rafmacalaba/data-use-mentions-extended huggingface dataset:
post supply of credit, effectively worsening the financial access of poorer borrowers.<sup>8</sup> Since, analogous to many households in developing countries, all households in the sample are producers as well as consumers (Banerjee and Duflo, 2007), this paper also relates to the literature on credit and investment constraints among low-income households and micro-entrepreneurs. Dupas and Robinson (2012) study the impact of access to savings accounts on microenterprise development, and provide evidence of significant barriers to saving that constrain productive investment. de Mel et al. (2008) provide unconditional cash transfers to micro-entrepreneurs in Sri Lanka and find similar evidence of significant investment constraints and high returns to capital. In an evaluation of a program that extended access to microfinance in India, Banerjee et al. (2010) find a positive but economically small impact on business creation. Karlan and Zinman (2010) study the effect of randomized access to credit on microenterprise investment in the Philippines and find no impact. Similarly, Kaboski and Townsend (2011) study a field experiment expanding access to microcredit in Thailand and find strong consumption effects but no impact on investment.

The present study differs from the literature on investment constraints in two important ways. First, in contrast to studies analyzing a credit expansion, I focus on a population of existing rather than new bank clients, who are excluded from bank credit due to high levels of pre-existing debt. Second, unlike programs expanding access to credit, debt relief (like a bankruptcy settlement) reduced debt on the books, but did not automatically provide beneficiaries with improved liquidity.

The remainder of the paper is structured as follows. Section two provides details on the debt relief program and eligibility criteria. Section three presents a simple model of the household’s investment decisio to motivate the empirical analysis. In section four, I outline the identification strategy. Section five describes the dataset and household survey. Section six presents the results and Section seven concludes.

> 8See also Djankov, McLiesh and Shleifer (2007
nationally-representative data, collected within a multi-topic framework and with emphasis on agriculture.

While the gender gap in Malawi is estimated at 25.4 percent at the mean, it ranges from 22.6 percent at the 10<sup>th</sup> percentile to 37.6 percent at the 90<sup>th</sup> percentile. The findings support the view that large and significant gender disparities in use of inputs and asset ownership are the central factors behind the gender gap, particularly in the first half of the agricultural productivity distribution. At the mean and the median, the differences in observable covariates are associated with 82 and 87 percent of the gender gap, respectively. Above the median, the percentage contribution of the endowment effect towards the gender gap declines steadily, whereby at the 80<sup>th</sup> and 90<sup>th</sup> deciles of the distribution of agricultural productivity, the structure effect, which is driven by gender differences in returns to factors of production, explains 30 and 34 percent of the gender gap, respectively.

Higher levels of household adult male labor and area under export crop cultivation on male-managed plots, in particular, widen the gender gap; a result that holds true across the vast majority of the agricultural productivity distribution. These disparities appear to be compounded by gender differences in the availability of time devoted to productive activities, as negative returns to household child dependency ratio on female managed plots are found to exacerbate the female structural disadvantage component of the gender gap at each decile of the agricultural productivity distribution. In addition, lower and declining returns to household adult male labor on female managed plots _vis-à-vis_ male-managed comparators across the agricultural productivity distribution might be suggestive of potential household adult male labor supervision difficulties on female-managed plots. These mutually reinforcing constraints appear to generate a female productivity trap; as such, policies need to prioritize and target the key factors underlying the gender gap.

Our study shows a number of factors that seem to be driving the gender
<!-- Start of picture text --> Age profile for Employment - India<br>100<br>g 80<br>5<br>E 60 _<br>5 ; o_oza :ai ;Ay<br>ola a —— i is<br>D 2 8itt —t—1996 Lg<br>15 20 25 30 35 40 45<br>Age<br>@ 1940-44 1945-49 —— 1950-54 —— 1955-59<br>1960-64 1965-69 — 1970-74 — 1975-79<br>1980-84 1985-89 — 1990-94 — 1995-99<br>—*> 2000-04<br><!-- End of picture text --> <!-- Start of picture text --> Age profile for Employment - Bangladesh<br>100<br>.)s 80<br>5 1975<br>E 60 1985 1980, 1970<br>é 1990 , 4965<br>»<br>® en ae a<br>D£2000204i ne638 Airo ——_ 3 —<——fore——thes1945 19403<br>e<br>520 5 30 35 40 45<br>Age<br>@ 1940-44 1945-49 —— 1950-54 —— 1955-59<br>1960-64 1965-69 — 1970-74 — 1975-79<br>1980-84 1985-89 — 1990-94 — 1995-99<br>> 2000-04<br><!-- End of picture text --> <!-- Start of picture text --> Age profile for Employment - Pakistan<br>100<br>=>4 80<br><7<br>£60<br>°<br>=<br>So 40<br>=2a<br>1986 1988 et | 672 —<br>2000<br>e<br>0<br>tp<br>15 20 25 30ops35 40 45<br>Age<br>@ 1940-44 1945-49 1950-54 — 1955-59<br>
are fundamental explanatory variables of macroeconomic outcomes in developing countries was revisited by Calvo et. al. (1993) who elucidated the resumption of international capital flows to Latin America in the early nineties based on the role of external factors. More recently, business cycle models have been extended to capture the role of external factors (Mendoza, 1995). However, the empirical literature has mixed results about the relative importance of domestic and external factors as sources of output volatility. On one hand, Hausman-Gavin (1995) conclude that domestic factors are the main cause of output volatility, and so do Hoffinaister and Roldos (1997) and Ahmed (1999)4. On the other hand, Joyce and Kamas (1997) show that the relative importance of each factor varies with the country. All these papers, except Ahmed's, have in common a sample period that covers, at the most, until 1994.

The prevailing external environment during 1998 and 1999 was very unfavorable for the region, as depicted by the evolution of regional terms of trade (Fig. 2) and the cost and availability of external financing<sup>5</sup> (Fig. 3 and 3A). Additionally, some countries,

> 4 Kamin-Babson show that the probability of currency crises in Latin America is determined fundamentally by internal factors. This result can be interpreted in favor of the domestic determinants of output volatility, given the association between currency crises and output growth.

> ' As a proxy for the cost of financing we include besides the real interest rate in the US, the non-Latin Brady bond sovereign spreads assuming it is completely exogenous to Latin America. In the following section we allow for endogeneity of the spreads. As a proxy for exogenous capital flows, to describe the availability of external financing, we use the current account of industrialized countries. In the last section we discuss the inclusion of both price and quantity variables of this market.
**Figure 2.3. Yemen: sectoral structure in 2020 (%)**

<!-- Start of picture text --> a. Value added b. Employment<br>Other<br>services<br>Manufacturi<br>Other ng<br>services Agriculture<br>Agriculture<br>Government<br>Mining<br>Government<br>Mining<br>Transport Manufacturi<br>ng<br>Transport Utilities Utilities<br>Constructio Construction<br>Trade n Trade<br>Other Government<br>Transport c. Exports services d. Imports<br>Constructio Government Transport Other<br>n Trade Trade services<br>Utilities Construction<br>Mining<br>Agriculture Agriculture<br>Utilities<br>Manufacturi<br>ng<br>Manufacturi<br>ng<br>Mining<br><!-- End of picture text --> Source: Authors’ calculations based on the 2020 Yemen SAM.

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the cost of sending and receiving remittances and thus are associated with reduced poverty and increased consumption among recipients.

Digital payments make remitting easier and quicker. Instead of having to pay someone to carry cash back to the village or waiting until they have the time and money to travel home, senders using digital remittance payments can use their mobile phone to send money instantly. This ease of use is associated with higher remittance payments. For instance, a study in Bangladesh found that poor rural households received larger remittance payments when they opened a mobile money account. This in turn reduced their dependence on borrowing, allowed them to spend more on food, and made them less likely to experience extreme poverty (Lee et al., 2021). A similar finding from in Uganda showed that opening a mobile money account was associated with a 36 percent increase in the total value of remittances and a 13 percent increase in per capita consumption (Munyegera and Matsumoto, 2016).

Curiously, another study in Bangladesh finds evidence of the opposite: that mobile money decreases consumption among recipients. An explanation for this finding is that recipients and senders view digital remittances differently. Unlike cash, digital remittances may be earmarked for specific purposes like education or health. As a result, recipients consume less when under financial stress despite receiving a payment (Lee et al., 2023).

The G20 has also recognized the importance of efficient cross-border digital payments, including remittances, and has brought together various stakeholders and standard-setting bodies to advance these efforts (G20, 2020). For example, the World Bank and the IMF provide technical assistance to to promote digitalization and improve transparency, with the goal of lowering costs and increasing the speed of cross-border payments.

# **4.5 How digital payments enable deeper financial usage**

Previous iterations of the Global Findex database found that digital payment recipients tended to cash out the money they received into an account. Data from the Global Findex 2021 finds that is changing, with 83 percent of digital payment recipients in
quality than our approach does—or, in other words, that our approach is more conservative in discounting the schooling of poorer-performing countries. Nevertheless, the _LAYS_ estimates derived under the two approaches are highly correlated.

# **Figure 5.8: Kaarsen’s (2014) approach versus LAYS**

<!-- Start of picture text --> Kaarsen’s Education Quality estimates (re‐ LAYS derived using Kaarsen’s Education<br>normalized) versus Learning‐per‐Year relative Quality estimates (re‐normalized) versus<br>to Singapore LAYS<br>0.0 0.2 0.4 0.6 0.8 1.0 0 5 10 15<br>TIMSS: Learning per year rel. to Singapore LAYS using TIMSS<br>Source: Authors’ analysis of Kaarsen (2014), TIMSS 2015, and Barro and Lee data (2013).<br>Notes: Numeraire country is Singapore. Illustration for case where learning starts at Grade 0 for TIMSS. For<br>relative learning per year: Correlation coefficient is 0.94. Spearman rank correlation is 0.93. For LAYS:<br>Correlation coefficient is 0.97. Spearman rank correlation is 0.97.<br>1.0 15<br>0.8<br>10<br>0.6<br>0.4<br>5<br>0.2 LAYS using Education Quality (Kaarsen)<br>Education Quality (Kaarsen) rel. to Singapore<br>0<br>0.0<br><!-- End of picture text -->

# **_5.4  Linear versus multiplicative combinations of schooling quantity and quality_**

A final consistency check compares LAYS with estimates that combine schooling quality and quantity in a different way. As explained in Section 2, the LAYS measure, like Schoellman (2012) and Kaarsen (2014),
instrumented TFPQ using Foster et al. estimates are also relatively unchanged with only Chile positive and Colombia now significantly negative at -.025, while Indonesia and Ethiopia remain statistically insignificant. TFPQ à la De Loecker et al estimates are _null_ for all countries, including Chile, although preserving the ranking found in the OLS estimates.

Chile remains the only country with a negative price elasticity, but now Colombia enters with a strongly significant positive coefficient while Indonesia and Ethiopia are positive but insignificant. Mark-ups broadly follow the previous pattern, albeit with Colombia gaining significance and Ethiopia losing it, but again, all but Chile showing negative coefficients. Marginal costs closely follow the pattern from the OLS results with Chile showing the only negative and significant value, but now with Colombia showing a higher positive and significant coefficient. In sum, the key magnitudes and patterns across levels of development largely hold.

# _Controlling for selection effects._

Since better firms may also be attracted to cities, ideally, we would like to control for, for instance, managerial quality or technological capabilities. Such data is only beginning to become available at the firm level. As an alternative, table 2, panel c reruns the OLS specification but with complete plant-level fixed effects. This is highly imperfect since, in the absence of a large number of plants moving from low to high density areas, we are also stripping out any plant specific agglomeration effects arising from being born in a particular density area. The results are, as expected, much weaker: there is a much less clear pattern of wage and TFPR elasticities with Indonesia alone retaining high and significant wage and TFPR elasticities and Colombia and Ethiopia now showing negative wage elasticities. This said, the TFPQ elasticities broadly retain the previous pattern with Chile showing the only positive and significant coefficient. Further, the previous pattern on prices and marginal cost showing Chile with the only significant and negative result continues to hold. Agglomeration continues to put downward pressure on mark-ups.

Cities may also attract a better quality of labor and the availability
Porta and Shleifer, 2014). Yet study after study has found an overlap in the distributions of measures of revenues and labor productivity, indicating that parts of both sectors are operating at the same points in these distributions.<sup>10</sup> While not definitive by any means, such over-lapping distributions suggest that some portion of the informal sector is operating more freely out of choice (perhaps avoiding a costly process of formalizing into a still-low-productive formal sector). The data for Harare’s informal and formal sectors fit this pattern. Figure 3 shows kernel densities of the log of labor productivity (in USD) for both formal and informal businesses. Since the values for informal businesses are recorded as of the last completed month, this value is multiplied by 12.<sup>11</sup> Values for the formal sector are calculated from the World Bank’s Enterprise Surveys.<sup>12</sup> _<u>Figure 3: The Relative Productivity of Harare’s Informal and Formal Sectors</u>_ <!-- Start of picture text --> Labor Productivity, Sales per Worker (log)<br>.3<br>.2<br>.1<br>Formal<br>Informal<br>0<br>0 5 10 15<br>Harare only. Log of revenues with adjustment. Informal monthly values multiplied by twelve to annualize value.<br><!-- End of picture text --> Formal and informal sectors may vary in terms of the sectoral composition, which may account for the differences shown in Figure 3. Table 1 does show that there is a notable difference in the types of activities engaged in by the formal and informal sectors. Nearly half of Harare’s informal sector is dedicated to food provision (sales of food, beverages, and bakeries). Only 10% of

> 10 Graphs in this vein have made frequent appearances in the literature on informality
Since the initial (before linking) growth rates of fossil fuels and CO2 emissions under the TIMES model are much lower as compared to those in the CGE model, it causes reductions in baseline emissions from the hybrid model (after linkage). The main reason for the higher baseline emissions under the top-down model is that it often excludes existing policies specific to sectors, sub-sectors and technologies not explicitly available in their database, social accounting matrix (SAM) or input-output (I-O) tables.

# **Figure 4: Economic impacts of meeting China’s NDC under stand-alone and hybrid modes of modeling**

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Stantcheva, S. 2022. “How to Run Surveys: A Guide to Creating Your Own Identifying Variation and Revealing the Invisible.” NBER Working Paper 30527, National Bureau of Economic Research, Cambridge, MA.

Vagliasindi, M. 2012. “Implementing Energy Subsidy Reforms: An Overview of the Key Issues.” Policy Research Working Paper 6122, World Bank, Washington, DC.

Vieites, Y., B. Andretti, M. Weiss, J. Jacob, and M. Hallack. 2023. “Effectively Communicating the Removal of Fossil Energy Subsidies: Evidence from Latin America.” Global Environmental Change 81.

World Bank. 2022A. Poverty and Shared Prosperity 2022: Correcting Course. Washington, DC: World Bank.

World Bank, 2022B. Indonesia Economic Prospects. Trade for Growth and Economic Transformation.

World Bank. 2023A. “World Development Indicators.” World Bank, Washington, DC. https://databank.worldbank.org/source/world-development-indicators.

World Bank 2023B. Angola Public Finance Review. World Bank.

Yemtsov, R., and A. Moubarak. 2018. “Good Practice Note 5: Assessing the Readiness of Social Safety Nets to Mitigate the Impact of Reform.” Energy Sector Management Assistance Program (ESMAP), World Bank, Washington, DC.

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# **1. Introduction**

There is a vast body of actuarial literature on design and ratemaking for crop insurance; for example Josephson et al. (2000) and Skees et al. (1997) document the methodologies implemented for the US Multiple Peril Crop Insurance (MPCI) program and Group Risk Plan (GRP) respectively, and Stohs and LaFrance (2004) propose a more sophisticated methodology for the MPCI program. However, despite the recent growth of crop insurance programs in low and middle-income countries, there is little literature on how to apply actuarial techniques outside high-income countries.

Any methodology for design and ratemaking should be robust and statistically efficient. Moreover in low-income environments where actuarial expertise is often lacking, it should be transparent enough for senior management to scrutinize and flexible enough to allow the inclusion of expert judgment.

Currently, many of the design and ratemaking procedures being used in developing countries are product-based approaches, where calculations are conducted separately for each product (Syroka 2007), rather than portfolio-based approaches, where calculations are conducted jointly for all products. By utilizing only the time dimension of historical data, these approaches are statistically inefficient (Efron and Morris 1975). There are a number of ways of incorporating the spatial dimension of data into statistical procedures, such as Hierarchical Bayes methods or seemingly unrelated regressions (SUR). However, these approaches may be challenging to implement and scrutinize when there is limited actuarial capacity. Empirical Bayes Credibility Theory is suggested as one way to increase the statistical efficiency of design and ratemaking while retaining transparency in intermediate calculations and the flexibility to apply expert judgment (Bühlmann 1967).

In this paper we document the design and rating methodology implemented by the Agriculture Insurance Company of India for the modified National Agricultural Insurance Scheme (mNAIS), which was piloted during the spring (Rabi) season 2010-11. The approach uses long term data series, available in India, to calculate premium rates that are stable yet reflective of regional differences and responsive to changes in risk over time. This enhanced
the period 1990-2010, ending in the third quarter of 2010. The resulting data set covers 60 countries or episodes of large devaluations. An episode of large devaluation was defined as the largest quarter-onquarter devaluation of each country’s REER. These data come from the IMF’s information notice system (INS). The value of exports of goods and services and the FDI series come from the IMF’s database on International Financial and Trade Statistics.

# **IV. Stylized Facts**

As a first approach to the data, Figures 1 and 2 present the cross-country averages of the REER, FDI and exports variables, normalized so that they equal zero in the quarter of the large devaluation and the units are in country-specific standard deviations. That is, we subtracted the value of each variable in the relevant quarter and divided the resulting series by each country’s standard deviation of each variable. This normalization facilitates the interpretation of these graphs.

Figure 1 illustrates that the identification of large devaluations resulted in a discrete change in the level of the REER before and after the identified maximum devaluations. It is noteworthy that on average during the two-year (eight quarters) after each episode the level of the REER was significantly lower than during the preceding two years. That is, the approach yields a significant and persistent devaluation treatment, which hopefully includes devaluations caused by different reasons across the country episodes rather than just reflecting episodes of devaluations driven by financial crises, just the type of unsystematically assigned event suitable for further data analysis. Alternative approaches used in the literature described earlier include the use of proxies for undervaluation or simply examine the effects of exchange-rate variations on other outcome variables. Both would be problematic for an event-study approach.<sup>8</sup> Figure 2 presents time-period averages of the normalized FDI and exports variables before and after the devaluation episodes, based on data from sub-samples of countries with data for all periods before and after the devaluation, and the list of countries for each sub sample appears in the table
_(i) Urban greening._ Based on review of existing Heat Action Plans and urban greening interventions, we established that a medium-ambition investment involves increasing the city's total tree canopy area by 10%, while a high-ambition investment entails a 30% increase. For example, if the city currently has a 10% tree canopy coverage, the medium-ambition plan aims to raise it to 20%, and the high-ambition plan seeks to raise it to 40% within the city boundaries. It was assumed that two trees can be planted on a 10 x 10-meter square. The unit cost for each additional tree planted was US$ 2.50. Maintenance cost per newly planted tree was estimated at US$ 5 per year based on US reference values with PPP conversion applied (Vogt et al, 2015).

(ii) _Early warning systems_ . Heat health early warning systems are designed to convert weather forecasts of potentially harmful conditions into alerts that prompt individuals and institutions to take action to prevent heat-related illnesses and deaths. In our study, we used international benchmarks to estimate the initial setup and annual operating costs of implementing such a system in a large Indian city. The startup costs were estimated at US$ 150,000, using data from the U.S. city of Philadelphia, adjusted for PPP and inflation (Ebi et al., ibid).

We based the operating costs on a model for managing heatwave alert systems, with cost references from Madrid, Spain. Rather than relying on a fixed annual cost, as done in Toloo et al. (2013), we used a per-heatwave-day cost approach, using data from Chiabai et al. 2018. The operating costs, converted from Spain to India using the PPP index, were approximately US$ 1,800 per heatwave day.

Additionally, some actions, such as operating the public heat advisory service (‘Heatline’) and deploying extra Emergency Medical Service (EMS) crews, incur direct costs, primarily in the form of additional wages.