rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table A8: Impacts of mental health on labor outcomes – Excluding language skills variables**

|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
|---|---|---|
||(1)|(2)|
|Standardized mental health<br>scores|-0.067***|-0.183***|
||(0.009)|(0.046)|
|_First stage of 2SLS (dependent va_|_riable is standardize_|_d mental health scores)_|
|Trauma experience*Year after<br>arrival|-0.045***<br>(0.009)|-0.066***<br>(0.016)|
|Kleibergen-Paap F stat.|17.692|19.635|
|AR 95-CIs|[-0.095, -0.032]|[-0.334, -0.123]|
|Dep. Mean|0.455|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|3,687|3,687|
|Number of individuals|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, residence area, and home ownership. Mental health scores are the sum of the 6- item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The
<!-- End of picture text --> Source: United Nations World Population Prospects: The 2017 Revision. Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa. A. The sample of each study differs from each other. Aiyar and Mody (2011): Indian states, 1961-2001; Bloom and Williamson (1998): 78 countries, 1965-90; Bloom and Canning (2004): Over 70 countries, 1965-95; Bloom et al. (2000): 70 countries, 1965-90; Amer and Cruz (2016): 160 countries, 1960-2010. B.C. Population weighted averages. The working-age population is defined as people aged 15-64 years. D. -F. GDP-weighted averages. Other factors include contribution from policy changes, cohort effects, changes in population growth, convergence-driven productivity growth, and investment. “Contribution” reflects percentage-point changes between the averages of 2003-07 and 2013-17.

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# 3. Data and Methodology

This section describes the data used in the paper and the construction of the main variables of interest. It then provides summary statistics and discusses some data limitations.

## 3.1. Data Sources

We combined high-quality georeferenced facility survey data with geospatial data on infrastructure and location-specific characteristics for this analysis, including: Service Delivery Indicators Data The Service Delivery Indicators (SDI) initiative gathers comparable international information on service delivery for education and health around the world. These data help to identify gaps, track changes over time, benchmark progress, stimulate evidence-based debate, and ultimately, influence how countries design policies and interventions to accelerate progress in human capital outcomes. Even when service coverage has been shown to have increased in many countries, the quality remains uneven, and the investments in expansion and access have not been matched with human development outcomes (World Bank, 2021). The SDI surveys develop a set of critical indicators that benchmark service delivery performance to provide valuable inputs for direction and policy making, increase public accountability, track progress over time and improve quality and human capital accumulation across countries (World Bank, 2018). Launched by the World Bank in partnership with the African Economic Research Consortium and the African Development Bank in 2010, the SDIs for health and education were piloted in 2010 in Senegal and Tanzania, implemented in Kenya in 2012. Since then, they have been scaled up to cover more than 15 African countries and adapted outside Africa, like in Iraq, Indonesia, Bhutan, and Moldova, where work is ongoing (Gatti R., et al., 2021).

The SDIs are nationally representative surveys that involve three dimensions of health care service provision: (i) providers’ knowledge, (ii) providers’ effort, and (iii) providers’ inputs to work. SDI surveys allow for assessing how these elements come together to produce quality health services in the same facility. Data collection is undertaken by enumerators visiting selected medical facilities in announced and unannounced visits and observing
The telecom offered a preselected set of subscribers the chance to switch to a postpaid plan with lower rates, and recorded who among these subscribers paid their bills on time. Because the telecom wanted to learn about the risks of transitioning different types of users in this initial exploration, it was permissive and unsophisticated in selecting customers to transition. It selected customers who used their phones sufficiently (who were more likely to benefit from postpaid billing) from across the distribution of credit bureau records.<sup>6</sup> These selected subscribers received a call inviting them to transition to a postpaid plan; those who opted in were switched from prepaid to the cheapest postpaid plan (providing approximately $30 in revolving credit each month). The data cover 7,068 subscribers who were offered postpaid plans and opted in, which is the relevant sample for assessing performance for the telecom. The telecom was aware that paying a phone bill was new for these subscribers, so if a subscriber did not pay their postpaid bill, they were notified by SMS and other channels that their bill was soon to become overdue. If consumers were more than 15 days overdue, their service was cancelled and they were reported to the credit bureau.<sup>7</sup> In our sample, 11% of consumers defaulted.<sup>8</sup> While this form of credit has different features from a traditional bank loan, so do many emerging forms of digital credit; for example, short term loan ladders are common: (Carlson, 2017). For each subscriber, the telecom pulled mobile phone transaction records (Call Detail Records, or CDR). In this setting, many subscribers also had formal financial histories maintained at the credit bureau; the telecom also pulled these records. Bureau records include a snapshot of the number of entities reporting, number of negative reports, balances in different accounts (including consumer revolving, consumer nonrevolving, mortgage, corporate, and tax debt), and balances in different states
Table 4. The <u>effects of COVID-19 exposure on educational outcomes by year (</u> _<u>continued</u>_ <u>)</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_C. Switched to public school_|||||
|Warning index x 2020|0.002|0.002|0.002|0.002|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Warning index x 2021|-0.001|-0.001|-0.000|-0.000|
||(0.000)***|(0.000)***|(0.000)|(0.000)|
||[0.001]|[0.001]|[0.131]|[0.130]|
|Pr(2020=2021)|0.000|0.000|0.000|0.000|
|Mean outcome|0.010|0.010|0.010|0.010|
|Adjusted R<sup>2</sup>|0.005|0.013|0.101|0.100|
|Observations|16,458,902|16,394,979|16,458,819|16,394,924|
|_D. Switched to private school_|||||
|Warning index x 2020|-0.002|-0
countries, Cura ̧cao, Palestine, and Sint Maarten, are ungraded.

# **B.3 Alternative Country Grades**

Henderson et al. (2012) note that it is difficult to disentangle which countries actually have poor quality national accounts from the Penn World Tables letter grades used in the main text. They instead turn to World Bank grades for a subsample of countries. Here, we show how our primary results regarding objective data quality rankings in Table 7 would change using this alternative grading scheme for the subset of 114 graded countries.

Table S7 presents the results in two scatter plots, where marker size is proportional to the number of countries. In Panel A, we show that while the Penn World Tables and World Bank grades have a strong positive correlation, there are important differences between the two rankings. In Panel B, we show how our objective measure compares with the alternative World Bank ranking. While also positively correlated, our ranking still represents a significant reordering of countries. Thus, our qualitative results discussed with respect to our objective rankings are the same if we were to conduct the discussion versus this alternative grading scheme.

# **B.4 2020 Misstatement**

Table 8 in the text list an abbreviated set of countries with understatement or overstatement in 2020. Tables S6 and S7 expand Table 8 Panels A and B, respectively, to show understatement and overstatement across all countries for which 2020 GDP data was available at the time of S13
*|
|||(1.26)|
|Share of male population|935.03**||
||(487.40)||
|Share 45 to 64|9.17***|1.05*|
||(3.82)|(0.51)|
|Share of unmarried||0.57*|
|||(0.33)|
|Share of households without children|-7.74***<br>(2.58)||
|Average household size||7.70|
|||(5.51)|
|Share of western immigrants|-1.83||
||(1.96)||
|Share of non-western immigrants||0.46*|
|||(0.25)|
|Share of water surface||14.25**<br>(5.62)|
|Share of overweight|8.74***|0.57**|
||(1.92)|(0.25)|
|Share exposed to noise above 50kmh|-10.16**||
||(4.22)||
|Mean PM2_._5|-6.70|0.40|
||7.79|0.63|
|_λ_|0.46*|-0.40**|
||0.13|0.17|
|_ρ_|-0.60***|0.61***|
||0.16|0.10|
|R2|0.20|0.26|
|AICc|3938.38|3019.20|
|N|355|355|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<
### **Remaining issues**

A remaining challenge for the analysis may pertain to reported FDI data. A first concern here may be that this does not capture FDI from all countries, but only OECD countries. However, due to the prominence of OECD-countries in outward FDI, we find that in most cases this data set captures a large share of a country’s FDI stock (see data section for details). In cases where the share of FDI from OECD countries is lower (e.g. Hong Kong SAR, China, or India), this would not introduce a bias (as the estimate would still be consistent), but changes the interpretation of the estimate to reflect an IPA’s ability to attract FDI _from OECD countries._ As such, this may potentially affect the external validity of the estimate, if it is expected that different strategies are more effective for non-OECD countries (but we are not aware of any such theories) _._ An additional limitation comes from the analysis of characteristics of effective IPAs. Using t-tests to distinguish between high-performing and other IPAs does not allow us to make causal inference about IPA characteristics to drive FDI dynamics. Instead, these results should be considered indicative only. This is partly because there may be other factors (e.g. strong political leadership) which may drive both IPA characteristics and FDI performance. These IPAs may then just be part of a government’s wider strategy of FDI sectoral targeting. It also does not allow us to gauge exactly how important specific characteristics are. That said, we do think that if there is a strong, consistent pattern emerging across all countries that see a strong FDI effect in sectors targeted by the IPA that it is not unreasonable to assume that at least part of this contribute this to the underlying IPA characteristics. In addition, to the extent that IPAs can align with the wider government’s sectoral growth strategy (or push sector-specific reforms through internal advocacy), this itself reflects an
and electricity availability. The sample sizes varied across census years (1970: _25_ percent; 1980: _25_ ; 1991: _12.5_ ; 2000: _5_ )., but all are representative at the município level, and thus are also reliable at the MCA level employed in this study. Income figures are compiled from monthly data, deflated to 2000 Real (R$).

The transportation cost (proxy for transportation connectivity) between all Brazilian municipalities and the nearest State capital and between all Brazilian municipalities and São Paulo come from Professor Newton De Castro at the Federal University of Rio De Janeiro, and available at www.ipeadata.gov.br.

Existence of Ports and Brazilian Regions dummies are from the Bureau of Statistics (IBGE) Municipalities Profile of 1999. Homicides are from DATASUS / Brazilian Ministry of Health dataset. Local government expenditures are from the Brazilian Treasury dataset of 1991 and 2000. Formal employment data are from RAIS dataset / Brazilian Ministry of Labor. Morandi and Reis (2004) capital stock data employed in our analysis come from Brazilian Economic Censuses of 1970, 1975 and 1980.

43
selected African countries. Specifically, we combine firm-product level customs transactions data on the universe of exporting firms in Senegal, Malawi, and Burkina Faso with data on NTMs in a reduced form gravity model of trade framework. We find no effect of standards and technical regulations on the extensive margin of trade in the sample of countries used in the study. On the other hand, we find that standards and technical regulations reduce trade at the intensive margins in both the agriculture and manufacturing sectors. This trade-reducing effect at the intensive margin is less pronounced for intermediate goods compared to consumable goods, as well as for large and medium-sized firms compared to small firms. Similarly, the trade-reducing effect is overturned for firms with relatively higher initial product quality in the manufacturing sector, while it is less pronounced for their counterparts in the agriculture sector. In addition, our findings indicate that the trade-reducing effect at the intensive margin does not vary by the level of development of the destination country, nor whether the export destination lies within or outside of Africa.

This paper enhances our understanding of the impact of NTMs on Africa’s trade in several ways. Most existing studies have focused on the effects of NTMs on African exports at the country level, primarily within the agriculture sector. It is well-established in international trade literature that firms, rather than countries, are the main participants in international trade. Therefore, by using firm-level data, we can examine how NTMs influence the export decisions of African exporting firms. This approach allows us to determine whether firms adjust at the extensive margin or the intensive margin, consistent with trade models featuring heterogeneous firms. Additionally, unlike most previous studies on Africa, we consider both the agriculture and manufacturing sectors. Our findings reveal an unequal impact of technical measures on these sectors, with the agriculture sector being most affected. Moreover, the use of firm-level data enables us to study how the impacts differ by firm characteristics, in addition to product and destination characteristics.

Our empirical results have important policy implications. First, our
of _SY Y_ ( _ω_ ), see (Uhlig, 2003); equation 3 demonstrates the maximization problem. Later, in the exercise identifying the primary business-cycle driver of investment, frequencies of 2 - 8 years are chosen.

Where _φi_ is an indicator vector that selects the _i_<sup>_th_</sup> impulse response vector. The solution to this maximization problem is the eigenvector associated with the largest eigenvalue of the denominator (which is _SY Y_ ( _ω_ )), evaluated over the relevant frequency _ω_ = [ _<u>ω</u> , ω_ ̄].

Given the limited sample size under consideration, and in a modification relative to existing implementations of spectral VAR methodologies, the MA-coefficient matrix D and _τ_ are constrained to the 1 - 10-year horizon, which has been shown to reduce estimation bias (Dieppe et al., 2019, 2021; Francis et al., 2014). That is, we instead maximize _f_<sup>_k_</sup> ( _α_ ), where _k_ reflects the horizon limit of the MA coefficients up to _k_ = 10 years.

## **3.2 Panel VAR Framework**

We extend this new spectral identification approach to a panel VAR, which takes the form: where _C_<sup>_n_</sup> , the constant, varies across countries, _n_ , while the slope coefficients _B_ , and the variancecovariance matrix of residuals Σ _u_ are both assumed to be common across economies. Additionally, dummy variables are included in certain economies during periods where inflation exceeds 20 percent. The estimated parameters _B_ and Σ _u_ can then be used to identify the effects of technology shocks using the spectral identification for each group.

# **4 Data and Estimation**

Typically, technology-identifying SVARs have been applied to quarterly datasets. Data shortcomings for EMDEs (with typically less than 10 years of quarterly data on employment or productivity) pose severe constraints. Hence, annual data are used to estimate the VARs. This choice significantly
of business support programs, and web searches to identify studies which met the following criteria: (i) the intervention was aimed at self-employment or job creation and was successful in either stimulating new or in to be more to sur- self-employment, getting existing self-employed firms likely vive; (ii) outcomes were measured for a period longer than 15 months after the intervention took place, to allow time for migration to occur; (iii) the follow-up survey tracked respondents who moved and did not suffer from extremely high attrition; and (iv) data were either publicly available or shared by authors upon request.

11
complemented survey data with administrative health facility data collected at the subdistrict-month level from the Indian Health Management Information System (HMIS).<sup>25</sup> This dataset provides monthly information from health facilities at the sub-district level, detailing the number of malaria tests performed and the positive cases identified. It separates the data for urban and rural health facilities, which is used for analyzing treatment effects specific to each setting. The dataset spans from April 2020 to May 2021.<sup>26</sup> We designated April to August 2020 as the pre-treatment period and September 2020 onwards as the treatment period, aligning with the dissemination of the campaign in the study states. The use of administrative records complements and expands upon the analysis of survey data in two important aspects. (1) The use of objective malaria indicators alleviates the concern that our results could be driven by social desirability bias and experimenter demand effect, as these might be present in survey responses but not in health facility records. (2)

> 24By employing time data collected for each survey question by Virtual Lab, we omitted observations where the 90th percentile of response time was less than 3 times the fastest response time. This procedure accommodates variations in respondents’ capabilities and response velocities.

> 25HMIS is a monitoring system that has been put in place by the Indian Ministry of Health & Family Welfare (MoHFW). It collects facility-wise monthly information on a variety of health outcomes, and processes and reports the information at Sub-district, District, State and National levels. The records in this dataset are considered to meet the required standards for completeness for health research in India (Sharma et al., 2016).

> 26Although data from previous periods is available, the total number of sub-districts per state and their boundaries were different and matching across years was imprecise. Hence, we excluded previous observations and focused on an homogeneous set.

15
real government spending for countries with high (low) bureaucracy quality, defined as bureaucracy quality above (below) the sample median. The difference between the two averages is not significant at the 10 percent level (p-value = 0.16). *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on International Country Risk Guide and WEO (IMF) data.
with the access rate from the 1960s to the 2000s. Access to water also improved significantly during the period of analysis. While one in four households had access to clean water during the 1960s, by the 2000s three out of four households were covered.

Figure 3. Evolution of the access to services in LAC over 50 years <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>1960s 1970s 1980s 1990s 2000s<br>Electricity Water Sanitation<br>School enrollment Primary completion<br>Coverage (in percentage)<br><!-- End of picture text -->

**Source:** Own elaboration based on IPUMS data.

Although the increase in primary completion and access to sanitation has also been significant, it has not improved as much as electricity, school enrollment or access to water. The increase in the coverage of sanitation has faced some challenges during the 1960-2000 period. Sewerage systems, which commonly need major specific fixed investments, especially for the layout of a public network, are the only dimension that stagnated jointly with GDP growth in the 1980s and 1990s. Even for large countries like Brazil, access to sewerage is still below 50 percent in many districts. Figure 4 shows the development of access to sewerage of the different subgroups in each country over time.

13
# **1. Introduction**

Vietnam is a rapidly developing country highly exposed to natural hazards. One of the major natural risks the country faces is riverine and coastal flooding, due to its topography and socioeconomic concentration: Vietnam’s coastline is 3,200 kilometers long and 70 percent of its population lives in coastal areas and low‐lying deltas (GFDRR 2015). Furthermore, climate change is expected to increase sea levels and the frequency and intensity of floods, globally and in Southeast Asia (IPCC 2014; World Bank 2014). Given the country’s concentration of population and economic assets in exposed areas, Vietnam has been ranked among the five countries most affected by climate change: a 1 meter rise in sea level would partially inundate 11 percent of the population and 7 percent of agricultural land (World Bank and GFDRR 2011; GFDRR 2015).

Even though climate change impacts are expected to primarily occur in the future, flooding already causes major problems in Vietnam, with some segments of the population more vulnerable than others (Adger 1999; World Bank 2010; World Bank and Australian AID 2014). In particular, evidence suggests poor people are more vulnerable than the rest of the population to natural disasters such as floods, as their incomes are more dependent on weather, their housing and assets are less protected, and they are more prone to health impacts (Hallegatte et al. 2016, Chapter 3). Poor people also have a lower capacity to cope with and adapt to shocks due to lower access to savings, borrowing, or social protection; and climate change is likely to worsen these trends (Hallegatte et al. 2016, Chapter 5).

Therefore, it is important to quantify how many people are exposed to floods, how this distribution of exposure falls upon regions and socioeconomic groups, and how climate change may influence these trends. Employing flood hazard maps and spatial socioeconomic data, this paper examines these questions in the context of Vietnam:

1. How many people are exposed currently