rafmacalaba/datause-encoder-data huggingface dataset:
# **E. COVID-19 related questions**

Table E.1: Impact of treatment on COVID-19 outcomes: bivariate regressions

|_Panel A_|(1)<br>Protective<br>measures|(2)<br>Effective<br>measures|(3)<br>Display<br>symptoms|(4)<br>Contract<br>change|(5)<br>Lost<br>income|(6)<br>Income<br>decrease|(7)<br>HH income<br>decrease|
|---|---|---|---|---|---|---|---|
|Treated|-0.389***|-0.398***|0.0341|0.0224|-0.0694|-0.0413|-0.00484|
||(0.003)|(0.002)|(0.509)|(0.678)|(0.160)|(0.408)|(0.918)|
||[0.039]|[0.023]|[0.985]|[0.999]|[0.839]|[0.986]|[0.994]|
|Constant|3.830***|3.769***|0.412***|0.520***|0.367***|0.670***|0.725***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Std. coeff.|-0.307|-0.322|0.0689|0.0448|-0.147|-0.0864|-0.0108|
|N|368|368|368|346|365|368|368|
|R-sq|0.0236|0.0260|0.0012|0.0005|0.0054|0.0019|0.0000|
|_Panel B_|(8)|(9)<br>|(10)<br>|(11)<br>|(12)|(13)<br>|(14)<br>|
||Transfers received<br>decrease|Food expend.<br>increase|Health expend.<br>increase|Educ expend.<br>increase|Transfers sent<br>increase|Savings<br>decrease|Borrowing<br>increase|
|Treated|0.00272|-0.0331|-0.00857|-0.0392*|0.0255|0.0363|0.0168|
||(0.957)|(0.478)|(0.816)|(0.091)|(0.365)|(0.482)|(0.702)|
||[0.956]|[0.991]|[0.995]|[0.645]|[0.981]|[0.995]|[0.992]|
|Constant|0.374***|0.291***|0.148***|0.0714***|0.0659***|0.560***|0.220***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Std. coeff.|0.0056|-0.0742|-0.0244|-0.1770|0.0944|0.0735|0.0399|
|N|368|368|368|368|368|368|368|
|R-sq|0.0000|0.0014|0.0001|0.0078|0.0022|0.0013|0.0004|

_Notes_ : Standard errors in parentheses, *, **, *** denote significance at 10%, 5% and 1% levels. List et al. (2019) p-values adjusted for multiple hypothesis testing in square brackets.
individually. This was primarily due to the complicated logistical circumstances surrounding fieldwork in South Sudan, in no small part due to rapidly deteriorating security conditions, but also due to constraints on the respondents’ time. Respondents were, therefore, playing the games against a pre‐ loaded hypothetical distribution of responses. Enumerators explained to the respondents that the other player would be another survey respondent elsewhere in South Sudan. The set of possible responses, in terms of the fraction of the endowment sent or returned, was equally distributed between [0.1,1] in increments of 0.1 . In no cases was the fraction of endowment sent or returned equal to zero.

# _List‐experiment_

Based on the results from the baseline survey, it was determined that the reporting of sensitive behaviors might have been untruthful. Methods to elicit more truthful responses were therefore employed in the endline questionnaire. For example, the rates at which respondents reported even simply knowing someone who may have participated in cattle raiding were close to zero, despite 63 percent of respondents reporting cattle raiding in their area in the baseline. Rates of reporting respondents’ own sensitive behaviors were even lower. Therefore, a set of list questions – also commonly known as the “item count technique” introduced by Miller (1984) – were added to the endline questionnaire. In these questions, the sample is split into a treatment and control group, and respondents in the control group are given a set of N statements and asked to answer with how many of these statements do they agree with/or would say yes to, without explicitly stating which ones. Respondents in the treatment group are given the same N statements + a sensitive item. The estimate of the true rate at which respondents agree with the sensitive statements is simply the difference in means, in terms of the number of statements, between the treatment and control groups. In the context of the endline survey, the sensitive behaviors pertained to violent behavior, including domestic violence, as well as cattle raiding. Direct questions were asked to the control group alongside the list question without the sensitive item, so as to compare results obtained through the list‐method. Below we report the list of sensitive items included in the experiment.

_Figure A4.1 List of sensitive statements included in the list experiment_

# **Cattle raiding:**

1. I know someone who has participated in cattle raiding, including myself.

2. I have participated in cattle raiding.

# **Violent behavior:**

1. I have had a verbal disagreement in the last month where the other person threatened me with violence.

2. I have had a verbal disagreement in the last month where I threatened the other person with violence.

50
Figure 3: Level change (in million) in female Figure 4: Change (%) in female employment from employment from BaU in 2020 BaU in 2020 <!-- Start of picture text --> 0.000 0<br>-0.200 -2<br>-0.400<br>-4<br>-0.600<br>-6<br>-0.800<br>-8<br>-1.000<br>-10<br>-1.200<br>Salaried Non-Salaried -12<br>Family Farm Other Farm Salaried Non-Salaried Family Farm Other Farm<br>Source: CGE model simulation results Source: CGE model simulation results<br><!-- End of picture text --> Total employment falls by more than 6 percent. Consequently, households’ labor income declines by 63 percent on average, with urban households are hit harder. The decline in labor income is due to the plummeting wage rates driven by the limitations on the substitution possibilities in production, the fall in overall price levels, and productivity losses due to COVID. Approximately 7 percent of the decline is due to labor productivity loss, while the decline in overall price levels contributes to 33 percent of the reduction. Around 10 percent is caused by production losses. The remaining 13 percent is due to restrictions on adjustments in input and factor mix, which causes a 6 percent decline in employment to have a much higher impact on wages.

The expansion in female-owned farm production compensates for losses in the income of rural households. The decline in earnings of women in urban areas is slightly less than men during the crisis, but note they continue to stay behind the BaU levels until 2025 and possibly beyond. The underlying driver is the sharp decline in the sectors where women are employed more. The recovery of the capital and labor use to BaU levels is slow after a significant reallocation of resources in the economy towards more maledominated sectors. In the absence of recovery measures, the new allocation of resources across sectors distorts gender gaps further and keeps employment and wage rates of women below the BaU. The smaller decline is not surprising given the already existing gaps in the earnings, but the more prolonged impact on wages implies widening pay gaps. Female labor in rural areas is affected less, thanks to increased agricultural activity. Note that wages for unpaid family labor are part of the farm income that is not paid to someone outside the home to do the work that is done by family members. Thus, a smaller decline in unpaid family labor wages implies a more modest reduction in agricultural income.

11
shocks on household income and poverty due to the lack of granularity in the weather data. Since we are not able to match households with weather data corresponding to their exact (or slightly offset) place of residence, but only at the district level, there may be underlying variation we fail to capture, as there is little to no variation across households living in the same district. Districts in Paraguay are reasonably small and differences in weather shocks may only vary slightly from district to district, so we expect this issue to be small. Also, as Asuncion and Central are the smallest districts, but simultaneously the most populated ones, we expect the results to be more robust in those locations, covering a large share of the Paraguayan population. Still, more geographically-coded survey data, potentially enhanced with self-reported shocks may help overcome this limitation, and remains a topic for future data collection and research. Third, there may be other weather shocks with devastating welfare effects, such as river flooding, which we do not or only imperfectly cover in our analysis.

# **7 Conclusion and Policy Recommendations**

In this study, we find that weather shocks have significant and heterogeneous impacts on poverty and income in Paraguay. Heat shocks emerge as the most pervasive threat, reducing household incomes by 5% in urban areas and 8.8% in rural areas, while increasing poverty rates by 1.7 and 4.2 percentage points, respectively. Urban areas face distinct challenges from flooding, which increases poverty by 1.9 percentage points, while rural areas are particularly vulnerable to both heat and drought shocks. These impacts are especially pronounced for specific groups: femaleheaded households in rural areas experience poverty increases of up to 7.1 percentage points during heat shocks, 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
because privacy protections tend to help build trust in digital transactions. The former variable’s effect is ambiguous, at least in the short run. On the one hand, such laws require that digital signatures have equal recognition under the law as properly signed paper contracts. On the other hand, such regulations require authentication procedures to ensure that parties involved in a transaction are real, which can deter the use of digital payments in contexts where authentication comes at the expense of privacy rights.<sup>14</sup> Finally, _Capabilitiesi2019_ is included to control for human capabilities that are positively linked to DFS usage as reflected in educational attainment. In the regressions below, we rely on mean years of schooling among adults aged 25 or older from the UNDP’s Human Development Index to proxy for this factor, because of its wide country coverage. Again, results are qualitatively similar when the schooling variable is replaced by the full HDI or the World Bank Human Capital Index.

Equation 2 adds to equation 1 the share of the of the population that made or received a digital payment in 2017 ( _DigPayi,2017_ ), which is from the penultimate version of the Global Findex dataset. It is included to control for the level of digital payment usage in a country prior to the pandemic.<sup>15</sup> Recall that our estimation approach for uncovering causal relationships is rooted in the assumption that the COVID-19 shock was unanticipated, and that its effects on digital participation depended on pre-existing conditions. For this reason, the explanatory variables are measured in 2019 to the extent possible. By controlling for the level of digital payment usage just prior to COVID-19 (and the trend in payment usage prior to that), equation 2 fully controls for developments in payment usage to that point. For these reasons, the coefficients for the remaining

> 14 For additional details on the construction of these indexes, see World Bank (2021, Chapter 6).

> 15 Note that the Findex data were not collected in 2018 or 2019, so 2017 is the last date prior to the pandemic for which indicators of DFS usage are available for a wide sample of countries via Findex.

14
relatively low interest rates (a median borrowing rate of 3%, mean rate of 3.8%). This may reflect an unwillingness to take on risk; village funds want to lend from equity, not debt. And if loan demand were to exceed the available supply, 63% of VF committees say they would still lend to all qualified borrowers, but in a majority of these cases they would reduce loans equally, effectively sharing the pain (see Table 14). Faced with excess loan demand, about one VF in seven would refuse some loans; and about one in eight would try to borrow more from the BAAC or another bank in order to accommodate the demand.

|**Table 14. Response of Village Funds to insufficient funds for lending**||
|---|---|
|How does the VF respond if there are insufficient funds relative to loan demand?|% breakdown|
|Still lend to all|2.1|
|Lend to all, reducing loans equally|35.8|
|Lend to all, reducing some loans by more|24.8|
|Refuse some loans|13.6|
|Borrow more from BAAC/GSB/KTB|12.7|
|Other|11.0|

Source: Village Fund Survey, 2010 Almost a third (32%) of village funds claim to have a AAA credit rating, and these funds are slightly more likely to want to borrow funds in order to lend more to their members. Most VF committees (91%) say they announce their financial results publicly.

# _Risk Mitigation_

Although every Village Fund needs to be concerned about managing risk, this is particularly true of VFs that borrow to on-lend. There are a number of possible ways in which risk may be reduced. An altruistic VF will try to minimize the burden of such provisions, consistent with some form of non-bankruptcy constraint. Informal mechanisms probably work better in small than large villages.

The first approach is to require borrowers to save, in the form of shares and deposits. Only members may apply for a loan, and 75% of VFs require members to buy at least one share, at a median cost of 20 baht (and mean cost of 44 baht). The median number of outstanding shares is 900, so the book value of total outstanding shares is roughly 18,000 baht (about US$525) per village fund. Aggregated over 78,000 villages, this represents equity of approximately US$41 million.

Fully 99% of villages reported that they then require a loan application fee, but the cost is modest: the median fee is 20 baht, and in 95% of cases the fee does not exceed 100 baht.

24
# **VIII.   References**

- Abidin, Mahani Z. 2001. Competitive Industrialization with Natural Resources Abundance: Malaysia, in Resource Abundance and Economic Development, ed. By R. Auty (Oxford: Oxford University Press), chapter 9.

- Acemoglu, Daron, Simon Johnson, and James A. Robinson. 2001a. “An African Success Story: Botswana.” Working Paper 01–37, Department of Economics, Massachusetts Institute of Technology (MIT), Cambridge, MA.

- Auty, Richard M. 1998. Resource abundance and economic development: Improving the performance of resource rich countries. Helsinki: The United Nations University – World Institute for Development Economics Research.

- Bevan, D; P Collier, J.W Gunning, 1999, The Political Economy of Poverty, Equity, and Growth: Nigeria and Indonesia, Oxford University Press.

- Easterly, W; and R. Levine, 2002, Tropics, Germs and Crops: How Endowments influence Economic Development, mimeo, Center for Global Development and Institute for International Economics.

- Engerman, Stanley L; and Kenneth L. Sokoloff, 1994, Factor Endowments, Institutions, and Differential Parths of Growth Among New World  Economies: A view from Economic Historians of the United States, National Bureau of Economic Research Working Paper N0. H0066.

- Gelb Alan et al. 1988 Oil windfalls blessing or curse? New York: Oxford University Press.

- German Overseas Institute (DÜI), May 2005. Context Matters – Rethinking the Resource Curse in Sub-Saharan Africa. Working Papers No1. Mathias Basedau.

- Harvey, Charles, and Lewis, S.R. 1990. Policy Choice and Development Performance in Botswana (New York, St .Martins Press).

- Haussmann, Ricardo, and Rigobon, R. 2002. An Alternative Interpretation of the Resource Curse: Theory and Policy Implications. Working Paper No 9424 (Cambridge, Massachusetts: National Bureau of Economic Research).

- IMF Working Paper WP/03, May 2003 – Xavier Sala-i-Martin and Arvind Subramanian. – Addressing the Natural Resource Curse: An Illustration from Nigeria.

- IMF, October 2007, World Economic and Financial Survey. Regional Economic Outlook. Sub-Saharan Africa.

- James A. Robinson, Ragnar Torvik, Thiery Verdier. May 2005, Political Foundation of the Resource Curse. Journal of Development Economics.

- Katz. Menachem, Bartsch. Ulrich, Malothra. Harinder and Cuc Milan. 2004. Lifting the Oil Curse: Improving Petroleum Revenue Management in sub-Saharan Africa (Washington: International Monetary Fund).

41
their database which contains information up to 2006 with information from the World Bank’s World Development Indicators (WDI) and the International Monetary Fund’s International Financial Statistics (IFS). These indicators of _domestic financial development_ can be grouped in 6 categories:

- (a) _Measures of (absolute and relative) size of financial intermediaries_ are captured by the assets of deposit money banks and assets of the financial system (deposit money banks and other financial institutions) as a percentage of GDP. We also include the ratio of deposit banks vis-à-vis the central bank assets and the ratio of liquid liabilities of a financial system to GDP.<sup>5</sup>

- (b) _The level of activity of financial intermediaries_ is measured as the private credit by deposit money banks (DMBs) and the extent of domestic credit to the private sector (undertaken by deposit money banks and other financial institutions). We call them _bank credit_ and _financial credit_ , respectively. They capture the financial intermediation with the private non-financial sector in channeling savings to investors. Note that all variables are expressed as a percentage of GDP in logs.

- (c) _Efficiency and structure of the commercial banking sector_ are measured by: (i) _overhead costs are_ the ratio of accounting value of the bank’s overhead costs as a share of its total assets. (ii) The _interest rate margin_ is the net interest revenue as a percentage of the commercial bank’s interest-bearing (total earning) assets. (iii) The _concentration of the banking system_ is proxied by the assets of three largest banks as a share of assets of all commercial banks.

- (d) The _depth of other financial institutions_ is approximated by the level of activity of insurance companies. We use the volume of life insurance premium and non-life insurance premium (i.e. life and non-life insurance penetration, respectively) as a share of GDP in logs.

- (e) _Stock market development_ . We use the stock market capitalization and the stock market total value traded to capture the level of activity or liquidity of domestic stock markets as a percentage of GDP in logs. We include the stock market turnover (ratio of the value

> 5 We should remark that liquid liabilities include currency in circulation plus demand- and interest-bearing liabilities of banks and other financial intermediaries. This is the broadest available indicator of financial intermediation (since it includes all financial sectors) and is a typical measure of financial depth (i.e. overall size of the financial sector without distinguishing financial sectors from between liability uses).

7
similar calculation is used for those with a secondary or higher education level. Omitted level for contract is none. Omitted level for Provident Fund is none. Standard errors are clustered at the respondent level. *, ** and *** represent statistical significance at the 10%, 5% and 1% levels, respectively.

# **Table** **<u>8: Conditional Logit Coefficient Estimates by Employment Type</u>**

||Private emplo<br>|yee|Govt employ<br>|ee|Casual work<br>|er|Self-employ<br>|ed|
|---|---|---|---|---|---|---|---|---|
||Coefficient (se)<br>(1)|WTP<br>(2)|Interaction (se)<br>(3)|WTP<br>(4)|Interaction (se)<br>(5)|WTP<br>(6)|Interaction (se)<br>(7)|WTP<br>(8)|
|Contract - 6 months|1.00***|18.5|0.22|24.9|-0.0074|16.6|-0.090|19.0|
||(0.085)||(0.23)||(0.18)||(0.12)||
|Contract - 1 year|1.47***|27.2|0.36|37.3|-0.042|23.9|-0.26*|25.3|
||(0.090)||(0.27)||(0.18)||(0.13)||
|Contract - long-term|2.33***|43.1|0.88**|65.5|0.066|40.1|-0.37*|41.0|
||(0.14)||(0.41)||(0.30)||(0.20)||
|Notice|0.021***|0.39|0.0026|0.48|0.0015|0.38|-0.0026|0.38|
||(0.0014)||(0.0040)||(0.0030)||(0.0020)||
|Hours|-0.023***|-0.43|-0.0083|-0.64|-0.0039|-0.45|0.0047|-0.38|
||(0.0020)||(0.0058)||(0.0044)||(0.0028)||
|Leave|0.029***|0.54|-0.0049|0.49|-0.0037|0.42|-0.0031|0.54|
||(0.0039)||(0.011)||(0.0084)||(0.0055)||
|Provident Fund|0.98***|18.1|-0.038|19.2|-0.026|16.0|-0.15|17.4|
||(0.086)||(0.23)||(0.18)||(0.12)||
|Perc. Change Income|0.054***<br>(0.0028)|1.0<br>|-0.0050<br>(0.0082)|1.0|0.0058<br>(0.0057)|1.0|-0.0062<br>(0.0040)|1.0|
|Observations|23,568||||||||

_Source_ : Authors’ analysis based on survey data.

47
# **Contents**

|**1**<br>**Intr**|**oduction**|**2**|
|---|---|---|
|**2**<br>**Con**|**sumer surplus with incomplete fnancial markets**|**6**|
|2.1|Model<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>7|
||2.1.1<br>Environment<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>7|
||2.1.2<br>Existence of model solutions . . . . . . . . . . . . . . . . . . . . . .|.<br>9|
||2.1.3<br>Graphical representation of the model. . . . . . . . . . . . . . . . .|.<br>9|
|2.2|Roy’s identity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>11|
|2.3|Consumer surplus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>12|
||2.3.1<br>Equivalent variation<br>. . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>12|
||2.3.2<br>Marshallian surplus . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>14|
||2.3.3<br>Uncompensated and compensated demand elasticities . . . . . . . .|.<br>14|
|2.4|Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>16|
|**3**<br>**Pra**|**ctical guidance on consumer surplus from fnancial technologies**|**16**|
|3.1|Defning, estimating, and graphing demand for fnancial technologies<br>. . .|.<br>17|
|3.2|Simplifying assumptions on dynamically compensated demand . . . . . . .|.<br>18|
|3.3|Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>21|
|**4**<br>**Con**|**sumer surplus from savings and microfnance**|**23**|
|4.1|Retirement savings (Dufo et al., 2006) . . . . . . . . . . . . . . . . . . . .|.<br>23|
||4.1.1<br>Context and experimental design<br>. . . . . . . . . . . . . . . . . . .|.<br>23|
||4.1.2<br>Demand elasticities and consumer surplus<br>. . . . . . . . . . . . . .|.<br>25|
|4.2|Commitment savings (Karlan & Zinman, 2018)<br>. . . . . . . . . . . . . . .|.<br>27|
||4.2.1<br>Context and experimental design<br>. . . . . . . . . . . . . . . . . . .|.<br>27|
||4.2.2<br>Demand elasticities and consumer surplus<br>. . . . . . . . . . . . . .|.<br>28|
|4.3|Microfnance (Karlan & Zinman, 2019) . . . . . . . . . . . . . . . . . . . .|.<br>30|
||4.3.1<br>Context and experimental design<br>. . . . . . . . . . . . . . . . . . .|.<br>30|
||4.3.2<br>Demand elasticities and consumer surplus<br>. . . . . . . . . . . . . .|.<br>32|
|4.4|Discussion of results<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>36|
|**5**<br>**Con**|**clusion**|**38**|

1