rafmacalaba/data-use-mentions-extended huggingface dataset:
financial intermediation does not operate efficiently due to such covariate risks in production, rural households cannot borrow from local credit markets or handle efficiently seasonality of income and consumption.<sup>19</sup> Over time, group lending and other innovative mechanisms have been developed in many places to deal with the moral hazard of rural lending. These measures have helped reduce loan default costs for lenders and handle covariate risks caused by agricultural seasonality. Also, where agriculture is highly seasonal, institutions such as MFIs have sought to diversify income by supporting rural nonfarm activities. To help reduce negative effects of covariate risks on income and consumption, they have introduced seasonal loans. Weekly loan repayment schedules and peer monitoring are enforced to avoid non-payment, especially during lean seasons.

The question is whether these measures are adequate to reduce the role of covariate risks in financial intermediation and to help households cope with the seasonality of income and consumption. No study has yet been done, to the best of our knowledge, to demonstrate whether financial inclusion and innovations have sufficiently reduced the negative consequences of covariate risks in financial intermediation in a rural setting.

The paper analyzes both household and institutional data from Bangladesh to examine both issues, namely, (i) whether seasonality still matters in household borrowing in a rural setting with extensive coverage of microfinance, and (ii) whether seasonality still matters in the portfolio management of the MFIs with innovative means to deal with the moral hazard of rural lending. Descriptive analysis of seasonal patterns of household borrowing data exhibits pronounced seasonality in that borrowing is the lowest during the lean season when households need to borrow most. On the other hand, monthly

> 19 Predictable seasonality does not pose a risk if the means to avert its negative consequences does not involve a great degree of uncertainty.

24
skills development using survey data and tracked their GPAs (up to one year post-treatment) via the official administrative school records in the country.<sup>4</sup> <mark>Students exposed to the intervention report improved self-regulation relative to students in the control condition in the academic quarter immediately after the intervention. This improvement comes primarily from the perseverance-of-effort facet of grit. Impacts are larger in the high-intensity treatment than in the lower-intensity treatment. Impacts are larger for female and Roma students, a group that has been traditionally disadvantaged in North Macedonia. Among Roma students, gains in GPAs become stronger over time, reachingup to 28% of a standard deviation one year post-treatment.</mark> The stronger benefits for disadvantaged students echo similar findings in a number of other social-psychological interventions in education (Yeager et al., 2019; Sisk et al., 2018; Paunesku et al., 2015; Cohen et al., 2009; Hulleman and Harackiewicz, 2009; Wilson and Linville, 1982).<sup>5</sup> <mark>While treated students report putting in more effort and being more industrious (i.e. the perseverance-ofeffort facet of grit), they score lower on the consistency-of-interest facet of grit. Thus, a potentially unintended consequence of the intervention is that it diminished interest for long-term goals. One plausible explanation for this result is that by teaching students the value of deliberate practice applied repeatedly to the same stretch goal, the intervention reduced interest in long-term goals – a potentially unintended consequence of deliberate practice itself. This could also be reflective of the intervention not intentionally targeting a specific interest, as well as of the varied interests corresponding to the age of the children participating in the intervention.</mark><sup>6</sup> <mark>It is also worth noting that separate research has shown that developing consistent interests (i.e., specializing) may, paradoxically, depend on diversification (i.e., sampling) of
23 underlying stability assumptions of the poverty prediction method appear to hold, particularly when models using non-consumption assets are employed.

# **4 Gauging Cost-of-Living Adjustments in Russia and Kenya**

Following an assessment of the validity of our poverty prediction method in Vietnam and rural China, we now turn to two applications that consider the all too common situation where temporal poverty comparisons need to be made in the face of uncertainty about the appropriate price deflator. Our first example concerns Russia between 1994 and 2003, straddling the 1998 financial crisis. Poverty during this period is tracked based on the Russian Longitudinal Monitoring Surveys (RLMS). These are nationally-representative surveys that track a panel of about 4380 dwellings. They are not representative at the regional levels. Data from rounds 5, 8 and 12, corresponding to years 1994, 1998, and 2003, respectively, are analyzed. The surveys have similar consumption modules between rounds as well as many other similar survey components.<sup>16</sup> Nonetheless, there are concerns that the data are quite noisy, especially around Russia‘s Financial Crisis in 1998, which was accompanied by a sharp devaluation (Luttmer, 2001; Wall and Johnston, 2008).

Conventional analysis of these surveys based on official price deflators documents a sharp _increase_ in recorded (consumption) poverty from 11.4% in 1994 to 33.8% in 1998, after which poverty is estimated to have fallen back to 11.1% by 2003.<sup>17</sup> While this trend has in general been accepted in the literature on Russian poverty, recent papers have raised some doubts. Stillman and Thomas (2008) find almost no effect of the 1998

> 16 See www.cpc.unc.edu/rlms for more complete information about the survey and sampling design.

> 17 Poverty figures are authors' calculations based on RLMS expenditure data.
sample of pre-primary students. Not all children in Egypt attend preprimary; indeed, there is substantial socio-economic inequality in access to pre-primary (El-Kogali & Krafft, 2015). In the general population of pre-primary aged children (including those not attending pre-primary), there may be different patterns of inequality in home environments. The children not enrolled in pre-primary might particularly benefit from pre-primary or might particularly suffer from low-quality or inequitable pre-primary if they attended pre-primary; our research is not able to assess these dynamics, and they remain an important area for future research.

The sample we used from Egypt was designed to be nationally representative of pre-primary students, however, there was substantial non-response in the parental sample, which we use to measure SES. As Table 3, in the appendix, shows, there are some differences between our parental sample and a nationally representative sample of parents of KG students. The respondents in our sample were of slightly higher SES. This bias in the sample will not necessarily bias our research questions on SES unless there is a differential relationship among the respondents.

Our data collection efforts were also in late 2021, during the ongoing COVID-19 pandemic. While children were again attending pre-primary in person, the pandemic may have affected outcomes in complex ways that we are unable to unpack. These results do not necessarily generalize to other contexts, although future research should investigate the relative role of pre-primary and home environments in other LMICs.

23
Using data from the PNAD-C collected during 2020, we present a validation exercise where we compare our projections of the average changes in income between 2019 and 2020 by quintiles of the initial per capita household income with the observed values according to the PNAD-C. We present two calculations: the first one excludes the Auxilio Emergencial transfer program (because our macro-micro simulation methodology does not consider government transfers), and the other one includes this program as part of household income.

23
|||||0.0041|
|EU Pair|0.2476<sup>∗</sup>|0.2476<sup>∗</sup>|0.2479<sup>∗</sup>|0.2479<sup>∗</sup>|0.2478<sup>∗</sup>|
|Observations|235,876|235,876|235,876|235,876|235,876|
|Exporter-year FE|Yes|Yes|Yes|Yes|Yes|
|Importer-year FE|Yes|Yes|Yes|Yes|Yes|
|Country-pair FE|Yes|Yes|Yes|Yes|Yes|

_Source_ : Authors’ estimations using WB-DTA 2.0 and ITPD-E data. Dependent variable: bilateral services trade _Xijt_ . PPML estimation with three-way clustered standard errors.

33
residing in cities with larger populations are more likely to implement both product and process innovations and undertake R&D. For product innovation, the predicted propensity when the independent variables are at their mean values is 31.4%. The coefficient corresponds to a marginal effect of 4.3 percentage points, or 13.5% higher from the predicted propensity. For process innovation, the estimated marginal effect is 3.7 percentage points, or 8.5% higher than the predicted propensity of 42.8%. For R&D, the estimated marginal effect is 2.8 percentage points, or 14.3% higher than the predicted propensity.

Other city characteristics are also correlated with innovation-related activities. The coefficients on average precipitation are negative and significant; coefficients on maximum temperature are positive and significant in models for process innovation and R&D; coefficients on minimum temperature are positive and significant; lastly, firms based in inland cities and/or cities with more slopes are, on average, more likely to innovate.

In terms of firm characteristics, younger firms are more likely to implement process innovations and conduct R&D. Firm size is positively associated with all three innovation-

> 20 We are grateful to a referee for this discussion on the requirements of an IV strategy. It may be noted that in an earlier version of the paper (Chen, Hasan and Jiang, 2020), the 1950s population of administratively defined cities corresponding to each natural city (sourced from the World Urbanization Prospects database) was used to instrument for the contemporary population of our natural cities. The IV estimates are largely in line with the results from Probit models.

13
- Sebastian, K. (2009). Agro-ecological Zones of Africa. https://doi.org/10.7910/DVN/HJYYTI, Harvard Dataverse, V2.

- Sedova, B. and M. Kalkuhl (2020). Who are the climate migrants and where do they go? evidence from rural India. _World Development 129_ , 104848.

- Shah, M. and B. M. Steinberg (2017). Drought of opportunities: Contemporaneous and long-term impacts of rainfall shocks on human capital. _Journal of Political Economy 125_ (2), 527–61.

- Shaw, L. H. (1964). The effect of weather on agricultural output: A look at methodology. _American Journal of Agricultural Economics 46_ (1), 218–30.

- Shee, A., C. G. Turvey, and L. You (2019). Design and rating of risk-contingent credit for balancing business and financial risks for Kenyan farmers. _Applied Economics 51_ (50), 5447–65.

- Simonsohn, U., J. P. Simmons, and L. D. Nelson (2020). Specification curve analysis descriptive and inferential statistics for all plausible specifications. _Nature Human Behaviour 4_ , 1208–14.

- Stallings, J. L. (1961). Weather and crop yields. _American Journal of Agricultural Economics 43_ (5), 1153–60.

- Steegen, S., F. Tuerlinckx, A. Gelman, and W. Vanpaemel (2016). Increasing transparency through a multiverse analysis. _Perspectives on Psychological Science 11_ (5), 702–12.

- Suri, T. (2011). Selection and comparative advantage in technology adoption. _Econometrica 79_ (1), 159–209.

- Survey and Census Division, National Institute of Statistics, Niger (NIS) (2014). National Survey on Household Living Conditions and Agriculture 2011. Public Use Dataset. Ref: NER ~~2~~ 011 ~~
primary channel through which protection has been ratcheted up around the world. We refer to such accords as Intellectual-Property-Related PTAs, or IPAs.

The TRIPS Agreement has been studied empirically in broad terms, with papers asking whether its adoption by members has affected such economic outcomes as trade and foreign direct investment (FDI). The role of PTAs that feature strong IPRs standards has attracted much less attention, however. These agreements, which have steadily proliferated since the mid-1990s, are a potentially significant determinant of trade and investment patterns and innovation activities. As such, they deserve systematic study, which we undertake in this paper. Specifically, we build on an existing working paper (Maskus and Ridley, 2020), which studied the impact of membership in IPAs, where one partner is the US, the EU, or EFTA, on the composition of aggregate and bilateral trade, controlling for compliance with TRIPS standards. Here we delve deeper by using specific aspects of such agreements, as measured by the World Bank’s database on the depth of trade agreements (Hofmann, et al, 2017).

The relationship between intellectual property standards and trade is theoretically ambiguous and depends on context. Most important, patents and copyrights are different from import barriers. A cut in import tariffs is a reduction in trade costs, implying generally higher trade. Lower trade barriers expose domestic firms to competition, typically reducing market power and markups. Intellectual property rights, however, create temporary monopolies in the use, including trade, of technologies and goods. These exclusive rights permit their owners to decide where, when, and how they will produce, export, license, and sell protected products and technologies.

A small number of studies have estimated the trade effects of TRIPS, using broad measures. Thus, Ivus (2010) found that a group of developing countries (those that were not British or French colonies) that were obligated by membership in the WTO agreement to adopt stronger patent reforms than a similar group that did not have to reform as much
# Mediating Variables

These are variables that might facilitate improvements in business performance as a result of improved mental health and well‐being of entrepreneurs. They include counterproductive work behaviors (CWB), social networking, perceived social support, individual entrepreneurial orientation (autonomy, risk taking, proactive decision making, information seeking, and so on), as well as overall work‐life balance. A brief description of each is provided below.

- **Perceived social support** was measured using the Multi‐dimensional Scale for Perceived Social Support (MSPSS). MSPSS measures perceived social support in three domains: family, friends and significant others (4 items each).

- **Psychological capital** was measured using the Psychological Capital Questionnaire (PCQ) 12 items version. The PCQ‐12 measures psychological capital across four domains (hope‐4 items, optimism‐2 items, resilience 3 items and self‐efficacy‐3 items).

- **Individual Entrepreneurial Orientation (IEO)** was measured using an adapted Individual Entrepreneurial Orientation (IEO) Scale (Sang and Lim 2009). Data were collected regarding the autonomy, innovativeness, risk taking, competitive aggressiveness, information seeking (pro‐active behavior) and impulsiveness to reflect the degree of change in behaviors over time.

- **<mark>Social networking</mark>** <mark>was measured using the composite scores of social networking with other business circles, as measured by the 4‐item Social Networking Questionnaire. The questionnaire tapped into 4 areas of entrepreneurial networking: 1) sharing of business ideas, new techniques or suppliers; 2) formation of connections with new potential customers, markets or other entrepreneurs; 3) gain of new customers, or financial support from other entrepreneurs; and 4) Sharing of tools, inputs, equipment or employees with other entrepreneurs.</mark>

- **<mark>Work‐life balance (WLB)</mark>** <mark>was measured
considered to be _process_ dimensions, while the middle two are _product_ dimensions. We could posit that process and product are equally important to evaluating statistical capacity, while making a judgment that the individual components are also of equal importance. If so, then the four dimensions would be consistent with Atkinson et al (2002) who observe that “the interpretation of a set of indicators is greatly eased where the individual components have degrees of importance that, while not necessarily exactly equal, are not grossly different.” 12 For fundamental discussions of flexibility in the context of production, see, for example, Kreps (1979).

13 Parts of this aspect might be captured in the technology or product dimensions: if the country is able to produce a best practice census, then it is plausible that it could produce comparably sophisticated products if and when needed. An analogy can be seen with Sen’s notion of capability, which evaluates a person’s well-being via sets of available options, or capability sets, which require information on alternatives that have not yet been chosen. The difficulty of obtaining counterfactual information has led many researchers to measure capability using actual achievements or functionings.

14 One related critique is that since the variables are based on past data, the resulting measure cannot be “forward looking” enough to convey the future possibilities for a given NSS. This inevitable consequence is analogous to the challenge of measuring “vulnerability to poverty” using household survey data. See Dang and Lanjouw (2017) for a recent study that addresses this challenge.

14
Policy Research Working Paper 9987

# **Abstract**

The accumulation of personal wealth, stemming from ownership and control of assets, plays a critical role in advancing women’s and men’s economic opportunities. Yet, it is an understudied dimension of inequality across the developing world. To study individual-level wealth inequality and gender differences in wealth, this paper leverages unique data from nationally representative, multi-topic household surveys that were conducted in Cambodia, Ethiopia, Malawi, and Tanzania and that interviewed men and women in private regarding their personal ownership and valuation of physical and financial assets. The analysis documents substantial gender inequalities in asset ownership and wealth, overall and for specific asset classes. Individual-level wealth inequality measures are substantially higher vis-à-vis comparators based on per capita household consumption expenditures and per capita household wealth, and intrahousehold wealth inequality has a substantial role in explaining overall wealth inequality. While land is a key contributor to wealth inequality across countries, there is cross-country heterogeneity in the relative contributions of asset classes. Self-reporting on asset ownership and valuation, the internationally-recommended best practice, is also shown to lead to higher inequality estimates compared to the business-as-usual survey practice of interviewing a single, most-knowledgeable household member to identify intrahousehold asset owners and values. The discussion expands on the implications of the findings for future surveys and methodological research.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gkoolwal@worldbank.org or tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names
Policy Research Working Paper 9592

# **Abstract**

This paper studies the effects of removing transport and trade barriers between Bangladesh and India on aggregate real income and the distribution of population and real income within both countries. The paper uses a spatial general equilibrium model calibrated to these two economies, along with road network travel time calculated using GPS data, to measure changes in economic outcomes given changes in trade costs across regions. The paper focuses on the Motor Vehicles Agreement between Bangladesh, Bhutan, India, and Nepal and full transport and trade integration between Bangladesh and India. The counterfactual exercises show that decreasing transport and trade barriers between Bangladesh and India can lead to up to a 7.6 percent increase in national real income for India and a 16.6 percent increase for Bangladesh.

This paper is a product of the Transport and Digital Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mdappe@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **What Drives the Development of the Insurance Sector?**

**An Empirical Analysis Based on a Panel of Developed and Developing Countries**

Erik Feyen Rodney Lester Roberto Rocha

**The World Bank**<sup>1</sup>

> 1 Erik Feyen is a Senior Financial Economist in the Financial Systems Department (efeijen@worldbank.org). Rodney Lester is a consultant and former financial sector Senior Advisor (rlester@worldbank.org). Roberto Rocha is a financial sector Senior Advisor in the Middle East and North Africa Region (rrocha@worldbank.org). We would like to thank Alain Ize, Heinz Rudolph, and Rodolfo Wehrhahn for detailed comments and Diego Sourrouille for valuable research assistance.
00590|
||||||(0.00630)|(0.00295)|(0.00261)|(0.00466)|
|Trade Openness|-0.0255|-0.00989|-0.0134|-0.00465|-0.0245|-0.00846|-0.0128|-0.00459|
|_(% of GDP, logs)_|(0.0231)|(0.0111)|(0.00993)|(0.0171)|(0.0234)|(0.0112)|(0.0100)|(0.0173)|
|Financial Openness|0.0220***|0.00474*|0.00663***|0.00893**|0.0202***|0.00418*|0.00593***|0.00802**|
|_(Chinn‐Ito Index)_|(0.00521)|(0.00249)|(0.00221)|(0.00385)|(0.00524)|(0.00249)|(0.00222)|(0.00387)|
|_External Factors_|||||||||
|Foreign Growth|0.00303*|0.000577|0.000132|0.00256**|0.00317*|0.000391|6.75e-05|0.00280**|
||(0.00171)|(0.000803)|(0.000716)|(0.00126)|(0.00169)|(0.000797)|(0.000711)|(0.00125)|
|VIX Index|-0.0186|0.0126|0.00461|-0.0280**|-0.0103|0.00617|