rafmacalaba/data-use-mentions-extended huggingface dataset:
These data can be used to identify nutritionally vulnerable women and children. The DHS also include a household wealth index based on a household’s assets and living conditions. We use this index as a proxy for household wealth. However, aggregate consumption may well be a better predictor of individual welfare (and nutritional status) than the DHS wealth index, which (for example) may not respond quickly to shocks. Wherever possible, we complement the DHS data with good-quality nationally-representative household consumption surveys from the World Bank’s Living Standards Measurement Study (LSMS).

We acknowledge that nutritional status is not all that matters to individual welfare; our findings may not hold for other dimensions of individual poverty. Yet, in the absence of better individual poverty measures, and given considerable evidence of unequal intra-household allocation of resources, it is important to investigate this issue. Our results are also relevant to policy makers who are specifically interested in reaching undernourished individuals viewed as having a health deprivation. There are various forms of direct interventions with the aim of improving nutrition, including direct nutrition supplementation and promoting better health practices.<sup>9</sup> Many of these are implemented through health clinics and delivery points other than the household. However, there is a growing interest in doing so more through household-based policies—by integrating nutrition programs within anti-poverty policies more broadly. We throw light on whether this might work.

Our principle finding is that, although the incidence of undernutrition tends to be higher in poorer households, the nutritional deprivations are spread quite widely through both the wealth and consumption distributions, such that the joint probability of being an underweight woman or child and living in the poorest household wealth quintile is low. This also holds when we use an augmented regression to control for various individual- and household-level factors which may influence nutritional outcomes. Our results point to the need for broad coverage in efforts to address undernutrition and, by extension, individual poverty, rather than subsuming this problem within household targeted antipoverty interventions. Data availability
the 2007-08 period. They use a CGE model calibrated to a 2003 Social Accounting Matrix (SAM) (see Arndt et al., and Thurlow(2008) for details of their model), along with the household survey data from 2002/03. The simulated price rises for food and fuel commodities range from 25-75%, under four different scenarios (varying the commodities experiencing the price rise and under differing assumptions for changes in land allocation for crops). Under their preferred scenario, they estimate a four percentage-point increase in national poverty rate, with the effect being larger in urban areas. The impact appears to be largely driven

> 1 Note that these observations by the authors are drawn based on the NBR values alone for each group of households (calculated based on the proxies for aggregate consumption and production ratios for households).

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found no statistically significant gap in the means of household consumption across surveys and national accounts, while household income means from surveys were 33 percent lower than GDP in national accounts. Deaton found that means for household consumption were 22 percent lower in surveys than in national accounts and means of income from surveys were 43 percent lower than means from national accounts.

We also find that the size of the gaps varies systematically along income levels of countries, with the discrepancy being largest for middle-income countries. In contrast to previous assessments of gaps, we find relatively small differences between income and consumption estimates across all countries. Overall, the gaps across NAS and HHS for income and consumption measures are similar, which contradicts Ravallion’s (2003) suggestion that the gap is mainly due to underreporting of incomes in surveys. In contrast to Deaton (2005) and Karshenas (2003), we show that the gap is narrowing as countries get richer for both income and consumption measures, possibly reflecting better integration of NAS and HHS data in highincome countries due to improved efforts to align survey and national accounts in recent years. We also find that growth rates from national accounts are higher than from surveys, particularly in middle-income countries, in line with the economic gradient in the gaps in levels.

We illustrate potential implications of these observed gaps for the measurement of global poverty and inequality. The implications depend on assumptions about the origin of the gaps and the corresponding adjustments made to the measures. We discuss implications of assuming that the gap is fully due to errors in survey data and that these errors are distribution neutral (proportionally uniform across the income distribution). This is similar to scaling up survey means to national accounts means, in line with the methods applied by Bhalla (2002), Sala-iMartin (2006) and Pinkovskiy and Sala-i-Martin (2014; 2016). Such adjustments generally show that both global poverty and inequality are lower and falling faster than measures based on surveys only.

We also examine a more nuanced explanation that assumes the gap in means
# **Can Information Influence the Social Insurance Participation Decision of China’s Rural Migrants?**<sup>_∗_</sup>

John Giles,<sup>_†_</sup> Xin Meng,<sup>_‡_</sup> Sen Xue<sup>_§_</sup> and Guochang Zhao<sup>_¶_</sup> _JEL Classification Codes_ : H53, H55, J46, J61, O15, O17, O53, P35

**Keywords** : Migration, Social Insurance, Information, Randomized Controlled Trial

> _∗_ The impact evaluation supporting this study is registered in the AEA RCT Registry (AEARCTR-0003265) and the human Subjects IRB was approved by the Australian National University Ethics Committee (2013/688). The authors are grateful for financial support from the following sources: the Australian Research Council (LP140100514), which has provided the core funding for the RUMiC surveys; the Research Support Board and Jobs Umbrella Trust Fund at the World Bank which funded the information intervention implemented in this paper. Further, Giles is grateful for support from the Southwestern University of Finance and Economics under the 111 Project Research Base (B16040) administered by China’s Ministry of Education.

> _†Lead Economist_ , Development Economics Research Group, The World Bank. 1818 H St NW, Washington, DC 20433. (tel. +1-202-906-0192; e-mail: jgiles@worldbank.org)

> _‡Professor_ , Research School of Economics, ANU College of Business and Economics, The Australian National University. (e-mail: xin.meng@anu.edu.au)

> _§Assistant Professor_ , Institute for Economic and Social Research, Jinan University. (e-mail: sen.xue@jnu.edu.cn)

> _¶Assistant Professor_ , Research Institute of Economics and Management, Southwestern University of Finance and Economics. (e-mail: guochangzhao@swufe.edu.cn)
If the higher level of disaggregation better reflects the true range of different products that countries could produce, then the 6-digit data are most appropriate. However, as discussed above, it may also be the case that the differences among products at the 6-digit level are not meaningful from a discovery standpoint. If this is true, and the higher level of disaggregation does not reflect the range of different products countries could produce, then going from 4 to 6 digits would bias the results towards those sectors that, for accounting reasons, are decomposed into a greater number of subcategories. It is not clear which is the case, therefore results for both levels of disaggregation will be reported throughout the paper.

Notwithstanding this issue, the results shown in Table 1 are quite interesting. When discussing areas of rapid export growth, researchers identify a relatively narrow range of dynamic products, such as clothing or electronics (Butkevicius, Kadri & Mayer 2002). However, results in Table 1 show that economic discoveries in the 1990s were not highly concentrated in certain ‘modern’ sectors. In fact, sectors considered to be more ‘traditional’, like foodstuffs & agriculture, chemicals, and metals, were are also important sources of discoveries. Chemicals and allied industries contain the highest share of discovery activity at either level of disaggregation. This result lends support to a more broad vision of discovery that is not focused on a certain group of manufactured products.

Considering discovery activity by country also gives some interesting results. Discoveries by country, using both HS 4-digit and 6-digit data, are shown in Table 2.

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# **6.1 Panel Analysis**

Our analysis may be subject to some omitted variable bias if countries with high statistical capacity share commonalities unrelated to their number qualitative articles. Below, rather than controlling for the number of qualitative articles, we add country fixed effects and year dummy variables to our OLS regressions, so that only within-country variation over time is used to identify our main relationships. Standard errors are clustered at the country level. Note that this specification may remove relevant variation if some countries have a constant high output of papers using data due to a constant high statistical capacity and vice versa.

Using this specification, the relationship between academic output using data and log GDP per capita is similar as previously estimated, with an elasticity between around 0.4 and 0.6 depending on the specification (Table 4). The relationship with population is not statistically significant under this specification in most cases. The SPI overall scores are not statistically significant either under this specification, but because the SPI overall scores only are available starting in 2016, the time series is relatively short. To overcome this shorter time series, an “extended SPI” index is created that incorporates data from the older Statistical Capacity Indicator (SCI), which the World Bank has produced since around 2004.<sup>4</sup> Using the longer time series in the extended SPI, a 10 unit increase in the extended SPI overall score is associated with an increase in academic output using data of around 0.1%. The estimated coefficient is statistically significant at the 5% level and similar in magnitude to the cross-sectional results that control for the number of qualitative articles. The extended SPI data sources score is also statistically significant at the 5% level.

_Table 4. Longitudinal Relationships between Number of Papers Using Data and Statistical Performance_

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|-
learning approach, we offer two potential interpretations for this difference. First, individuals may have stronger prior beliefs about the exchange rate than about the inflation rate. This interpretation is consistent with the evidence documented in Section 3.1 showing substantially more interest in learning about the exchange rate than the rate of inflation, presumably because it is more consequential for everyday economic decisions. An alternative interpretation is that individuals do not trust the precision of the signal. That is, they are less likely to trust expert forecasts about inflation than about the exchange rate. However, as we do not provide specific information about the sources of inflation and exchange rate forecasts used in our experiment, this interpretation seems less likely.

# **6.4 Effect of Information on Spending**

Having shown that our information treatments are effective at shifting beliefs, we turn to their impacts on consumption. The main goal of our experiment is to test whether changes in macroeconomic expectations affect actual spending, as measured in administrative data covering the universe of credit card transactions for bank customers in our sample. To examine this question, we estimate the following regression equation: Note that the right-hand-side of equation (6) is identical to the learning equations in (4) and (5). The only difference is that the dependent variable is now a generic outcome _Yi,t_ +1. For example, this dependent variable may be the average monthly spending in the 3 months post-treatment. Recall that in the set of control variables ( _Xi,t_ ), we include the pre-treatment spending, which exploits the persistence in spending patterns to help reduce the variance of the error term and improve statistical power (see McKenzie, 2012).

Table 4 reports the results. In columns (1) and (2), we estimate the relationship between the information shock and the resulting change in self-reported macroeconomic expectations. The results confirm that participants update their macroeconomic expectations in response to the information provided to them through our experiment. In Table 4, columns (3
class, on the other hand, are located in households from the 8<sup>th</sup> income class and above. We see, then, that the household income classes are highly positively correlated with the occupational wage earning classes.

# **4. The simulations**

This paper presents results for the central Doha scenario and for the full-liberalization scenario.

## **4.1 Model closure**

In choosing a model closure we aimed to mimic the GTAP model that generated the foreign price scenario. On the supply side, we fixed total national employment by occupation, with jobs moving freely between sectors and regions<sup>13</sup> . The model allows substitution between occupations, driven by relative wages. Similarly capital is fixed nationally but is mobile between sectors and regions. The Land stock (used just in the Agriculture activity) is fixed<sup>14</sup> . Since agriculture is an activity that produces 11 products, land is allocated to these competing products through relative prices, allowing the crop mix to change. On the demand side government and investment spending are fixed<sup>15</sup> , and a fixed trade balance enforces the national budget balance, 13 There is a tension between our GTAP-like closure and Brazilian reality. The microdata show substantial unemployment of less-skilled groups in all regions. For the microsimulation we assumed that jobs created (or lost) in a region were allotted to (or taken from) households in that region. An alternate scenario, where fixed real wages replaced national labour constraints, yielded results similar to those reported here.

14 The factor market closure causes the model to generate percent changes in prices for 10 labour types, capital and land, ie, price changes are uniform across regions. Percent changes in demand for each of the 12 factors vary in addition by sector and region. Each adult in the PNAD microdata is identified by region and labour type; those employed are also identified by sector. Changes
# **I.  Introduction and Summary Findings**

This paper examines the ways in which lower-income households obtain basic financial services in urban communities in the United States and in Mexico. In addition, the paper discusses the efforts that private sector and government organizations are making to lower the cost or improve the quality of those services. The goal of the paper is to summarize available information on these issues and to assess the rationale and challenges facing the strategies that both countries are employing to improve the financial services available to lower-income households.

In the paper, we give particular attention to “unbanked” households, by which we strictly mean households that do not have deposit accounts with any regulated deposit-taking institution. But we also use the term more broadly to include lower-income households that use high-cost informal-sector non-bank lenders when they seek consumer loans. In many cases, these are unbanked households, but they may also include households with deposit accounts.

As noted above, our emphasis in the paper is on lower-income households living in large urban areas. Two considerations explain this focus. First, in both countries, the best survey data on the use of financial services among lower-income households applies only to urban areas. Second, while there are undoubtedly many similarities among the unbanked in rural and urban areas, there are also differences in the financial institutions with which they interact and differences the reasons that these households are unbanked. To try to cover both the rural and urban unbanked in two countries in one paper, would make this rather long paper much longer and less focused.

Before plunging into the details, it is useful to review some distinguishing features of the two countries that explain some major differences in the extent to which lower-income households are unbanked, their use of non-bank financial services, and strategies for improving financial services to the unbanked. Most importantly, there is a substantial difference in typical household incomes across the two countries. On a purchasing power parity basis, national income per capita in Mexico in 2002 was U.S. $8,
price<sup>3</sup> and turnover should benefit local vendors, but disadvantage buyers (Cunha et al, 2011; D’Aoust et al, 2013). If, however, there exist financial, social or behavioral constraints that are relieved by demobilization transfers, demobilization allowances would be expected to be invested in productive assets. In this case, the impact on consumption and assets of beneficiary households should be positive and long-lasting, but the magnitude of the consumption increase should be rather small (Gertler et al, 2012). Spillovers on nonbeneficiaries at the community level are expected to be marginal. Examining which of the consumption or investment scenarios prevails is an empirical question, which we tackle in the empirical analysis.

We use a household panel dataset to capture the direct and indirect effect of the DDR program on beneficiary and nonbeneficiary households. The first wave of the panel was collected in 2006, while the reintegration phase of the demobilization of CNDD+ combatants was taking place. This wave is representative at the provincial level. The second wave was collected in 2010, in the midst of the reinsertion phase for FNL combatants, and about five years after the demobilization of CNDD+ combatants. The timeline below summarizes graphically the various components of our assessment (figure 1).

Figure 1: Timeline <!-- Start of picture text --> Conflict Coup Arusha Peace Demobilization Demobilization<br>Onset d’État Agreement CNDD-FDD+ FNL<br>1993 1996 2000 2004 2009<br>1 2 1 2<br>Data collection (2006, 2010)<br>1 Cash transfer<br>2 In-kind transfer<br><!-- End of picture text --> Our identification strategy exploits several key facts: (i) not all households hosted excombatants; (ii) not all households with ex-combatants benefited from allowances; (iii) the waves of the demobilization program took place before and in between the two survey rounds of the panel; and (iv) some
and an Employment and Unemployment Survey (EUS). Round 50 was carried out from July 1993 to June 1994 and EUS covered a sample of approximately 565,000 individuals (115,400 households); Round 55 took place between July 1999 and June 2000 and EUS covered about 595,500 individuals in 120,400 households. This study uses the 1993-94 and 1999-00 EUSs.<sup>**4**</sup> EUS instruments are broadly similar for the 50<sup>th</sup> and 55<sup>th</sup> rounds, and gather information about demographic characteristics of household members, weekly time disposition, and their main and secondary job activities. Twenty principle job activities are defined consistently across the two survey rounds for all household members. These activities distinguish all respondents and their family members as: self-employed, regular salaried worker, casual wage laborer, unemployed, or domestic worker, among others. Employed respondents are asked a wide-range of questions about their main job (industry, occupation, type and size of enterprise, etc.), as well as about their subsidiary economic activities. Only those who work at regular salaried jobs or as casual laborers are asked to report their weekly wages (both cash and in-kind).<sup>5</sup> The sample selected for this analysis consists of those employed and working for wages (leaving out those who are self-employed and work for profit and unpaid family workers), see Figure 1. We define total wages as the sum of weekly cash and in-kind wages from the principal activity. We adjust nominal wages for differences in price levels across urban and rural areas and states, and for temporal changes in price levels between 1993 and 1999, using the indexes developed by Deaton and Tarozzi (2000). Thus, all wages in the paper are expressed in terms of the 1999 average of India’s rural prices.

Workers engaged in salaried occupations were asked about the type of enterprise where they carried out their
Second, a striking feature of the Indian data is that one knows the gender composition of the workforce. Most notably, we are not aware of large-scale datasets studies that are able to unite owner’s gender, employee gender, and localized competition in the manner that this study does.<sup>5</sup> In the case of unorganized sector manufacturing and services sector establishments, one also knows the gender of the business owner. This provides exceptional details about gender-based employment patterns that stretch over a long period of time. This offers a very rare opportunity to study gender-based outcomes, especially in a developing economy setting. As establishments also have geographic and industry identifiers, one can make very precise assessments of discrimination behavior. For example, our work asks whether gender discrimination is more or less prevalent in leading or lagging regions and states. How does this relate to the local education of the workforce or the quality of physical infrastructure in these regions?

Our descriptive work suggests that there is a clear pattern of gender segmentation in both manufacturing and services. For example, in the unorganized manufacturing sector, about 90% of employees in female-owned business are female, while this share is 81% in the case of services. Further, the extent of gender segmentation in India’s female-owned as well as male-owned businesses has increased over the years. Beyond these core results, we also note that since 2001, the share of female entrepreneurs and their resulting employment has increased in the informal manufacturing sector.<sup>6</sup> For the services sector, these statistics are, however, much lower in level as well as in growth terms. Finally, although the share of female employment is slightly lower in services than in manufacturing, it has shown dynamic growth from 2001 to 2006.

These broad patterns mask the varying trends in female entrepreneurship, employment, and gender segmentation across leading versus lagging regions as well as among the industries within manufacturing and services. Our state and industry level descriptive statistics suggest that the