rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10373

# **Abstract**

It is widely recognized that climate hazards impact the poor disproportionately. However, quantifying these disproportionate hazard impacts on a large scale is difficult given limited information on households’ location and socioeconomic characteristics, and incomplete quantitative frameworks to assess welfare impacts on households. This paper constructs a household-level multidimensional poverty index using a synthetic household dataset of 43 million people residing in the coastal zone of Bangladesh. Households are spatially linked to the critical infrastructure networks they depend on, including housing; water, sanitation, and hygiene; electricity; education; and health services. Combined with detailed cyclone hazard data, the paper first quantifies risks to households, agriculture, and infrastructure. It then presents a novel framework for translating critical infrastructure impacts into the temporary incidence of service deprivations, which can contribute to temporary deprivations and hence multidimensional poverty. The paper uses this framework to evaluate the benefits of various adaptation options. The findings show that asset risk due to flooding is US$483 million per year at present, increasing to US$750 million per year in 2050 under climate change. Households face an average infrastructure service disruption of two days per year, which is expected to increase to 4.6 days per year in 2050. This, in turn, would incur a temporary increase in multidimensional poverty (7.2 percent of people are multidimensionally poor at the baseline) of up to 94 percent (2.9 million people) 30 days after an extreme cyclone event (a 1-in-100 years event) at present and 153.9 percent (4.8 million people) in the future. The paper quantifies the large welfare benefits of upgrading embankments, showing how apart from significant risk reduction, these interventions reduce service disruptions by up to 70 percent in some areas and can help up to 1.6 million (0.23 million under current and proposed programs) people from experiencing some form of temporary poverty. Overall, the paper identifies poor households exposed to climate impacts, as well
property owners in control clusters and find no significant difference between the groups, suggesting no discernible spillover effects from the program using our originally specified empirical strategy for estimating spillovers (Table 6, Panel A). In Table 6, Panel B we report similar results for disconnection-ineligible property owners suggesting that the enforcement intervention had no observable spillovers on paying property owners either.

# **8.4 Heterogenous Treatment Effects on Payment Behavior**

We consider two sets of sub-group analysis. First, the calculus for resident property owners is likely to be different to that for non-resident property owners (the former would be more directly affected by service disruption in the enforcement intervention, and potentially more accessible in the engagement intervention). Second the constraints and decisions to make payment may be different when considering the intensive (getting payers to pay more) versus the extensive margin (inducing those that have never made a payment to start paying). The appendix Tables A4 and A5 present sub-group analyses for property owner residency status and the intensive vs. extensive margin of payment respectively. In both cases we find no clear evidence of strong differences across these subgroups.

# **8.5 Water Access and Use**

To measure social and economic costs of the enforcement intervention we use survey data collected nine months after the intervention, as reported in Section 7. Estimated impacts are presented in Table 7. The enforcement intervention had little effect on compound connections to water and sanitation services. The majority of the 96 disconnected compounds were reconnected after agreeing to pay a portion of their balance. We observe 27 of these compounds made a payment after being disconnected, with 20 of these payments being made within one month of the disconnection. The remaining compounds were reconnected without requiring a payment if they agreed on a plan with the utility. Compounds receiving the enforcement intervention have statistically indistinguishable piped water and sanitation connection rates at endline. This remains true when considering whether the piped water connection is currently working. We also find little evidence of illegal connections based on enumerator
education does not seem to increase the gains from e-commerce.

This finding suggests that holding other observable factors equal, having vocational training is better than general education in facilitating e-commerce success. In other words, skills attained from vocational training could be more relevant to e-commerce than general education. For example, regular high school curriculums usually do not include management, business operation, or any technical skills, instead, they include liberal arts and science classes that may not be directly related to e-commerce. However, technical training in vocational schools can be targeted on specific skills. For example, curriculums in vocational schools can include web page design and retailing that are helpful to e-commerce operations. Additionally, vocational training, such as gardening, cooking, and tailoring, can give e-shop owners the practical skills and knowledge needed to understand the markets for the products they may produce and sell through e-commerce. In the Taobao Village Survey, data also show that e-commerce training is one of the top three supports e-tailers need from the government. These pieces of evidence, while far from conclusive, suggest that collective action from the villagers or government support in designing complementary training programs can go a long way to promote further e-commerce development.

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treatment dummies are statistically significant, suggesting that neither financially literacy nor the degree of indebtedness affects the product offered. Auditors are nonetheless offered relatively more expensive products: without late payments, the average difference in the total cost of credit for control auditors is 11.7 percent and 17.4 percent once late payment costs are included. We conclude that Prediction 3 is supported for the matched credit products but not for matched savings products. Appendix tables 4 and 5 include products offered that could not be matched in the market products database by matching them to either the cheapest product or the most expensive product that the institution could have offered. Since every product offered is matched, the number of products in Appendix tables 4 and 5 coincides with the number of auditorinstitution interactions. When we use all products, Prediction 3 is supported for both credit and savings products. Overall, the results seem to conform well with the predictions of the Gabaix and Laibson (2006) model.

Why do staff provide little information voluntarily and offer relatively more expensive products? Table 7 tries to address this question by reporting results from the staff survey conducted in Peru and Ghana. About 70 percent of the staff in each country have university degrees. In Peru there are no

> 37 When the total cost of credit and savings products are regressed against the 9 auditor characteristics and the treatment dummies, the p-value associated with the F-test that all auditor characteristics are jointly zero is 0.992 for credit and 0.172 for savings. This suggests that staff has limited ability to tailor the terms and cost of the product to the characteristics of the client.

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# **Annex 2: Indexed Indicators**

# **Accountability Index (including indicator sources)**

|**Ad**|**justed Accountability Index**|
|---|---|
|•<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•|Accountability, Transparency & Corruption in the Public Sector (AfDB/WB)<br>Accountability, Transparency & Corruption in the Public Sector (AfDB)<br>Accountability, Transparency & Corruption in the Public Sector (WB)<br>Corruption & Bureaucracy (WB)<br>Corruption in Government & Public Officials (EIU)<br>Diversion of Public Funds (WEF)<br>Accountability of Public Officials (EIU)<br>Public Sector Corruption Investigation (GI)<br>Public Sector Corruption Bodies (GI)<br>Corruption Investigation (GI)<br>Prosecution of Abuse of Office (BS)<br>Accountability, Transparency & Corruption in Rural Areas (IFAD)|
|**Sub**|**-Indicators removed for endogeneity**|
|~~•~~<br>~~•~~<br>~~•~~|~~Access to Information (GI)~~<br>~~Public Information (GI)~~<br>~~Access to Legislative Processes & Documents (GI)~~|

_Source: Mo Ibrahim Index of African Governance Indicators, 2017_

# **Service Delivery Indices (including indicator sources)**

||**Education Service Delivery**|**Health Service Delivery**|
|---|---|---|
|**Ov**<br>(pro|**erall Education Index*
_m_</sup> _t_<sup>_, a_</sup> _t_<sup>_f_).Until2004,divorcedidnotexistinChile.Forsimplicitywetreatdivorce,</sup> marriage annulment and de facto separation as equivalent in the model. The separation probability is specified as a logistic model, Upon separation, a couple’s non-pension assets _At_ are split evenly between the two individuals who then become single households.

Recall that one feature of the pension reform was a change in the rules governing pensions upon divorce. Prior to the reform, divorce could lead to a loss of rights to a spouse’s pension benefits. After the reform, in the event of a divorce or annulment, a judge can rule that

> 28See table B8 for the actual bracket values and corresponding marginal tax rates.

> 29We obtain these probabilities from life tables that are specific to Chile and are conditional on age and gender (RV-2004, from Circular 1314, published by the Superintendencia de Pensiones).

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from metals, minerals, agriculture, food and beverages have a negative and statistically significant effect on economic growth. Economic growth is positively related to billionaire net worth, with the coefficient being statistically significant at the 1% level. These findings stand whether billionaire or country fixed effects are employed. The instruments reject under-identification. The instruments also pass the overidentification test, especially when billionaire fixed effects are used, indicating that the validity of the instruments cannot be rejected. The instruments are also strong, given that they pass the weak identification test, exceeding the Stock and Yogo critical values.

The findings thus far point to a plausible mechanism whereby top incomes increase in the face of growth or commodity terms of trade shocks. We test whether this is conditional on the degree of market contestability/competition and quality of institutions in the economy. We use the sample average of the control of corruption quality of governance indicator. This captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand. We also use the sample averages of the World Economic Forum’s indicators on the intensity of location and market domination. The former measure considers the distortive effect of taxes and subsidies on competition, the extent of market dominance, and competition in services. The market dominance indicator measures perceptions of whether corporate activity is characterized by a few business groups or many firms. For all the indicators, higher values imply better governance/market contestability.

Table 6 reports the findings. All interactions between governance/competition and commodity price shocks have negative and statistically significant coefficients. The same results are found when the governance/competition variables are interacted with resource rents. The results indicate that in countries with more contestable markets and good governance, top incomes are less likely to increase as a result of positive commodity price shocks. These findings are consistent with Andersen et al. (2017) that finds that exogenous shocks in petroleum income increase hidden wealth in offshore accounts for economies where institutional checks and balances are weak.<
# **1. Introduction**

Rural labor markets are a subject of widespread interest mainly because of the large number of rural population in the western hemisphere and the ways in which the rural population migrates and exports their produce. In Mexico alone, there are some 25 million rural people, most of them living in poverty.

The agricultural sector contributes just 5 percent to Mexico’s GDP while it provides employment to 45 percent of the rural workers in semi-urban areas (localities with rural population less than 15,000) and 56 percent in more dispersed rural areas (locations with less than 2,500 inhabitants). However, labor productivity and returns tend to increase as production shifts from grains to more export oriented crops such as fruits and vegetables, which has occurred in the last decade in Mexico. Moreover, export oriented crops also give a boost to rural nonfarm sector employment.

Today’s rural population in Mexico is no longer dependent on agricultural income alone. The rural population receives its income from various sources apart from agricultural activities, namely from off-farm or nonagricultural activities, remittances, and transfers. Moreover, the share of off-farm, transfers and remittances in total income is rapidly increasing for the rural population (see Verner 2004). Labor is the most important asset the poor have and nonagricultural jobs pay the highest wages in rural areas.

Households and locations operate in multiple, interacting activities and sectors, for example rural nonfarm and agriculture are complements and migration affects dynamics at the household and community levels. This has implications for policy because it requires a policy approach that is territorial in a double sense: it takes account of spatial differences and spatial interactions; and it recognizes multiple activities.

This paper tries to shed some empirical light on income generation and employment in the rural agricultural and nonagricultural sectors in Mexico. The analyses are based on ENIGH ( _Encuesta nacional de Ingresos y Gastos de los Hogares_ ) surveys from 1992 to 2002 and ENET ( _Encuesta Nacional de Empleo_ ) surveys from 1995 to 2003<sup>
Klass et al., 2006) among others. Summary statistics for the sample of analysis are provided in Table A1.

# _2.2 Commodity Windfalls_

Data on commodity price shocks are obtained from the IMF (Gruss and Kebhaj, 2019). The commodity terms of trade index is based on international prices of up to 45 individual commodities, constituting broad categories of energy, metals, food and beverages, and agricultural raw materials. We calculate commodity price shocks by taking the first differences of the log of the price index as shown in equation (1) below.

Where is the natural log of the real price of commodity _j_ in year _t_ . Ω represents the commodityand country-specific time-varying weights, which are based on three-year rolling average trade flows over jj,tt cc,jj,tt the previous three calendar years. Similar measures of commodity windfalls have been used by Arezki PP and Brückner (2012).<sup>7</sup>

> 7 We employ a similar measure to calculate specific commodity sub-indices. There are marginal differences in terms of weights, but the methodology is largely the same.

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over time), housing quality, livestock, and productive assets. For

> 11 All survey instruments and enumerator manuals for the survey can be found at https://sites.google.com/site/decrgberkozler/datasets.

> 12 An important issue in survey design, given the capture of assets at both the group and household levels, is avoiding double-counting the same assets. In an attempt to avoid this, we carefully distinguished group assets as those considered to be owned collectively by the group, and in the household survey we asked separate questions about individually-owned assets versus group-owned assets (typically livestock) that might be on household land or supported with household labor and expenditure, but were not owned by the household.

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generate lower yields, but when fertilizer and other inputs are provided, there is no difference across genders. In contrast, one study (Tiruneh et al., 2001) finds that in Ethiopia, female-headed households are less likely to adopt improved varieties (in this case, wheat). Additionally, a number of studies in Kenya and Zambia document the lower adoption of improved maize among female-headed households (Kumar, 1994; Saito, Mekonnen, and Spurling, 1994; Ouma, De Groote, and Owuor, 2006).

In cases in which there is lower female adoption of improved varieties, the main factors seem to be overall production constraints rather than norms with respect to women’s use of new technologies. A number of studies in Kenya<sup>15</sup> argue that women’s lower adoption of technology is due to lower access to land, labor, and credit and lower levels of education, which are related to market failures in these areas. A solution may be to provide a more complete package of support. For example, the study by Gilbert, Sakala, and Benson (2002) shows that providing technology together with inputs and extension seems effective.

# **_Technology—Extension and Other Agricultural Information Outreach_**<sup>**_16_**</sup>

In terms of cross-country data, the 1988–89 FAO survey of 97 countries found that 5 percent of extension resources were devoted to women, and only 15 percent of extension personnel were female (FAO, 1993). The fact that these are the most recent comprehensive data provides some evidence regarding the small amount of attention this subject receives.

However, the situation has likely improved since that survey. In terms of access to extension, recent figures come from a set of 2010 World Bank and IFPRI surveys in three countries. These surveys found that in Ethiopia, 20 percent of women compared with 27 percent of men had been visited by an extension agent. In Ghana, the figure was much
# Source: Authors based on OECD, 2012a

# • _Other non-tariff measures (NTMs) and business climate_

Non-tariff measures (NTMs) and business climate/environment at large influence both the competitiveness of domestic companies and the attractiveness for foreign investors. In other words, they potentially affect a country’s participation in GVCs, whatever the governance structure of the GVC.

The _Doing Business_ (WB) and _Global Competitiveness_ (WEF), among others, provide a list of the measures that matter to the operation of business, as well as an indication of countries’ relative performance against selected criteria. The range of measures is very large, from the regulatory environment to the functioning of markets (e.g., state trading enterprises and government procurement). Considering that trade within GVCs is often associated with transfers of knowledge and technology, protection of intellectual property and competition is a tipping decision point for many industries. The cost of administrative burdens is also magnified through the lens of GVCs, where management coordination is a challenge per se.

Political stability, governance and corruption are also decisive factors in the decision to include a country in a GVC (see Figure 10 and Figure 11) This relates to security (including of assets and personnel) and predictability, which are the key drivers of intra-GVC trade and on-time delivery to the consumers. Within GVCs, suppliers are often expected to meet the lead firm’s CSR codes, raising a number of challenges for audit and execution in small developing countries’ firms (UNCTAD, 2012).

**Table 4. Improving the business environment: Policy objectives and performance indicators**

||**Policy objectives**|**Performance indicators**|
|---|---|---|
|**Inte**|**llectual property protection**|•<br>Ease of doing business index (IFC, WTI 3.1, WDI)|
|•|Improvement of IP regime and administration to comply
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- We always compute skilled birth attendance and antenatal care utilization rates for births in the past two years. The comparison databases, by contrast, use births in the reference period of the original survey question – e.g. births over the last 5 years for DHS, over the last year in the second MICS wave, and over the last 2 years from the third MICS wave onwards.

- We apply a consistent definition of full immunization in terms of both required vaccines and the age by which a child has to have received them to be considered fully immunized, whereas the vaccines and age-groups vary within and across the DHS and MICS comparison databases. For example, some MICS surveys measure full immunization at age 18-29 months and others at age 12-23 months, whereas we consistently use the 15-23 months age-group.<sup>23</sup> Also, full immunization rates in the MICS comparison databases vary in whether they consider pentavalent vaccination as an alternative to standard DPT vaccination, while we consistently consider it an alternative.

- As mentioned above, we use a consistent definition of ARI (cough and rapid breathing) across all DHS surveys to define the sample for which we compute our ARI treatment indicator.<sup>24</sup>

- We use a consistent definition of modern contraception methods across all surveys in the database which includes the lactational amenorrhea method (LAM). LAM is considered a modern method in STATcompiler and DHS reports but not in MICS reports and the MICS data in WHO’s Health Equity Monitor.

> 23 We depart from the 12-23 months age-group that is used in most DHS and MICS reports to account for the fact that immunization schedule age-ranges for the first dose of measles vaccination vary internationally from 9 to 15 months. Countries with higher measles prevalence are recommended to immunize children at an earlier age, despite the vaccines being more effective when administered later (World Health Organization 2017).

> 24 Our definition of appropriate care-seeking for children with ARIs excludes _other_ public or private providers since it