rafmacalaba/data-use-mentions-extended huggingface dataset:
read directly from the bill (Table 1). Second, a further 30% of households were unwilling or unable to show their utility bills to the interviewer, but were able to recall the value of their most recent monthly bill. In these cases, the local utility tariff structure was applied to the reported expenditure in order to infer the physical consumption of the service. For the remaining 35% to 45% who neither presented bills nor recalled expenditure, imputation techniques were used to infer expenditure based on household characteristics.

3.6 Some evidence of selectivity effects was found with regard to the sub-sample that provided coherent information on household expenditure. Both for water and electricity, high-income households were more often able to report expenditure. In the case of water, this may simply reflect higher rates of service coverage in the upper income quintiles. However, in the case of electricity, where coverage is practically universal, this explanation is no longer relevant.

**Table 1: Summary of data availability on utilities expenditure**

||**Water**|**Electricity**|**Gas**|**Telephone**|
|---|---|---|---|---|
|Percentage of households that showed bill|27.9|35.4|26.5|23.7|
|Percentage of households that verbally reported expenditures|29.7|29.8|30.4|28.7|
|Percentage of households that did neither of the above|42.4|34.8|43.1|47.6|
||100.0|100.0|100.0|100.0|

_Source:_ OPSM, May 2002 3.7 Nonetheless, these data limitations in practice do not materially circumscribe the conclusions of this analysis. In reality, expenditure data were only used to evaluate the efficacy of existing social policies in the Metropolitan Area (for
estimate). Since the purpose of this scoping review was to provide an overview of the existing evidence regardless of methodological quality or risk of bias, the articles included were not critically appraised (Tricco et al. 2018).

# Results

## Search 1: Evidence on the link between diet quality, health/nutrition outcomes and economic costs (all three components)

### **Search results and characteristics of selected studies**

The study selection process for the first literature search objective is shown in **Figure 2** . The total number of records screened, after removing duplicates was 704; 142 full texts were screened. We identified a total of 20 studies that included data from LMICs and estimated the burden of unhealthy food choice by quantifying the impact of individual dietary factors (dairy products, salt, sugar, sugar-sweetened beverages, ultra-processed foods) or poor-quality diets (measured by healthy eating indices) on diet-related NCD outcomes (cardio-vascular diseases, diabetes, chronic disease) (see **Table 2** ). A comparison of key findings in terms of mortality, DALYs and health care costs are summarized in **Table 3** .

Most of the studies were based on the GBD data and methodology. Several studies estimated the cost (mortality and DALYs) of cardiovascular disease or the NCD burden attributable to the dietary risk factors, using the GBD dataset for 204 countries (B. Zhang et al. 2023; Dong et al. 2022; Qiao et al. 2022) or in specific country contexts (Brazil, Mexico) (Dávila-Cervantes 2020; Machado et al. 2022). Globally, in 2019, 6.9 million CVD deaths and 153.2 million CVD DALYs were attributable to dietary risk factors, with high sodium, low whole grains, low legumes, low fruits and high red meat being the top risks (B. Zhang et al. 2023; Dong et al. 2022). This increased to 7.9 million deaths and 187.7 million
**<u>Table 6: Estimated values of output (in US$) from different sources, aggregate and by groups</u>**

||**Recall**<br>(1)|**Disposition**<br>(2)|**Diary**<br>(3)|**Ra**<br>(2)/(1)|**tios**<br>(3)/(1)|
|---|---|---|---|---|---|
|Cash crops|48.62|57.14|27.24|1.18|0.56|
|Food crops seasonal|135.4|164.50|297.43|1.21|2.20|
|Food crops continuous|178.07|270.26|275.58|1.52|1.55|
|Fruit & Vegetables|10.85|43.41|14.87|4.00|1.37|
|Total|372.94|535.31|615.12|1.44|1.65|

_Source:_ Own computation from 2005/06 UNHS and harvest diaries 21
prices and the real economy, including employment. This strand of literature has focused on the mechanisms by which consumption and employment may be affected by energy price increases. On the one hand, higher energy prices are expected to reduce discretionary income, as consumers have less money to spend after paying their energy bills. Consumers may also increase their precautionary savings and delay or forgo purchase of energy-using durables (Bernanke, 1983, Pindyck, 1991). These effects imply a reduction in aggregate demand, output and the corresponding employment in response to an energy price increase. On the other hand, higher energy prices are also likely to cause reallocation of capital and labor away from energy intensive industries as consumers switch to more energy efficient durables and as firms also switch away from production technologies that are energy-intensive. In the presence of frictions in capital and labor markets, these sectoral shifts will cause resources to be unemployed, at least in the short term (Davis, 1987; Hamilton, 2008).

While most studies of oil shocks and economic activity focus on the United States, there are a few recent notable exceptions. Miguel, Manzano, and Martín-Moreno (2003) examine oil shocks and the Spanish economy. The study suggests an immediate 0.3 percent drop in hours worked in response to a onestandard deviation oil price shock. Cuñado and De Gracia (2003) compare macroeconomic responses to oil shocks among fifteen European countries. This study ignores effects on employment but instead considers effects on industrial production indexes; however, the study is useful for showing significant differences among some countries in economic responses to oil prices across Europe. For example, industrial production initially increases and then falls in the United Kingdom, but in Germany and several other countries the decline in production is immediate. Papapetrou (2001) considers oil shocks and the Greek economy and finds that oil price shocks explain a significant proportion of the fluctuations in 4
/sup><br>(0.04)|||||||
|Self Awareness||0.36<sup>_∗∗∗_</sup><br>(0.04)||||||
|Emotional Regulation|||0.31<sup>_∗∗∗_</sup><br>(0.04)|||||
|Self Control||||0.35<sup>_∗∗∗_</sup><br>(0.04)||||
|Perseverance|||||0.37<sup>_∗∗∗_</sup><br>(0.04)|||
|Personal Initiative||||||0.35<sup>_∗∗∗_</sup><br>(0.05)||
|Problem Solving|||||||0.34<sup>_∗∗∗_</sup><br>(0.05)|
|Control Mean|-0.03|-0.02|-0.03|-0.02|-0.01|-0.02|-0.02|
|Observations|849|849|849|849|849|849|849|

Notes: OLS regressions controlling for strata dummies used in the randomization and baseline value of the outcome. Soft skills were only measured at baseline and endline. Robust standard errors shown in parenthesis. The controls presented in the table are the baseline measures of each index.

36
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the mid-1980s. He investigated the presence of an economically meaningful, stable narrow money demand relationship using annual data for Australia for the period 1970–2009. He employed an Auto-regressive Distributed-lag (ARDL) cointegration approach. The results suggested the presence of a long-run equilibrium relationship between real narrow money balances, real income, a representative domestic interest rate (e.g., the yield on Australian government short-term bonds) and the nominal effective exchange rate of the Australian dollar. The statistical tests suggested no significant instability in the narrow money demand relationship despite financial deregulation and innovation in Australia since the early

> 9 Near-monies is defined as savings accounts and money market mutual funds that are close substitutes for M1.

11
# **C Introduction**

The gender disparity in commuting barriers is pervasive, affecting women in developing and developed nations. Recent OECD findings reveal a substantial gender commuting gap, with men averaging 33.4 minutes per day compared to women’s 21.9 minutes, indicating a 31.1% difference (OECD, 2016). Developing countries exhibit even more pronounced patterns; Indian women, on average, spend a mere 8 minutes daily on employment-related travel, compared to men’s 36 minutes.<sup>22</sup> ILO (2017) reports that lack of transportation decreases women’s probability of participating in the labor market by 16.5 percentage points among developing countries. Given the established inof barriers on women’s labor a natural arises: can fluence commuting supply, inquiry reducing these barriers influence women’s labor supply decisions? Whether policies directly addressing commuting costs would help increase female labor supply or not is far from obvious: social norms regarding household chores and traveling alone also significantly impact female labor supply, as evidenced by prior studies (Fanning Madden, 1981; Turner and Niemeier, 1997; Lee and McDonald, 2003; Abe, 2011; McQuaid and Chen, 2012).

Our study sheds light on the trade-offs women face when commuting cost is reduced by exploiting the rollout of a free busing scheme, the _Pink Slip Program_ , in two states of India. India is a pertinent setting for exploring this issue. There is an overwhelming gender gap in commuting time and modes used. The 2011 Census of India reveals that 30.2 % of women travel to work on foot, and only 24.6 % use any transportation, highlighting the limited access to transportation options for many women. In contrast, only 20 % of men travel on foot, and more than 50 % of men use some transportation. There is also evidence that women use slower modes of transportation to commute to work as faster modes are usually more expensive (Anand and Tiwari, 2006
less reliant is the U.S. on the targeted foreign country for its own exports (-0.720). In specification (2), the U.S. authorities are also more likely to impose a trade remedy, the larger is the recent surge in product-level imports under investigation (0.107). The only estimates that run counter to the theory are the variables designed to capture the “injury” to the domestic industry (change in employment, change in capacity utilization), though these estimates are not statistically different from zero.<sup>27</sup> Since the estimates presented in table 6a are not of particular concern to the ultimate question of interest regarding the question of whether to formally challenge a U.S.-imposed remedy at the GATT/WTO, we will not further discuss them here.

# **5.2 Regression Equation: Determinants of Foreign Country Decisions to Formally Challenge a U.S. Remedy with a WTO Dispute**

Table 6b provides results from our estimation of the decision of a foreign country to challenge a U.S.imposed trade remedy at the WTO.

## **5.2.1 Expected benefits and costs to dispute initiation**

Consider initially the first three rows of table 6b which describe whether the size of the market access benefits from the U.S. removing the imposed trade remedy are associated with the WTO member’s decision to formally challenge the measure with a dispute. First, the size of lost imports matters. The larger the dollar value of the investigated products in the U.S. import market in _t-1_ (the year before the trade remedy investigation), the more likely is the affected foreign country to challenge the measure (0.056). Since the import data is converted to logs – the size of the effect is not particularly easy to interpret. Nevertheless, the impact of an increase from the mean value of the explanatory variable of 16.857 (the
|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|
|||<br>|Panel B: Excludin<br>|g sample weight<br>|s<br>||
||DV: Δ Log cou|ntry-industry lab|or productivity|DV:|Δ Log bilateral e|xports|
|Δ Log estimator for technology|0.267***||0.265***|0.648***||0.636***|
|flows from the United States,|(0.078)||(0.078)|(0.112)||(0.107)|
|lagged five years|||||||
|Δ Log estimator for technology||0.064|0.059||0.473***|0.459***|
|flows from the United States,||(0.071)|(0.069)||(0.111)|(0.105)|
|contemporaneous|||||||
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3. The first-stage and reduced-form performance of the lagged technology flows, the preferred instrument, is contrasted with contemporaneous flows.
|Ln(number of banks)|The number of banks used in the analyses each year for each country|Datastream &<br>BankScope|
|---|---|---|
|Political Globalization|Index of political globalization|KOF Index of<br>Globalization|
|Security market liberalization|Government policies related to development of securities markets and<br>restrictions on foreign investors|Abiad, Detragiache and<br>Tressel 2009 (IMF)|
|Social Globalization|Index of social globalization|KOF Index of<br>Globalization|
|Stock Market Capitalization / GDP|Value of listed shares to GDP, calculated using the following deflation<br>method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is stock<br>market capitalization, P_e is end-of period CPI, and P_a is average<br>annual CPI|Financial Structure<br>Database (World Bank)|
|Stock Market Turnover Ratio|Ratio of the value of total shares traded to average real market<br>capitalization, the denominator is deflated using the following method:<br>Tt/P_at/{(0.5)*[Mt/P_et + Mt-1/P_et-1] where T is total value traded, M<br>is stock market capitalization, P_e is end-of period CPI P_a is average<br>annual CPI|Financial Structure<br>Database (World Bank)|
|Trade / GDP|Total exports plus total imports to current GDP|World Development<br>Indicator (World Bank)|

# **Table A2.  Summary Statistics of Country Variables**

This table shows the summary statistics of country-level variables used in this paper. The variables are described in detail in Table A1.

35
stock of capital in developing countries was financed by foreign savings consequent upon growing financial integration. They constructed a ―self-financing ratio‖ for the period 1991 to 2001 using a 10-year horizon for 47 developing countries and 22 OECD countries using data for the period 1981-2001. The first step was to derive an estimate of the capital stock in year t minus 10 using the national income accounts. The next step was to add the cumulative national investment over the 10-year period to this initial capital stock, and then divide this by the initial capital stock plus the cumulative national savings over the same period to obtain the selffinancing ratio, with suitable adjustments made for depreciation.<sup>5</sup> The purpose was to see how the self-financing ratio varied over the 1990s in response to growing financial integration and to compare the 2001 ratio with that in 1991.

APR‘s main findings were first, that the average self-financing ratio for developing countries did not change much over the 1990s, global financial liberalization notwithstanding. Second, there did not seem to be a growth bonus associated with greater external financing of the domestic capital stock. To the contrary, countries with higher self-financing ratios grew significantly faster. Third, more volatile self-financing ratios were associated with lower growth; but this result disappears when a proxy for the quality of institutions was included in the crosscountry regressions. But the strong, asymmetric effect of self-financing on growth remained: a rise in the self-financing ratio from 1.0 to 1.1 was associated with increasing the average per capita growth, from 2.8 percent to 4.4 percent, while a drop from 1.0 to 0.9 lowered per capita growth from 2.8 percent to 2.2 percent.<sup>6</sup> Self-financed growth is not a new idea. Feldstein and Horioka (1980) wrote a seminal paper asking how internationally mobile capital really was. In regressing national investment rates on national savings rates for 21 OECD countries using data from 1960 to 1974, they
reached by Japan in the 1960s and Hong Kong SAR, China; Korea; Singapore; and Taiwan, China, in the 1980s. To continue growing dynamically against the background of declining wage competitiveness, China will have to follow the path of the earlier Asian ―geese‖ and start to relocate its labor-intensive industries to low-income countries.<sup>23</sup> Indeed, this is already happening. A large share of China‘s outward foreign direct investment in Africa, which had reached $9.33 billion by the end of 2009, has gone to manufacturing (22 percent), second only to the share in mining (29 percent). And China is building six economic

> 22 <mark>Data from Oxford Analytica, March 28, 2011.</mark>

> 23 Based on the estimation by Maddison (2010), China‘s per capita income (measured in purchasing power parity) was 6,725 international dollars in 2008, the same level as in Japan in 1966, Korea in 1986, and Taiwan, China, in 1983. These economies started to relocate their labor-intensive manufacturing industries at that income level, Japan to the East Asian Tigers and Korea and Taiwan, China, to mainland China.
, allowing to track changes over time; however, indices are not informative to understanding distributional aspects of prices, affordability issues, or the degree of price segments underserved in a market. When housing markets mature, properties' characteristics and attributes may be more stable over time. Hence, changes captured by price indices reflect price movements over a relatively constant set of properties in the market. However, in many EMDEs, there are dynamic changes to the type of properties being built by formal developers. Due to the nascency of markets, formal developers and builders in EMDEs are evolving to expand their portfolio to also cater to housing solutions to HHs with lower or even informal incomes. In such circumstances, aggregate price indices may not be fit for purpose and may suffer from biases due to rapidly changing underlying property types.

The wide gap of publicly available property price data in EMDEs is a key impediment to extending the understanding of housing markets widely available from developed economies to these markets. Insights on the determinants of property prices and their drivers over time are heavily researched subjects, but exclusively rely on studying this dynamic in developed economies. If comparable property price data were available in EMDEs, they could provide crucial insights on potential inefficiencies along the housing value chain. Analyzing property price data in emerging economies can also help, for instance, to point towards constraints in developers’ ability to access finance, prohibitively high construction costs, or regulatory bottlenecks in land acquisition and titling. In addition, property price data in EMDEs can also help point towards affordability issues and potential ramifications on HH consumption and spending. The width of these challenges is often difficult to grasp as the housing value chain is very interconnected and complex, 2
efforts could differences in up investigate gender performance rating, promotions, project allocations, or the impact of career interruptions due to children.

# **2 Data and Institutional Features**

The WBG’s Human Resource Longitudinal Database was assembled for the purpose of this study. It is structured in a panel from 1987 to 2015 with staff uniquely identified through a universal, permanent personnel identifier (UPI). Data from each year are a snapshot taken on June 30 that contains information on the staff’s universal personnel identifier (UPI), compensation and benefits (for instance, salaries), personal background (gender, age), professional situation (for instance, professional grade), location (HQ or country-office based), role and movements within the organization (promotions and lateral moves), and information on the yearly performance rating (SRIs) from 2000 onward.<sup>12</sup> For the purpose of applying our decomposition, we consider the annual monetary compensation of each employee, the grade at which they were hired, their gender and nationality.<sup>13</sup> Our data cover all staff employed by the WBG between 1987 and 2015. Among staff members, the WBG grades run from GA to GL (the president of the WBG). Grades GA–GD are the grade levels for administrative and client support (ACS) staff. GE corresponds to analyst level. GF and GG contain the bulk of professional technical staff. Staff in the GH level, the first leadership position at the WBG, can be either in a technical or managerial role. GI (director) through GK (vice presiables (wages) can be explained by differences in characteristics (experience or education) versus differences in returns to these characteristic (see the seminal papers Oaxaca 1973 and Blinder 1973; Fortin et al. 2011 for a recent survey of the literature; and Filmer et al. 2005 for an application to a 1997 crosssection of World Bank personnel files).

> 12The Technical
. Thus, cash and direct deposit recipients each have the opportunity to receive the same amount of money by visiting the bank once, and they each incur the cost of a visit within a narrow window.

We first examine the impact of the savings default on bank transactions, using the administrative data described in the previous section. We compare outcomes for respondents who received MK 25,000 in cash to those who received the same amount deposited directly into their savings accounts, by estimating the following equation: The coefficient _β_ measures the effect of the savings default. We control for the average of the outcome variable in the 90 days prior to the transfer ( _Yi_ 0). The specification includes village and week-of-first-survey fixed effects. Standard errors are robust to heteroskedasticity rather than clustered because randomization is at the individual level, a decision that is conservative in light of our emphasis on null results. We report results for each of the three outcomes – deposits, withdrawals, and net deposits – in four time horizons to observe 9
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