rafmacalaba/data-use-mentions-extended huggingface dataset:
consider only the overall effect of all potential sources of distortion on the economy.<sup>7</sup> In contrast, other papers take what is referred to as the ”direct” approach, picking a particular policy or institution and using quantitative models to assess its contribution to the misallocation of resources and its impact on aggregate TFP.<sup>8</sup> This paper contributes to the latter line of research by focusing on the static and dynamic effects of one specific mechanism, bribing, that generates misallocation.

This paper is relevant to Argentina because the country’s weak institutional capacity, high perception of corruption and repeated high-profile corruption scandals in recent years have caused a loss of faith in institutions, which this paper shows to play a key role in enabling and perpetuating idiosyncratic distortions across establishments. More generally, because difficulties in doing business not only affect investment decisions but also generate incentives to obtain private rents, simplifying the regulations around starting and running businesses in order to reduce those incentives is of first-order importance.

In the following section, I present the facts on bribing and development that motivated this research. In section 3, I present the model that will try to account for these facts. In sections 4 and 5, respectively, I present the strategy for calibrating the model and the model’s quantitative results. Section 6 concludes and gives some policy recommendations.

# **2. Data**

The establishment-level data I use to calibrate this model are from the WBES. The data were collected through interviews, typically with managers, aimed at understanding the major obstacles establishments face in their day-to-day operations. A convenient feature of the surveys is that they were administered in a similar form within continents, which allows for cross-country comparisons. The initial rollout of the survey was conducted in 2003, with subsequent and more complete rollouts conducted within a three-to-four-year span. For this paper, I use the data set from 2006–2018 provided by the World Bank. This data set contains firm-level observations for
<!-- Start of picture text --> Figure 7. Linkage to manufacturing GVCs by industry (%): Non-resource<br>rich LICs<br>100<br>90<br>80<br>70<br>64<br>60<br>50<br>47<br>40 42 40<br>30 35 34 33<br>20 20<br>10<br>0<br>-10<br>-20<br>-30<br>Upstream Downstream Δ GVC 2015/1995<br>Δ Upstream 2015/1995 Δ Downstream 2015/1995 GVC linkage 2015<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

28
fluctuations; and _Controls_ stand for additional control variables such as country trends, as above. In these regressions, net capital flows are calculated using the standardized versions of _CIF_ and _COD_ . The trade balance in goods and services is also scaled by trend GDP, demeaned and standardized by its standard deviations at the country level.<sup>20</sup> Our measure of business cycles is based on real GDP in constant units of local currency.<sup>21</sup> More specifically, we use the growth rates in real GDP, which should capture accurately the current state of the economy over the business cycle.<sup>22</sup> The results are reported in Table 3. They show that net capital inflows are strongly associated with capital inflows by foreigners for all income groups. For high-income countries, they are also strongly correlated with capital outflows by domestic agents. However, such association is not as strong in middle- and low-income countries, where larger coefficients are estimated for _CIF_ . Note that net capital flows are calculated as the difference between _CIF_ and _COD_ , and are thus, by construction, correlated with our dependent variables. To partly avoid this correlation, we use the trade balance in goods and services as an alternative measure of capital flows to the extent that it captures the other side of the balance of payments. The estimated coefficients confirm the previous results. The trade balance is strongly correlated with capital flows by foreigners, and more so than flows by domestic agents in middle- and low-income countries.

Regarding the dynamics of gross capital flows during the business cycles, we find that gross capital flows expand during good times, while during bad times, they decline. In other

> 20 The data on the trade balance are from the IMF's _Balance of Payment Statistics Yearbooks_ .

> 21 Real GDP in constant units of local currency comes from the World Bank’s _World Development Indicators_ . This information was complemented with data from the IMF’s _World Economic Outlook 2009_
Figure 4: Historical weighted average producer loss ratio for major crops, 2000-2008 <!-- Start of picture text --> Chilly<br><!-- End of picture text --> Source: Data from AICI

# _Strengths of NAIS_

The NAIS has significant advantages as compared to both multiple peril crop insurance (MPCI) and weather indexed insurance. First, individual MPCI would have been prohibitively expensive, or even impossible on technical and administrative grounds, in a country such as India with so many small and marginal farms (Hazell, 1992). Further, the method of using an ‘area based approach’ has several other merits including, most importantly, the mitigation of moral hazard and adverse selection.

Second, the mismatch between claim payments and losses incurred, known as basis risk, from weather indexed insurance is usually considered to be higher than that from area yield index insurance (Carter et al., 2007). This is partly because area yield insurance can cover more perils than weather based insurance. Also, in an Indian context insurance unit size is typically smaller under NAIS than WBCIS, due to limited weather station infrastructure, leading to an increased ability of NAIS to cover localized perils.

Finally, by using the banking system both to collect farmer insurance premiums and to channel payments, the NAIS has low supply-side transaction costs. This low-cash and transaction point intensity, together with the ‘area based approach’, has enabled low leakages in the channeling of claims.

# _Challenges faced by NAIS_

However, despite NAIS insuring 25 million farmers, 95 million are not yet insured. Ignoring the 11% of farmers for whom purchase of NAIS cover is a compulsory precondition for taking out a loan for agricultural purposes, only 6% of farmers voluntarily purchase cover. This is particularly surprising given the large subsidies afforded to NAIS: for every 1 rupee of farmer premium paid between 2000 and 2008, NAIS disbursed 3.5 rupees in claim payments. The challenges faced by NAIS fall into the following seven
scenarios for demographic change and development in the SACU economies are considered following the approach of Ahmed et al. (2016) where a global dynamic simulation model is applied in tandem with a microsimulation model based on harmonized household and labor force survey data. Demographic changes in the different economies are considered against the backdrops of different policy outcomes in the areas of educational improvement, mobilizing savings, and improving employment rates. When considering the scenarios of individual interventions, Lesotho and Swaziland benefited the most from improvements in educational attainment rates by achieving higher economic growth and lower poverty rates. For other economies, interventions that had greater impacts were those that focused on improving employment ratios, through higher labor force participation, reducing unemployment, or eliminating gender gaps in the labor market. The paper thus provides a comparative analysis across several policies highlighted by the literature (e.g. as in Bloom, 2
because the effects of abating global emissions of greenhouse gases on aggregate economic damages necessarily accrue more to higher-income countries. Unlike adaptation, emissions mitigation does not specifically target the poor. The major gains in poverty averted by following the optimal abatement strategy would indeed occur on a longer time horizon, by 2100 and beyond.

**Table 2. Climate change impacts on world poverty (at the $2 a day level)**

||Number|of poor (mi|llions)|Headco|unt poverty r|ate (%)|
|---|---|---|---|---|---|---|
|Scenarios|2005|2055|Change|2005|2055|Change|
|Baseline|2,069.4|1,259.1|(810.3)|32.3|14.1|(18.2)|
|BAU|2,069.4|1,269.2|(800.2)|32.3|14.2|(18.1)|
|Difference from baseline|..|10.1|10.1|..|0.12|0.12|
|Optimal abatement|2,069.4|1,268.5|(800.9)|32.3|14.2|(18.1)|
|Difference from BAU|..|(0.7)|(0.7)|..|(0.01)|(0.01)|

Source: authors own estimates.

Even though aggregate impacts on poverty seem to be modest by mid-century it does not imply that the impacts will be equally distributed among the population. To analyze how climate change will affect specific sectors of the population one needs to use household-level data and explicitly model the channels through which future warming will affect economic activity.

# **
(0.027)|(0.027)|(0.437)|(0.226)|(0.224)|(0.304)|(0.107)|
|Control mean|0.248|0.285|8.919|4.935|13.111|43.454|6.075|
|Control SD|0.432|0.452|5.529|3.482|3.163|5.292|2.838|
|Observations|901|884|901|884|873|833|882|
|H0: IPT-G=0 p-values|0.889|0.597|0.532|0.329|0.704|0.759|0.088|
|FDR adj. q-values|0.889|0.889|0.759|0.759|0.759|0.759|0.441|

Notes: This table shows ITT estimates of the impact of IPT-G on the primary and secondary mental health outcomes, comparing the control group to the IPT-G only group; The full sample is used in Panel A as the Cash treatment arm was implemented after the Rapid Resurvey; All regression models include randomization block fixed effects centered and fully interacted with treatment; Standard errors clustered at the club level in parentheses; Estimates at endline are weighted to account for the probability of selection for intensive tracking; Columns 1-2 show results for the primary outcomes, indicators for whether the PHQ-8 score is _≤_ 4 or the GHQ-12 score is _<_ 3; Columns 3-7 show results for the secondary outcomes, the PHQ-8 and GHQ-12 raw scores, the Rosenberg Self-Esteem scale, the Child and Youth Resilience Measure-Revised, and Locus of Control, all in terms of their raw scores; The rows marked as _H0:..._ show the p-value for a t-test of the indicated null hypothesis on the regression coefficients and the corresponding
# **3 Income and the margins of transport consumption: stylized facts**

Households choose which transport modes to use and how much to spend on transport goods and services. We identify several extensive and intensive margins of interest behind transport consumption. In poorer countries, many households mostly walk and do not consume transport goods or services. The carbon footprint of such countries will therefore increase as first-time users start consuming transport goods and services. As they get richer, households then choose between public and/or private transport options and spend more on transport goods and services. While private transport might be more efficient, it is also more carbon-intensive than public transport. The total carbon footprint of private transport will depend on vehicle ownership as well as vehicle use. For each margin, this section presents a series of global stylized facts between income and transport consumption.

## **3.1 Income and first-time transport users**

**Fact 1: In poorer countries, a large share of households do not consume transport goods or services.** Figure 2 uses LSMS household data to plot the average share of households not consuming transport goods or services per country against country GDP per capita. There are large differences between shares of households with zero-transport expenditures across countries. The percentage of zero-transport expenditure observations is very high in countries like Sierra Leone and Malawi (82% and 65% respectively), while it is null in South-Africa. Figure 2 shows that there is a negative correlation between the percentage of households with zero transport expenditures and country GDP per capita.<sup>5</sup> Poorer countries tend to have a higher share of households not spending money to increase their mobility. As countries get richer, the share of households not consuming transport goods or services decreases.

> 5

11
Altruistic parents have a reason to care about their children‘s preferences for work because if the grown-up children suffer from economic hardship, the parents will want to help them. The stronger the work norms of the children, the less will the parents have to help them. Altruistic parents have, therefore, an economic reason to instill strong work norms in their children. However, if the welfare state takes over the obligation to help children who happen to suffer economic hardships, the parents have less incentive to instill work norms in their children. Lindbeck and Nyberg develop a theory that formalizes these intuitions. Interestingly, data from the World Values Survey in 1998 are consistent with their account. The seven countries that rank lowest in terms of the share of respondents who consider a willingness to work hard important to learn at home are advanced European welfare states. Denmark, where only 2 percent of the respondents hold this view, ranks last out of 42 countries, followed by Sweden, Finland, Norway, the Netherlands, Austria, and West Germany.<sup>11</sup>

# **4. Conclusion**

Over two decades ago, when economists were laying the groundwork for an economics in which information was endogenous, Stiglitz (1985) argued that the standard theory ―is not robust to slight alterations in the informational assumptions. It is but a special—and not very plausible— ̳example‘ among the possible set of informational assumptions which can be employed to characterise an economy‖ (p. 21). Here we have suggested that the assumption of exogenous preferences—unaffected by the outcomes in society—is but a special and not very plausible example among the possible set of assumptions about preferences that can be employed in explaining economic outcomes.

A considerable body of research suggests that institutions may have effects that go far beyond the incentives that they provide – they may create framing and priming effects that influence the accessibility of memories and the perception, interpretation and, hence, the meaning of facts. It
# I. Introduction

Global poverty, as measured by the international extreme poverty line of US$1.90 a day, is becoming increasingly concentrated in Sub-Saharan Africa. It is also increasingly concentrated among households engaged in subsistence agriculture in environments characterized by uncertainty from conflict, vagaries of the weather and international food price fluctuations (World Bank 2016). Understanding how to achieve sustainable increases in household incomes in this context is key to ending extreme poverty.

Uganda offers important lessons in this regard. Uganda experienced conflict, drought and price volatility in its most important agricultural commodities in the decade from 2003 to 2013. This was also a decade in which Uganda recorded impressive rates of poverty reduction. Uganda saw the second fastest percentage point reduction in extreme poverty per year in Sub-Saharan Africa.<sup>2</sup> The proportion of the Ugandan population living beneath US$1.90 a day fell from 62.2 percent in 2003 to 32.2 percent in 2013. Understanding the drivers of this reduction is important both for offering lessons on how to end extreme poverty in Uganda, but also for other countries in the region that have not experienced such a remarkable reduction in poverty.

Understanding the drivers of poverty reduction is a complex task, and an analysis of observational data does not always allow the causes of this progress to be uncovered. Nevertheless, careful analysis of a nationally representative panel data set collected from 2006 to 2012 in combination with data on conflict events, weather and prices allows us to describe the type of income growth households experienced and to assess the importance of these external events in determining progress.

Uganda’s progress in reducing poverty in earlier years from 1993 to 2006 is a remarkable story of success that has been well told. Annual reductions in the national poverty rate of 1.9 percentage points resulted from the restoration of peace and stability to much of the country after Yoweri Museveni came in to power in 1986, the series of economic liberalization reforms that were implemented in the 1990s (such as
underestimate (overestimate) measured land productivity. Second, of all the productivity measures, labor productivity per worker measured at the plot-level is most likely to be overestimated by the recall method. This is because listing bias in persons reduces the denominator and (unlike with labor productivity per hour) is not counteracted by positive recall bias in hours per person-plot conditional on being listed. Third, even if labor productivity per worker is overestimated at the plot-level, it could still be underestimated at the household-level. This is because listing bias in plots reduces measured output (in the enumerator), but may not necessarily reduce the number of workers (the denominator), unless some workers worked exclusively on non-listed plots.

To assess the impact on different measures of productivity empirically, we use data on crop production collected at endline. As discussed in section 2, the endline survey fielded a standard agricultural production module to all households, which asked to report the quantity and value of crop harvested during the preceding agricultural season (for each plot and crop). Hence households in the weekly visit and recall groups reported on their plot-level harvest in the same manner. Using these harvest data, we measure plotlevel yields and labor productivity for maize as the maize harvest in kg or in cedi per acre, per labor hour, and per worker.<sup>13</sup> All measures are winsorized at 1 percent and the analysis is conducted at the plot-level. **Table 13** presents regression estimates of recall bias.

**Table 13: Yields and labor productivity in maize production, recall and weekly visit groups**

||Landpro|ductivity|Laborproducti|vity (per-hour)|Laborproductiv|ity (per-worker)|
|---|---|---|---|---|---|---|
||Cedi/acre|Kg/acre|Cedi/hour|Kg/hour|Cedi/worker|Kg/worker|
||(
incentives, and sample respondents using the exact same questionnaire twice within a two-week lag time. Analysis of the data shows that error variances between the two identical surveys do not differ significantly, but the larger the utility difference in a choice task, the larger the probability that the respondent will choose the same alternative in the retest. Morten and Olsen also determine that the longer the time a respondent takes to answer the choice sets in the retest, the lower the probability that they will choose the same alternatives in the retest as they did in the original test.

Our study assesses how households in Ethiopia value different attributes of improved cookstove technology, and if these values are stable over a period of three years. This work contributes to the existing body of work on the stability of WTP values elicited from CE surveys using a test4
# Conflict and the Composition of Economic Activity in Afghanistan<sup>1</sup>

Virgilio Galdo, Gladys Lopez-Acevedo and Martin Rama<sup>2</sup>

**Keywords:** Afghanistan, Conflict, Economic Activity.

**JEL:** D74, E21, F35, I32, O17

- 1  The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

- 2  All authors are with the World Bank. They are grateful to Tarek Ghani and Ethan Kapstein for providing access to an aggregated version of their dataset of detailed call records from Afghanistan. They are also grateful to Claudia Nassif and Tobias Haque for insightful comments on a previous version of the paper, and to Rucheta Singh for excellent research support.
Figure 4: Trust dividends of better government performance <!-- Start of picture text --> Satisfaction with social services and institutional trust Satisfaction with economic performance and institutional trust<br>1 - Not at all 2 3 4 - Very 1 - Not at all 2 3 4 - Very<br>Satisfaction with social services (index) Satisfaction with economic performance (index)<br>(a) Social services (b) Economic performance<br>80 65<br>70<br>60<br>60<br>55<br>Probability of trust (%) Probability of trust (%)<br>50<br>40 50<br><!-- End of picture text --> Data source: World Bank survey on trust (2021). Note: the estimated predictions are derived from a logit model regressing trust in the executive (coded as a binary variable) on the range of independent variables presented in our model and controlling for relevant geographic and socio-demographic variables.

Satisfaction with the quality of public processes – and in particular with public integrity and policy credibility – is associated with a higher trust dividend than satisfaction with policy outcomes. As shown in Figure 5, integrity and credibility matter substantially for institutional trust – arguably more than government performance. Public integrity has a large positive pay-o↵: individuals who have a very positive perception of public integrity are 43.6 percentage points more likely to trust the government than individuals who have very negative perceptions (respectively 82.3 percent vs 38.7 percent report trusting government). Individuals who believe that policy announcements are often or always followed by actions are more likely to trust legislative and executive institutions by 12.5 to 18.4 percentage points, compared to those who do not believe in the credibility of policy announcements (67.6 percent of those who think that announcement are always followed by actions trust executive institutions, compared to 55.6 percent of those who think it is
### **_<u>Health</u>_**

Social public spending on health care by the National Central Government was close to 2.8 percent of GDP and amounted to 21.5 percent of public social spending, making it the third item in importance. The Colombian Health System consists of two regimes, one contributory and one subsidized. The contributory health system is mandatory for all formal employees and pensioners, who contribute according to their ability to pay. Taxpayers and their beneficiaries are guaranteed access to the mandatory health plan (POS) and must join a Health Promotion Entity (EPS) that they can freely choose from a set of options. The EPS is responsible for managing financial and health risks. The rate for the contribution is 12.5 percent of salary, where the employer contributes 8.5 percent and the employee 4 percent; for their part, the selfemployed and independent contractors pay the full rate. The Firm Tax (CREE) helps finance this scheme to the extent that it replaced employer contributions for workers with less than 10 monthly minimum wages.<sup>25</sup> The subsidized regime enrolls people who do not have payment capacity; that is, they are categorized as poor or vulnerable by the SISBEN.<sup>26</sup> This population also joins an EPS and is guaranteed access to the POS.<sup>27</sup> The subsidized health care system is financed with resources from the National General Budget and with contributions from local administrations. The total coverage between subsidized and contributory scheme is 94.8 percent. The unenrolled without payment capacity, called Poor Uninsured Population (PPNA), can receive medical care in public hospitals for free.

# **III. Data, methodology, and assumptions**

## i. Data

### **_The National Household Budget Survey (ENPH)_**

The ENPH is a nationally representative survey conducted by the National Statistical Office (DANE).