rafmacalaba/data-use-mentions-extended huggingface dataset:
Mac became priced lower than other value meals and temporarily stopped being advertised for sale, raising speculation over artificial distortions leading to underpricing.<sup>47</sup> Moreover, findings by Portes and Atal (2014) suggest the Big Mac index is at most a qualitative indicator of the direction of movements in the nominal exchange rate in rich and economically stable economies, while being less effective in forecasting movements in emerging markets.<sup>48</sup> In a similar spirit, CommSec, a unit of Australia’s commonwealth bank, constructs an iPad index in which price differences derive from currency misalignments but are also largely attributed to transportation costs, taxes, and to some extent price discrimination (due to Apple’s large market share). By this measure, Argentina ranks the most expensive place to buy an iPad as of September 2015, costing almost triple that in the US, suggesting an overvaluation of the peso of approximately 64% without adjusting for transport costs and tariffs (see Figure 11). Argentina has high trade barriers, such as tariffs and currency controls, which prevent iPad price adjustment in line with the world market. Therefore, we expect this estimate to overestimate the peso’s overvaluation, such that controlling for these market imperfections, would suggest a lower overvaluation. The caveat of Apple’s potential price discrimination, however, remains.

Rather than comparing prices of single commodities across countries, other measures of PPP equate consumption baskets. This is the spirit of the OECD measure of different price levels by calculating the PPP ratio for private final consumption expenditure.<sup>49</sup>

# **_Purchasing Power Parity (PPP)_**

Lastly, we calculate the peso’s misalignment according to the theory of purchasing power parity, which equates price levels across countries and implies that the real effective exchange rate–measured as the nominal effective exchange rate adjusted for relative consumer price indices–should be constant. If the real exchange rate is below (above
particularly if fertilizer use is as low as it is in Uganda.

We observe that returns to fertilizer are present across all crops, and high for a range of prices. For beans and maize, the median returns are 74 percent and 66 percent respectively. There are positive returns for both fertilizer and improved seeds across the entire range of prices, but the returns to investing in hybrid seeds and fertilizer together are no higher than investing in just fertilizer alone.<sup>11</sup> For matooke and coffee, tree‐based crops, median returns are 66 and 39 percent, respectively. Positive returns are present at all prices for matooke and for the top 75 percent of prices for coffee.<sup>12</sup> Robusta coffee requires the highest application rate of fertilizer (190 kg per hectare compared to 45 kg per hectare for matooke). Thus, at these rates of return, we should expect high rates of fertilizer use.

That said, the latter returns assume normal weather conditions, and under poorer weather conditions the financial returns likely disappear. If the yield gain is halved under bad weather conditions as some studies suggest (Christiaensen and Dercon 2011, Rosenzweig and Udry 2017), the financial returns become negative for all coffee prices and all but the top 25 percent of maize and matooke prices. In contrast, the return to fertilizer use on beans remains positive across the full range of prices (see Figure 3.7). The expected return for fertilizer use will depend on the full distribution of possible returns under all weather and price outcomes, which is not possible to estimate with the available data. However, if farmers are risk 11 These returns are also specific to the particular seed used and region, the Western region. For beans we used the returns from K132 and Kanyebwa seeds from Kaizzi et al. (2012).

12 Yield increases are taken from Nyombi et al. (2010) for matooke and van Asten et al. (2017) for coffee.
lends support to the model.

# Table 1. Growth: panel regression analysis - GMM

_Dependent variable: Annual growth in real GDP per capita Sample: 31 countries, 2010–2014 (unbalanced panel) Estimation: GMM-IV system estimation_

|_Variable_||
|---|---|
|_Infrastructure Stock_|_0.041***_|
|_(first principal component of stocks)_|_(0.015)_|
|_Control Variables_||
|_GDP per capita_|_-0.292***_|
|_(last period, logs)_|_(0.037)_|
|_Trade openness_|_-0.094***_|
|_(trade volume as % of GDP, logs)_|_(0.026)_|
|_Human capital_|_0.254_|
|_(Penn World Table - human capital index, logs)_|_(0.169)_|
|_Institutional quality_|_0.011*_|
|_(WGI for government effectiveness, logs)_|_(0.006)_|

31 For example, there is a sizable heterogeneity in the extent to which growth responds to trade liberalization (Wacziarg and Welch, 2003; Chang, Kaltani, and Loayza, 2005). Greenaway et al. (1998) suggested that trade liberalization negatively affects growth in the short run. Trade share in GDP has negative impact on growth for heavily regulated economies (Bolaky and Freund, 2004). Michaely et al. (1991) and Panagariya (2004) provided case studies indicating that in an unstable macroeconomic environment and with an overvalued exchange rate, the effect of trade liberalization is likely to be reversed. Wacziarg and Welch (2003) also reported that restrictive macroeconomic policies can explain why some countries did not experience higher growth following trade liberalization. They stressed that currency overvaluation can undo trade liberalization. Finally, results may also depend on measures of trade openness (Ciccone
the error terms. Both methods employ cluster random effects. Model 3.1 employs non-standardized variables. Model 3.2 employs variables in TA 2 standardized by those in NPS 2020/21. Food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. Imputed poverty rates for TA 2 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2020/21 deflated to 2022 prices using the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.6.

34
Appendix 12 reports the OLS results distinguishing between intermediate and final imports.<sup>14</sup> Import data in these regressions draw on the Economic Census data. There are no significant correlations between imports and labor market outcomes when all controls are included. However, the coefficients between final imports and both the labor force participation and employment rates are negative when no controls are included (column 7), while they are insignificant for intermediate imports (column 4). This could point to a more favorable role of intermediate imports relative to final imports for labor market outcomes through the employment channel, suggesting that imported inputs complement domestic or export production, thereby increasing employment. Interestingly, the results find a negative association between the expansion of final imports and inward migration, but no link with intermediate imports. There is no relationship with the informality rate.

Differentiating by broad sector of traded imports – manufacturing versus services – reveals that the observed overall pattern is driven by manufacturing imports (Appendix 13). The results are in sharp contrast for services imports (Appendix 14). Here, we find a positive and significant association of both intermediate and final services imports with labor force participation and employment rates. The coefficient between intermediate services imports and employment rates is slightly larger, which could imply that access to intermediate services for domestic or export production has larger employment benefits. Focusing on the income channel, however, the findings are less clear. Both final and intermediate services imports are positively associated with total labor incomes in Mexico, but only the estimates for final services imports are statistically significant. Finally, we find no association between services imports and labor markets through the migration and informality channels.

# 4.4 Imports for export production: GVC spillovers

Finally, we analyze the role of GVC participation for local labor market outcomes. Because many municipalities have zero changes in GVC participating establishments over the period 2004-14, our analysis focuses on GVC participation measures at the more aggregated state level. Correlates with labor market outcomes in that case are
ASEAN|13.2|14.6|16.9|20.9|17.2|18.2|23.4|19.8|15.2|16.3|20.0|20.3|
|EU(15)|57.3|65.9|62.4|61.8|52.9|63.2|61.0|59.4|55.0|64.5|61.7|60.6|

Sources: IMF Direction of Trade Statistics.

Notes: Shares for Bhutan are based on partner data (mirror statistics). Please note the presence of discrepancies between FOB and CIF values in mirror statistics.

Intra-regional trade patterns in South Asia suggest that all members increased their relative importance in the region toward the late 1990s.<sup>9</sup> Although the relative importance of the

> 9 Pitigala and Baysan (2001) argue that the revival of intra-regional trade in the late 1990s has ostensibly occurred due to unilateral trade reforms rather than regional or bilateral agreements. However, increased

8
# **ANNEX 1: DATA SOURCES AND DEFINITIONS**

There is no official definition of “city” or “agglomeration” in Brazil. The lowest administrative level consists of more than 5000 municípios. However, these vary greatly in size and many functional economic and population agglomerations consist of a number of municípios. In this paper, we therefore follow the example of a study of Brazilian urban dynamics by IPEA, IBGE and UNICAMP (2002). It defined agglomerations based on their place in the urban hierarchy from “World Cities” (Sao Paulo and Rio de Janeiro) to subregional centers. For each agglomeration, this study identified the municípios that were a functional part of the urban area. The municípios belonging to each agglomeration were then further classified into eight categories according to how tightly they are integrated in the agglomeration, from “maximum” to “very weak”. The main criteria used in these classifications were centrality, function as a center of decision making, degree of urbanization, complexity and diversification of the urban areas, and diversification of services. These were measured by a range of census and other variables such as employed population in urban activities, urbanization rate, and population density. We modified this classification slightly by also including smaller municípios to existing agglomerations if their population exceeded 75,000 population and more than 75 percent of its residents lived in urban areas in 1991, or if they were completely enclosed by an agglomeration.

The agglomeration definitions developed by IPEA, IBGE and UNICAMP (2002) are based on município boundaries valid at the time of the Brazilian Population Census of 1991 and the Population Count of 1996, while our study captures dynamics from 1970 to 2000. During this time, many new municípios were created by splitting or re-arranging existing ones. In fact, the number of municípios increased from 3951 to 5501 during these three decades. To create a consistent panel of agglomerations for the 1970 to 2000 period, we therefore used the Minimum 36
increase in concerns about climate change – but this is not what we observe.

We substantiate these results with Google trend data. The competing priority hypothesis would require the relative number of searchers about climate change to decline between 2016 and 2023. But the opposite is observed: Between 2016 and 2023, the Google trend index of search topics increased by 31 percent for environmental and climate change topics and 47 percent for global warming; it rose by 36 percent for health, 31 percent for education, 16 percent for immigration policies and border issues, and by 2 percent for public safety. We find analogous changes in the index at the country level.<sup>6</sup>

> 6 These results are available from the authors on request.

10
stringency and vaccine acceptance are significantly stronger in societies with weaker social norms about vaccine acceptance, i.e., in societies where individuals do not feel social pressure to get vaccinated due to their perception that the majority of their peers (or other relevant reference group) will not accept a vaccine. These stronger correlations are also found where trust in government is low. Lockdowns and stringent restrictions plausibly convey a stronger signal of COVID-19 risk in these societies, rather than in societies with tighter social norms and with populations trusting their government more. Higher vaccine acceptance in these societies tends to pre-exist the implementation of stringent non-pharmaceutical interventions. These findings are in line with evidence from other studies (Blair et al., 2022; Lazarus et al., 2021; Jabar et al., 2021; Mannan and Farhana, 2021; Kerr et al., 2021).

Our paper contributes to three branches of the literature on vaccine uptake and adoption of other preventive health measures. Firstly, a large body of work exists on individual drivers or correlates of vaccine take-up. In developing countries, these studies mainly focus on child immunization rates and find that mothers’ characteristics such as education and household socio-economic status are significantly correlated to the probability of immunization (Devasenapathy et al., 2016). Education continues to play a role for adult vaccination, along with acceptance of vaccines for other diseases. Maurer et al. (2009) show that the intention to vaccinate against the novel H1N1 virus is strongly associated with uptake of seasonal influenza vaccinations in the US. The association between individual characteristics and vaccine take-up is often non-monotonic. For example, in a study conducted in Indonesia, illiterate women and women with secondary education are more likely to get their children vaccinated than women with primary education (Streatfield, Singarimbun, and Diamond, 1990). The authors argue that this is possibly due to higher social compliance with local authorities among women with no education, and greater awareness of the protective function of vaccines among women with higher education.

In addition
(Love et al. 2023). While insurance products hold significant potential, they have not been rigorously evaluated with women-led businesses. IFC and AXA (2015) estimate that women’s spending on insurance premiums will triple its current size, reaching $1.4 trillion to $1.7 trillion by 2030. Battaglia et al. (2023) assert that flexible loan repayment options can serve as an insurance mechanism and enhance business performance. Bianchi and Bobba (2013) suggest that predictable income from cash transfers can decrease risk aversion and promote business investment. Groh and McKenzie (2016) designed and evaluated an innovative product for entrepreneurs to cover macroeconomic risks, but they did not observe significant effects on business outcomes.

Innovations in design are necessary in this area, including those tailored to the needs of women-led businesses. For instance, products that offer business interruption protection for women entrepreneurs, such as permitting the suspension of loan payments during maternity leave, could be beneficial. Considering women’s dual roles as caregivers and entrepreneurs, solutions that enable them to manage risk and provide protection during personal crises or business interruptions hold promise.<sup>26</sup>

## 4.7 Reducing gender biases among financial intermediaries

Some studies suggest that women investors are more inclined than their male counterparts to fund women-led businesses (IFC et al. 2019), highlighting the importance of increasing women’s representation in leadership roles within banks and investment funds. However, two studies in Türkiye reveal that both male and female loan officers exhibit implicit biases against women, although such biases diminished with experience (Alibhai et al. 2019c is based on a loan-application experiment and Brock and De Haas 2023 conduct lab-in-the-field experiments). Implementing gender intelligence training may be an effective strategy to combat unconscious bias.<sup>27</sup> Many financial institutions do not track sex-disaggregated data, leaving them unaware of potential gender biases in their services. Therefore, collecting and analyzing sexdisaggregated data should be a strategic imperative for financial institutions,
agents to reduce the weight attached to the inflation target in forming expectations (thereby imparting greater persistence to inflation), because default is associated with a perceived increase in the risk of monetization and thus higher future inflation. In a sense, therefore, inflation expectations depend essentially on the fiscal stance–in line with the empirical results of Celasun, Gelos and Prati (2004), based on survey data for Turkey. Although they do not provide a formal characterization of their argument, they note (p. 494) that “...A credible fiscal consolidation is probably the key to reducing inflation, because inflation expectations will decline only if the public perceives that the need to monetize fiscal deficits or inflate away the debt stock has come to an end.”<sup>41</sup> The expected nominal depreciation rate, EDEPR, which affects portfolio decisions and the pricing rules of commercial banks, is defined as a weighted average of its past value and expected changes in the real exchange rate, measured as the difference between expected domestic inflation (given in (130)) and foreign inflation, FINFL, with a one-period lag: EDEPR = χEDEPR−1 + (1 − χ)(EINFL − FINFL−1), (132) where 0 < χ < 1. Thus, when domestic inflation is expected to exceed foreign inflation, that is, when the real exchange rate is expected to appreciate, agents will also expect the nominal exchange rate to depreciate, to prevent a loss in competitiveness.<sup>42</sup> Note that in the model exogenous changes in the probability of default lead in general to an inverse correlation between credibility and the government bond rate. An increase in PDEF , for instance, raises directly the bond 41They also found that inflation expectations appear to be forward-looking, rather than backward-looking. However, this result is not consistent with those obtained by Agénor and Bayraktar (2002), who found that forward- and backward-looking components have similar weights in expectations.
18 (2016), Philippines (2017), Senegal (2016, 2017), South Africa (2016), Tajikistan (2017), and TimorLeste (2016).<sup>20</sup> Newly added country-specific surveys on the health equity side of HEFPI include the 2015 Kenya Integrated Household Budget Survey (KIHBS) and the fourth round of the 2016 Malawi Integrated Household Survey (IHS).

# 3.4. <u>Subnational data points</u>

Generally, HEFPI data points come from nationally representative surveys. There are, however, a number of exceptions: because data on the prevalence and treatment of noncommunicable diseases from low- and middle-income countries are extremely scarce, we include data points from subnational STEPS surveys for indicators where no data point (from any year) is available from a nationally representative survey. Most of these subnational surveys sample capital cities where epidemiological and healthcare use patterns likely differ from those of the general population – a caveat HEFPI users should bear in mind. Table 4 lists the subnational surveys on the health equity side of HEFPI, together with the number of data points we source from them, and the subnational area they are representative of.

> 20 So far, we have only included the following indicators from these new DHS surveys: 4+ ANC visits, skilled birth attendance, full child immunization, receipt of oral rehydration salts for children under five with diarrhea, and formal health care provider visits for children under five with acute respiratory infections.
, on average 34% lost their job in the first four months of the pandemic. The rate of job loss was much higher in Nigeria (61%) compared to the other countries

> 6 Ambel, McGee and Tsegay (2021) analyzed the survey sample and selection process of the HFPS used in this paper, they found that the contacted samples were biased toward wealthier households with higher living standards. The coverage rates (i.e., the share of households in pre-COVID-19 with phone numbers) for Ethiopia, Malawi, Nigeria and Uganda were: 79%, 73%, 99%, and 73% respectively. While the response rates (i.e., the households interviewed as a share of the initial sample) were: 60%, 74%, 65%, and 93% respectively.

> 7 In each HFPS round, respondents were asked whether they worked during the past week. In round 1, respondents who worked at the time of the survey were _not_ asked about their pre-pandemic employment status, so we assume that everyone employed at the time of the HFPS first round was also working before the pandemic. HFPS data for Latin America show that this assumption has minor effects on the job loss estimates, because few people began working immediately after the pandemic started (Khamis et al. 2021a).

7
relevant to the questions at issue.

# _Information published by providers_

Although much regulatory information is routinely obtained from banks and other financial service providers for regulatory purposes and also, for many banks, published for the benefit of shareholders or the general public, relatively little of this has a bearing on the small customers of interest here. For most banks the aggregate value of deposits, or loans of such customers is very small as a fraction of the banks’ total business, and information about numbers of accountholders is not always retained or known. Some relevant information should be easily available from providers but is not commonly published even though not very market sensitive. This would include such information as number and location of branches and ATMs, number of accounts, size distribution of accounts, both deposit and lending and pricing of services.

> 22 This count includes the Business Environment and Enterprise Performance Surveys (BEEPS) for Easter Europe and Central Asia which retain the smaller sample size, wider sectoral coverage and more limited questionnaire more characteristic of the earlier wave.

> 23 It mystifies this author as to how the firm is expected to make this distinction given the fungibility of money, not only between working capital and fixed investment, but as between existing real capital and new investment.

17
year by the policy: CT, GA, MA, MD, ME, NC, NH, NJ, NY, PA, RI, and VT. However, because of data limitations we observe a bank balance sheet before and after the policy only for the following six states: MA and ME in 1812; NH and RI in 1822; NY in 1839; CT in 1850. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

39