rafmacalaba/data-use-mentions-extended huggingface dataset:
Table A2 in the appendix describes the definitions and sources for the remaining data series. We use G7 GDP growth to measure global growth; using U.S. GDP instead made little difference for the results. Global inflation is measured by the log-difference of the U.S. GDP deflator, while the global short-term interest rate is given by the 3-month U.S. Treasury Bill rate. Finally, real commodity prices are constructed deflating the UNCTAD index for all commodities with the U.S. GDP deflator.

In accordance with the discussion in the preceding section, we use as conditioning variables **z** in (1’) a measure of trade openness (log imports plus exports as a fraction of GDP), the Chinn-Ito index of capital account openness, and an index of commodity intensity, to capture countries’ degree of productive specialization in commodities. Following Leamer (1984, 1995), we measure commodity intensity by net exports of commodities per worker.<sup>12</sup> The model consisting of (1) (or (1’)) and (2) is block-recursive. Hence we estimate the GDP growth equation (1) and the global block (2) separately. In each case, we use Akaike, Hannan-Quinn, and Schwarz information criteria to determine the optimal number of lags. Although the three criteria are asymptotically equivalent, in small samples the Akaike criterion tends to select the highest number of lags, and the Schwartz criterion the lowest. Noting that the number of lags selected by the three criteria is not too different, we chose to use the Hannan-Quinn criterion as a compromise between the least and most conservative criteria. Specifically, for the domestic growth equation the Schwartz criterion selects 1 lag of the endogenous variable and only the contemporaneous global variables, while the other two criteria select 1 lag for the endogenous

> 12 We compute aggregate net exports as the sum of net exports of Leamer’s six commodity clusters: (i) petroleum, (ii)
Furthermore, we do not include candidate variables that are applicable to only one segment of the population, such as farming assets and crop production, that only apply to household farmers. Although we do include categorical variables on the sources of livelihood (e.g., farming, aid, wages, etc.). Finally, we avoid variables that capture a temporary state of the household, such as a recent sickness or other shock to the household. However, a PMT could be designed for specific use cases or combined with additional information to target such households, e.g., households impacted by recent hazardous weather and that have a low PMT score.

A limitation of this analysis is that the 2014/15 household survey is outdated, particularly with the multiple shocks Sudan has experienced in recent years. With support from the World Bank, the Sudan Central Bureau of Statistics has started preparing a new household budget and poverty survey to update poverty and other socio-economic statistics. The survey was due to be fielded in 2022, but has been paused following the military takeover in October 2021. Survey preparations should resume once things return to normalcy. The PMT methodology presented in this paper can readily be applied to new household survey data when it becomes available.

# _Methodology_

In an effort to avoid overfitting on specific in-sample characteristics of the data, we split the household survey data into a training and a testing set using a stratified random sample based on the outcome variable of log per capita household consumption expenditure (Kuhn and Johnson 2019, Chapter 3).<sup>13</sup> All modeling decisions are based on the training data, before arriving at a small number of potential models that are applied to the testing data to explore model performance on unseen, out-of-sample information. Statistical analyses are performed using the statistical programming language R and the Tidymodels package (R Core Team 2022; Kuhn et al. 2020).

To select a subset of variables from all candidate variables, we use the Least Absolute Shrinkage and
Neither methodology proposed by Nagengast and Sterher (2016) manages to break up the whole domestic and foreign value-added exported by a country across the different bilateral flows. However, this probably goes beyond the scope of their analysis, which focuses on the value-added components produced and absorbed by two bilateral partners. Since an important aspect in their study is the role of final demand in generating bilateral trade balances, a sink-based approach seems ideally suited. As mentioned, the proper measure of this component is given by the **VAXIM** of equation (39), which differs from the measure proposed by Nagengast and Sterher (2016). In fact, they only classify as ‘domestic value-added absorbed by direct importers’ what is embedded in goods that do not leave this country again, assigning the remainder to the double counted component. In this way, they do not take into account what we have classified in the **2b** and **3c** components in equation (D.2), underestimating the domestic value-added absorbed by direct importers.

# **6 Concluding remarks**

The diffusion of international production networks over the last three decades calls for new tools in order to evaluate supply and demand relationships between countries which can no longer be adequately gauged by gross trade flows. For this reason, inter-country input-output tables and new methodologies that exploit these data have been developed to measure trade in value-added terms and countries’ participation in GVCs. These tools have been extensively used in a large number of applications. However, on the one hand, several empirical issues have been not properly addressed so far; on the other hand, the emergence of different methodologies for the value-added accounting of trade flows has raised doubts as to the correct way of measuring the phenomena.

In this paper we propose a general scheme for breaking down bilateral and sectoral exports flows according to the source and the destination of their valueadded content. This framework can be differentiated according to alternative approaches and perspectives that are instrumental to addressing different empirical
Policy Research Working Paper 9942

# **Abstract**

Despite a policy consensus that closing gender employment gaps will boost economic growth, relatively little is known about the size of these gains in many developing countries. This paper develops a new Gender Employment Gap Index (GEGI), which is equal to the size of long-run GDP per capita gains from closing gender employment gaps. The GEGI is simple and transparent and can be easily constructed using closed-form expressions for almost all countries using macroeconomic employment rate data by gender. The basic variant of the GEGI is the gap between male and female employment as a share of total employment.

The full GEGI is similar, but instead of using an aggregate employment gap, the full GEGI is the weighted average of a “better employment gap” and “other employment gap.” The basic and full GEGIs are similar (correlation of 0.97), and both average 19 percent across countries. This means that GDP per capita in the long run would be almost 20 percent higher if female employment were exogenously increased to be the same as men’s (other things being equal). The paper also provides an application for the Pacific Islands, for which a simple measure like the GEGI is particularly important given the lack of alternative estimates.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at spennings@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings
competing claims about the factors associated with high levels of conflict.<sup>5</sup> Other intermediate approaches to surveys or local fieldwork for the systematic mapping of violence and conflict include the use of national and, increasingly, local media sources (Tadjoeddin 2002; Welsh 2003).<sup>6</sup>

# **_3.1. The PODES Data_**

The Central Bureau of Statistics’ (BPS) Village Potential series (PODES) is a longstanding tradition of collecting data at the lowest administrative tier of local government. It collects detailed information on a range of characteristics – ranging from infrastructure to village finance – for Indonesia’s current 69,000 villages and neighborhoods.<sup>7</sup> The latest PODES was surveyed at the end of 2002 as part of the 2003 Agricultural Census.

5 Other sources of information on conflict include the police, health care providers, morgues, and the Indonesian army (TNI). However, it is very difficult to use these data sources for cross-provincial comparisons, in part because of large gaps in the data but also because common definitions (for example of violence types) are not used in different areas.

6 In the absence of any primary data, the United Nations Support Facility for Indonesian Recovery (UNSFIR) attempted to create such a dataset by compiling newspaper reports on violent conflicts (Tadjoeddin 2002). However, in large part because of the use of national media, which often do not record localized conflicts, the resulting data massively underreports levels of conflict. The UNSFIR dataset captured only 1,093 incidents of conflict over an eleven-year period (1990-2001). In contrast, PODES documented almost 5,000 villages as reporting conflicts in 2002 alone. Newspaper reports will also be subject to biases. For example, newspapers may only report high profile violent conflict. At the same time, coverage may differ significantly across regions. Areas with high readership or local correspondent penetration may also yield regional over-reporting biases. The PODES
of efficient logistics services and improvements in both the hard and soft international logistics infrastructure. The score of overall logistics competence (and quality) in Brazil – as captured by the Logistics Performance Index (LPI) in 2016 – is below Mexico, Turkey, India, China and South Africa (Figure 9). Brazil’s overall integration in GVCs is low compared to international peers in part because of relatively lengthy and costly procedures to import and export.<sup>15</sup> Indeed, firms’ integration to GVCs critically depends on their

> 12 The World Bank STRI data set focuses on policies and regulations that discriminate against foreign services or foreign service providers, as well as certain key aspects of the overall regulatory environment that have a significant impact on trade in services.

13 These results are also valid when using OECD STRI; the latest numbers from 2017 show that Brazil scores worse than Mexico, Chile and Colombia for accounting, architecture, engineering and legal services and for commercial banking and insurance. For telecoms and retail, the value of the Brazil STRI index is zero.

14 The overall tax burden on services is heavier than in other sectors: while the average tax burden on the production and consumption of goods and services is 19.4 percent, it is much higher in the services segments most critical to other sectors of the economy: the tax toll exceeds 23 percent in transport and business services, 27 percent in IT services and over 30 percent in utilities (OECD, 2016). This tends to be particularly burdensome for firms that operate in supply chain organizations as they are not allowed to claim full credit for indirect taxes paid on services inputs in lieu of the "physical credit" principle in ICMS and they cannot claim credit for inputs in ISS.

> 15 This forces firms to adopt costly hedging strategies and complicates their ability to engage in just-in-time production or react quickly to demand shifts. Evidence suggests that inventory-holding costs can vary from 15 percent of the cost of goods per year to as much
similarities of content, available slots, and their time of the day (among other logistical factors) in order to secure the comparability of listeners. This selection criteria alleviates the risk of confounding factors having an influence on the variables of interest.

# **_III. Data_**

# **_A. Sampling Framework_**

This study was conducted in two regions of Western Senegal: Thiès and Diourbel. The two regions were selected based on an exhaustive review of national radio signals, density of weekly local markets called _Loumas_ , and proximity to the capital. Both were also among the eight Senegalese regions selected by Lighting Africa as priority targets for its information campaign.

Thiès and Diourbel are the most populated regions in Senegal after the Dakar region, with 1.7 million and 1.4 million inhabitants, respectively. Although they have the highest population density after Dakar<sup>19</sup> both regions remain greatly agricultural: about 52 percent and 43 percent of households live from agricultural production in Thiès and Diourbel, respectively, close to the country average (50 percent).

Within the two regions, we identified weekly local markets and searched for villages within a radius of 8 km to those markets. Those villages were considered proximate enough that a villager without access to transportation could visit the market and return to the village during daylight in one day. We only considered villages with approximately more than 100 inhabitants and national radio signal. Among the 169 eligible villages, we randomly selected 150 to be part of the experiment.

At baseline, enumerators visited two villages per day, completing as many surveys as possible with a minimum of 40 surveys per village. The village center was identified as the starting point for the listing in each village, which was typically the community center or village leader’s office, and the exercise systematically worked through proximate residential properties. A household was defined as a group of individuals living together and putting together part or all of their resources to meet their basic needs (housing and
# **3 Research Design**

We explore the e↵ects of CBI using cross-national panel data. We test both the overall prediction (CBI increasing inequality) as well as each of the mechanisms (the e↵ect of CBI on financial regulations, labor market, and social policies). None of these variables has a natural and unique proxy. This forces us to rely on several variables that capture the underlying theoretical quantities of interest. We present them briefly here. All variables are summarized in Table 1.

|**Su**|**mmary **|**statistics**||||
|---|---|---|---|---|---|
||Mean|Median<br>S.D.|Min.|Max|Obs.|
|CBI|0.50|0.20|0|1|4369|
|GDP (log)|23.68|2.38|17|31|9070|
|Population (log)|15.00|2.30|8|21|11937|
|Income share of bottom 10%|2.35|1.07|0|5|1000|
|Income share of 10-20%|3.94|1.31|1|7|1001|
|Income share of 20-30%|5.04|1.35|2|7|1001|
|Income share of 30-40%|6.06|1.32|3|8|1001|
|Income share of 40-50%|7.12|1.25|4|9|1001|
|Income share of 50-60%|8.34|1.13|5|10|1001|
|Income share of 60-70%|9.84|0.93|7|12|1001|
|Income share of 70-80%|11.91|
there is a small gap between the final two interviews and some seasonal activities just after harvest time may be undercounted and missed in our estimates. This would likely lead to an understatement of long recall losses. We show robustness (and strengthening) of our main results using all four interviews in Appendix Table 1.

To identify the number of jobs done by individuals over the full study period, we linked reported activities across survey rounds, so that if a respondent reported working the same job in all four

> 10 This is necessary because for a 90-day recall period, there are 4 calendar months in which the respondent may have worked. To estimate the months worked or hours worked, we must calculate the relevant fraction of the first and fourth month options.

> 11 We calculate these quarterly measures by using the question in the 12-month recall module that asks respondents to indicate which months they worked in each job, and then use the survey dates from the quarterly interviews to reflect the same time periods covered by the short recall questions. We use the day of survey for each household, so the period covered varies slightly by household based on the date of interview.
risk-sharing mechanisms (which typically operate through gift giving or interest-free loans) are less available to landless households than to other households. Finally, the last four rows of Table 1 show that landless households are also economically disadvantaged in terms of their ownership of productive assets aside from land, and have lower material well-being in terms of ownership of durable consumption goods, housing quality, and consumption expenditures.

# _4.2 Balance at baseline_

In order to establish the validity of the randomized assignment to early rollout, we test for pretreatment balance across treatment and control groups in the primary outcomes of interest, as specified in the pre-analysis plan, and other important household characteristics. Differences in means for each variable are estimated through linear regressions in which controls for stratification variables are included and standard errors are clustered at the panchayat level. Results, shown in table A1 (column 1), indicate that households in treatment areas are 2 percentage points more likely to include an SHG member. This difference, while small, is significantly different from zero at the 5% level. Relatedly, the mean outstanding SHG debt is also higher in treatment areas at the 5% level, though the difference is small in economic terms (50 rupees). Monthly interest rates measured at the household level are higher in treatment areas, and this appears to be driven by informal lending rates. Informal lending rates measured in the FGD data, on the other hand, are well balanced. An index of productive assets, constructed according to the method proposed in Filmer and Pritchett (2001), is 0.1 points lower (0.06 of a standard deviation) in treatment areas as well. We also test for differences among landless and landed households in treatment versus control areas (columns 2 and 3). Again, we find small differences in SHG enrollment and debt, and in asset holdings across treatment and control groups. The difference in interest rates reported at the household level appears to be driven by landless households. Attrition, at 3%,
partially, to compensate for the negative impact of these disasters.

Regarding the impact of the different shocks on the current account, the figure suggests that, with the exception of Geological disasters, the current account tends to go to surplus (deficit) as a result of a negative (positive) shock, in other words, in this countries the current account is countercyclical: they borrow in good times and “lend” (increase their position) in bad times. In this regard, these countries do not seem to behave differently from emerging markets, which have been documented to have a highly procyclical access to capital markets (see Agiar and Gopinath, 2004). It is important to keep in mind that the dynamic response of net exports was computed as a residual from a linear approximation using the average shares of the other GDP components, so it also captures inaccuracies in the approximation and in the shares used. However, given the magnitudes observed in the IRFs, it is unlikely that the explicit consideration of these errors could reverse the conclusion about the cyclicality of the current account. Overall, these findings suggest that negative external shocks are not directly linked with significant increases in the level of external debt of low-income countries. On the contrary, these countries seem to accumulate debt while in good times. Of course, the results also imply that, during bad times, these countries are not able to borrow, and most likely have to serve their debt obligations with a reduced level of output and income. It is in this regard that negative external shocks may put these countries under a lot of stress, but, as mentioned above, this situation is not different from that experienced by emerging markets, so, the evidence does not support any structural difference between these groups of countries in this regard.

# **6.2 The determinants of aid**

Much has been recently discussed on academic and policy circles about the adequacy of the level of aid to low-income countries and its effectiveness (see for example, Burnside
Policy Research Working Paper 9431

# **Abstract**

This paper investigates whether the relative self-esteem level of spouses can lead to within- household competition for inputs and affect economic gender inequality in the home. Using data on smallholder farmer couples in Côte d’Ivoire, the paper examines the relationship between spouses’ self-esteem and income-earning in agriculture. Although the link between own self-esteem and crop income earning is positive, there is a “battle of the sexes” in which one spouse’s self-esteem is negatively related to the other’s income earning, particularly income earning in higher-value, export-oriented agriculture. Women’s outcomes are more sensitive to their own self-esteem (positively) and to their partners’ (negatively) than men’s. This negative relationship is driven by individuals during middle age, when self-esteem is considered most stable. A key channel through which self-esteem appears to matter is by increasing control over household land, which is a scarce but crucial input to agricultural production. In addition to confirming the importance of noncognitive skills for poverty reduction in rural settings, the findings highlight the importance of their impact on intra- and inter-household inequality, especially in the presence of interlocking market failures constraining the supply of inputs to production.

This paper is a product of the Gender Innovation Lab, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at adonald@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper
. For this comparison of earnings, we restrict the sample to wage workers with secondary education or more.

The median earnings of teachers identified in our study surveys are about $680 per month (measured at the 2011 purchasing power parity (PPP) exchange rate) (Column 1, Table 4). There is, however, very large cross-country variation in these monthly earnings. Teachers make $100 per month in the Democratic Republic of Congo, $220 in Liberia, $400 in Niger, $660 in Ghana, $800 in Tanzania, $1,600 in Zambia, and $2,300 in Namibia.

One often-used way to put these numbers in context is to express teacher earnings in relation to GDP per capita. Column 3 of Table 4 reports that, on average, teachers in the sample earn 300 percent of GDP per capita, again with large variation across countries. Teachers in Burkina Faso and Zambia earn around 500 percent of GDP per capita and, at the other extreme,

> 27 There are far more missing data among secondary school teachers and so those results are not informative.

15