rafmacalaba/data-use-mentions-extended huggingface dataset:
in Ghana, one of the higher-income countries in the region, only 56 percent of adults use any kind of financial product. This figure rises to 81 percent in Lesotho, but falls to just 22 percent in Mozambique. It is interesting to note that Pakistan has a very similar access profile to Tanzania; both countries have high levels of financial exclusion, and use of informal products is about three times more prevalent than use of formal products. Awareness of basic financial products and concepts vary from country to country as well, but is generally also low, with many people never having heard of savings accounts. However, the financial literacy data from the FinScope surveys is limited in that it generally focuses only on awareness of financial products and providers, and not on other dimensions of financial literacy, such as numeracy or capability.

Other national financial literacy surveys have been conducted in the U.K. (Atkinson et al., 2007), Austria (Fessler et al. 2007), Australia (Worthington, 2004), Poland (Szafranska and Matysik-Pejas, 2010), Singapore (Media Research Consultants, 2005), Fiji (Sibley, 2010), and Ireland (O’Donnell and Keeney, 2009), although many of these tend to focus on broader measures of financial capability. The specific results from these surveys are not presented here due to lack of direct comparability.

The comparable surveys find that financial literacy is low everywhere, though still lower in low-income countries. Lusardi and Mitchell (2011b) find in the U.S. that only about 65 percent of respondents correctly answered the first question or the second question, while only half gave the correct response to the third question. Table 1 shows that these scores are fairly comparable to those in other high income countries. For instance, Sekita (2011) finds that slightly worse performance on the financial literacy test for individuals in Japan—the correct response rate on the first two questions is 50 percent, and 40 percent on the last question. In
a disadvantage relative to males in access to information (Inci et al., 2017) and financial resources (de Andr ́es et al., 2021), which may also affect their decisions to adopt and use technologies in their businesses.

To explore the links between technology and the gender gap in productivity, we leverage a comprehensive database from the Firm-level Adoption of Technology (FAT) survey conducted in 11 countries. In addition to productivity measures and information on top managers’ gender, the FAT database offers a unique opportunity to explore technology adoption at the firm level and overcome a significant gap in the literature. For instance, most studies examining the technological gender gap have focused on gender ownership and a few technologies related to the agriculture sector (Hirpa Tufa et al., 2022; Teklewold et al., 2020; Doss and Morris, 2001) or on individual technologies such as internet connection (Allison et al., 2023) or e-commerce (Lashitew, 2023). In contrast, the FAT survey provides granular information on more than 300 technologies across almost 50 different business functions, allowing us to study the gender lens in various sectors and technologies.

Our results reveal a significant productivity gap between male- and female-managed establishments, with estimates ranging from 24 to 66 percent. Regarding technology adoption for general business functions, our analysis shows that female-managed enterprises are just as likely to adopt the most advanced technologies, except for Enterprise Resource Planning (ERP). In contrast, we find more significant differences in adopting advanced technologies associated with sector-specific business functions. Finally, the results show that technology adoption correlates positively with firm performance and that female management positively affects performance in more technologically sophisticated establishments.

Our analysis contributes to the expanding body of literature investigating the moderating factors influencing the gender gap in productivity. Specifically, the research by Allison et al. (2023) focuses on female ownership and finds that the negative association between female ownership and firm performance can be partially mediated by firms’ access to finance, technology usage,
physical capital that determine income.<sup>6</sup> We have however included a categorical variable to indicate whether the household fell in the lowest income quintile. This is not intended as an income variable, rather it captures the special constraints faced by the poorest segments of the population in terms of access to credit and insurance. This lack of access prevents a poor household from relying on outside markets to reduce income risk and is a major reason why child labor is predominant among poor households (Grootaert and Kanbur, 1995).

_Cost of schooling._ Since schooling is the main competing time use for children, it stands to reason that the cost of schooling would be an important determinant of the likelihood of child work (Siddiqi and Patrinos, 1995). The CILSS contains information on household expenditures for education, but these cannot be included as explanatory variables in the model because they are endogenous to the child labor decision (by definition, expenses on education are incurred only for children for whom the decision was made to enroll them in school). We have hence averaged, for each cluster in the survey, household expenditures on education per enrolled child. This average can be considered an independent measure of the cost of schooling in that cluster, and this variable has been included as a regressor in the model. We have also included the distance to the school (also averaged by cluster) as a partial measure for the opportunity cost of school attendance. Unfortunately, a direct measure of

> _6_ The model we estimate below is a reduced form equation. In a structural model, it would be appropriate to have a separate equation to determine household income as a function of

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charges, voters are highly aware of both resource and preference costs. Fiscal decentralization with ear-marking essentially means that taxes are benefit levies. Increasing the quantity of production of road quality will result in a higher ear-marked tax bill, making the opportunity cost of road services much clearer to the voter. The country studies in the next section allow us to gain a better understanding of these issues.

# **_Decentralization of Construction Expenditures_**

Figure 2(a) shows the relationship between decentralization of construction expenditures and performance as obtained from the data set described in section 4. On the ordinate we have the countries in increasing degrees of decentralization of construction in terms of percentage local government expenditure for road construction. On the abscissa, we measure performance in terms of preference costs<sup>1 4</sup> , defined as the cost of roads in bad condition (roads not paved (million $/km)) and resource costs, defined as unit cost of construction (expenditure on construction per km (million $/km)). In figure 2(b) we present the same costs normalized by GNP per capita to adjust for differences in technology. The determination of the proper level of decentralization for construction of roads involves minimizing total cost of provision, expressed as the sum of preference and resource costs for a given level of effort.

A first look at the graph shows that there are multiple-equilibria of decentralization, as countries attain different levels of unit costs with increasing degrees of decentralization. The countries selected in the case study in figures 2(a) and (b) fall into four categories. Brazil, the Philippines and Indonesia'<sup>5</sup> are all centralized with regard to financing of their road construction operations falling under the category [0-10] percent decentralization; Argentina, Colombia and Germany are more decentralized with the central government and local governments sharing the fiscal responsibly for construction activities. Moving further to the right, in South Africa, financing of construction works is with local
# **Vietnam’s Evolving Poverty Map: Patterns and Implications for Policy**

Peter Lanjouw, Marleen Marra and Cuong Nguyen<sup>1</sup> JEL classification: I31, I32, O15 Keywords: Poverty, inequality, small area estimation, population census, household survey, targeting efficiency, Vietnam.

> 1 Development Economics Research Group (DECRG) World Bank, DECRG,World Bank, and National Economics University, Hanoi.  This study is an outgrowth of analysis undertaken as part of the Vietnam Poverty Assessment managed by Valerie Kozel under the POV Sector Board.  We are grateful for her advice and guidance. The study is funded by the World Bank. We would like to thank Tran Ngoc Truong and Lo Thi Duc (GSO) for assistance in data analysis.

Email: planjouw@worldbank.org; mmarra1@worldbank.org; c_nguyenviet@yahoo.com
domestic sales, firms also optimize their output decision between exports to the three possible export regions, based on relative prices the three regions and their constant elasticity of transformation production function for shifting output between the regions.

Tourism is one of the sectors in this category, and given its importance in the Tanzanian economy, it deserves its own discussion. All sales to tourists are considered exports, i.e., the sector does not produce for the domestic market. Based on a survey (discussed below), we have that the inputs of the sector are domestic transportation services, hotel and restaurant services, clothing, gold items, and certain food products, as well as foreign inputs such as international travel expenditures. Given that we have data only on tourist expenditure by sector and on tourism imports and exports, we assume that the output of this sector is produced without value added. But the tourism sector demands domestically produced services and goods that lead to valueadded in the Tanzanian economy. Moreover, the sector is intensive in services and this way we capture the impacts on services liberalization on services exports. We discuss the special data issues related to this sector in the data section below.

# **Goods Produced Subject to Increasing Returns to Scale**

These goods are differentiated at the firm level. We assume that manufactured goods may be produced domestically or imported for firms in any region in the model. Firms in these industries set prices such that marginal cost (which is constant) equals marginal revenue; and there is free entry, which drives profits to zero. For domestic firms, costs are defined by observed primary factor and intermediate inputs to that sector in the base year data. Foreigners produce the goods abroad at constant marginal cost but incur a fixed cost of operating in Tanzania. The cif import price of foreign goods is simply defined by the import price, and, by the zero profits assumption, in equilibrium the import price must cover fixed and marginal costs of foreign firms. Domestic firms set prices using the
alone had no impact on female co-ownership of land until the 2010 Land Act made co-ownership the default option for common-law marriages, an awareness campaign had been launched, and procedures for systematic initial registration were designed to put such rights into practice.

This study contributes to several research strands: First, although there is ample evidence of land and credit linkages at the macro level, to the best of our knowledge ours is the first study to document the credit- and land-market activation effects of land titling at the parcel level. Historically, the type of land rights and land ownership structure affected the evolution of cooperative credit institutions (Suesse & Wolf 2020), financial system operation (Rajan & Ramcharan 2011), real estate cycles (Rajan & Ramcharan 2015), and long-term development (Devijlder & Schoors 2020).<sup>2</sup> On U.S. Indian reservations, credit access has been identified as the main channel that supported greater development on parcels with fully, compared to only partially transferable property rights (Dippel et al. 2020). Establishment of property registries and adoption of secure transaction laws by Indian bands has also been linked to greater economic development (Dippel et al. 2021). In India, registry computerization that improved registry access has been shown to expand urban mortgage lending at the level of registry offices (Deininger & Goyal 2012).

We add to such macro-studies by using administrative data to provide micro evidence on the impacts of systematic first registration separately from those of policy and institutional reform. While each of these interventions has significant impact, differences in target variables and the time profile with which effects materialize make them highly complementary: while systematic first registration expands registry coverage, female co-ownership, and investment in the short term, the thrust of policy reform is to translate greater coverage into market activity and credit access, especially for female co-owners. and, in a hypothetical scenario without policy reform, systematic initial registration would actually reduce the number of registered transactions.

A second strand of literature relate
<br>(**_T_**)|Urban<br>structure|Circuity_avg_1996|From the street network: the average<br>ratio between an edge length and the<br>straight-line distance between the<br>two nodes it links (Boeing, 2017)|OpenStreetMap street network data within<br>the urban extents from deblurred and<br>corrected DMSP-OLS NTL RC 1996<br>image.|
||Connectivity|Intersection_density<br>_1996|From the street network: the node<br>density o the set of nodes with more<br>than one street emanating from them<br>(Boeing, 2017)|OpenStreetMap street network data within<br>the urban extents from deblurred and<br>corrected DMSP-OLS NTL RC 1996<br>image.|
||Connectivity|Street_density_199<br>6|From the street network: the sum of<br>all edges in the undirected<br>representation of network graph<br>divided by the urban extent area|OpenStreetMap street network data within<br>the urban extents from deblurred and<br>corrected DMSP-OLS NTL RC 1996<br>image.|
|III. Land<br>use (_L_)|Sprawl|Sprawl_1996|Normalized difference between the<br>share of areas with population<br>density below the regional average<br>density and the share of areas with<br>population density above the<br>regional average density (Fallah et<br>al., 2011)|1990 Population count at pixel level from<br>from GHS (GHS_POP_GPW41990_G<br>LOBE_R2015A_54009_250_v1_0 at 250<br>meters of spatial resolution) within the<br>urban extents from deblurred and corrected<br>DMSP-OLS NTL RC 1996 data.|
health sticker he could display, and so that he could open a bakery and wholesale bread. A final case was of a fish market vendor, who had used the 40,000 LKR grant to buy a freezer which he could then use to increase and maintain his stock, and then increased advertising and used his registration to print a receipt book he could use to give receipts to restaurants who wanted fish. In all these cases, there were clear changes in the business operation, and in the attitude and vision of the owners. Hence, we believe this growth at the top tail of the distribution is genuine. But such firms were rare. Most firms that had formalized had not seen such improvements.

# **5.4 Impact on Mediating Channels**

The above analysis suggests that formalization has not had significant impacts on key outcomes for most firms that formalized, but that it has helped a handful of firms which grew substantially. Our follow-up surveys span a period of 15 to 31 months after treatment. This should be a sufficient period to detect effects for most firms. Nevertheless, one concern is that the standard errors in Table 5a are relatively large, reflecting relatively low power to detect effects. The CDF in Figure 3 is comforting in this regard, as it shows strikingly similar distributions of profits for treatment and control for most of the distribution. However, a further approach to determining whether formalization seems to be benefitting 18
Are these differences in sectoral composition and scale being driven by differences in plant-level productivity across establishments? We next turn to this issue by examining the sources of productivity growth among different categories of plants. Using both the static Olley-Pakes (1996) decomposition and the dynamic Olley-Pakes decomposition (DOPD) developed by Melitz and Polanec (2015). Our preferred method is the Melitz and Polanec (2015) decomposition but utilize the static decomposition method when panel data are not available. As discussed by OlleyPakes (1996), this method is more directly linked to theoretical models of firm dynamics developed to analyze the sources of market share reallocations across heterogenous firms (see, for example, Hsieh and Klenow, 2009; Collard-Wexler, Asker and de Loecker, Bartlesman, Haltiwanger, and Scarpetta, 2011; and Restuccia and Rogerson, 2017).

# **V. Results**

Tables 3 and 4 report the results of the Olley-Pakes decomposition methods. Several patterns are worth noting. First, aggregate productivity growth in Ethiopia (Table 3) outstripped that of both Côte d’Ivoire and Tanzania (Table 4). In Ethiopia, aggregate productivity increased by 76 percent between 2006 and 2016 whereas it increased by only 6 percent in Côte d’Ivoire over a similar period (2004 to 2014).<sup>4</sup> The weak performance of the Ivorian manufacturing sector is not surprising given the country’s two civil wars and extreme political instability during this period. However, growth rates rebounded following the peace agreement that ended the Second Ivorian Civil War (2010-2011). As reported in Table 4, aggregate productivity increased by 30 percent in

> 4 Table 5 reports that aggregate productivity growth increased by 47 percent between 1996 and 2009.  Note our estimates contradict earlier findings by Newman et al (2016) drawn from the Groningen Africa Database who estimate manufacturing productivity changes based on aggregate (sectoral) data.

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. To explore this issue fuither, we graph the estimated probability of benefiting from investments made by INFES, PRONAA, and FONCODES on the number of standard deviations that the income of household i in di ,trict j is above or below the mean income in district j when both household and district incomes are calculated from the 1996 INEI survey. Figure 5.10 shows that the nonparametric regressior lines for FONCODES school infrastructure and school breakfast programs are "humped", peakir gat about one standard deviation above mean district income. Within a given district, househo **Js** that are somewhat better-off than their counterparts are more likely to benefit from FONCODE', investments. This suggests that there was essentially no (positive) intra-district targeting o- FONCODES resources in 1996.

> 12 The amount of targetinrg may be limited by the nature of FONCODES projects, especially those which involve the construction of infrastructure. It may be possible, however, to distinguish between communities of different characteristics within a district--in particular in the larger districts, which also tend to be less homogeneous. Means testing may also be possible for some of the "special" projects.

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of the two education indicators and only one of the access to basic services indicators.

> 19The indicator based only on eligible households (41.8 percent) and the indicator with the above province-level assumptions (43.6 percent) are nearly identical at the national level.

> 20Additionally, a subset of the other standard MPM indicators have a handful of missing observations in both surveys. For each individual indicator, we report and rank estimates using all non-missing observations. But for the multidimensional poverty indicator, we calculate the measure for only households with no missing observations in any of the indicators. However, this last restriction drops only a handful of households in each survey (e.g., 133 households out of 16,021 were dropped in the 2016-2018 DHS).

> 21There was a substantial worsening of school attendance between attendance indicators in the 2009 HIES and the 2016-2018 DHS. However, the 2009 estimates appear to be an outlier when compared to estimates reported from the 2011 Population Census and the 2022 Socio-Demographic and Economic Survey, and little could explain such a change over the time period. And this stands in stark contrast to the very little change in any other well-being indicator presented in Section 4. Details available from the authors upone request.

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# **4 Analysis**

In this section, we report intent-to-treat (ITT) estimates of the impacts of the mechanization treatment on households assigned to the treatment group. Treatment assignment was random within strata, so the impacts of the intervention on a given outcome _Yi_ can be measured using the following regression: where _Yi_ represents outcome _y_ for household _i_ , Treat _i_ is an indicator variable equal to 1 if household _i_ was assigned to receive oxen in the first wave, **Xi** is a vector of unbalanced demographic variables, and _δstratum_ is a series of strata fixed effects. As treatment was assigned at the individual/household level, we report heteroskedasticity-robust standard errors.<sup>13</sup> In addition, we examine impact heterogeneity by whether households live in districts where women are more or less involved in oxen tasks. Specifically, we run the following regression: where High _j_ is an indicator variable equal to one for households living in districts where the share of oxen tasks conducted by women within the district exceeded the median across all districts. We restrict to control households for this calculation, since our goal is get as close as we can to capturing baseline-level norms. As a robustness check, we-run this analysis using pre-program Demographic and Health Survey (DHS) data to divide districts into ones that are more conservative (below-median women’s decision-making power over their health care, making large household purchases and visiting family and friends) versus less conservative, as described in Section 3.2. We confirm that results are unchanged.

## **4.1 Household-level impacts**

Table 4 presents intent-to-treat impacts on agricultural yields and area cultivated, showing that traction oxen increase yields and lead farmers to bring a larger area under cultivation.<sup>14</sup> Panel A of Table 4 focuses on treatment effects for first season agricultural outcomes, showing that assignment to the oxen treatment arm led to significant increases in agricultural output, even though the oxen were distributed mid-season and recipient