rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Agent banking holds the promise of facilitating financial inclusion for underrepresented and marginalized groups, including women, through improved accessibility, convenience, and reduced costs. As an alternative to bank branches, agents represent physical access points that enable customers to make deposits, withdrawals and money transfers, and to repay loans (Lyman et al., 2006; Siedek et al., 2008; Flaming et al., 2011). In contrast to personnel of a bank branch that likely change from visit to visit, the agent is usually the same contact for clients. Customers therefore choose agents based on location, or personal characteristics of the agent such as gender, religion, ethnicity or age.

In this paper we investigate whether gender plays a role in the choice that customers make about which agents to transact with. Research to date says little about matching between agents and clients, or the potential implications of agent gender on clients’ financial access and utilization. This is an important policy question since closing the gender gap in financial access is a goal of institutions such as the World Bank, the International Finance Corporation (IFC), and the United Nations. According to 2017 Global Findex data (Demirguc-Kunt et al., 2018), the gender gap in financial institutions account ownership was 9 percentage points (35% of males versus 26% of females had an account).

We study the role of gender in financial transaction decisions using data from FINCA DRC, one of the largest microfinance institutions (MFIs) in the Democratic Republic of Congo. The Democratic Republic of Congo is a country characterized by large gender inequalities–it was ranked 176th out of 189 countries in the 2018 Gender Inequality Index (United Nations Development Programme, 2018). These gender inequalities impede women’s full participation in social and economic life.

Females only represent 39% of FINCA DRC’s customers and 23% of all agents. In this context of maledominated agent networks, both genders transact predominantly with male agents since there are simply more of them. But while male agents comprise the majority of the network, the distribution of male and female agents across locations is somewhat similar, suggesting that almost all FINCA DRC customers have a choice of the gender of the agent.

Our regression results indicate that clients are more likely to transact with agents of their own gender. In our main regression model, we find that women are significantly more likely to transact with female agents (odds ratio 1.518, p-value=0.000) and this tendency increases both with the value of transactions and the customer’s balance. These patterns hold when controlling for customer, agent experience, and market-level covariates and when we restrict the analysis to markets with substantial representation of female agents. We also confirm gender matching using dyadic regressions. The results show that both men and women have broadly similar tendencies to transact with clients of their gender and similar age.

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2 Quite a few studies have been carried out to drive home the costs of these special protection arrangements. Individual sector studies for textiles: (Horkre (1985); Tarr and Horkre (1984); Cline (1987); Keesing and Wolf (1980)]; for automobiles [Winston and Associates (1987); Tarr and Morkre (1984); FeenstLa (1984, 1985a, 1985b); Dinopoulos and Kreinin (1986)]; for steel [Tarr and Morkre (1984)] are among the most vilely known studies thav provide estimates of the welfare costs of NTBs in the US. Perhaps the most comprehensive recent estimates come from the 31 case studies of special protection by Hufbauer, Berliner and Schott (1986). All these studies, using a partial equilibrium (PE) framework. rovide a very useful range of estimates of the costs of non-tariff px lon. These studies are especially valuable when taken individually because the assumptions of PE analysis provide an adequate approximation of reality even thougk. the behavior giving rise to the demand and supply curves used in the estimation are not always properly spelled out. But, as we discuss below, when it comes to aggregating the costs of all restrictions taken together, the underlying assumptions are much less tenable.

This paper deals with the problems of PE analysis by presenting estimates from a static ten-sector computable general equilibrium (CGE) model of the US economy calibrated to the year 1984 when QRs in textiles, and autos were in effect, and those on steel were in negotiation. To our knowledge, the treatment of QR-associated effects here is more satisfactory than in previous CGE applications. 'While the US-negotiated VER on automobiles has expired, demand for NTB protectionism in the US remains strong and may be on the rise because of presidential campaign politics. A new round of estJmates of the costs of protection is therefore all the more welcome.
noticeable.<sup>22</sup> All applications for review requesting the renewal of antidumping duties in force had every chance of being accepted.

As regards the cases with final positive determinations (i.e. measures were approved), Figure 4 shows that during the period under study, only in five years<sup>23</sup> – 1988, 1989, 1990, 1992 and 1995 – all cases were concluded with affirmative determinations. In general terms, a greater acceptance is observed in the 1993-1999 period (save for 1995), whereas fewer approvals occurred in the following years, precisely when a flexible exchange rate system was in place again. Given the differences among the measures adopted, it is impossible to assert whether the government tried to counteract the effects of currency appreciation on certain sectors by resorting to antidumping duties.

Figure 4 Share of Approved Antidumping Cases – Brazil: 1988-2002 <!-- Start of picture text --> 120<br>Trade liberalization Fixed exchange rate Flexible exchange rate<br>100<br>80<br>60<br>40<br>20<br>0<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Approved Approved excluding reviews<br><!-- End of picture text --> Source: DECOM, SECEX, MDIC Reports, several years. Author's calculations.

Note: Accepted cases were those concluded with a final positive determination of dumping and injury.

22 A more rigorous assessment would require the conduction of econometric tests such as those by Leidy (1997), Knetter and Prusa (2000), Francois and Niels (2003), and Irwin (2004).

23 The fact that all the requests submitted in the first three years – 1988, 1989 and 1990 – were accepted does not bear the same significance as in 1992 and 1995, as in the first three years of the antidumping implementation in Brazil, there were few requests for investigations.

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Table 2: Effect of Years of Schooling on Number of Children Born

||**Number of**<br>**Children Born**<br>(OLS)|**Years of**<br>**Number of**<br>**Schooling**<br>**Children Born**<br>(First Stage)<br>(Reduced Form)|**Number of**<br>**Children Born**<br>(2SLS)|
|---|---|---|---|
||(1)|(2)<br>(3)|(4)|
|||A. Census + DHS||
|Years of Schoolingizy|-0.120<br>(0.016)<br>[0.000]|||
|Add’l Years of Free<br><br> <br>||0.131<br>-0.057<br><br>||
|Schooling<br>�<br>_I_<sup>_F P E_</sup><br>_zy_<br>�||(0.034)<br>(0.016)||
|||[0.001]<br>[0.001]||
|�<br>Years of Schoolingizy|||-0.437<br>(0.090)<br>[0.000]|
|First Stage F-Statistic||14.80|14.80|
|Number of Clusters<br>N|32<br>83005|32<br>32<br>83005<br>83005|32<br>83005|
||,|,<br>,|,|
|||B. Census Only||
|Years of Schoolingizy|-0.097<br>(0.015)<br>[0.000]|||
|Add’l Years of Free<br><br><br> <br>||0.154<br>-0.064<br><br>||
|Schooling<br>�<br>_I_<sup>_F P E_</sup><br>_zy_<br>�||(0.025)<br>(0.014)||
|||[0.001]<br>[0.000]||
|�<br>Years of Schoolingizy|||-0.417<br>(0.074)<br>[0.000]|
|First Stage F-Statistic||37.62|37.62|
|Number of Clusters|32|32<br>32|32|
|N|69,083|69,083<br>69,083|69,083|
|||C. DHS Only||
|Years of Schoolingizy|-0.130|||
||(0.017)<br>[0.000]|||
|Add’l Years of Free<br><br> <br>||0.112<br>-0.059||
|Schooling<br>�<br>_I_<sup>_F P E_</sup><br>_z_<br>�||(0.046)<br>(0.021)||
|_y_<br>||[0.021]<br>[0.007]||
|�<br>Years of Schoolingiz|||-0.529|
|y|||(0.165)<br>[0.001]|
|First Stage F-Statistic||5.93|5.93|
|Number of Clusters|30|30<br>30|30|
|N|13,922|13,922<br>13,922|13,922|

_Source_ : Author’s analysis in panel A is based on data from the Ethiopian census of 2007 and from the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016; each data source is used separately in panels B and C.

_Note_ : The dependent variable is years of schooling in column 2 and is number of births in the other three columns. Years� of Schooling _izy_ is the reported number of years of schooling from the data; Years of Schooling _izy_ is the predicted number of years of schooling, instrumented with the free primary education (FPE) intensity measure, _Izy_<sup>FPE</sup> . All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age when multiple survey waves are included. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

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establishments, but the coverage seems to be nearly the same. For 1963, on the other hand, the total of insured was only about 86 percent of employees enumerated by the census and differences between sub-groups were in some cases substantial; footwear, wearing apparel and textile goods with metal products, excl. machinery, accounted for almost the whole difference between the two series. The picture conforms to the gradual extension of social insurance to small establishments and communities. It seems safe to conclude that compared to the census, the coverage of social insurance improved significantly from 1960 to 1984. If wages depend upon size of establishment, we should thus expect a bias in the SII averages for daily wages.

The censuses of manufacturing in 1963 and 1980 both give breakdowns by large and small establishments, the former defined as having 10 or more persons engaged (not only employees). The information is strictly about annual average wages computed as annual wage bill over average number of employees. Table VI.App.2.3 shows these by big and small establishments (thus defined) for 1963 and 1980. Since the composition of employees by big and small establishments changed somewhat over the period, big establishments increasing relatively, wage indexes were computed. For the sake of comparison also SII wages for all insured and for manufacturing separately are shown in this table.

Table 3.3 discloses three important features of wages i manufacturing: (1) Wages are much higher in big than in small

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# **9. Summary and Conclusions**

Assessing the impact of multilateral trade liberalization on poverty is a challenging assignment. As Winters (2002, p. 43) notes: “Tracing the links between trade and poverty is going to be a detailed and frustrating task, for much of what one wishes to know is just unknown. It will also become obvious that most of the links are very case specific.” The research reported here is an attempt to make known a few more of these “unknown” linkages. As such, the approach has been heterogeneous and opportunistic, calling on experts in this field to undertake in-depth studies in countries for which appropriate data and analytical infrastructure are available. All of this research capacity has been directed towards the analysis of the trade policy question which is central in many policy makers’ minds today – namely: What are the likely poverty impacts of a successful Doha Development Agenda? And what elements could be added to enhance this outcome?

As noted previously, the approach taken by the project ensures consistency of methods in the global analysis of the multilateral trade reform scenarios – and also in the methodology for incorporating these results into the national analyses. However, at the country level, different authors have had the liberty to take a variety of approaches depending both on the particular circumstances facing their countries and their own analytical interests. This is why we have two studies of the Brazilian economy – one of which focuses on near term impacts across heterogeneous individuals, households and

> by exempting a few special products from liberalization. But given the multitude of products and countries we cannot identify such cases here. Besides, we find it implausible that developing countries could leave the exemptions door open in any significant fashion without industrialized countries also squeezing their sensitive products through the same opening.

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34 <u>Table 2. Seasonal Variation in Diet Feasibility and Cost, 2013–2017</u>

||(1)<br>**Household**<br>**With Feas**<br>_Estimated Perc_|<br>**-Months**<br>**ible Diet**<br>_ent Feasible_<sup>‡</sup>|(2)<br>**Seasonal Variation**<br>**in Diet Cost**<br>**_if feasible_**<br>_Seasonal Factors_<sup>_*_</sup>|(3<br>**Seasonal**<br>**in Die**<br>**_imputing_**<br>_Seasonal_|)<br>**Variation**<br>**t Cost**<br>**_infeasible_**<br>_Factors_<sup>_*_</sup>|
|---|---|---|---|---|---|
||Individualized<br>Diets|Household<br>Sharing|Individualized<br>Diets<br>Household<br>Sharing|Individualized<br>Diets|Household<br>Sharing|
|January|91.23|<br>58.41|<br>-1.70<br>0.63|4.19|<br>42.24|
|<br>February|89.69|52.51|3.28<br>3.13|-3.61|31.30|
|<br>March|88.80|52.25|-0.13<br>0.66|-10.89|-7.99|
|April|91.89|52.03|0.85<br>2.25|-13.42|-9.50|
|<br>May|96.24|57.29|7.20<br>4.33|-0.38|-11.70|
|<br>June|95.19|52.42|4.85<br>2.49|18.47|21.83|
|July|93.05|48.31|-0.96<br>-1.81|8.30|-18.43|
|<br>August|93.91|52.64|-5.41<br>-4.43|-4.94|-51.53|
|<br>September|89.15|53.26|-6.63<br>-5.79|5.10|-38.56|
|<br>October|90.73|59.73|-3.65<br>-2.08|-11.71|-33.40|
|November|94.44|67.53|2.58<br>0.82|-1.61|11.61|
|December|93.82|65.26|-0.28<br>-0.21|10.49|64.14|
|**_Seasonal Gap_**|7.44|19.22|13.83<br>10.12|31.89|115.67|
|Mean Availability<sup>†</sup>|89.72|60.18||||
|<br>(% Household-Months)|(0.91)|(1.56)||||
|<br>Median Cost, per capita per day<sup>†</sup>|||1.79<br>2.26|||
|<br>(2011 US$ PPP)|||(0.03)<br>(0.04)|||

‡ Calculated as in equation (9), interpreted as the percent of households with a feasible diet on average each month. The seasonal gap in feasibility is the percentage point difference between the most feasible and the least feasible month.

* Seasonal factors of diet cost calculated as in equation (8) interpreted as the percentage point difference in average cost in that month relative to the average over all months of the year. † Standard error in parentheses.
Table 5: Tetrachoric Correlation Matrices for JQI components (2007, 2011, 2014 and 2020)

||(1)<br>Social<br>Security<br>Benefts|(2)<br>Minimum<br>wage<br>compliance|(3)<br>Low incidence<br>of child labor|Components<br>(4)<br>Low incidence<br>of relative<br>underpayment|(5)<br>Low incidence<br>of compensatory<br>moonlighting|(6)<br>Labor<br>stability|(7)<br>Low incidence<br>of overqualif-<br>cation|
|---|---|---|---|---|---|---|---|
|Panel A: 2007||||||||
|Social Security Benefts|1.00|0.77|0.58|0.45|-0.09|0.16|0.01|
|Minimum Wage Compliance|0.77|1.00|0.68|0.88|0.06|-0.01|-0.01|
|Child Labor|0.58|0.68|1.00|0.38|0.00|0.05|0.09|
|Relative underpayment|0.45|0.88|0.38|1.00|0.05|-0.01|0.00|
|Compensatory moonlighting|-0.09|0.06|0.00|0.05|1.00|0.04|0.01|
|Labor stability|0.16|-0.01|0.05|-0.01|0.04|1.00|0.02|
|Incidence of overqualifcation|0.01|-0.01|0.09|0.00|0.01|0.02|1.00|
|Panel B: 2011||||||||
|Social Security Benefts|1.00|0.70|0.60|0.40|-0.13|0.11|0.06|
|Minimum Wage Compliance|0.70|1.00|0.68|0.87|0.11|-0.03|-0.02|
|Child Labor|0.60|0.68|1.00|0.31|-0.04|0.05|0.08|
|Relative underpayment|0.40|0.87|0.31|1.00|0.04|0.01|-0.04|
|Compensatory moonlighting|-0.13|0.11|-0.04|0.04|1.00|0.02|-0.01|
|Labor stability|0.11|-0.03|0.05|0.01|0.02|1.00|0.03|
|Incidence of overqualifcation|0.06|-0.02|0.08|-0.04|-0.01|0.03|1.00|
|Panel C: 2014||||||||
|Social Security Benefts|1.00|0.69|0.64|0.38|-0.10|0.04|-0.04|
|Minimum Wage Compliance|0.69|1.00|0.66|0.89|0.05|-0.09|-0.05|
|Child Labor|0.64|0.66|1.00|0.32|0.06|0.00|0.04|
|Relative underpayment|0.38|0.89|0.32|1.00|0.01|-0.03|-0.05|
|Compensatory moonlighting|-0.10|0.05|0.06|0.01|1.00|0.02|-0.02|
|Labor stability|0.04|-0.09|0.00|-0.03|0.02|1.00|0.04|
|Incidence of overqualifcation|-0.04|-0.05|0.04|-0.05|-0.02|0.04|1.00|
|Panel D: 2021||||||||
|Social Security Benefts|1.00|0.71|0.59|0.37|-0.09|0.06|0.00|
|Minimum Wage Compliance|0.71|1.00|0.61|0.88|0.08|-0.03|0.06|
|Child Labor|0.59|0.61|1.00|0.25|0.10|0.01|0.21|
|Relative underpayment|0.37|0.88|0.25|1.00|0.01|-0.01|0.01|
|Compensatory moonlighting|-0.09|0.08|0.10|0.01|1.00|-0.02|0.05|
|Labor stability|0.06|-0.03|0.01|-0.01|-0.02|1.00|0.05|
|Incidence of overqualifcation|-0.00|0.06|0.21|0.01|0.05|0.05|1.00|

_Notes:_ Based on data from EH 2007-2021.

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<!-- Start of picture text --> Figure 1: Global Growth Convergence in Service is much faster<br>1.6 Convergence in services productivity<br>1990-2010<br>1.4<br>China<br>1.2<br>India<br>1<br>0.8<br>Malaysia y = -0.0523x + 0.7331<br>0.6 Ethiopia<br>0.4<br>0.2<br>Brazil<br>Thailand<br>0<br>-0.2 Tanzania<br>-0.4<br>-0.6<br>6 7 8 9 10 11 12<br>ln(services VA per worker), early 1990s<br>change in ln(services VA per worker), early 1990s to late 2000s<br><!-- End of picture text --> Source: World Development Indicators. Ethiopia data from Martins (2014). Labor productivity calculated by the ratio of total sector value-added t employment in sector. Underlying accounts are in 2005 constant international USD. Values taken from earliest year available from 1990-1993 and available from 2005-9. Overall conclusions do not change if line is quadratic or cubic in ln(initial VA per worker).

6
**Figure 2. Population Structure and COVID-19 death rates**

<!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>0 5 10 15 20 25 30<br>Percentage of the population 65 years and older<br>Sub-Saharan Africa Developing Countries Industrial Countries<br>COVID-19 deaths (per million people, logs)<br><!-- End of picture text --> _Source: Our World in Data COVID-19 database_ 24
Policy Research Working Paper 7326

# **Abstract**

A significant portion of the world’s forests that are eligible for Reducing Emission from Deforestation and Forest Degradation, known as REDD+, payments are community managed forests. However, there is little knowledge about preferences of households living in community managed forests for REDD+ contracts, or the opportunity costs of accepting REDD+ contracts for these communities. This paper uses a choice experiment survey of rural communities in Nepal to understand respondents’ preferences toward the institutional structure of REDD+ contracts. The sample is split across communities with community managed forests groups and those without community managed forest groups to see how prior involvement in community managed forest groups affects preferences.

The results show that respondents care about how the payments are divided between households and communities, the severity of restrictions on firewood use, the restrictions on grazing, and the fairness of access to community managed forest resources as well as the level of payments. The preferences for REDD contracts are in general similar between community managed and non-community managed forest resource respondents, but there are differences, in particular with regard to how beliefs influence the likelihood of accepting the contracts. Finally, the paper finds that the opportunity cost of REDD+ payments, although cheaper than many other carbon dioxide abatement options, is higher than previously suggested in the literature.

This paper is a product of the Environment and Energy Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at sdissan2@gmail.com.

_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
will be, e.g., 10 (gain from tax evasion) if he does not report the sale. However, if a customer uses a credit card, the seller’s payoff will be zero (no gain) because he should honestly report his sale, or, e.g., ‐20 (loss from the detection and punishment) when he does not report his sale. Therefore, the equilibrium, in most cases, will be the worst case, the shaded area of the left matrix of Table 5 (Not use CC – Not report). That is because a seller has a strong incentive not to report sales to maximize payoffs when customers do not use credit cards. Sometimes, in expensive deals, a seller can collude to share the tax benefit with the customers; such payoffs are displayed in parentheses.

Introducing TIETP to give tax benefits to end‐consumers, however, can alter the equilibrium. TIETP will give end‐consumers incentives to use credit cards, making the payoffs to end‐ consumers 2, for instance, as shown in the right payoff matrix in Table 5. The payoffs of business sellers are the same as without TIETP. In that case, the highest possibility will be the case where end‐consumers request to use credit cards and business sellers report the sales accordingly (the shaded area of the right matrix; Use CC – Report). Nonetheless, again, the possibilities remain for collusion between sellers and buyers and benefit sharing. For example, sellers can offer customers lower prices by sharing the tax evasion benefit and can request that their customers not use credit cards, especially for expensive transactions, the payoffs of which are displayed in parentheses in Table 5. TIETP clearly cannot eliminate all underreporting. However, we can expect that, in most ordinary retail transactions, such as in restaurants, bars, pharmacies, and other small retail stores, the equilibrium will be “credit card use and honest report.” Table 5 Payoff Matrix of Non‐TIETP and TIETP

|<Wi<br>|thout TIET<br>End‐co|P> <W<br>nsumer|ith TIETP<br>End‐co|><br>nsumer|
|---|---|---|---|---|
|Seller|<br>Not use<br>CC|<br>Use CC|Not use<br>CC|Use CC|
|Not report|0(5)<br>10 (5)|Not report<br> <br>0<br>‐20|0(5)<br>10 (5)|2<br>‐20|
|Report|0<br>0|Report<br>0<br>0|0<br>0|2<br>0|

_Source:_ Authors’ conception.

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