rafmacalaba/data-use-mentions-extended huggingface dataset:
where F is the log of greenfield investment plus one from the US to country in month in a specific year. is a row vector of gravity variables, which include an indicator II jj variable equal to one if country is a member of the North Atlantic Treaty Organization tt GG ZZ (NATO), the log of the population-weighted distance between the US and country , an jj indicator variable equal to one if country is the US, a dummy variable equal to one if jj country is an English speaking country, as well as the log of country _j_ ’s GDP and jj population in the previous year, and time fixed effects . jj jj αα The indicator variable for N N alliance is used as a proxy for the importance of “friends” or strategic military allies. A positive coefficient that increases over time, especially after the Russian invasion of Ukraine, would suggest an intensification of friend-shoring in nations that provide higher security to US businesses. The coefficients on and B capture the sensitivity of investment announcements to physical proximity to the FFD ttyyD yy US. Larger negative coefficients for or larger positive coefficients for B yyBBy in recent years would indicate an intensification of nearshoring activity by US companies. FFD ttyyD yy yyBBy Finally, the coefficient on the dummy variable identifies the home bias of the greenfield investment from the US, reflecting the extent to which investment within SSyySSyy DDBBccDDtty US national borders differs from investment to other countries, capturing the importance of reshoring. Additionally, we also include controls for population and GDP to account for destination market size and economic conditions.

# _2.2. Empirical strategy II: Triple-difference approach_

In a second exercise, we take advantage of the investment data, which allow us to compare US and EU investment flows—the only economies for which we have internal investment. We implement a triple-difference approach and compare the coefficients for distance, 6
observed and notional wage changes are allowed if measurement error is present: ∆yit<sup>o=</sup> xit−1βt + εit + ∆eit.

It is worth stressing once more that measurement error is not forced onto the data. Assuming that ∆eit is normally distributed with a positive variance in the population would imply that the probability of a firm correctly reporting individual wages would be zero, a feature most likely inconsistent with the nature of our data. Instead, our assumption that a fraction, qt, of the wages, is measured without error allows us to examine the relative importance of this feature in the two data sets we study.

Under these assumptions, the likelihood of each observation is calculated and all parameters are estimated by maximum likelihood. Intuitively, the estimator uses the local asymmetries in the wage change distributions that we have discussed in the descriptive section in order to identify DNWR and DNWR. However, instead of identifying them unconditionally, by, for example, visually inspecting the previous histograms, the estimator exploits the asymmetries in wage changes conditional on individual and job characteristics. As for the identification of potential measurement error, the estimator has two main features that help assess its relevance. First, since measurement error is expected to follow a continuous distribution, its presence is less likely when the size of the spike at zero is large. Second, since measurement error is expected to displace actual wage freezes, we should observe excess probability mass around zero and relatively smooth asymmetries in other parts of the wage change distribution when measurement error is important.

It is important to note that not all forms of measurement error will necessarily result in an underestimation of DNWR. Most notably, in the presence of rounding errors, wage freezes would actually be over-represented and not the opposite. For example, Smith (2000) uses data from the British Household Panel Survey (BHPS) and finds that self-reported reported wage freezes between two consecutive years are less likely to happen when individuals double check their pay slip before reporting
in each state.<sup>28</sup> A two-stage clustered design is assumed with clusters (defined above) serving as primary sampling units (PSUs) selected in the first stage within each domain and then a sample of 10 households within each cluster is selected in the second stage. With these design features established, the trimmed census data was analyzed to identify the parameter estimates for per capita income (mean and standard deviation) for each state and the target standard error implied by the parameter estimates that corresponds to an _RSE_ of 7.5 percent. The minimum sample size required for state _s_ , given these parameters, under simple random sampling (SRS) design is then obtained as: where _σs_ is the standard deviation of per capita income in state _s_ , _RSE_<sup>_tgt_</sup> is the target standard error of 7.5 percent in state _s_ , and _y_ ̄ _c_ is the mean per capita income in state _s_ . The minimum sample size under SRS must then be adjusted to account for the clustered design. This design effect due to clustering is accounted for by estimating the intra-cluster correlation ( _ICC_ ) for per capita income within each state using the trimmed census data. The _ICC_ estimates can then be applied to the SRS size obtained above to arrive at the minimum sample size for state _s_ under the clustered design employed here, given by: where _DEFFs_ is the design effect in state _s_ , _npsu_ is the number of households selected within each cluster (10 in this case), and _ρs_ is the _ICC_ for per capita income in state _s_ . The minimum number of clusters to achieve _ns_ (assuming 10 households per cluster) was calculated and then the final (household) sample size established by multiplying the number of clusters by 10.<sup>29</sup> Taking the sample size requirements from above as fixed, the sample in each simulation is selected in accordance with the two-stage design. PSUs within each state, referred to here as clusters,
# III.  DATA

The data I use is the National Family Health Survey (NFHS) conducted in 2005-06, which is a large survey of representative households from all 29 states of India. The survey includes a Household Schedule which provides a list of members in each household and basic socioeconomic information such as religion, caste, wealth status, and durable goods ownership. In addition, a Women’s Schedule provides the information needed to pin down the treatment status—state of residence, year of marriage, and religion—for all women between the ages of 15 and 49 in each household. It also provides information on sociodemographic variables such as years of education, work status, husband’s education and occupation, and on variables denoting status within the household such as participation in own healthcare and household decision-making, and requiring permission to go somewhere or maintain contact with friends and family.

The outcome variables that are based on questions related to mobility are dummy variables denoting: (i.) whether the woman is allowed to go to a health clinic by herself; and (ii.) whether she is allowed to go to the market by herself. Outcome variables based on questions related to bargaining power within the marriage are dummies denoting whether she has a say in (either alone or jointly with her husband): (iii.) how the husband’s money is spent; (iv.) her own healthcare decisions; (v.) big household purchase decisions; (vi.) small household purchase decisions; and (vii.) decisions about visiting her family and friends.

Table 1 shows summary statistics. There are 93,274 married women in the data overall who constitute my primary sample. Women in reform and non-reform states are similar in average age and age at marriage. Women in the reform states have higher years of education on average than women in non-reform states, and this is particularly the case for the cohorts of women married post-reform,<sup>14</sup> who are
We examine the bivariate relationship between demand and key firm and owner characteristics in more detail in Figure 5. This figure allows us to see how demand varies with different characteristics at different prices. We see the demand curves look similar for men and women, and that demand is higher for those who are wealthier, more risk-seeking, with higher sales, higher expected returns to training, and younger owners. We also test (not reported) whether the slopes vary by characteristic, and find that younger owners and those who have higher expected returns from training have more inelastic demands (flatter slopes).

Table 6 looks further at the correlates of willingness to actually pay. Column (1) repeats the last column of Table 5 in terms of the BDM WTP. Column (2) examines the correlates of actually paying for training in the BDM exercise, conditional on drawing a positive price to pay, and what that price was. We see that only education is significant, with more educated individuals being more likely to pay for training. Column (3) shows this continues to hold when using LASSO to reduce the number of coefficients. Columns (4) and (5) look at who reneges on payment after having a WTP greater than the price, and finds those with more education are less likely to renege. Finally, Columns (6) and (7) present the correlates for paying for the course in the TIOLI sample. Price offered is negatively correlated with paying, as we saw before. We see demand is higher for wealthier owners, and those with larger firms (more employees), as with the BDM. We find that those who think their practices compare well with other firms have lower demand. In contrast to the BDM, we find a marginally positive impact of age on demand (but note that the magnitude is small, and that the TIOLI sample is younger to begin with than the BDM sample).

# **4.3 How Does Pricing Affect Training Attendance?**

We
- Morón, E., E. Salgado, and C. Seminario. 2012. “Financial Dependence, Formal Credit and Firm Informality: Evidence from Peruvian Household Data.” Working Paper 288, Inter-American Development Bank, Washington, DC.

- Nurboja, B., and M. Košak. 2017. "Banking efficiency in South East Europe: Evidence for Financial Crises and the Gap between New EU members and Candidate Countries," _Economic Systems_ 41(1): 122-138.

- Ohnsorge, F., and S. Yu. 2021. _The Long Shadow of Informality: Challenges and Policies._ Washington DC: World Bank.

- Ohnsorge, F., Y. Okawa, and S. Yu. 2021. “Chapter 4 Lagging Behind: Informality and Development” In _The Long Shadow of Informality: Challenges and Policies_ , edited by F. Ohnsorge and S. Yu. Washington, DC: World Bank.

- Okawa, Y., and E. van Wincoop. 2012. “Gravity in International Finance.” _Journal of International Economics_ 87(2): 205-2015.

- Olson, D. and T. Zoubi. 2017. “Convergence in Bank Performance for Commercial and Islamic Banks During and After the Global Financial Crisis.” _The Quarterly Review of Economics and Finance_ 65: 71-87.

- Orsi, R., D. Raggi, and F. Turino. 2014. “Size, Trend, and Policy Implications of the Underground Economy.” _Review of Economic Dynamics_ 17 (3): 417-436.

- Pappadá, F., and Y. Zylberberg. 2017. “Austerity and Tax Compliance.” _European_ –

- _Economic Review_ 100: 506 24.

- Perry, G., W. Maloney, O. Arias, P. Fajnzylber, A. Mason, and J. Saavedra-Chanduvi. 2007. _Informality: Exit and Exclusion_ . Latin American and Caribbean Studies. Washington, DC: World Bank.

- Pleninger, R., J. Sturm. 2020. “
where **'A,** is the price index of goods from the component groups that make up the aggregate at node A.

This aggregation structure implies that the share of total expenditure on imports at node A that come from category Ai will be Equations (1)-(3) summarize the quantity, price, and expenditure share at a representative node.

In practice, data on the value of trade in each category is normally available but data on the quantities of imports are unavailable or unreliable. This means that it will not be possible to separately identify the bA4 parameters from the data. Therefore, there is no loss of generality in normalizing prices of all imports to 1 in the initial situation. Under the assumption that the country in question is small and that goods from different source countries are imperfect substitutes, the domestic price of the imported goods in the tariff line data will be (1+ **j** ) q*,, where q*, is the exogenously given foreign price of goods in the tariff line with index ijk from the country whose index is Im and **r°jjkhn** is the ad valorem tariff imposed in the initial situation. The normalization of domestic prices to 1 in the initial situation is thus a normalization of world prices to 1/(1 + **i'ijkh,,)** in the initial situation. Assuming that prices in the rest of the world remain unchanged, the new domestic price will be **Ojk,n,=** (1 _+ Tlj,k,,)/_ (1 **+°i7jj,,),** where **r!j,l,** is the new tariff rate .

> Under this normalization, *A = 1 for all nodes A and sub-indices i in the initial situation

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11 within the range of income Ginis recorded for other developing countries. For instance, the income Ginis recorded in the most recent edition of World Development Indicators suggest that Gini coefficients of per capita household income range from a low of 0.420 (Bolivia) to a high of 0.601 (Brazil).<sup>20</sup> In Table 2 the sources of rural income for Egypt and Jordan are presented by income quintile group. The results suggest that nonfarm income plays a radically different role for the poor in the two countries. While in Egypt the poor -- that is, those in the lowest quintile group - receive almost 60 percent of their total per capita income from nonfarm income, in Jordan the poor receive less than 20 percent of their total per capita income from this source. Furthermore, while in Egypt the share of nonfarm income in total income generally falls as income rises, in Jordan the share of nonfarm income in total income typically increases.

The reasons for this dramatic difference have to do with land. In Egypt the cultivated land base is 100 percent irrigated and highly productive with yields for the main field crops (wheat and rice) among the highest in the world. The large number of people living in rural Egypt therefore all seek access to agricultural land. However, in fact, the very real land constraints in rural Egypt -- 75.7 percent of the households in this sample own no land<sup>2</sup> ' - force most people (and especially the poor) to earn their livelihood from outside agriculture. In Jordan the situation is quite different. Only 30 percent of the cultivated land base is irrigated, and the main field crops (wheat and barley) are generally grown under rainfed conditions and so yields are low.<sup>22</sup> People in rural Jordan thus do not press for land access, since the really attractive economic rates of return are to be found not in agriculture, but rather in
so we follow previous studies in pre-treating the data in ways that reduce the volatility of the outcome variables. Inherent variability in clearance times and in firms’ monthly import activity generates a high degree of volatility in the outcome variables even in logs. We further smooth the fluctuations in the outcome variables by calculating the 3-month moving average (i.e., the average of the current and previous two months) for each of the outcome variables a)-e) for both treated and control firms. These moving averages are the actual outcome variables we use in the impact evaluation of the IHC program.<sup>35</sup> These variables nonetheless remain more volatile than is common in most SCM studies.

The SCM uses a weighted combination of non-IHC firms to construct a synthetic control firm that resembles in the most relevant economic characteristics the IHC firm before the outset of the IHC program. We rely on the administrative trade data to define the vector of conditioning variables that are used to construct the synthetic control firms. For each firm and month we calculate the share of imports originating in the EU, the share of imports cleared under a special customs clearance code (other than IHC), and the share of imports in each of 10 broad commodity groups or sectors. Appendix Table 1 provides the detailed definitions of the conditioning variables. In particular, note that the use of sector data allows us to account for seasonal patterns in imports across sectors and for supply or demand shocks that would apply to firms trading in the sector. The vector of conditioning variables also includes the relevant outcome variables in specific pre-treatment periods that are discussed below.

The purpose of the conditioning variables is to ensure that the synthetic control firm not only matches the treated firm in terms of the dynamics of the outcome variable, but also matches important characteristics of the treated firm in the pre-treatment period. The share of imports originating in the EU is useful for conditioning the construction of the synthetic control firm if shocks to the outcome variable are related to the
_terly Journal of Economics_ , 119(2), 647–678.

- Timmer, M., T. van Moergastel, E. Stuivenwold, G. Ypma, M. O’Mahony, and M. Kangasniemi (2007): “EU KLEMS Growth and Productivity Accounts Version 1.0,” University of Groningen.

- Verhoogen, E. (2008): “Trade, Quality Upgrading and Wage Inequality in the Mexican Manufacturing Sector,” _Quarterly Journal of Economics_ , 123(2), 489–530.

- Waugh, M. (2010): “International Trade and Income Differences,” _American Economic Review_ , 100(5), 2093–2124.

- Yi, K.-M. (2003): “Can Vertical Specialization Explain the Growth of World Trade?,” _Journal of Political Economy_ , 111(1), 52–102.

- (2010): “Can Multistage Production Explain the Home Bias in Trade?,” _American Economic_

- _Review_ , 100(1), 364–393.

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case of new regulations and firn entry and exit. Regulations may affect firm entry and exit in complicated ways, They may impose entry barriers to new finns and impose exit barriers to inefficient (or state-owned) firms. Av "snapshot" of firms at a given point in time (a crosssection) provides limited information on this matter. Only by surveying firms over time and across countries can we begin to understand how new regulations or other policy interventions affect firm entry and exit.

Entry and exit also lead to attrition bias in the sample-another reason to obtain panel data. Properly accounting for both entry and exit of firms is therefore important to understand industry evolution. Attrition bias may arise when firms disappear (exit) because of mergers, acquisition, consolidation, or bankruptcy, or when a firm is included in the sample only if it survives up to the point of interview. Without accounting for attrition, the sole reliance on surviving firms will inevitably bias (usually upward) performance measurements. The additional survey rounds necessary for constructing panel data can help account for this bias by deterrnining which firms have disappeared and why. In addition, panel data can help better understand firn entry.<sup>4</sup> New entrants may differ significantly from surviving firnms. Moreover, a high death rate of firms is not necessarily worrisome if it is accompanied by a high birth rate. To deal with such issues, firm surveys should resample some firms annually and strive to add new entrants.

Surveys should also define industries as precisely as possible and offer detailed informnation of the industry analyzed. When this type of micro data is collected in panel data sets, it becomes possible to compare the technological progress of particular industries in different countries. Such data allow us to better address important unanswered questions, such as why inter-firm dispersion in productivity is greater in industrialized economies than in developing countries (as suggested in Tybout, 1998). A variety of reasons (such as a smaller extent of market and excessive regulation) have been suggested.
and hence may be reducing information asymmetries.

To further understand the mechanisms through which eBay reduces the impact of distance on bilateral trade flows, we interact distance with measures of exporter corruption and information-availability. Our corruption measure is from the World Governance Indicators. Our measure of country information-availability is the number of Google search results for the country name. The idea is that there is more available information about countries that have more Google results. The marginal effects of distance as a function of corruption and country popularity are reported in Figure 7 (drawn from Table 6). The higher the level of corruption in the importing or the exporting country, the larger the distance-effect difference between online and offline flows. Similarly, the lower the degree of country information, the larger the difference in distance coefficients. Furthermore, these differences in distance effects are largest when exporters, rather than importers, are corrupt or unpopular. This confirms that due to eBay’s ’cash-in-advance’ system, it is trust in the exporter that matters most.

Finally, as noted earlier, the difference in the effect of distance could be due to a selection of ’international’ buyers rather than a ’technology’ effect. While the appended model including importer-eBay and exporter-eBay fixed effects partly corrects for these selection effects, buyer and seller characteristics might also affect the impact of distance. For example, online buyers may tend to be richer and rich individuals may prefer purchasing goods from faraway countries. Ideally, we would like to observe individual characteristics of buyers online and offline, but we do not have access to that data. Thus, we check whether distance matters less online at different levels of income inequality and internet penetration in the importing country. The idea is that in highly unequal societies with low internet penetration only a few privileged ’international’ buyers have access to internet and buy on eBay. In these countries buyers on eBay and offline are likely to be most different. As reported in Figure 8 (drawn from Table 7), we find the biggest differences
%|
|Computer available for classes|48%|11%|41%|53%|30%|48%|53%|41%|50%|
|Teacher has International<br>Computer Driving License|64%|34%|58%|71%|54%|67%|70%|65%|69%|
|Teacher has Intel certification|22%|10%|19%|36%|19%|32%|33%|27%|32%|

Source: Authors’ calculations, based on NAfKE 2006 and 2008 surveys 9
**Figure 4.7: Right panel: Hotels and coastal erosion in Nha Trang. Left panel: Locations of hotels sourced from OpenStreetMap in Vietnam**

Source: Deltares et al., 2017 (erosion data), OpenStreetMap, 2019 (hotel locations), Google Maps (background image).

- 4.6. Saline intrusion and agricultural production

In the Mekong River Delta, saline intrusion harms agricultural production in dry years. This analysis quantifies the extent to which production of rice, other crops, and orchards were affected by saline intrusion in 2016. The location of agricultural production is based on the land-use maps described in section 3.1 (figure 4.8.a), while hazard information is extracted from the saline intrusion maps introduced in section 2.4 (figure 4.8.b). Overlaying these two datasets allows quantification of the production area affected by different levels of salinity per province and agriculture product. A brief overview over the results is given in figure 4.9.

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