rafmacalaba/data-use-mentions-extended huggingface dataset:
Our projections on poverty rates, using a microsimulation tool explicitly tailored to the dynamics of Pakistani households’ labor and non-labor income flows, show that poverty in 2023 stands at about four percentage points above its 2019 level. This result represents a break with the trend of continuous poverty reduction in the past decade. It hides heterogeneous impacts across different population groups, particularly those more reliant on labor income from the manufacturing sector and those more exposed to the detrimental effects of rapidly increasing food prices.

Our results are quantitatively competitive against commonly used poverty projection alternatives, with the advantage of allowing for distributional analysis. When comparing our model with off-the-shelf alternatives, we identify two main advantages. First, we allow for nondistributionally neutral changes in welfare distribution, an important factor considering that welfare impacts in households correlate with their initial characteristics in terms of location, labor supply, and consumption patterns. Second, we tailor the model to the characteristics of the social transfer programs in Pakistan, which implies greater modeling flexibility and the opportunity to simulate different policy interventions.

The analysis and projections developed in the paper showcase how using various economic data sources can be useful to assess the current situation in the absence of current data. The results highlight the vulnerability to falling into poverty or destitution that Pakistani households face when confronted with shocks. The analysis also highlights that labor income has been the main driver for poverty reduction, resulting from people moving to better-paid employment opportunities during normal times. It also shows that when shocks occurred, informality increased (and served as a cushion to prevent unemployment), indicating that informal employment helped people stay economically engaged, albeit in low-productivity, low-wage activities.

The base model presented provides a foundation on which future work could iterate and improve, especially in modeling its inputs subject to policy-specific questions and data availability across different contexts. A better understanding of how labor market dynamics play a role in people’s socioeconomic well-being will allow for improvements in the model presented in this paper and ultimately help inform policy.

17
such as Chad, Jordan, Kenya, Niger and Uganda (e.g., World Bank 2021b).

However, the political economy and data privacy can make these efforts challenging. For host countries, FDPs may be only one of the many marginalized groups they may be concerned with. With limited budgets, statistical agencies need to justify prioritizing one group over another. Furthermore, data privacy is particularly relevant for FDPs. National sampling frames are constructed based on census information, including personal information such as addresses, phone numbers, names as well as GPS locations in some cases. The UNHCR and national governments generally work together to collect registration data but there is some information that may not be shared between the two sides. For example, national statistical agencies protect the national sampling frame and cannot share the underlying cartography that would be required for UNCHR to amend the sampling frame with counts of FDPs. Even if both agencies would be able to create a trusting relationship allowing close collaboration, questions remain on whether FDPs would need to agree with sharing their information with a government agency. This becomes even more relevant in the context 8
the view that ambiguity aversion may influence adoption of index insurance (Bryan, 2010), in the Gujarati data the partial correlation of insurance demand with risk aversion is negative initially, but positive in the more recent data. This suggests that ambiguity about index insurance can fall over time, with salutary consequences for risk management.

_Initiative in Peru to reduce losses associated with El Niño._ In a new collaboration between GlobalAgRisk, with the support of the Bill and Melinda Gates Foundation, and the United Nations Development Programme, Peru has initiated a new insurance model to mitigate losses 16
217***<br>_(0104)_|
|VF could lend out another 1m baht safely|-|||_._<br>0.572***<br>_(0.193)_|
|VF wants to borrow more from BAAC, GSB, etc.|-|||<br>0.464***|
|||||_(0154)_|
|Memo items:||||_._|
|<br>Number of observations|2,143|||1,963|
|R2|0.014||||
|pfor endogeneity||||0.055|
|<br>Source: Village Fund Survey 2010 and SES 2009. Robust stan|dard errors in pa|rentheses. * p<|0.05, ** p<0.01,|<br>*** p<0.001.|

29
248)|12,850 (58)<br>GHS -2018/2019:<br>13,157 (-)<br>MICS – 2016/2017:<br>89,033 (1,619)|GHS1 – 2010/2011:<br>9,542 (-)<br>GHS2 – 2012/2013:|GHS1 – 2010/2011:<br>9,542 (406)<br>GHS2 – 2012/2013:|||
|10<br>11<br>12<br>13||||||9,940 (-)<br>GHS3 – 2015/2016:<br>9,864 (51)<br>GHS -2018/2019:<br>9,901 (-)|9,940 (776)<br>GHS3 – 2015/2016:<br>9,864 (6)<br>GHS -2018/2019:<br>9,901 (-)|||
|14<br>15<br>16<br>17||||||MICS – 2016/2017:<br>33,040 (45)|MICS – 2016/2017:<br>60,455 (9)|||

Source: Authors’ own estimates based on GHS 1 – 2010/2011, GHS2 – 2012/2013, GHS3 – 2015/2016, GHS4 – 2018/2019 and MICS 2016/2017 Note: Numbers represent sample sizes of the respective age samples with numbers in parentheses representing the number of observations with missing information for the respective poverty measure. For the health deprivation, additional completed age categories are marked in blue, indicating that there is data available for individuals in these age groups from the MICS, but for comparison not used in the main analysis of this study. For the additional deprivations sample sizes are as follows: Not-in-school deprivation:
selectively dropping observations can “cut off” the mode corresponding to the global maximum, resulting in a discrete (and negative, in this case) jump to the local mode. Indeed, it was not difficult to find data-generating

> 23 When iterated to convergence, the mean value of the estimated δ was 0.998 with a standard deviation of 0.032, and the asymptotic t-ratios on δ were in excess of 30.0 in all simulated datasets.  The data-generating process thus seems to result in a very well-identified model; that is, the instruments are not weak.

18
>Quintiles of per capita MIPP Quintiles of per capita MIPP<br>Education Primary Education Pre School<br>Bolsa Familia Rural Pension<br>Education Young Adult Education Upper Secondary<br>BPC Other<br>Health Benefits Education Tertiary<br>Unemployment Benefits Salario Familiar<br>Market Income Pensions<br>Abono Salarial Market Income Pensions<br>Concentration shares (% ) Concentration shares (% )<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal.

37 Given the high level of labor informality in Brazil and the strict unemployment benefit rules, one might be surprised by the fact that the quintile with the highest concentration share (26.2%) in terms of the unemployment insurance (UI) is the first one. It is important to keep in mind that the first MIPP quintile captures low-income individuals, including currently unemployed individuals that could have been formally or informally employed. Since the unemployment benefit is included in the disposable income concept, but not in the MIPP one, it is plausible to observe in the first MIPP quintile a (high-skilled) unemployed individual who is a UI recipient. When replicating the analysis of the concentration shares using the disposable income, we find that the quintile with the highest share in terms of the UI is the second one (23.24%), what reflects the more “intuitive” results of concentration of UI benefits in other quintiles of the distribution.

27
- _Additionality._ Private investment in a company or project may occur independent of public participation, particularly when the expected financial returns are attractive. In this case, estimated multipliers would be overstated.

- _Projections._ Multipliers may refer to expected rather than actual investment volumes.

- _Expectations._ Multipliers can generate mechanistic or otherwise unrealistic expectations.

# **Box 6. The Public Capital Multiplier**

The concept of a public capital multiplier was first prominently used in the development of the Europe 2020 Project Bond Initiative (PBCE), adopted in 2012. It is defined as the ratio of total investment to public funds invested in a certain project. The multiplier can be calculated at the fund level and at the project investment level. The combination of the two levels results in the total or overall multiplier, as follows:

- Fund (or investment vehicle) multiplier = total size of fund or facility / public capital

- Investment multiplier = total invested in projects / fund size

- Total multiplier = total investment volume / public capital

Publicly available information on multipliers is very limited. Whereas the public capital multiplier is an explicit measure of success for and an integral part of the public disclosure of funds created by the European Union/European Investment Bank (EIB), other SIFs do not report their multipliers. The multipliers reported in the table below are therefore the result of estimates based on press releases and other publicly available data.

## **<u>Capital Multipliers of Selected Funds: Preliminary Estimates</u>**

|**Fund**|**Year**|**Capitalization**|**Fund Multiplier**|**Investment Multiplier**|**Total Multiplier**|
|---|---|---|---|---|---|
|EFSI|2015|€21 billion|1x|6.7x|6.7x|
|Marguerite|2010|€710 million
At age 5, we estimate a 0.208 S.D. increase in scores on the physical health and well-being domain and a 0.115 S.D. increase in scores on the emotional maturity domain for the overall sample. However, these effects do not persist to age 8. We generally observe null effects in 2016, with one negative impact on the communication skills and general knowledge domain (- 0.136 S.D). These results are largely consistent with the fade-out literature in early childhood education, where interventions aimed to reduce early childhood inequalities have large initial impacts but these dissipate over time (see for instance the studies included in Tanner et al., 2015). Our results suggest that the impact on emotional maturity may be concentrated among those classified as poor in our data.<sup>13</sup> There are no other statistical differences across subgroups – either by wealth or by parenting practices.

One note of caution in interpreting our results is warranted. The data suggest that measurement error may contribute to the null effects on the EDI that we observe in 2016. In the raw histograms of the EDI domains presented in Appendix Figure 1, we find evidence of ceiling effects in 2016 that are not present in 2013. The exception in 2013 was ceiling effects on the communication skills and general knowledge domain, which may also explain the negative impact on this domain identified in 2016.

13 See Appendix Table 5.

12
- b) More volatility of sales is associated with higher financial savings;

- c) Higher cost of capital is associated with higher financial savings;

- d) Increase in financial savings leads to increase in physical savings (i.e. investment) in about 2 years.

Despite the data limitations, two important findings are consistent across the two very different datasets. The fact that these findings are consistent in both datasets reaffirms the usefulness and credibility of these results.

First is the finding that larger firms invest more (physical saving), which is essential for maintaining competitiveness and growth. The study points out that one important reason small firms invest less is lack of access to credit. Thus, improving access to credit for small and medium enterprises is likely to have a positive impact on investment.

Second, despite the financial deepening, the use of credit products has been declining during the last decade. This result is observed in both datasets. Specifically, usage of credit products in ICA data has declined from 23% in 2003, to 21% in 2005 and 18% in 2007 surveys. Among the listed firms, the debt levels relative to assets have declined from about 25% average (20% median) in 2001 to below 20% average (and below 15% median) in 2008. This trend raises concern and more research is needed to understand the reasons for such “deleveraging.” The reasons could be supply-side constraints, with banks unwillingness to lend at rates attractive to corporate sector or crowding out by the government sector, or demand-side weaknesses, such as lack of productive growth opportunities. Our data do not allow such analysis.

Large literature has established a solid link between access to finance, productive investment and growth on the macro and micro levels. The results of this study confirm that firms in Egypt, especially small and medium firms, would benefit from increased access to finance which is likely to support firm investment into productive assets (i.e. physical savings) and stimulate growth.

Based
workers in the short term would crowd out private investment, it has potential to reduce sufficient credit flow to private sector. Thus, private sector job creation through small business development needs to be encouraged to put less pressure on labor transfer.

This paper's contribution is to explore what such a path may look like, using estimated production functions for the state and non state industrial sectors to simulate the productive effects of moving labor and capital from the less efficient state sector to the more efficient private sector. Clearly, we do not take the specific results we get too seriously since the production functions we estimate aggregate indiscriminately a variety of firms small and large, and with very different problems. However, we trust that the order of magnitude we get are robust and thus, symptomatic of the existing Chinese economic structure. This sense is reinforced by two factors: that the data set we use exhibits many characteristics that have been documented by other sources, such as the decline in the efficiency of SOEs and the rise in the importance and efficiency on non-state firms; and that results are roughly robust to the choice of estimation year.

Our paper measures labor and capital productivity in both state and non-state industrial sector through estimating aggregate production function using 30 provincial pooled data from 1994 to 1997. Low labor and capital productivity, stagnated labor productivity and overpayment are found to be prevailing in state sector over time. Our three-year separate and averaged estimation all show a sizable gain from labor retrenchment. In particular, our 1996 simulation results estimate that 43 percent of labor (or some 23 million state-owned enterprise workers) and 70 percent of capital are redundant. An estimated benefit of 43 percent of labor transfer alone will 21
et al. 2019). The authors do not solely rely on web scraping for all 40 countries. They instead relate to national accounts data and implement web scraping as a “fallback methodology” in case of missing data. As the authors use both methods, national accounts as well as web scraping, for a sub-sample of European countries, they can compute the median level of estimated upward bias arising from the use of listed rather than transaction prices, which is then applied as a correction factor to improve comparability of price level data obtained with the two methods (Bricongne et al. 2019: 6). _HouseLev,_ to the best of our knowledge, is the most comprehensive web scraping project of private property prices, primarily focused on developed economies.

Advanced price estimation techniques have also evolved with the increased usage of big data approaches to collecting property price data from listing websites. Traditionally, the hedonic price model, which draws on Lancaster’s consumer theory, has long been the predominant model to estimate property prices (Lancaster 1966; Rosen 1974). Property prices are modeled in multiple regression analysis, assessing the association between property price and several hedonic attributes through parametric estimation (Oladunni & Sharma 2016). Attributes frequently applied in hedonic price models include, for instance, number of bathrooms, number of bedrooms, area size, neighborhood, or accessibility of the property (e.g., Borba & Dentinho 2016; Can 1992; Krol 2013). While very simple in their interpretation, hedonic price models require the fulfillment of strong model assumptions, including functional form of the conventional hedonic pricing model, homoscedasticity, independence, and the absence of multicollinearity (e.g., Anderson 2000; Pérez-Rave et al. 2019).<sup>10</sup> In recent years, the applicability of alternate methods to the hedonic price estimation has expanded and machine learning (ML) has emerged as an alternative to predicting house prices (Borde et al. 2017; Čeh et

> 10 For a
behavioral responses to taxation (e.g., Blundell and MaCurdy, 1999), we assume that income effects are negligible (so that _ξz_<sup>_u_=</sup><sup>_ξ_</sup> _z_<sup>_c_and</sup><sup>_ξ_</sup> _h_<sup>_u_=</sup><sup>_ξ_</sup> _h_<sup>_c_).27</sup> After briefly discussing what the baseline results imply about the drivers of income inequality, our main analysis concerns performing the inversion from labor supply decisions to primitives under different assumptions on the relevant elasticities, highlighting how deviations from the baseline estimates change our inference about the determinants of income inequality. We show that (1) larger differences between the income and hours elasticities with respect to the tax rate (i.e., larger effort elasticities) and (2) larger income effects will both lead us to infer that preference heterogeneity is increasingly important in driving income differences between rich and poor. We also discuss briefly at the end of this section how our findings change if allow for heterogeneity in the elasticity schedules (roughly in line with the findings of Gruber and Saez, 2002) and how we can account for labor market frictions using survey data on actual and preferred hours.

# **5.1 Data on Incomes and Hours Worked**

We will use data on incomes and hours worked from the American Time Use Survey (ATUS), which is a survey conducted on a subset of individuals who have participated in the CPS.<sup>28</sup> In addition to income data, the ATUS asks respondents to meticulously detail all of their activities on a particular (random) “diary day”. We then assume that this noisy “diary day” measure is representative of this individual’s average daily hours worked. We also do not have days worked per year, so we impute that all individuals work 250
financing pilots. Although the survey was not designed to be representative at the national or district level, it covered about 26 percent of the health zones in the country and is representative at that level.<sup>7</sup> In each health zone covered by the survey, five health centers were randomly chosen to be included in the sample, without distinguishing between health centers offering the minimal package of services and referral health centers offering a more complete package. In the catchment area of each of the chosen health centers, a village or urban quarter was randomly selected to be included as a sampling unit for the household survey. In addition to the health centers, health facility surveys were also conducted in general referral hospitals in most health zones. Appendix A provides further details on the sampling frame and overall study design.

Within the selected villages, 4,834 households with women who had live births in the 24 months prior to the survey were randomly selected for the household survey. The focus of the survey was the care received during the most recent pregnancy. Women who reported receiving at least one antenatal consultation were asked a series of recall questions about different components of the consultations, ranging from physical examinations and diagnostic tests to preventive treatments and counseling.

The facility assessments covered a wide range of aspects, ranging from staffing and management to infrastructure and availability of equipment and supplies. Observations of antenatal consultations were conducted in 316 facilities that were randomly selected for an in‐depth assessment (see appendix A). Most of the health centers offer antenatal consultations only one day per week; on average, antenatal care services were offered 1.3 days per week. Exit interviews were conducted with the women who received antenatal care in these facilities. For 22 percent of the women for whom an exit interview was completed, an observation of the consultation was not conducted.<sup>8</sup> A key advantage of using data collected through direct observations is the ability to overcome potentially large recall biases in maternal
matched samples where unmatched observations will be inevitably dropped, Smith (1997) showed that matched samples can increase the efficiency of estimates.<sup>13</sup>

# _(iv) Data Sources_

The sample in this analysis consists of 6,399 children ages 6–9 years from both urban and rural areas of Bangladesh. The data are put together from DHSs 2007, 2011, and 2014, which are nationally representative at the division level. The dependent variable is dichotomous and indicates whether a child aged 6–9 is enrolled (1) or not enrolled (0) in primary school at the time of the survey. The primary variable of interest is the concentration of unimproved sanitation at the baseline within each household cluster. We consider this variable to be reflective of the child’s early exposure to unimproved sanitation. The variable is obtained by geomatching household clusters over a seven-year period.<sup>14</sup> Clusters from DHS 2000 were geomatched with DHS 2007 to estimate the exposure to unimproved sanitation when children in the 2007 sample were 1 or 2 years old.<sup>15</sup> Here we make two assumptions: (i) a household is likely to have unimproved sanitation if the majority of its neighboring households have it; and (ii) the families stayed in the same household and household cluster over the seven-year period. The percentage of children enrolled in primary school at the baseline was also obtained using the geomatching technique. This variable is defined as the proportion of primary schools in a cluster. Finally, the lagged electricity ratio was calculated using the same procedure.

The indicator “URBAN” takes the value of 1 if the household is located in an urban area and 0 otherwise. In our analysis, household wealth quintiles are constructed using a principal component analysis (Filmer & Pritchett, 1999) that accounts for housing conditions, ownership of durable goods, land, etc. Information on the type of water source and sanitation facility used by the household is