rafmacalaba/data-use-mentions-extended huggingface dataset:
situation, the TLSS 2007 was interrupted and resumed in January 2007. All households interviewed previously were reinterviewed. It is, however, still possible that some biases may remain. In particular, it is possible that some of the results we obtain are not due to exposure to the 1999 violence but to exposure to the civil upheaval in 2006.

In order to control for this potential exposure to the violence in 2006, we explore a variable in the 2007 dataset that captures whether an individual has been absent from home in the past 12 months for security reasons.<sup>27</sup> Our calculations show that individuals who have been absent from home for security reasons in 2006 are all residing in Dili. Therefore, we believe that this dummy reliably captures quite well the level of exposure to the 2006 violence.

> 27 Those that declare being absent from home for security reason represents the 2.7 % of our sample.

30
# UP = ratio of urban to total population

Contrary to most econometric studies, we do not try to explain FDI inflows but rather the FDI that does not arise from market size and the natural resources available in the host country. Therefore, the dependent variable used in the regression is our business climate indicator as defined by equation (1). As discussed earlier, we assume that FDI inflows respond to a change in GDP or natural resources with perfect elasticity. To check the robustness of this assumption, we have also estimated the same equation but with FDI inflows as a dependent variable and GDP and natural resources as explanatory variables. We found the respective elasticities of 0.91 and 0.92 and 1.4 and 1.2 in our panel and cross-country regressions (see table 4, third column)." A brief explanation might be necessary for our selection of explanatory variables, which has been partly driven by the availability of data in the World Bank's database. 12 The economic growth rate should influence positively the business climate for FDI as it reflects an improvement in economic performance. Most recent studies have also evidenced that the degree of openness, as measured by the trade share in GDP, should influence positively foreign investors through trade liberalization and higher competitiveness. The illiteracy rate should be inversely related to the availability of relatively skilled labour - a major factor in the location decision of TNCs. The number of telephone lines per 1,000 people is viewed as an indicator of infrastructure and communication development. Finally, the recent literature has argued that investors can be lured by concentration of other companies or customers, since it reduces their transport costs and there are evident economies of scale in the development of backward and forward linkages. This argument might be partially captured by the share of urban population (as a percentage of total population). Note we will also test the relationship 'Wheeler and Mody (1992) found that market size had a positive influence on capital expenditures by manufacturing affiliates of United States TNCs
first, the business environment, including policies affecting the degree of competition, and second, firm characteristics, and in particular firm capabilities. For example, using data for 25 industries in 25 European countries and a difference-in-difference methodology, Andrews, Nicoletti and Timilotis (2018) find that both market incentives (for example, administrative burdens on start-ups and barriers to entry in services, both measured from the OECD product market regulation index, and barriers to exit and reallocation, as measured by their employment protection Index) as well as firm capabilities (low managerial quality, lack of ICT skills and poor matching of workers to jobs) explain cross-industry-country variations in adoption of various DTs such as enterprise resource planning and customer relationship management software, and cloud computing.

A more recent categorization focusing on developing countries has emphasized that the main factors associated with the adoption of smartphones and computers by enterprises, as well as of more sophisticated DTs relying on these access technologies, are related to ability to pay and willingness to use, as well as other factors:<sup>5</sup> Ability to pay for DTs

- **Affordability of DTs** , namely the ability to pay for DTs, is linked to both their costs (relative to expected enhanced returns to enterprise revenues) and access to finance, including the

> 4 For a review of the recent literature on Africa, see “Impacts of digital technology use on jobs and poverty” in Begazo, Blimpo and Dutz (2023, forthcoming), and on developing countries more broadly, see Hjort and Tian (2021).

> 5 For a review of the theoretical and empirical literature on technology adoption and diffusion, see Comin, Cirera and Cruz (2022), chapter 6. For adoption drivers of DTs including with network effects (like digital platforms), see Cusolito (2021). The SME digital lag in OECD countries is attributed to similar factors though with different relative emphases, including insufficient capital and missing complementary assets (ability to pay), lack of
**Figure 1: Timing** **<u>of typical climate events and the IFLS3</u>**

<!-- Start of picture text --> Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><---- ----- 1999 ----- ----| |----- ----- ----- ----- ----- 2000 ----- ----- ----- ----- ----- ----|<br>"rainy season year"<br>Onset IFLS3, late Jun-end Oct<br>(20 cm) Post-onset 90 day<br><!-- End of picture text -->

The timing of these events in relation to the IFLS3 survey is illustrated in Figure 1. Considering that the degree of rainfall variability can differ across areas and that households may

> 5 This is the amount of rainfall needed to moisten ground sufficiently for planting.  It is believed that about 100 cm of rain are needed throughout the season for cultivation.

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br>.8 .15<br>CCDF .6 .1<br>.4 Difference .05<br>.2 0<br>0 -.05<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C10. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Experience of discrimination indicator removed (weights within the Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C10.1. FOSD: Counting Vector

<!-- Start of picture text --> C10.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>1 .05<br>.8<br>0<br>.6<br>CCDF<br>.4 Difference<br>-.05<br>.2<br>0<br>-.1<br><!-- End of picture text --> C10.3. SOSD: Social Sustainability Index C10.4. Rank correlations 58
The remaining 69 percent were self-employed of which about 73 percent worked in agriculture/fishery/forestry activities.

> 21 The figure is computed using the household weights available in the VHLSS 2004. The corresponding figures are 0.7 million for “foreign”, 4.8 million for “state” (including government and SOEs), and 2.4 million people for “private” sector employers. The number of workers employed by foreign firms computed from the VHLSS 2004 is smaller than that taken from the _Enterprise Survey_ data (see Table 1), most likely because the VHLSS data exclude migrants who hold a temporary or no registration status.

> 22 The large gender gap in the foreign sector partly reflects the younger average age of female workers relative to male in this sector and the low-wage industries for which women work. Controlling for personal and other characteristics, the gender gap is found to be largest in the informal wage sector followed by foreign and domestic private sectors (see Sub-section 3.3 below).

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decline in labor earnings. The decline in employment is captured by the microsimulation model. However, the declines in labor earnings are larger than the loss predicted by the private consumption component of National Accounts.<sup>18</sup> In fact, in 2020 Brazil implemented the Emergency Employment and Income Preservation Program <mark>consisting in a reduction of the working day (by 70, 50, or 25 percent) with a proportional reduction in earnings in exchange for employment stability. The evidence is also in line with previous findings that show earnings reductions are an important margin of adjustment strategy during shocks in comparison to employment reductions (Gaurav et al., 2020).</mark>

**Figure 11. Observed changes in employment and labor earnings between 2019 and 2020**

Employment declines (%) Labor earnings declines (%) <!-- Start of picture text --> Q1 -17.1 Q1 -7.9<br>Q2 -10.3 Q2 -10.1<br>Q3 -10.1 Q3 -9.5<br>Q4 -4.5 Q4 -12.5<br>Q5 -6.5 Q5 -14.8<br><!-- End of picture text --> Notes: Estimations obtained using the PNAD-Continua 2019 and 2020.

These results suggest that the macro-micro simulation model requires additional fine-tuning of the assumptions to better take into account labor income losses. For example, protecting employment positions can go hand in hand with a reduction in working hours and a corresponding reduction in labor income that needs to be taken into account. As explained in Section 3.1, workers in some sectors (public administration, utilities, health, and extraterritorial agencies) were protected from any shock in the model. However, the actual data shows that although these jobs were protected, labor incomes did decline in the utilities and health sectors. Similarly, although public sector nominal wages cannot decline, their purchasing
market fluctuations. One way to help these adults get financial services is to increase the use of digital payments in agricultural supply chains.

Digitizing payments for the sale of agricultural products offers opportunities for increasing the use and thus the potential benefits of accounts for those who already have an account. At the same time, Global Findex data show that digitizing agricultural payments is also a proven way of increasing account ownership. According to the 2017 edition of the database, 40 million adults with an account in developing economies opened their first one to receive payments for the sale of agricultural products.

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# **3.2 Econometric specification**

To causally estimate the impact of _Formosa_ , we employ two different data sets: (1) the Labor Force Surveys 2015-2016 (LFS), which provides information on fishery earnings and employment, and (2) VIIRS Boat-Detection Module, which provides satellite data on boats detected at night in coastal Vietnam. To analyze the LFS, we perform a set of difference-indifferences (DiD) regression analysis of the form: where the subscripts refer to an individual _i_ surveyed in month _m_ in 2016.<sup>18</sup> _yim_ is the dependent variable at the individual level. We investigate the effect of _Formosa_ on several primary fishery earnings and employment, including monthly fishery and total incomes, weekly work hours, and the probability of working secondary jobs. The standard DiD indicator terms are As mentioned, our analysis exploits both the location-specific and industry-specific characteristics of _Formosa_ . Therefore, we measure the disaster’s impact by employing two separate sets of control groups in all subsequent regressions:

1. Geographic control group: all individuals who worked in saltwater fishery before May 2016 and lived in the non-affected region. We purposefully narrow the geographic location of our control group to only the southern provinces distant from the Formosa’s location in order to mitigate any possibility that part of the control group could be contaminated due to potential spillovers of _Formosa_ . Specifically, we include all fishery workers living in the provinces South of Phu Yen, with Phu Yen being approximately –

600 km away from Hue the southern-most _Formosa_ -affected province.<sup>19</sup> 2. Industry control group: all individuals who worked in _Formosa_ -unaffected industries before May 2016 and lived in the affected region. To identify unaffected industries, we cross-check on different government’s official sources regarding _Formosa_ compensations to the affected industries. In the main analysis, we adopt three major industries that

> 18 We also report results for a placebo test using data from the LFS 2015.

> 19 We also
a booster listing exercise and survey in neighboring GNs in order to attain a larger sample for our intervention, re-interviewing those interviewed in the original sample. After dropping those firms that had closed since the first baseline, this gave a sample of 1,533 firms. Appendix 2 provides more details on the sampling methodology.

# **3.2 The Intervention**

Our main intervention consists of a temporary wage subsidy to firms with the purpose of encouraging owners to hire an additional full-time employee. The April 2009 survey – taken before anyone was made aware of the wage incentive program – asked for information about each employee currently working at the enterprise. In early July, we notified those assigned to the wage incentive treatment that we would pay a flat amount of 4,000 LKR per month for a period of six months if they hired an additional employee working at least 30 hours per week, and a flat amount of 2,000 LKR per month for a further two months. The employee had to be someone living outside the owner’s household and could not be an immediate family member (spouse, parents, siblings, and children). Participants were told that payments would start in August 2009 and, regardless of when the worker was hired, end by May 2010. In other words, workers had to be hired by October 1, 2009, for the full amount of the subsidy to be paid. The subsidy represents about half of the earnings of a typical unskilled worker. It is also approximately half the minimum wage, which in Sri Lanka is set by Wage Boards and ranged from approximately 7,000 to 8,000 LKR per month during the time of the intervention. Note that the minimum wages only apply to formally registered workers.

Several studies of the impacts of wage subsidies on workers in developing countries have found employers reluctant to register hired workers formally in the social security system where they would have to pay labor taxes (e.g. Galasso et al, 2004; Groh et al
2011 the value was $40.<sup>12</sup> The direct value

> 9 To check this we compute the sectoral average wage for laborers by dividing the total compensation of employees by the total number of employees. For several country-sector pairs this computation generates unreasonably high or low average wage values.

> 10 We do not provide global trends for jobs given the limited availability of the data for several countries.

> 11 Where indirect value added can take into account either the indirect contribution via backward or forward linkages to exports, we focus on the backward linkages in this report unless otherwise noted. Total value added accounts for both the direct and indirect value added. At the country level, the indirect value added is the same when considering either the forward or backward linkages.

> 12 According to the TiVA Database, the trade-weighted average share of foreign value added in gross exports across all countries in the world in 2011 is 25%. Combined with our estimates, this suggests the domestic capital value added share of gross exports is 35%.

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38 Table 3: Topics of most-cited 50 publications by decade

||1970s|1980s|1990s|2000s|
|---|---|---|---|---|
|Health and its value|3|1|4|2|
|Efficiency and equity|2|2|3|4|
|Determinants of health and ill-health|2|4|6|9|
|Public health|2|5|2|6|
|Health and the economy|5|4|5|4|
|Health statistics and econometrics||1|5|9|
|Demand for health and health care|3|3|3|1|
|Medical insurance|9|3|4|3|
|Supply of health services|12|9|3|2|
|Human resources|6|4|3|3|
|Markets in health care||3|||
|Economic evaluation|1|2|5|3|
|Total|45|41|43|46|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. The 50 most-cited publications were then assigned either one of the 12 topics listed in the appendix or a ―topic not assigned‖ code. The latter included publications that were too general (some were actually textbooks) some had no economic content despite being in EconLit, and some had no health content despite the JEL code.
empirical analysis allows us to compare the predictive power of FARP’s rainfall variables versus weather shock variables used in the production analysis, namely flood and drought indices. As results show, aid distribution was not necessarily greater in areas that suffered the greatest maize yield losses due to weather shocks, even when controlling for other geographic targeting criteria used by FARP and MVAC, such as predominant livelihood strategies. This allows us to generate additional instrumental variables by using categories of targeting outcomes under weather shocks and under no weather shocks, as explained more fully after the aid distribution regression results are reported.

To estimate aid distribution at the TA-level, we employ a random effects model with robust standard errors, using both sets of weather variables as follows: where _TAjtAid_ is aid distribution in TA _j_ at time _t_ ; _SijFARP_ is a vector of weather variables that influence FARP targeting; _SijCrop_ is a vector of weather variables that predict crop outcomes; _TACjt_ are TA-level variables that include other targeting criteria as well as additional control variables; and, _eTA_ are robust _ijt_ standard errors.

The second step is to investigate how aid receipt, weather shocks, and aid receipt under weather shocks affect six different household-level outcomes, namely (i) non-food expenditures per capita, (ii) value of food consumed per capita, (iii) food expenditures per capita, (iv) calorie consumption per capita, (v) food consumption score (a measure of dietary diversity),<sup>4</sup> and (vi) school participation rate.<sup>5</sup> Because the survey covers one year with widespread flood followed by another year with widespread droughts,

> 4 WFP (2008) provides more information on the measurement of the food consumption score.

> 5 School participation rate is the number of school-aged children (5-17) that are attending school divided by the total number of school-aged children. The measure is not defined for households with no school-aged children.

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# **Appendix B. The Construction of the Services Policy Reform Index**

In order to make the services policy information amenable to quantitative analysis, we translated the policy changes into a sector-specific reform index, taking values from 0 to 5.<sup>26</sup> Our primary concern was to maintain comparability across sectors, because our empirical strategy measures firms‟ exposure to upstream services reform by means of a weighted sum of the state of reform in four services sectors. Common definitions of what level of reform constitutes a given value of the index were used to preserve comparability. We started out with a general template of degrees of openness that is not specific to any sector, and then adapted this template to the specificities of each of the four services sectors.

In our general template, we attach an index value of 0 to a situation where hardly any progress has been made and the public sector is either the only relevant provider of services or has an extremely strong grip on private providers. A level of 1 indicates at least some scope for private sector participation and some liberalization of operational decisions, combined with some very limited scope for foreign participation (limited, for example, by low FDI ceilings or announced only as intentions). In order to qualify for an index value of 2, we required that there be only a limited degree of interference with operational decisions by public authorities, a substantial price liberalization, and clear scope for foreign participation even if only in narrowly defined segments and as minority participations. Still, the state remains a dominant actor in the sector. An index of 3 implies significant scope for private providers, including foreign ones, a noticeable competitive pressure on the public incumbents from new entrants, and explicit possibilities for foreign equity participation. A level of 4 is equivalent to little public intervention into the freedom of operation of private providers, the possibility of majority foreign ownership, and the dominance of private sector entities. Finally, a level of 5 would be equal treatment of foreign and
, we cross-check on different government’s official sources regarding _Formosa_ compensations to the affected industries. In the main analysis, we adopt three major industries that

> 18 We also report results for a placebo test using data from the LFS 2015.

> 19 We also experimented with including all provinces south of Hue, as well as including an additional province of Quang Nam in the control group and obtained robust estimates. Results available upon request.

11
important effect on the performance of these countries. We next ask whether this has been the case empirically.

# _Empirical Assessment_

In this sub-section we assess the empirical relevance of the saving based poverty trap models discussed above. We first ask whether the cross-sectional and time-series behaviour of saving is consistent with models of poverty traps. We then ask whether there is any evidence that in the past aid has helped countries to escape from saving-based poverty traps.

Consider first the nonlinear relationship between saving and the level of development that is required for a poverty trap to exist in the Solow model. In the left-hand panels of **Figure 5** we plot the empirical counterpart of this relationship. In each panel we have gross domestic investment rates on the vertical axis, and on the horizontal axis we have the capital stock.<sup>13</sup> The top panel plots data for all countries, the second only for low-income countries, and the third only for countries in Sub-Saharan Africa. In all these graphs we have fitted a third-order polynomial relationship to pick up any non-linearities that might be present in the data. These non-linearities do appear to be present in the cross-sectional relationship between investment rates and the level of development. In all three cases the patterns are broadly similar, with sharp increases in saving at very low capital stocks, then a fairly flat or even declining section, and then finally again an increasing section. Note however that these turning points occur at very different levels of the capital stock per capita, as the scale for the top graph is very different from the bottom two panels. In particular, for the low-income and Sub-Saharan Africa samples the turning points all occur at capital stocks less than $1500 per capita. Interestingly also, some of these nonlinearities are statistically significant: the quadratic and cubic terms in the first and third panels are statistically significant at the 5 percent level. At the same time, however, a visual inspection of the three graphs suggests that the shape of these nonlinearities is likely to