shock to inflation, or a one-time shift in price level. Deviation is the largest for Japan, about 2 percentage points, and the smallest for Czech Republic. On average, the deviation is about 0.7 percentage points. Reflecting the temporary nature of the shock, the debt reduction effect is concentrated in the first year.

# **Differences across countries**

The difference in the size of the initial debt reduction across countries is mostly explained by the initial debt level, as the debt reduction is measured in terms of percent of GDP (Figure 2). Most of the variation in the size of debt reduction disappears when the initial level of debt-to- GDP ratio is set at 84 percent, average for the sample (