rafmacalaba/data-use-mentions-extended huggingface dataset:
- 72 -

by the number of students) of the length of the various curricula that exist within each of the distinguished levels of VTE.<sup>20</sup> / The benefit streams are the streams of salary increments resulting from the received education. They were computed from the earnings equation in Table 19 in which the relevant VTE variables were set at zero to obtain the non-VTE earnings stream and then at the values corresponding to the completion of a VTE cycle to obtain the VTE earnings stream. The differences between the two streams is the benefit stream of a given type of VTE.<sup>2 1</sup> / Table 24 shows the starting and final values of the benefit stream for each type of VTE.

The duration of the benefit stream depends on the average age of graduation and the retirement age. In C8te d'Ivoire, the latter is 55 years for all employees, except upper level staff, for whom it is 60 years. We therefore assumed the latter age to apply to graduates of post-secondary VTE.

- 20/ When students repeat grades, the actual average duration of a curriculum will exceed the intended duration. Since we had no data on repetition rates, we were unable to adjust the cost streams to take account of repetition.

- 21/ The calculation of the benefit streams in this fashion means that the benefit streams apply to VTE graduates who obtain wage employment, which is the case for the vast majority (see Table 8). Two refinements to the calculation could, however, be considered: (i) the benefit streams could be computed as a weighted average of earnings of employee graduates and earnings of self-employed graduates (see, however, the caveat in section 7.5 regarding the analysis of the earnings of the self-employed); and (ii) the benefit streams could be corrected for the probability of VTE graduates to be unemployed. This requires, however, information on the average duration of unemployment, which cannot be derived from the CILSS.
- Carter, M. R., & Lybbert, T. J. (2012). Consumption versus Asset Smoothing: Testing the Implications of Poverty Trap Theory in Burkina Faso. _Journal of Development Economics_ , _99_ (2), 255-264. <u>https://doi.org/10.1016/j.jdeveco.2012.02.003</u>

- Chen, R., Yan, H., Liu, F., Du, W., & Yang, Y. (2020). Multiple global population datasets: Differences and spatial distribution characteristics. _ISPRS International Journal of Geo-Information_ , 9(11), 637. <u>https://doi.org/10.3390/ijgi9110637</u>

- Cheng, Y. T., Lung, S. C. C., & Hwang, J. S. (2019). New approach to identifying proper thresholds for a heat warning system using health risk increments. _Environmental Research_ , 170, 282-292. https://doi.org/10.1016/j.envres.2018.12.059

<mark>Coles, S. (2001). An Introduction to Statistical Modelling of Extreme Values, Springer, pp 208.</mark> Corral, P. (2023). wsample (Version v1.0.0-beta) [Computer software].

<u>https://github.com/pcorralrodas/wsample</u> Dang, H.A.H. and Dabalen, A.L. 2018. Is Poverty in Africa Mostly Chronic or Transient? Evidence from Synthetic Panel Data. _Journal of Development Studies_ 55 (7): 1527–47.

Dang, H.A.H., and Lanjouw, P.F. 2017. Welfare Dynamics Measurement: Two Definitions of a

- Vulnerability Line and Their Empirical Application. _Review of Income and Wealth_ 63 (4): 633–60. <
manner, by applying the GDP growth forecasts to the full distribution of household consumption.<sup>29</sup> Since the survey was conducted in 2017, to simulate the impact of these new taxes and COVID-19 on households’ welfare in 2021 and 2022, we first nowcast household income and consumption using a growth rate of real GDP multiplied by an income-GDP elasticity and inflation, assuming household income grows at the same rate across the entire distribution, and then apply the new PIT and GST rules on the nowcasted income.

# **_Simulation #1: PIT only (fiscal incidence as of 2021)_**

As expected, the PIT reform results in a small reduction of income taxes, primarily for households in richer deciles (figure 13, panel a). Households in the bottom two deciles, as before the reform, pay near-zero income taxes. Simulations suggest that poverty using consumable income is estimated to be 8.7 percent with the new PIT schedule (combined with assumptions on income growth), compared to 9.3 percent previously. These results are shown in Table 8 under 2021 simulations.

Direct taxes have become slightly more progressive, as measured by the Kakwani index which increased from 0.38 to 0.42 after the PIT reform. The burden of taxes has fallen throughout the distribution and has become slightly more concentrated among richer households (table 9). The inequality-reducing effect is smaller as the Gini index based on consumable income is estimated to be slightly higher than before at 36.3. Estimates of the marginal contribution suggest that the poverty-increasing effect of PIT, albeit previously small, has nearly disappeared.

# **_Simulation #2: PIT + GST (fiscal incidence as of 2022)_**

The simulation for 2022 reflects the combined impact of the PIT and GST reform. The impact of GST on poverty is negative, as the estimated poverty rate based on consumable income is 9.3 percent, similar to the poverty rate before any fiscal reforms. In fact,
sector for the following countries: Albania; Azerbaijan; Bulgaria; Belarus; Czech Republic; Estonia; Croatia; Hungary; Kazakhstan; Kyrgyzstan; Lithuania; Latvia; Poland; Romania; Russian Federation; Slovakia; Slovenia; and Ukraine.

For each cell in the matrix with intermediate coefficients in the Armenian data we apply the GTAP coefficients to the corresponding variable. The lower (upper) bound is set equal to the minimum (maximum) of the values calculated for the countries listed above. That is, for each input-output coefficient, we constrain the rebalancing procedure such that each coefficient in the balanced Armenian table we create lies within the observed bounds of the coefficients of the 19 Transition countries we consider.

# **Results**

Table 1 reports calculations based on the supply-use table provided by the National Statistical Office of the Republic of Armenia. In each sector, as an accounting identity, we must have that domestic production plus imports (total supply) is equal to domestic consumption (for intermediate and final use) plus exports (total demand). The excess demand columns show the extent that total demand exceeds or is less than total supply (both in Armenian DRAM and as a percentage of total supply). Excess demand in the original data varies from +75% in the case of electricity to -48% in the case of mining. These percentages show very large inaccuracies in the data at the sector level, and must be corrected to have a correct input-output table or a table that can be used in our model.

“Official” estimates of total GDP are available on the website of the National Statistical Office of the Republic of Armenia. We compare the official estimate with the supply-use table and our balanced table in Table 2.For the supply-use table, the imbalance is below and beyond official GDP depending on the method used. GDP in our balanced table is less than 1% off official GDP, and consistent across methods.

The adjustments in the components that make up GDP are slightly larger (see
Table 3 Government Size and External Risk: OLS and Instrumental Variable Regressionsa (Dependent Variable: Government Consumption/GDP in 1985 International Prices)

|Panel A<br>Independent Variables|OLS|OLS|IVb|IV|
|---|---|---|---|---|
|Openness|0.152***|0.044|0.157***|0.049|
||(0.033)|(0.049)|(0.037)|(0.051)|
|Volatility in Terms of Trade|-0.050|0.539|-0.204|0.438|
||(.249)|(0.345)|(.249)|(0.368)|
|Openness*STOT||0.973***||1.017***|
|||(0.344)||(0.389)|
|Nunber of Observations|348|348|336|336|
|R-squared'|0.5614|0.5696|0.5308|0.5388|

|Panel B|||||
|---|---|---|---|---|
|Independent Variables|OLS|OLS|IV|IV|
|Openness|0.135***|0.077*|0.132***|0.068|
||(0.031)|(0.042)|(0.036)|(0.045)|
|Terns of Trade Increases (TOTI)|0.234|-1.788**|0.246|-2.068**|
||(.580)|(0.896)|_(.615)_|(1.013)|
evidence in Giné et al. (2008), which presents correlates of the determinants of insurance participation using an earlier 2004 household survey. In this earlier study, insurance take-up is found to be decreasing in basis risk between insurance payouts and income fluctuations, increasing in household wealth, and decreasing in the extent to which credit constraints bind, based on self-reported measures of financial constraints as well as proxies such as wealth. This study also finds suggestive evidence consistent with a role for trust and networks; namely, participation in village networks and measures of familiarity with the insurance vendor are strongly correlated with insurance take-up decisions, and risk averse households are found to be less, not more, likely to purchase insurance.

# _B. Self-reported explanations for non-purchase_

As a second source of non-experimental evidence, Table 9 presents household qualitative self-reports, based on our 2006 surveys as well as on the earlier 2004 Andhra Pradesh survey, about the reasons why non-purchasing households did not buy rainfall insurance.

In 2006, the most common single reason cited by households in both samples is “insufficient funds to buy insurance,” with 81% of households in Andhra Pradesh citing it as the most important reason for non-purchase. Explanations relating to the quality of the product, such as “it is not good value” and “it does not pay out when I suffer a loss”, are much less frequently cited by households, and relatively few households cite “do not need insurance” as a reason for non-purchase (2.8% in Andhra Pradesh and 25.2% in Gujarat).

This qualitative evidence appears consistent with our experimental results, where the treatment involving random liquidity shocks has by far the most significant effect on insurance participation rates. The responses appear consistent with the view that liquidity constraints matter significantly for purchase decisions, and also inconsistent with a view that households simply have little interest in insurance against rainfall risk.

Finally, in the Andhra Pradesh sample, a common response to the 2004 survey is “do not understand the product.
foreign presence lowers the average dispersion of a sector's productivity, but they also observe that the effect is more significant in sectors with simpler technology. This is interpreted to mean that foreign presence forces local firms to become more productive in sectors where best practice technology lies within their capability, but that there are no significant transfers of modern technology. Furthermore, they find no significant effects of foreign presence on the rate of productivity growth of local firms, and interpret this as additional support to the conclusion that technology spillovers do not occur.

Aitken and Harrison (1991) use plant-level data for Venezuelan manufacturing between 1976 and 1989 to test the impact of foreign presence on total factor productivity growth. They conclude that domestic firms exhibited higher productivity in sectors with a larger foreign share, but argue that it may be wrong to conclude that spillovers have taken place if MNC affiliates systematically locate in the more productive sectors. In addition, they are also able to perform some more detailed tests of regional differences in spillovers. Examining the geographical dispersion of foreign investment, they suggest that the positive impact of FDI accrued mainly to the domestic firms located close to the MNC affiliates. However, effects seem to vary between industries. Aitken and Harrison (1991) is also one of the few studies, apart from Katz (1969), where inter-industry spillovers from foreign investment are discussed explicitly. As noted earlier, they assert that forward linkages generally brought positive spillover effects, but that backward linkages appeared to be less beneficial because of the foreign firms' high import propensities (although there were differences between industrial sectors).

Cantwell (1989), who investigates the responses of local firms to the increase in competition caused by the entry of US multinationals into European markets between 1955 and 1975, also argues that positive technology spillovers did not occur in all industries. His analysis differs notably from the other studies discussed in this section - he does not focus on productivity, but rather on changes in the market shares of foreign and local firms - but his
, we show the country level differences in quintile rank. The results vary depending on the norm in question and gender. For example, in Spain (ESP) men are equally gender progressive in both the Gender Equality at Home survey and the WVS (difference in rank is 0); whereas Spanish women are equally progressive in the male breadwinner norm (difference in rank is 0) but are ranked more progressive in our survey measure than the WVS (difference in rank 1). While in most countries there is little difference in the rank across the two surveys for the male breadwinner norm; for the female caregiver norm there are some discrepancies, e.g. Tunisia and Uzbekistan have a difference of 4. Using the Gallup World Poll we present comparisons in Table A6 and show that in the majority of countries there is a less than 10 percentage point difference with our survey measure.

> Specifically, we selected the dataset year that most closely aligned with our collection of the Gender at Home data in 2020. This data is referenced as Haerpfer et al. (2022)

> 17Gallup world data was subtracted at the country level using the Appendix of Bursztyn et al. (2023 _b_ )

> 18The overlap in country coverage is 68% between the WVS and the Gender Equality at Home survey; and approximately 50% with the Gallup World Poll 2020.

11
. Finally, municipal headquarters not only provide essential administrative services but are acceptable proxies for principal local markets. Education was considered for inclusion but a suitably comprehensive geospatial data set could not be located.

The Rural Access Programme (RAP), a donor-funded infrastructure support program active in Karnali, shared shapefiles for the 92 individual proposed road segments specified in Karnali’s draft Provincial Transport Master Plan as of early 2019 and the average per-kilometer budget charge associated with the different 9
Beck, Levine and Loayza, 2000).

While these observations are compelling, we know little about the impact of financial structures on export activities, and even less so about their impact on the dynamics of the export sector (entry into, and exit from, export). The goal of this paper is to help fill this gap. To this end, we exploit rich data on the structure of financial systems from the Global Financial Development Database (GFDD) as well as detailed, disaggregated information on the dynamics of the export sector from the World Bank Exporter Dynamics Database (EDD). Our data refer to a panel of 62 countries, across all geographic regions and income levels, for the 1997-2014 period. The EDD database contains detailed information at the level of 2-digit ISIC industries on the number of exporting firms and on exporters’ entry, exit and turnover rates drawn from exporter-level customs data which cover the universe of annual exporter transactions. Further, we are able to construct various indicators of the concentration of the export sector. On the financial side, we capture financial structures through widely used indicators of the importance of banks relative to financial (stock and 2
1.15)|1,610|
|Number of children aged 13-17 years in household|0.22*|(0.13)|[0.33]|0.79|(0.98)|1,610|
|Number of girls aged 13-17 years in household|0.06|(0.10)|[1.00]|0.39|(0.65)|1,610|
|Number of boys aged 13-17 years in household|0.16**|(0.07)|[0.33]|0.40|(0.66)|1,610|

**_Notes_** :

- The table shows the regression results on not pre-specified household composition outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The outcomes that require definitions are: Child hunger last week is defined as an indicator variable that equals 1 if any child in household had to go to bed hungry in the past week and 0 otherwise. Learning activities are done in the last 24 hours and include homework, e- learning, educational programs and videos, and reading. Child alertness is the respondent’s answer to “On a scale of 1-7, with 1 being tired and 7 being alert, how does (child name) feel right now? ” and is normalized by deducting the control group’s mean and then dividing the result by the standard deviation of the control group.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age).

- In the parentheses are robust standard errors
|0.45|0.00|1.00|
|Children [=1]|831|0.14|0.35|0.00|1.00|
|_Panel B: Prosociality Indicators_||||||
|Donated to Venezuelan Org. [=1]|831|0.74|0.44|0.00|1.00|
|Donated to non-migrant Org. [=1]|831|0.81|0.40|0.00|1.00|
|One can trust Venezuelan Migrants (4=Strongly agree)|831|2.70|0.67|1.00|4.00|
|<br>ProsocialityIndex (Kling)|831|0.16|0.98|-3.00|1.00|
|_Panel C: Attitudes Indicators_||||||
|List experiment: Annoyed by 4 situations|420|2.03|0.73|0.00|4.00|
|List experiment: Annoyed by 3 situations|409|1.82|0.60|0.00|3.00|
|Venezuelan are poor due to lack of self effort [=1]|829|0.14|0.35|0.00|1.00|
|Keep Venezuelans out of neighborhoods (4=Strongly agree)|830|1.90|0.76|1.00|4.00|
|Same opportunity for Venezuelans to apply for a job [=1]|829|0.58|0.49|0.00|1.00|
|Government should help Venezuelans (4=Strongly agree)|830|2.74|0.72|1.00|4.00|
|Increase gov. spending to help Venezuelans (4=Strongly agree)|830|2.49|0.80
# **1 Introduction**

Latvia was one of the hardest hit countries in the world during the 2008-2010 global financial crisis; during 2008-2010, Latvia’s gross domestic product (GDP) contracted by 21 percent. During 2009 alone, Latvia’s GDP contracted by 18 percent (Figure 1). Household budget surveys indicate that poverty rates increased by eight percentage points in one year - from 10.1 percent in 2008 to 18.1 percent in 2009 (Ajwad, Haimovich, and Azam, 2012). Net job creation was negative as layoffs rose sharply. Between 2008 and 2010, 126,000 jobs were lost, equivalent to 11.2 percent of the pre-crisis workforce. In 2008 Q3, unemployment rates began to rise and reached a peak of about 21 percent in 2010 Q1, compared to about seven percent in 2007-08. Government administrative data show that 40 percent of workers laidoff were not eligible for unemployment benefits, primarily because they failed to meet the requirement of having paid nine months of contributions into the unemployment insurance fund in a 12-month period.<sup>1</sup> In September 2009, in response to rapidly rising unemployment rates, the Government of Latvia launched a public works program known as the Workplaces with Stipends (WWS) program, darba praktiz ̄e ̆sana, simtlatnieku programma, or the 100-Lats-programma.<sup>2</sup> The program aimed to strengthen the social safety net in response to the unprecedented drop in economic activity.<sup>3</sup> Specifically, the program created temporary labor-intensive employment for people who had lost their jobs but were ineligible for unemployment benefits; also, the program benefitted communities through maintenance activities.

Public works programs are an important safety net intervention used widely around the world. Countries have introduced public works programs with diverse objectives such as

> 1Unemployed workers ineligible for unemployment benefits comprised a variable percentage, but as the crisis dragged on, the percentage rose to more than 50 percent as many more workers lacked sufficient
outcome measure from the _2014 Reform Efforts Survey_ : respondent evaluations of the World Bank’s helpfulness during

> 27ESW products are relatively inexpensive. In 2012, the World Bank delivered 874 ESW products to its client countries at total cost of $34,266,769. In 2013, it delivered the 772 ESW products to its client countries at a total cost of $24,503,120. In 2014, it delivered 1,199 ESW products to its client countries at a total cost of $38,588,088. The average unit cost of an ESW product during this time period (2012-2014) was $34,376. These summary statistics are drawn from the World Bank’s Business Intelligence Database.

> 28With some exceptions, the results for the control variables are mostly consistent across the columns in Table 1. Our measure of per capita income ( _lngdp_ _~~p~~ c_ _~~1~~ st_ ) and its squared term ( _lngdp_ _~~p~~ c 1st_ _~~2~~_ ) together suggest that increases in per capita income are negatively associated with the World Bank’s level of agendasetting influence, but that this effect dampens at higher levels of per capita income. The population size variable ( _lnpop 1st_ ) is negatively signed in all but one model, but only statistically significant in model (6). The aid dependence variable ( _aid gni_ _~~1~~ st_ ) is negative in all model specifications but never statistically significant (and close to zero). We also find no statistically significant relationship between the strength of democratic institutions ( _polity2_ _~~1~~ st_ ) and the World Bank’s level of agenda-setting influence, and the sign of the coefficient changes depending on the model specification. We find some evidence that the World Bank enjoys less policy influence in countries with left-wing governments ( _left_ _~~1~~ st_ ), but this estimate fails to achieve statistical significance in most models
investigate the role of services reforms on downstream or economy‐wide productivity performance, in particular those that use firm‐ level data.<sup>2</sup> Several studies stand out in this regard. One is by Arnold _et al_ . (2011) that shows how increased foreign participation of firms in services sectors caused an improvement in downstream manufacturing sectors in the Czech Republic. In this paper the authors use economy‐wide indexes of services reform employing the EBRD data base on services policy reform and interact those with a services input reliance coefficient using Czech input‐output tables. Another study is done by Arnold _et al_ . (2015) in which the authors undertake a similar empirical strategy but then for India by means of a comparable approach which is comprised of interacting a self‐constructed reform index for India with a comparable services input dependency ratio for the Indian economy. They also show that for India reform in services has had a positive impact on downstream manufacturing firms. In addition, this study also exploits sector‐specific indexes as opposed to only an economy‐wide index of services reform.

Two separate studies associated with quantifying services linkages on downstream manufacturing productivity focus solely on the role of Foreign Direct Investment (FDI).<sup>3</sup> Fernandes and Paunov (2012) 1 Figure 1 shows the level of regulation of EU countries using the OECD NMR regulations database for the year 2006 and 2013, which is the timeframe of our empirical analysis and is interpolated for missing series. Note that other indexes of restrictiveness in services are on a one‐year basis only such as the OECD’s Services Trade Restrictiveness Index (STRI) which to date has only collected data for 2014 and the World Bank’s STRI that extends to developing countries for the year 2009 alone.

2 Some earlier studies that analyze the effects of services reform on economy‐wide performance without using firm‐level data are Mattoo _et al_ . (2006) and Eschenbach and Hoekman (2006). The