rafmacalaba/data-use-mentions-extended huggingface dataset:
# **I Introduction**

The costs and consequences of climate change are enormous and multifaceted (Carleton and Hsiang, 2016; Graff Zivin and Neidell, 2013; Intergovernmental Panel on Climate Change, 2022; Isen, RossinSlater and Walker, 2017; Park, Behrer and Goodman, 2021), with monetized impacts estimated to be as large as 20% of annual global GDP within a generation (Nordhaus, 2007). On current trajectories, the world is on track to experience 2.7°C warming above pre-industrial levels within the next century, far above the global goal of 1.5°C (Climate Action Tracker, 2022). Individual behavior change and government policy are needed to dramatically alter the trajectory of emissions. Despite the urgency and scale of the challenge, current efforts are underwhelming, in part because sizable populations around the globe remain skeptical about climate change and policies to tackle it (Bechtel, Scheve and van Lieshout, 2020; Dechezleprˆetre et al., 2022; Sunstein et al., 2017). Surprisingly little is known about how to overcome such resistance. One promising approach is the accumulation of human capital through increased educational attainment.<sup>1</sup> More educated individuals may be better equipped to understand the complexities of climate science and have more awareness of the risks of climate change. Descriptive correlations suggests this might be true: a global survey found people with more education were more likely to see climate change as a major threat (Pew Research Center, 2019). More education might also yield transferable skills across occupations, encouraging voting for policies which promote less-polluting industries, such as renewable energy subsidies. Yet determining the causal effect of human capital accumulation on pro-climate beliefs and behaviors is challenging. People who choose to pursue more education are, by revealed preference, forward looking and thus more concerned with the future consequences of climate change. It might not be education that is causing pro-climate beliefs and actions, but rather time preferences. Reverse causality is another challenge:
consumption of households in Ethiopia, with rainfall shortfalls and food price increases being the most important. A moderate drought reduces consumption by 9 percent for many rural households in drought prone areas and the rapid inflation witnessed in the first half of 2011 caused consumption losses up to 14 percent among many urban households. We find considerable heterogeneity of impacts, with uneducated households in urban areas suffering most from food price increases and those not covered by the safety net in rural areas suffering most from rainfall losses.<sup>4</sup> When covered by the safety net, beneficiaries are better able to withstand shocks than non-beneficiaries, although this cannot be fully attributed to the presence of the program.

Vulnerability to poverty using our approach is 42 percent in rural areas, much higher than poverty (headcount just under 30 percent); vulnerability is 5 percentage points less prevalent than poverty in urban areas. This is on account of poverty having been measured in a year of good conditions in rural areas (good weather) and bad conditions in urban areas (high food price inflation). It highlights how a one-year snapshot of poverty such as afforded by a national consumption survey is not necessarily reflective of the relative vulnerability of different groups in the population, and also, that drought and inflation do not always coincide. Analyzing our various vulnerability measures by gender of the household head yields surprising results: female-headed households are more vulnerable in urban areas, the opposite is true in rural areas. PSNP targeting in rural areas appears to be good, but there are many households in rural woredas that are not covered by the program, and many vulnerable households in urban areas too.

The paper is structured as follows. Section 2 outlines the empirical method and data used to estimate the impact of weather and price shocks on household consumption. Section 3 discusses the literature on vulnerability measurement and how the estimated impact of weather and prices on consumption can be used to measure vulnerability to poverty. Section 4 presents results on the impact of weather and prices on consumption
, while households engaged in primary sector activities see income declines of up to 10% during heat shocks and 12.5% during droughts.

The evidence provided in this study suggests that Paraguay’s considerable poverty reduction over the past decades may be threatened by the increasing frequency and intensity of weather shocks. In spite of that, as the country recovers from the negative economic impacts induced by the COVID-19 pandemic and the protracted drought that followed, poverty levels have fallen to 22.7% in 2023 (Instituto Nacional de Estadística, 2024).<sup>9</sup> To build on this progress in reducing poverty in the face of climate change, it is increasingly important to improve resilience to weather shocks and protect the incomes of the most vulnerable to avoid setbacks.

In light of these findings, policy makers and practitioners in Paraguay should consider several strategies to mitigate the negative impacts of weather shocks on vulnerable populations.

> 9Note that this poverty level is not strictly comparable to previous years in our data, as poverty levels are being revised to reflect the population level observed in the 2022 Census.

27
# 1. **Why this survey?**

There exists a huge number of anecdotal evidence that uncertainty on laws, policies and regulations hurts private sector development in many LDCs. Typical examples are the study by De Soto (1989) on the problems of infornal firms in Peru, the description by Klitgaard (1990) of the uncertainties for doing business in Equatorial Guinea, or the analysis of institutional uncertainty in Nicaragua by Borner, Brunetti and Weder (1995). Such case studies show that unpredictable state action can have large costs in terms of economic development. If the private firms cannot be sure which regulations apply in the near future, whether private contracts are unarbitrarily enforced, or whether their property will be protected against violence private firms typically react by cutting back on long-term investment. The considerable sunk costs of most investment projects create large disincentives against binding any resources to long-term investment projects if the firm operates in uncertain environments. The theory of irreversible investment (e.g. Dixit and Pindyck 1994) has reinforced the argument that such uncertainties are particularly costly in terms of aggregate investment.

Given the case study evidence and the theoretical arguments on investment irreversibility, the reliability of government activity should be at the forefront of an analysis of the sources of differences in economic development. Due to lack of adequate data, this is not the case. Research and data on the sources of development is mainly based on broad cross-country data sets that allow direct comparisons of government policies and other country characteristics. Such comparable cross-country data on the degree of reliability of government activity has, however, not been available.<sup>3</sup> This survey's aim is to fill that gap by creating a comparable, quantitative data set on different aspects of the degree of institutional uncertainty as perceived by private entrepreneurs. In contrast to case study work, this data is created for a broad cross-section of countries making it possible to calculate individual indicators that can be used in standard cross-country analysis.

> **3**
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in around 40 Sub-Saharan countries, including the DRC. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for the DRC, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that the DRC is a fragile state trying to catch up with other low-income countries (LICs) in the region, both fragile-state and LIC African benchmarks will be used to evaluate the DRC’s situation. Detailed comparisons will also be made with immediate regional neighbors in Central Africa.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, in order to make comparisons across countries, indicators had to be standardized to place the analysis on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

During the period from 2001 to 2005, per capita economic growth in DRC was on average 2.1 percent higher than during the period from 1991 to 1995. Despite this improvement, growth levels, which oscillated between 4 and 8 percent in the early 2000s, still fell short of the sustained 7 percent per year needed to meet the Millennium Development Goals (MDGs). Improved telecommunications infrastructure has been the main driver of this change, contributing 1.1 percentage points to the country
<!-- Start of picture text --> Subject expenditures (% of endowment)<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Loans are NOT available Loans are available<br>Expenditures (% of endowment)<br><!-- End of picture text -->

**Figure 6: Subject expenditure by treatment and income strata – Conspicuous consumption treatments**

Note: The error bars reflect mean the standard error of the mean. The figure reports data from the conspicuous consumption treatments (unequal endowments with observability) with and without loans.

To establish the effects of this finding on inequality, we construct a measure of inequality as an individuals’ income as a share of the group’s total income.<sup>17</sup> We construct this measure in two ways, once before the consumption phase (which is the initial relative income directly attributable to the effort task), and once after the consumption phase (which reflects both inequality due to the effort task as well as the consumption and borrowing decisions). The difference between these two measures gives us the change in income share due to consumption decisions (purchasing and interest payments). Figure 7 displays the change in income share in our conspicuous consumption treatment, broken out by income strata, with and without loans. When loans are not available, the income shares of all income groups generally do not significant change after consumption. However, when loans become available, the income shares of poorer strata decrease, implying worsening inequality. For the lowest income strata, the availability of loans significantly reduce their income share ( _p<0.05_ ). We observe no significant reductions in any other income strata, with the third highest income strata lower, but not significant ( _p=0.349_ ), the second highest income group showing a significant increase in income share ( _p<
13 competitiveness of inputs is offered most frequently (Jordaan 2013; Ivarsson and Alvstam 2010; Franco, Sanfilippo, and Seric 2015; Alfaro-Urano, Manelici, and Vasquez2019).<sup>9</sup> To enhance the effectiveness of support, MNC affiliates screen (potential) local suppliers to ensure that they identify those firms that are most likely to be able to benefit from the support. Characteristics of domestic firms that facilitate the impact of support include firm size, high employment growth, having previous experience with working as supplier to MNC affiliates, and having experience with producing for international markets (Jordaan 2011b; World Bank 2018). Importantly, selection effects also occur among domestic firms, as firms with more capabilities are more likely to commit to (try and) become suppliers to MNC affiliates (Javorcik and Spatareanu 2009). As a result of these selection effects, the impact of the support that MNC affiliates provide is enhanced, as it is targeted to those firms that are motivated and more likely to be able to use the support to improve performance.

_Firm-level capabilities (absorptive capacity)._ The importance of firm-level capabilities is also indicated by findings on drivers of productivity spillovers. It is increasingly accepted that productivity spillovers are facilitated or even conditional on domestic firms having sufficient capacity to absorb and implement new technologies (Crespo and Fontoura 2007; Jordaan 2016). Because absorptive capacity cannot be directly observed, econometric studies use a variety of firm-level characteristics that are assumed to be related to this concept. For instance, World Bank (2018) uses WBES data for 122 developing host economies and finds that productivity spillovers materialize only among the more dynamic domestic firms, characterized by high employment growth.

_Firm size_ . Evidence on the effect of firm size is more mixed. Several studies examine the notion that larger domestic firms are more likely to benefit from spillovers, given that they have more resources to invest in absorbing and implementing new technologies. For various host economies, Farole and Winkler (2014), Gorodnichenko, Svejnar
Data on Central Govemment Employment, Education and Health employment are taken from Republic of Barbados<br>Ministry of Finance and Public Service Commission data and relate to 1995. Local Government in Barbados is non-<br>existent.<br>Employment in State-owned enterprises is taken from WB Report No. 15185 CRG Public Sector<br>Modernization in the Caribbean.<br>GDP per capita, Government average wages and wages and salaries as multiple of per capita GDP are based on<br>calculations emanating from this data.|
|**Belize**|Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995<br>and are for 1994. Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995 and refer to<br>1994 and source I:_Labor_force_sample_surveys and_General household sample_surveys.<br>General Government employment is taken from information provided by Public Sector Modernization in the<br>Caribbean.|
|**Bermuda**|Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995<br>and are for 1994. Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1991<br>and source l1l, Code 1:_Employment_office_statistics_and do not include persons who are already in employment.|
|Bolivia|Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995<br>and are for 1994.<br>Data relating to Central Government employment are drawn from AR on Civil Service Reform project and relate to<br>1993. Central Government employment figure is broken down into 117,596 permanent employees, 16,642<br>temporaries and 1,180 consultants (line positions financed under International Cooperation projects). Central<br>Government entails Central Ministerial departments and local administration extensions. Central Government<br>employment does not include teachers (numbering about 80
clearly too large to be a true measure of income volatility.

It seems informative to compare our estimates of the permanent component of income risk, _σ_<sup>2</sup> _ε_<sup>, with the estimates obtained by the extensive empirical literature on U.S. labor market risk</sup> using annual income data drawn from the PSID. Most of these studies find an average value of around _._ 0225 for the annual variance _σε_<sup>2(Carroll and Samwick (1997), Gourinchas and Parker</sup> (2002), Hubbard, Skinner and Zeldes (1994), and Storesletten, Telmer and Yaron (2004)), with a value of _σε_<sup>2=</sup><sup>_._0324beingtheupperbound(MeghirandPistaferri,2004).Assuming</sup> that these income shocks are i.i.d. over time (the maintained random walk assumption), this means that these studies have found a quarterly variance of _σε_<sup>2=</sup><sup>_._0056,withonestudy</sup> estimating _σε_<sup>2=</sup><sup>_._008.Thus,theaveragevalueofourestimatesofpermanentincomerisk</sup> is in line with the estimates that have been obtained by the previous literature on U.S. labor market risk, although our estimates lie somewhat on the high end. Notice that our estimates are obtained using a five-quarter rotating panel, whereas Carroll and Samwick (1997), Gourinchas and Parker (2002), Hubbard, Skinner and Zeldes (1994), Meghir and Pistaferri (2004), and Storesletten, Telmer and Yaron (2004) use the PSID data with a panel dimension of many years. Thus, as long as Mexican workers face similar amounts of permanent labor income risk as U.S. workers (or more), this result suggests that most income shocks we label “permanent” in this paper indeed persist for
8 discover that they are HIV negative may decrease their demand for risky sex, especially if prevalence is high among potential sexual partners (e.g. Ahituv et al., 1996)<sup>11</sup> .

# **3 Data**

The data were collected in 2007 and 2008 in 4 provinces of Mozambique (Maputo City, Maputo Province, Sofala, Manica). The survey was designed to collect data in order to assess the impact of the scale-up of ART in Mozambique. The project delivering ART began in 2004 and had a 4-year duration. The HIV/AIDS patients of the survey were identified at the health facility where they received treatment and were interviewed at home along with the rest of the household. The questionnaire includes information on consumption, time use, labor force participation and earnings, and education as well as other health measures of the identified patient and their household members. It also included questions on adherence to treatment, health of adults and children, anthropometric measurements, and quality of life.

A group of comparison households was included in the sample, in which there were no identified HIV positive persons, to control for general trends in socio-economic circumstances. The comparison households were randomly selected in the neighborhood of each HIV household. The first wave of the survey, conducted between August and December of 2007, included 658 HIV households and 341 comparison households. In the second wave, one year later, HIV households that could not be found and interviewed were not replaced, but comparison households were. The panel consists of 896 households interviewed in both waves: 616 HIV households and 280 comparison households. At the individual level, there are 616 identified HIV positive patients, 2579 individuals living in HIV households but not

> 11Using the National Longitudinal Survey of Youth (NLSY-1979) Ahituv et al. (1996), estimate that a 1 percent increase in the prevalence of AIDS in the state of residence increases the propensity to use a condom significantly and up to 50 percent for the most
# **3. Data**

## 3.1. The PSLM survey and the estimation sample

Our analysis is based on the data from the Pakistan Social And Living Standards Measurement (PSLM) Survey. It consists of eight irregularly spaced rounds (2001, 2005, 2007, 2010, 2011, 2013, 2015, and 2018) and contains individual-level data on various aspects including education, health, employment, as well as household-level data on assets, income, and expenditure. It has national coverage and despite changes to the survey structure over survey rounds, we obtain a consistent cross-section for the purpose of our analysis in each reported year.

Our population of interest includes all Pakistani households not in the formal sector. The relevant dimension of formality for our purposes is whether a household is participating in a formal pension program, but we do not have a direct measure of this in the survey. With informality around 88% of the labor force and public employment around 8% in the period covered by the survey, this leaves at most 4% of employment covered by private pension schemes, and possibly much less. In the absence of a direct measure of pension coverage, we keep all households where neither the head nor the spouse is working for the public sector in our sample.

Table 1 shows some characteristics of the households in the data: household heads are 46 years old on average; 28% of heads have primary education and 18% have secondary education; 66% of households are rural; and they earn and consume around PK20,000 (2011 PKR) on average.

_Table 1 Summary statistics for the main variables_

|||||||Mean by su|rvey round|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||N|Mean
In terms of the value of trade covered, trade policy changes in medical goods outweigh those in food products by a factor of 3 or higher (Table 2, panel a). At the same time, trade reforms in both product groups outweigh the newly erected barriers in our sample. We estimate that the export curbs in medical goods and medicines covered international trade worth $135 billion. Whereas the many import reforms in the same sector covered $165 billion of 2019 trade. In the case of food and agri-food products, the comparable totals are $39 billion and $42 billion, respectively. The instability of trade patterns is an important caveat for the estimates presented above. Basing our estimates on trade values in medical supplies and equipment that pre-date the COVID-pandemic may misrepresent the scale of the problem. Global demand for medical supplies and equipment spiked when the pandemic encompassed ever more countries. The estimates presented for these products below may thus understate the magnitudes of implicated trade.

Table 2 (panel b) disaggregates the information by policy measure, while Table 3 looks at the share of 2019 trade affected by the trade interventions over time.<sup>12</sup> The data show the importance of export bans as trade restrictive measures in medical goods and tariff changes among the trade liberalizing measures. In terms of the evolution of the share of trade covered by policy interventions over time, we observe a surge in February-March-April 2020 for both trade liberalizing and restrictive measures on medical and food products. Thereafter, the trade coverage of trade restrictive measures slightly declines, while the coverage of liberalizing measures stabilizes.

**Table 2: trade coverage of measures tracked by GTA database (US$ bn)**

Panel a: Annualized totals of 2019 world trade

||**Export restraints**<br>|**Import reforms**<br>|
|---|---|---|
|Medical|134.6|165.2|
|Food|39.