rafmacalaba/data-use-mentions-extended huggingface dataset:
|1,306|
|Adjusted R-squared|0.334|0.204|0.350|0.369|0.385|0.254|
|Mean headcount<br>poverty rate (percent)|16.847|16.847|30.152|30.152|45.559|45.559|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Poverty rate is calculated using subnational GDP from Kalkuhl and Wenz (2020) and the poverty lines of $1.90, $3.20, and $5.50. Poverty rates and weather variables in the long-differences model are measured by the difference between averages of the earliest 10-year period (1979–1988) and averages of the latest 10-year period (2009–2018). The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 54
# - 3 -

course, important policy issues which emerge from the experiences of both developed and developing countries with reference to the divestiture of state-owned enterprises and the contracting out of public services. The literature, however, does not provide a distillation of such strategic issues and their implications for privatization decisions from the perspective of LDCs.

Based on a survey of the literature, this paper reviews the privatization experience of both developed and developing countries with a view to highlighting the major strategic issues which seem to emerge from this wide range of experience. The primary objective here is to compare experiences and pull together the key policy issues in a summary fashion. These are issues which LDC governments embarking on privatization and donors who advise them may wish to consider carefully even if all of them are not equally relevant to all countries. The paper, however, does not evaluate privatization techniques nor does it assess the Bank projects which have divestiture components as they have been examined by others.<sup>6</sup> We first examine the privatization experience of the U.K. and U.S.A. as a backdrop against which to assess the experience of LDCs. The reasons for the differential performance of the two groups of countries and some of the lessons and emerging issues associated with privatization are then discussed. Privatization here refers to the (1) sale of all or some of the assets of public enterprises or other public entities; (2) the 6/ Shirley, M., Guidelines for Bank Lending for State-Owned Enterprise Sector Reform, World Bank, 1988; Vuylesteke, op.cit; Savas, op.cit.
as they have been built to answer different questions about risk. Such simulated hazard event sets focus upon a primary damage discriminator, such as wind speed for storm or ground shaking for earthquake, which is subsequently translated into impact on exposure through damage functions describing vulnerability.

A criticism of catastrophe models is that they are not forward looking regarding climate change.<sup>9</sup> Since the consequences of climate change are already present, however, any recent historical record for atmosphericrelated natural hazards comprises a climate change signal, alongside other potential modalities, such as the El Niño–Southern Oscillation (ENSO). Model sensitivity to these climate aspects can be explored in a catastrophe modeling environment and considered as another dimension of uncertainty. In recent years, “climate conditioning” of catastrophe models for differing time horizons and emissions scenarios (such as in Bates [2023]) has received much attention and is an active research frontier, attracting many new entrants to natural hazard risk modeling (see Fielder et al. 2021). Model adjustment can range from a simple shifting of event frequencies in stochastic hazard tables to much fuller incorporation of and alignment with GCM approaches. It must be remembered that there may be considerable uncertainty around future climate patterns (such as rainfall across the Sahel), and it may be challenging to adequately parameterize them without suggesting inappropriate levels of exactitude. Scenario modeling is one typical approach under high uncertainty. Additionally, exposure and vulnerability components of risk (such as urbanization, informal settlements, and flooding) can change much faster than climate trends. Since this work increasingly falls under financial regulation and disclosure regimes (e.g., NGFS 2022), explicit climate modeling for catastrophic risks will see much growth in the near future, with translation of approaches to low-income regions through use of underlying global data sets. This paper’s analysis focuses on past and current weather patterns and does not include an estimation of future changes due to global warming.

# 3. Data on Hazards and Welfare

##
# **_Sectoral convergence_**

Given the patterns of cross-country convergence are reflections of and shaped by countries’ sectoral composition, it is insightful to investigate sectoral convergence. Using data on more than 100 countries, Rodrik (2013) shows strong evidence of unconditional convergence in manufacturing labor productivity, as well as _σ_ -convergence for a sub-sample of countries. However, there is no aggregate convergence because of a rather small share of formal manufacturing employment and the slow pace of industrialization in low-income countries. In addition, Kinfemichael and Morshed (2019a), using 95 countries, find evidence of unconditional convergence for aggregate labor productivity, primarily driven by the fast convergence in services. Kinfemichael (2019) also reports similar results.

By contrast, Herrendorf et al. (2022), based on a new dataset for low-income countries during 1990-2018, uncover that cross-country differences in productivity gaps (with high-income countries) are larger in manufacturing than in the aggregate, and no observed tendency for productivity to converge in manufacturing. They also find a positive correlation between manufacturing labor productivity growth and aggregate and other sectors’ labor productivity growth, suggesting weak growth of manufacturing employment is not necessarily the force behind the lack of convergence in lowincome countries. The seemingly sharp contrast findings between Rodrik (2013) and Herrendorf et al. (2022) are partly attributed to the role and size of the informal sector in developing countries. Rodrik (2013)’s findings in support of unconditional convergence are based on data on larger establishments in the formal manufacturing sector.<sup>6</sup> When the sample coverage extends to small and informal establishments and workers, which are highly prevalent in developing countries, as in Herrendorf et al. (2022), the data does not reveal unconditional convergence in manufacturing.

An earlier study by Wong (2006) decomposes the pattern of _β_ -convergence into within- and between-sector contributions and show that both the within-sector growth and between-sector reallocations. Among the OECD countries during 1970-1990, Wong (2006) shows sizable
between influence variables and employment levels<sup>48</sup> . Nonetheless, the risk factors identified in our model remain valid even if they are not perfect predictors.

Examining job losses by sector in the CMMHH using its distinct sector classification, we see a higher degree of cross-sector disparity in PFPS job losses than using the WBES categories. As shown in Table 5-10, the percentage of “PFPS” workers which were permanently laid off or suspended was highest for _manufacturing_ (39 percent), _and accommodation and food services_ (39.0), but were essentially nil in _construction and utilities_ and _ICT_ sectors

|_Table 5-9 Jordan: Model-Predicted versus S_<br>_Sector-Level PFPS Job Losses, Winter 2020_|_urvey-Measu_<br>_/ Wave 2_<br>Model-<br>predicted|_red Projections of_<br>Survey<br>Measured|
|---|---|---|
|1. Chemicals|13.2|17.3|
|2. Food, drink, and tobacco|20.8|8.0|
|3. Garments|23.8|37.4|
|4. Hotel, restaurant, and transportation|25.8|27.2|
|5. Machinery, electronics, and construction|34.9|39.2|
|6. Metals and non-metallic minerals|13.9|6.8|
|7. Wholesale, retail, and other services|26.6|29.5|
|8. Wood, paper, publishing, and printing|27.3|7.3|
|Total<br> Source: CFUWBES2. See notes to Table 5-8.|25.2|25.5|

(which are not categories in the WBES.) As shown, when individuals were asked about impacts in the past 60 days, they reported a high level of temporary or permanent layoffs relative to the total shown in column 1, confirming that
Table D.5: Effect of Years of Schooling on Number of Children Born: Alternative Samples and Specifications - Analysis of Results from Table 2

||Non-|MTI|Nation|al Sample|Nation|al Sample|
|---|---|---|---|---|---|---|
||Region<br>Efect o|s Only<br>f FPE|Efect of F<br>(Separate|PE and MTI<br> Instruments)|Efect of F<br>(Combined|PE and MTI<br> Instrument)|
||Census|DHS|Census|DHS|Census|DHS|
||+ DHS|Only|+ DHS|Only|+ DHS|Only|
||(1)|(2)|(3)|(4)|(5)|(6)|
||||A|. Baseline|||
|�<br>Years of Schoolingizy|-0.437|-0.529|-0.273|-0.365|-0.257|-0.321|
||(0.090)|(0.165)|(0.109)|(0.147)|(0.117)|(0.156)|
||[0.000]|[0.001]|[0.012]|[0.001]|[0.028]|[0.040]|
|First Stage F-Statistic|14.796|5.93|14.09|6.91|38.28|19.56|
|N|69083|13922|205141|24898|205141|24898|
||,|,|,|,|,|,|
||||B. Coh|orts: 1968 - 1990|||
|
# **1. Introduction**

Agriculture is the backbone of the economy in rural areas that house nearly 70 percent of the population in low-income countries. The importance of agriculture for development is recognized recently during the formulation of the Sustainable Development Goal (SDG) Target 2.3, which requires doubling of agricultural productivity and incomes of small-scale food producers. In Africa, the average share of rural household income tied to agriculture could be as high as 69 percent (Davis et al., 2017), and the research has shown that compared to non-agricultural growth, agricultural growth translates into higher rates of poverty reduction (Dorosh and Thurlow, 2016).

Yet, despite the importance of agriculture for livelihoods, obtaining accurate and timely estimates on crop production and yields remains a significant challenge. The weaknesses in agricultural data in turn undermine the ability to gauge the performance of the sector; assess the extent to which interventions translate into gains in production, productivity and welfare, both within and across agricultural households; and ultimately, anchor policies in evidence (Carletto et al., 2015). In the context of root, tuber and tree crops, the primary concern around the accuracy of prevailing approaches to data collection in large-scale household and farm surveys that underlie official statistics is arguably the reliance on respondent recall.

For cassava, the crop that is at the center of our analysis and that could be planted to hedge against the risk of seasonal crop failure and/or food insecurity during the lean season, harvesting typically takes place over an extended period and as needed. Irrespective of using a given agricultural season, or a more extended time frame, such as 12 months, as a reference period, respondents are likely to underestimate production as they attempt to recall the accounts of continuous harvests that take place throughout the year and often in small quantities (Friedman et al., 2017; de Nicola and Gine; 2014).P1F<sup>2</sup> And in the specific case of using an agricultural season
provision of microcredit and cash subsidy to increase agricultural productivity and facilitate the transition to non-farm employment. Households can receive one-time support for purchasing seeds and fertilizers that encourage them to cultivate high-value crops and livestock. At the village and commune levels, the program invests in basic infrastructure, including electricity, irrigation, markets, roads, schools, and health care facilities.

# **3. Data and descriptive analysis**

# **3.1. Data sources**

Our main data source is the Vietnam Household Living Standard Surveys (VHLSSs) spanning over 16 years from 2004 to 2020. The VHLSSs are conducted biennially since 2002 by the General Statistics Office of Vietnam (GSO) in collaboration with the World Bank. The VHLSSs cover around 45,000 households from around 3,000 enumeration areas and provide detailed socio-economic data on households and their members. One key advantage of the VHLSSs is their comprehensive coverage, including all districts in the country with the exception of a few islands. Thus, these surveys cover all the districts that participate in the 30A Program, as well as districts with a poverty rate close to the threshold of 50% in the 2006. The VHLSSs are representative at the provincial level.

We focus on the rural sample, since the rural population accounted for 98% of the total population in the 30A districts in 2008. Moreover, we limit the analysis to households living in districts with a poverty rate greater than 40% in 2006, such that the control group comprises districts with a poverty rate ranging between 40% and 50% in 2006. There are 65 control districts, which is approximately equivalent to the number of treatment districts. Consequently, our final sample includes a total of 127 districts (62 program districts and 65 control districts). We also conduct various robustness checks using different bandwidths, resulting in varying numbers of districts in the analysis.

9
the 99% confidence level.

In Table 6, we confirm the impact of CB reform on employment growth using the ORBIS sample but the magnitudes we find are larger. Introduction of CBs leads to an over 5 percentage point increase in employment growth both for the overall sample (5.29%) and for MSMEs

> 23 We are unable to use survey (country-year variation) fixed effects as in Table 3, because CB reform is a countryyear variable.

22
low malaria districts (type 2) to targeting all travelers that enter only specic health post catchment areas in those ve districts (type 1), using the cell phone data for both. Targeting specic travelers rather than only areas is 257% more eective with a budget of $400,000.

The previous scenarios consider either targeting particular travelers or targeting particular areas, but it is possible to combine the two types of targeting to target particular travelers going to particular areas in certain months. This would not be possible without the cell phone data, which provides extremely granular information on spatial movement of individuals across time so that we know how many travelers enter a specic area in a given month and this can be used to calculate the cost of targeting each of these travelers and the benet of quickly identifying and treating any cases they may have brought through travel. Figure 8 shows the cost curve in this scenario where travelers from a particular district to a particular health facility area are targeted in a specic month.<sup>28</sup> This curve is compared to the previous curves of just targeting travelers, just targeting areas, and also the best strategy without using cell phone data of targeting travelers based on incidence in 2012. Targeting both travelers and areas leads to 52% better performance on average compared to the non-cell phone data strategy when focusing on a budget of under $400,000. Using cell phone data for targeting both travelers and areas compared to just targeting travelers is 19% more eective on average with a budget under $400,000.

There are two important limitations in conducting this type of targeting. The rst is related to potential risk of targeting areas based on movement information from a previous year, given that population movement patterns may change drastically from one year to the next. Other research that has used cell phone data for several years in Namibia nds very consistent short term movement patterns across three years (Milusheva et al. 2017, Wesolowski
kWh generated from<br>coal|BSO|No|
|Energy|Fuel oil prices|Average household prices<br>per kWh generated from<br>fuel oil|BSO|No|
|Prices|Household<br>electricity<sup>19</sup><br>price|Electricity price for<br>household consumers<br>(consumption band 2500-<br>5000 kWh)|EUROSTAT|Yes|
||Household gas<br>price|Gas price (consumption<br>band 20-200 GJ)|EUROSTAT|Yes|
||District heating<br>price|Average household price<br>per kWh generated from<br>district heating|BSO|No|

> 17 - - <u>https://ec.europa.eu/eurostat/web/household budget surveys/methodology</u>

18 Information on ability to keep home cool is particularly important in the context of climate change and the increasing likelihood of heat waves. The World Bank rapid surveys collected in June 2023 collect information to construct this indicator in Bulgaria. This information could potentially be collected through specific ad hoc modules of the national EU-SILC, as recommended by the latest EC recommendation on energy poverty in October 2023. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:L_202302407 19 Household electricity and gas price are considered complementary indicators, as they affect affordability but do not indicate affordability per se.

8
SAIFI), and the System Average Interruption Duration Index (SAIDI) for each of the DISCOs. Only the Islamabad (IESCO) utility was able to meet the allowed limits set by the regulator (SAIFI = 13 per annum; SAIDI = 14 minutes). However, the report published by the regulator<sup>27</sup> indicates that there were considerable inaccuracies in the collection of these data by the DISCOs and that these statistics cannot be taken as realistic.

> 27 _Performance Evaluation Report of All Distribution Companies for 2015-2016_ . National Electric Power Regulatory Authority.

27
father. Thus, FG parents attempt to finance the higher savings through higher earnings.

**Borrowing.** In Panel A of Table 9, we examine if there is also a corresponding rise in debt for FG families relative to FB families due to expected dowry. Column (1) shows that for average expected dowry (=INR 26,120), FG families are 6 percentage points more like to have borrowed the year before the survey, although we lose significance once caste-state-year fixed effects are included. In the absence of dowry expectations, FG families are _less_ likely to have borrowed than FB families. Each INR 10,000 rise in expected dowry raises the likelihood of borrowing in a year by 3 percentage points for FG families.

Like employment, REDS also has data on the borrowing history of each household. However, more than 90 percent of the loans in the data were taken out during seven years before the survey, suggesting that there is serious recall bias in the historical loan data. Keeping this caveat in mind, we use specification (4) to estimate the effect on total amount borrowed by FG families each year, relative to FB families, to finance future dowry. We restrict the sample to 1980-2008 to minimize recall bias. Panel B of Table 9 shows that the triple-interaction coefficient is positive (though insignificant) implying that borrowing post-birth is larger in FG families as expected dowry rises.

On the whole, we find that FG families finance the increased savings after the birth of their daughter by increasing fathers’ labor supply and by borrowing somewhat more.

# **6 Discussion**

The birth of a daughter in dowry-paying societies, such as India, implies an almost certain increase in parents’ future expenditure. Faced with such a lumpy expense, one way parents of daughters could finance dowries is through borrowing around the time of marriage. Indeed, in our data, households are more likely to borrow during the year of marriage than otherwise. The likelihood that a household applied for a loan
> 6 <u>http://www.oecd.org/tax/tax-policy/revenue-statistics-in-africa-2617653x.htm</u>

> 7 Identifying the individual beneficiaries of general infrastructure spending as well as the individuals who might bear the burden of a corporate income tax is difficult with only a standard household budget survey.  In addition, investment in infrastructure in the current fiscal year will also provide benefits in the future that are impossible to allocate to the households identified in the household survey.

6
the ability of policy-makers to affect the structure of cities and the functioning of housing markets was severely circumscribed.

Not surprisingly, given this presumption, most of the land market concerns of Bank strategy focused on the microeconomic aspects of land use regulations. Like the focus on the empirical regularity of housing demand, this perspective led to a neglect of idiosyncratic, localized conditions. Subsequent world events, however, such as the fall of the Soviet Union and the end of Apartheid in South Africa permitted cities in the part of the world outside Clark’s ambit to be subjected to empirical analysis. Based largely on the first set of comparable data on more than 50 cities - much of which is summarized in Bertaud (2003) -- the results provide increasing evidence that urban spatial patterns diverge substantially from those predicted by the standard urban models.<sup>21</sup> Moreover, the divergences are particularly strong in environments where policy superseded market processes in determining urban land use.

Figure 1 presents the density gradients for two very different but market-oriented cities – Paris and Atlanta -- as well as two of the more extreme policy-controlled city density structures, Johannesburg and Moscow. Cumulatively, these four images provide both some sense of how much policy can disrupt both the functioning of land markets and the underlying structure of cities and housing markets, as well as just how idiosyncratic urban spatial patterns can be across cities.

> 21 The bibliography provides a number of citations to this data. The most comprehensive source is the website: http://alain-bertaud.com/

16
spillovers from FDI in services has been recognized in the literature (Blind and Jungmittag 2004). Foreign-owned firms tend to enjoy some technological advantages that make their engagement with the domestic market profitable. In the service sectors, these largely involve intangible assets, which, given their only partial excludability, can generate knowledge spillovers to firms that utilize the services. Indeed, evidence suggests the presence of information and knowledge flows from foreign firms to their domestic customers and a consequent learning-from-supplier effect (Crespi et al. 2008). There is also evidence that increasingly goods embody some accompanying services, which themselves can be considered a form of innovation in products. Both forms of FDI-induced innovation would result in an increase in investment in intangible assets. Ultimately, investment in intangible assets has been found to increase productivity, not only in relation to its R&D component, but also in the form of organizational capital, ICT, and firm-specific human capital (Syverson 2011; Battisti et al. 2015), and stimulate firm growth (Hosono et al. 2020).

# **6. Conclusions**

This paper examines productivity dynamics of publicly listed firms in Pakistan, and the extent to which integration in the global marketplace explains their productivity growth. We rely on firm-level data on publicly listed firms in non-banking sectors in Pakistan over the period 2012-2017. We adopt a standard approach in the literature to estimate productivity and measure changes in input tariffs and upstream FDI presence. Estimating the effect of integration on productivity is challenging because integration policies are shaped by governments that respond to some extent to private sector interests. We show that in the case of Pakistan the concern is valid, and, following the literature, we address this identification problem by focusing on a narrow window for which this concern is dissipated by the data.

22