rafmacalaba/datause-encoder-data huggingface dataset:
# Gender Differences in Poverty and Household Composition through the Life‐cycle: A Global Perspective<sup>1</sup>

Ana Maria Munoz Boudet, Paola Buitrago, Benedicte Leroy de la Briere, David Newhouse, Eliana Rubiano Matulevich, Kinnon Scott, Pablo Suarez‐Becerra with the Global Solution Group on Welfare Measurement and Capacity Building.

JEL codes: I3, J1, O1 Key words: Poverty, Gender, Lifecycle, Women, Household 1 Ana Maria Munoz Boudet is a Senior Social Scientist with the Poverty and Equity Global Practice at the World Bank, David Newhouse and Kinnon Scott are Senior Economists with the same group. Benedicte de la Briere is a Lead Economist with the Gender Global Theme Department at the World Bank, Eliana Rubiano Matulevich is an Economist with the same team. Paola Buitrago and Pablo Suarez‐Becerra are both Economist Consultants with the World Bank. The authors can be contacted at <u>amunozboudet@worldbank.org. The World Bank’s Global Solution Group on Welfare Measurement and Capacity Building is a team</u> of statisticians and economists responsible for producing the global monitoring data base (GMD)used for this study. This paper was commissioned as a background paper for UN Women’s flagship reports “ _Turning promises into action_ : Gender equality in the 2030 Agenda for Sustainable Development” and the upcoming _Progress of the World’s Women_ . Funding was provided by UN Women. The team would like to thank Ginette Azcona, Shahra Razavi, and Papa Seck from UN Women for their comments and feedback. The team also benefited from comments by Caren Grown, Carolina Sanchez‐Paramo and Carlos Silva from the World Bank.
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 5. Tanzania’s power tariffs among the lowest in Africa, 2005**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: Eberhard and others 2009.<br>Figure 6. Power costs and tariffs in Tanzania, 2005<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>-<br>historical commercial incremental industrial residential average<br>cost effective cost, effective effective revenue<br>tariff optimal tariff tariff<br>trade<br>US cents<br>effective residential tariff at 100 kWh (US$ cents)<br>Chad Cape Verde Madagascar Uganda Burkina Faso Senegal Kenya Rwanda Cameroon Niger Benin Côte d'Ivoire Namibia Tanzania Ghana Lesotho South Africa Mozambique Malawi Ethiopia Congo Nigeria Zambia<br><!-- End of picture text --> _Source:_ Eberhard and others 2009.

**Figure 6. Power costs and tariffs in Tanzania, 2005**

_Source:_ Derived from Eberhard and others 2009; Rosnes and Vennemo 2009.

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examples of this literature applied to cross-country aggregate data.<sup>2</sup> These conventional tests can be informative about whether perfect risk sharing holds empirically. But once their null hypothesis has been rejected –as is almost invariably the case –in general they cannot say much about the extent of imperfect risk sharing present in the data. Put di¤erently, in most cases one cannot draw inferences about the degree of risk sharing just from the magnitude of consumption correlations or estimated regression coe¢ cients. To do this in a rigorous and meaningful way, one needs a model describing the channels through which the risk sharing arrangement is implemented.

One leading example is the model of partial risk sharing developed by Crucini (1999). It assumes that, prior to any income realization, agents contribute a common fraction of their incomes to an income pool in exchange for the right to the same common fraction of the pooled income after shocks are realized. Thus, the fraction of the income that agents contribute to the pool has the natural interpretation of a coe¢ cient of partial risk sharing. After income shocks are realized and transfers made according to the risk sharing agreement, agents act as permanent income consumers borrowing and lending at a ...xed interest rate. This basic framework has been used by Crucini (1999), Crucini and Hess (2000), Asdrubali and Kim (2008), and Artis and Ho¤man (2008) to obtain estimates of the extent of partial risk sharing. However, this setting su¤ers from a major limitation, in that all participants in the risk sharing arrangement are assumed to contribute the same fraction of their income to the common pool. This makes the framework unsuitable for a situation in which di¤erent agents or countries may engage in di¤erent extents of risk sharing.

This is precisely the focus of this paper. It analyzes to what extent di¤erent countries are able to diversify their idiosyncratic risks, and relates their respective degree of risk sharing to selected country characteristics, in particular regarding their international asset portfolios. To do this, we develop an expanded version of the model in Crucini (1999) that allows each agent / country to contribute a di¤erent fraction of their income to the income pool,

> 2Cochrane (1991) and Townsend (1994) present early implementations of risk sharing tests based on household level data.

2
20 to assess how the contribution of vertical specialization to productivity growth has changed between the 1990s and the 2000s.

In Constantinescu, Mattoo and Ruta (2016b), we estimate the impact of (forward and backward) vertical specialization on productivity growth at the country-sector level. Specifically, we use the following panel estimation with multiple sets of fixed effects: country/industry (μci), country/year (νct) and industry/year (τit):  ln LPcit =  + β  ln LKcit +   ln BVScit-1 +   ln FVScit-1 + μci + νct + τit + εcit, where c denotes country, i is industry, and t year. The left-hand side variable LP is the change in labor productivity, measured as value-added divided by employment. The key explanatory variables are the change in backward vertical specialization (BVS), which is the foreign content in own exports, and the change in forward vertical specialization (FVS), which is own value added re-exported by the direct importer. As standard, the analysis also controls for the capital stock per employee (LK), which is capital divided by employment, where capital is derived by cumulating gross fixed capital formation.<sup>7</sup> The key insight of this regression analysis is that backward linkages have a positive impact on productivity growth, although the effect is not large, while forward linkages do not appear to have a statistically significant impact.

Specifically a one standard deviation increase in backward specialization increases productivity growth of the average sector by 0.06 percent.

> 7 The sample in Constantinescu, Mattoo and Ruta (2016b) consists of 1995-2009 annual data (as 2010 and 2011 are excluded due to lack of capital data) for 14 manufacuring sectors and  39 countries (Russian Federation is excluded due to prevalence of commodity exports).  Data come from the World Input-Output Database (Timmer et al, 2015), obtained by interacting Census data (interpolated for years between Censuses) with yearly trade data. Vertical specialization indicators are computed based on the methodology described by Wang, Wei, Zhu (2015), using an algorithm developed in Quast and Kummritz (2015).
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The estimation results show that proximity to agrobusinesses has a positive impact on rice production: The elasticity with respect to AGAI is 0.16. When disaggregating into input- and output-oriented firms, this impact is mainly explained by the effect of input-oriented agrobusinesses. While _AGAI_i_ has a positive and significant coefficient of 0.22, the coefficient of _AGAI_o_ is negative and insignificant. This is consistent with the above finding that the advanced input variables, fertilizer and irrigation, have large elasticities. It can be interpreted to mean that input-related agrobusinesses, such as input dealers and equipment suppliers, are important to stimulate rice production further in Madagascar. Since they are mainly located to major urban areas, transport infrastructure is critical to connect them to local farmers.

Of course, the possible impact of output-oriented agrobusinesses may have been captured by market accessibility ( _MAI_ ) to a certain extent. When all accessibility indices are included, the impact of MAI dominates all other effects. However, the relative importance of input-related agrobusinesses remains unchanged. Therefore, it can be concluded that in Madagascar, access to input-related agricultural services is more critical for rice production growth.

**<u>Table 4. Spatial autoregressive model with the small positive number specification</u>**

||<br>Spatial r|<br>egression||<br>Spatial r|<br>egression||<br>Spatial r|<br>egression||
|---|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||
|ln_A_|0.038|(0.011)|***|0.047|(0.011)|***|0.045|(0.011)|***|
|ln_L_|0.097|(0.020)|***|0.094|(0.020)|***|0.095|(0.020)|***|
|ln_R_|0.100|(0.009)|***|0.101|(0.009)|***|0.101|(0.009)|***|
|ln_I_|0.133|(0.010)|***|0.136|(0.010)|***|0.136|(0.010)|***|
|ln_F_|0.518|(0.011)|***|0.511|(0.011)|***|0.512|(0.011)|***|
|ln_KM_|-0.055|(0.011)|***|-0.065|(0.011)|***|-0.064|(0.011)|***|
|ln_MAI_|0.247|(0.028)|***|||||||
|ln_AGAI_||||0.144|(0.025)|***||||
|ln_AGAI_i_|||||||0.172|(0.051)|***|
|ln_AGAI_o_|||||||-0.012|(0.051)||
|constant|-7.090|(0.393)|***|-7.992|(0.348)|***|-7.920|(0.357)|***|
|Obs|3,198|||3,198|||3,198|||
|Wald||||||||||
|chi2|7343.2|||7263.4|||7288.1|||
|Spatial para|meters:|||||||||
|_λ_|1.554|(0.056)||1.652|(0.047)||1.650|(0.049)||
ambiguities about the place of Islam in public life, and international developments such as the Iranian revolution have also played a role in inflaming sectarian violence. Using terrorist profiling techniques based on a review of case studies and past terrorist attacks, Hudson (1999) finds that although there is no single terrorist personality, there is little evidence to support the notion that terrorists are psychologically disturbed individuals. Hegghammer (2006) constructs the profile of radical militants in Saudi Arabia and concludes that socioeconomic factors provide a limited explanation for their radicalization, but Jenkins (2011) finds that terrorist recruits in the US tend to be young and relatively uneducated.

McCauley and Moskalenko (2008) study the mechanisms of political radicalization defined as a dimension of increasingly extreme beliefs, feelings, and behaviors in support of intergroup conflict and violence. Using real world case studies and a survey of the literature, they conclude that radicalization occurs in a context of group identification or from the dynamics of intergroup conflict rather than from the vicissitudes of individual psychology. Walters et al. (2013) explore the sources and consequences of radicalization and terrorism through a compilation of interdisciplinary research, theory, and analysis by authors from eight countries. They conclude that violent conflict and extremism in Arab countries is the result of a combination of internal and external factors, including resource dependence, tribalism, social inequality, prolonged existence of oppressive hereditary regimes, militarization, and corruption.

The findings of the few empirical studies on the determinants of radicalization are mixed. Blomberg et al. (2004b), who estimate the relationship between economic growth and terrorism using Markov processes, find that economic contractions lead to increased likelihood of terrorist activities, particularly in democratic, high income countries. Their conclusion that economic growth fluctuations tend to be the main driver of terrorism is problematic as they are not able to 6
_56_ invariably converged to the boundary. As a result, we have adopted the standard, augmented Cobb-Douglas function as our principal specification.

Tables 6.1-6.13 contain our estimates of various specifications of the Cobb-Douglas value-added functions. Tables 6.1-6.3 report sample-wide estimates with industry-specific fixed effects, and Tables 6A-6.13 contain parameter estimates by industry. Each table gives results based on deflated value-added in domestic and world prices. The world price data on value-added are based on the calculations of the effective rate of protection that were computed for each firm in our sample by a World Bank financed project carried out at the Senegal's Ministry of Finance. The availability of value-added data in both domestic and world prices enables us to assess the effects of the numerous variables in the context of both domestic distortions and the more objective scarcities of the world market.

# 6.1 Overall Production Function Estimates

Tables 6.1 and 6.2 report the instrumental variable (IV) and ordinary least squares (OLS) estimates, respectively, of three major specifications of the Cobb-Douglas production function. In each table, columns 1-3 contain estimates based on value-added in domestic prices; estimates in columns 4-6 are based on valueadded in world prices. Ihe instruments used are canonical correlation variables derived from firm-specific dummy variables interacted with time and with the value-added deflator (see e.g., Bowden and Turkington, 1984). The variables which we instrumented are the labor input variables, capital stock, and the percentage of output that is exported. The IV and OLS estimates yield similar results, with the OLS estimates being occasionally more significant.

As the estimated coefficients in Tables 6.1 and 6.2 indicate, total factor productivity appears to be unrelated to enterprise ownership, except for the Syro-Lebanese firms, which display a significantly (30-50 percent) inferior level of productivity. The results challenge three traditional notions, namely that (1) private firms are more productively efficient than public ones; (2) foreignowned firms are more productively efficient than domesticallyowned ones; and (3) Syro-Lebanese enterprises are more productively efficient than domestically-owned firms and, possibly, other foreign-owned firms. The results hold for various
such a randomized framework, a simple comparison of post-intervention outcomes between participants and non-participants is a measure of program effect.<sup>9</sup> In contrast, when a nonrandomized evaluation method is adopted, it is important to account for these correlations or endogeneity in program assessment; otherwise, the results will be biased. There are alternative non-experimental methods that may be relevant to assess program effects. Using a particular method in a program evaluation would ultimately depend on available data and its characteristics (e.g., whether the data is drawn from a cross-sectional or panel survey (Ravallion 2006; Khandker, Koolwal, and Samad 2009). Although randomization is beyond the scope of this study, we use non-experimental design based on both cross-sectional and panel surveys to estimate the effects of PRIME vis-à-vis regular microfinance on seasonality of income and consumption, as well as hunger and poverty.

# **4. Survey Data and Its Characteristics**

The available data to evaluate PRIME comes from a survey conducted by the Institute of Microfinance (InM). PKSF carried out a pre-intervention data-collection survey to identify its target group for PRIME. Along with its partner organizations (POs), PKSF conducted a village census that listed households to determine which were ultra-poor and which were also vulnerable to seasonal food deprivation. The ultra-poor were then selected based on the previously mentioned criteria.<sup>10</sup> After identifying the ultra-poor target households of PRIME using these criteria and focus group meetings, a small survey questionnaire was administered by the PKSF and its POs with the help of the InM (who designed the survey

> 9 The PKSF has not allowed the InM to do a random design of PRIME program evaluation.

> 10 This is the overriding eligibility condition of regular microfinance as practiced in Bangladesh.  The ultra-poor, who have much less than 50 decimals of land, also belong to this target group; however, given the program design, which includes maintaining a weekly repayment schedule and attending weekly meetings, the ultra-poor often find it difficult to participate in regular microfinance programs.

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# **II. Longer-Term Lending in Europe and Emerging Markets by European Banks**

# **_<u>Europe</u>_**

**Bank lending standards in Europe have deteriorated significantly under recent stress conditions.** First they deteriorated considerably as the US subprime mortgage crisis unfolded in 2007 and reached a peak in 2009 in the wake of the default of Lehman Brothers in September 2008. Credit supply weakened significantly in 2011Q4 again when the European crisis deepened. Despite European Central Bank (ECB) action which loosened lending conditions significantly in 2012Q2, they have recently tightened again.

**European banks have systematically tightened their standards for longer-term loans more than for short-term loans and banks expect this trend to continue in 2013Q1** (Exhibit II.1). At the same time, demand for longer-term loans has fallen more than for shorter-term loans, particularly in 2009 (Exhibit II.2). Note that both trends mask substantial heterogeneity between peripheral and core countries. In the periphery, both supply and demand for long-term loans have deteriorated substantially more compared to the core, which underscores the impact of austerity programs, impaired financial markets, and financial fragmentation. Consequently, credit growth has been negative in the periphery since 2011. While initially still positive, credit has been contracting in the core since August 2012 as well (Exhibit I.3).

<!-- Start of picture text --> II.1 Credit supply conditions tightened significantly II.2 ...while demand for longer-term loans has<br>during stress periods, particularly for long-term fallen substantially more in 2009 and 2012<br>loans...<br>Overall European Credit Supply Conditions Overall European Credit Demand<br>Net percentage of banks indicating tightening relative to Net percentage of banks indicating higher demand<br>previous quarter relative to previous quarter<br>70 30<br>60<br>20<br>50<br>40<br>10<br>30<br>20 0<br>10<br>0 -10<br>-10<br>-20<br>-20<br>-30<br>-40<br>-50<br>Long-term loans<br>Short-term loans<br>Long-term loans (Forward looking)<br>Short-term loans (Forward looking) Long-term loans Short-term loans<br>Source: ECB Lending Survey 2013Q1 Source: ECB Lending Survey 2013Q1<br>2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1<br>2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1<br><!-- End of picture text -->

**Banks have used various means to tighten their credit terms including increasing prices, shortening maturities, and demanding more collateral** (Exhibit II.4). By the end of 2008,

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disclosing the cumulative costs of payday loans in amounts (rather than percentages) significantly reduces the demand of such loans.

The remainder of the paper is organized as follows. Section 2 presents the experimental design and the different treatments; Section 3 reports the empirical strategy, Section 4 presents the results and Section 5 concludes.

# **2. Experimental Design**

Individuals from around the capital city of Peru and Mexico were invited to participate in the experiment to test different disclosure forms. During recruitment, individuals were told that they would earn money making decisions but no details were provided about the nature of the decisions. Experimental sessions were conducted in 2013 in Mexico and 2017 in Peru. They took place in a room set up in a way to ensure that communication between subjects was not possible. A total of 600 subjects in Peru and 1,071 in Mexico participated in 57 sessions (10 sessions for each product in Peru and 20 and 17 for credit and savings, respectively, in Mexico), with around 30 subjects per session (see Table 1 for details). Prior to the sessions, a subset of participants in Mexico received SMS and live calls with tips about the terms that were important for financial decision-making. In particular, prospective participants in credit sessions were told to verify the total amount to be paid, including interest payments, commissions and insurance premia. Participants of savings sessions were told to choose the accounts offering the highest yield. Online Appendix OA2 contains the scripts to the live calls and the text of the SMS. While there is an extensive literature on messages as reminders (see, for example, Karlan et al., 2016) here we test messages as a way to disclose information.

Subjects only participated in one experimental session that lasted between 1.5 and 2 hours. Each session started with a 20-minute survey, then three rounds of decisionmaking in Peru and 5 in Mexico where subjects were instructed to choose the product that best fit their needs, followed by an end-of-session survey. The initial survey included questions on demographic characteristics, knowledge and preferences of financial institutions, factors that affect subjects’ financial decisions, and financial literacy.

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**Figure 1: Food Price Index, 1960-2012**

<!-- Start of picture text --> Food Price Index<br>(2005=100)<br>300<br>250<br>200<br>MUV-deflated<br>150<br>100<br>Nominal<br>50<br>0<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012<br><!-- End of picture text -->

**Source** : Based on World Bank data.

**Figure 2: Elasticities (Absolute Values)**

<!-- Start of picture text --> a. S/U ratio elasticity b. Crude oil elasticity<br>0.7 1960-2004 1960-2012 0.6 1960-2004 1960-2012<br>0.6<br>0.5<br>0.5<br>0.4<br>0.4<br>0.3<br>0.3<br>0.2<br>0.2<br>0.1<br>0.1<br>0 0<br>Maize Wheat Rice Soybeans Palm oil Maize Wheat Rice Soybeans Palm oil<br>Source : Authors’ calculations<br>Note : S/U = stock-to-use<br><!-- End of picture text --> — 29 —
dealers, and commercial entities). Therefore, banks are allowed to sell insurance products, provided they conduct the appropriate training programs for their employees according to the insurance products that the bank will sell.

Insurance companies that belong to financial groups are also subject to the Financial Groups Law (Ley para Regular las Agrupaciones Financieras). This law regulates the organization and operation of financial groups, and it establishes general consumer protection rules.

# **The Bancassurance Market in Mexico**

The relationship between banks and insurance companies, among other operational, strategic, and technological synergies, enables insurers to take advantage of a massive distribution channel with benefits in terms of intermediation cost.

## **Financial Groups**

In 2011 the Mexican insurance industry<sup>15</sup> comprised 102 insurers, out of which 15 were integrated into a financial group. This set of insurers participated with 32.1 percent of the total premium of the insurance market, 9.9 percentage points higher than in 2005. The average premium growth of this group during the 2005–2011 period was 14.2 percent, 6.8 percentage points higher than the average premium growth of the total market. This growth suggests that, among other drivers, insurers belonging to financial groups have taken advantage of the bank distribution channel (table 4.1).

## **Table 4.1 Insurers That Are Part of Financial Groups: Market Share (Top 5)** **_percent_**

|Insurer|Market Share<br>||
|---|---|---|
||2005|2011|
|Seguros Inbursa|6.1|7.0|
|Seguros BBVA Bancomer|4.2|5.8|
|Seguros Banamex|2.4|4.5|
|Seguros Banorte Generali|3.0|3.7|
|Pensiones Banorte Generali|a|3.0|
|Aseguradora Interacciones|1.2|~~a~~|

## _Source:_ CNSF.

a. This insurer was not part of the top five in that year.

Distribution of insurance products through the banking system has allowed insurance companies that belong to financial groups to reach new market segments and to offer standardized insurance products linked to banking products. Examples are Motor insurance linked to auto credits, and Life and P&C insurance sold in conjunction with 60