rafmacalaba/datause-encoder-data huggingface dataset:
IHEs nationwide.

In 2016, the policy was implemented for Kankor exam test-takers applying for their freshman year in that academic year. The MoHE introduced a biometric system for applicant registration and verification<sup>14</sup> in the same year to address allegations of fraud and corruption against the Kankor exam in previous years. The MoHE publicly announced the biometric registration system, but not gender-specific changes, in the enrollment approach of treated concentrations in the first-year of implementation<sup>15</sup> . Therefore, the level of awareness among applicants regarding the changes in the enrollment method—from open competition to gender-specific considerations—remained ambiguous. The absence of a public announcement suggests that this information was not evenly distributed across applicants and regions, at least during the first year of adoption. This lack of advertisement has profound implications on students’ academic decisions. First, a lack of complete information beforehand might have led to misaligned expectations regarding the chances of acceptance. Second, students might have been excluded from the concentrations for which there was a higher likelihood of acceptance due to the gender quota allocation. Finally, students from privileged backgrounds might have accessed this information through informal channels, while other applicants remained uninformed, raising equity concerns.

Additionally, I report the t-balance test for the primary outcomes before the introduction of the gender quota in Table B3.1. The results show that the overall share of female applicants and the share of female applicants from low-SES groups who matriculated in treated concentrations were significantly lower than the control group mean. The quota policy, which serves as a quantity intervention, aims to improve the rate of female students by distributing seats to gender-specific proportions in treated concentrations. On average, this rate was greater than the actual share of female students in the period

> 14This universal change does not threaten the identification strategy as it affects all applicants equally. However, the introduction of the biometric system might discourage certain groups from participating in the Kankor exam.

> 15I verify that concentrations received official communication from the MoHE about the gender quota implementation. However, I found no evidence indicating that this decision was communicated to test-takers across the country, at least before the Kankor exam for the 2016 academic year.

12
and categorize the fiscal risks from PPPs in terms of direct versus contingent and explicit versus implicit liabilities. Informed by the framework of Budina, Polackova Brixi, and Irwin, this paper conceptualizes early termination risk as a contingent liability but does not distinguish between the explicit and implicit dimensions, in order to provide a common method of valuation of the risk regardless of differences in PPP–contracting practices across countries.

A standard method of evaluating fiscal costs and risks from infrastructure PPPs has been the PPP Fiscal Risk Assessment Model developed by the International Monetary Fund (IMF) and the World Bank (2019). This model qualitatively evaluates various sources of fiscal risks and presents a level of priority for actions against risks. Irwin (2007) describes quantitative methods for valuing exposure to different types of risks from PPPs. Although he does not provide a specific method for valuing fiscal risks from early termination, the methods he proposes align with the value-atrisk methodology applied in this study, which identifies a probability distribution for the occurrence of an event and determines the probabilistic loss from the event. This paper proposes a way to estimate the probability distributions for early termination and apply them using a value-at-risk methodology.

**3 Public-Private Partnerships in Infrastructure in Developing Countries**

The active portfolio of PPPs in infrastructure in developing countries grew rapidly in the 1990s. After plateauing around 2000, the portfolio grew exponentially in 2002. Growth has slowed since 2012. The size of the portfolio in dollars has grown consistently since 1990 (figure 1, panel a). The size of the portfolio as a percentage of developing world’s GDP grew at a rapid pace only during the 1990s (figure 1, panel b).<sup>3</sup> Between 1999 and 2008, investment growth slowed, falling behind GDP growth. After 2008, the size of the PPP portfolio as a percentage of GDP started growing again. At the end of 2020, cumulative investment in the active portfolio of PPP projects was about $1.66 trillion,<sup>4</sup> according to the World Bank Private Participation in Infrastructure Project Database (PPI),<sup>5</sup> which corresponds to about 5.25 percent of the developing world’s GDP.

> 3 The GDP of the developing world was calculated as the total GDP of low- and medium-income countries as classified by the World Bank, plus Romania, for which data are available until the end of first half of 2020 but which was reclassified as a high-income country in 2020.

> 4 All dollar figures in the study are expressed in 2020 US dollars, inflated using the US Consumer Price Index (CPI) series.

> 5 See https://ppi.worldbank.org/en/ppidata.

6
As mentioned, one of the key identifying assumptions of this DDD strategy is that the difference between EZ and EC areas and their surrounding cities would have grown the same in the absence of the tax incentives. This is a more relaxed condition compared with a DID strategy which would have required that the EZ and EC areas would have grown the same over time. Nonetheless, this strategy can be still weakened from the unobserved spill-over effects from the EZ and EC areas to their surrounding cities and if nature and rate of spill-over varies over time between EZ and its surrounding areas vis-à-vis EC and its surrounding areas.

To overcome this issue, exploiting a unique feature of the Texas EZ program, Freedman (2013) uses an RD approach to estimate the effect of this program on local labor market. In Texas EZ program created in 2003, participating businesses receive a combination of state and local benefits for up to five years, which can take of several forms. They can apply for state sales and use tax refunds (tied to amount of capital investment and number of jobs created) of up to $1.25 million over five years on qualified expenditures on machinery and equipment, building materials, electricity and natural gas, and construction labor. Local communities must also offer incentives to designated projects, which may include tax abatement, utility rate reductions, public service expansion (for example, road improvements), tax increment financing, expedited permitting, or other incentives. In contrast to EZ programs of other states, businesses in Texas need not necessarily locate in an EZ to receive benefits, nor does locating in an EZ guarantee that a business will receive benefits. To be eligible for benefits, a business located in an EZ must ensure that 25 percent of its new employees will meet economically disadvantaged or EZ residence requirements. A business located outside an EZ is eligible to receive benefits if it commits that 35 percent or more of its new employees will meet economically disadvantaged or EZ residence requirements. More so than the number of jobs that exist in distressed areas, the program might be expected to increase the number of jobs held by the residents of those areas.

The distinct feature of Texas EZ program that Freedman (2013) utilizes is that unlike most other states, where localities often must apply for EZ designations, Texas designates areas as EZs on a noncompetitive basis. Any census block group in which 20 percent or more of the residents have income below the federal poverty line is automatically designated as an EZ. Using this fixed cut-off rule based on the poverty rate, the study employs the RD with the identification 28
**Figure 4: NTL intensity in CAR and its synthetic control, 2005-17**

<!-- Start of picture text --> 5500<br>5000<br>4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>Seleka occupation CAR Synthetic CAR<br>5500<br>5000<br>4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>Seleka occupation CAR Synthetic CAR<br>Nighttime lights intensity<br>Nighttime lights intensity<br><!-- End of picture text --> 13
# **Figure B9: Zinc**

<!-- Start of picture text --> Log scale<br>1.00<br>Medium-term cycle Transitory component<br>0.75<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>-0.75<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.5<br>Medium-term components Transitory components<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 50 —_
In Figure A2, we show that the same bunching behavior observed in 2015-2020 was also present to some extent in the previous period. In 2011, when the threshold was 500,000 litas, we observe some amount of bunching at that level, and the bunching moves to 1,000,000 litas when the policy changes in 2012-2014. Immediately after the introduction of the Euro, the bunching again moves from the equivalent 1,000,000 litas to the new €300,000 threshold—reinforcing the message that the bunching behavior is indeed a response to the change in tax rates.

**Balanced panel of firms.** In our main analyses, we use an unbalanced panel of firms - not all firms file every year, so our sample is changing every year. While there is no reason to believe that firms are exiting or entering precisely around the €300,000 threshold, we replicate our main finding of the bunching behavior for a balanced panel of firms that file every year in Figure A3. The result is qualitatively similar: we observe the same behavior of an excess number of firms declaring turnover precisely below the level that separates the 5% and 15% tax rate regimes.

**Number of employees and bunching behavior.** While our focus throughout this report is on the turnover threshold that separates the two regimes, the number of employees is also a determinant of the tax rate faced by firms: the preferential 5% tax rate is only applied to firms that declare less than €300,000 in turnover and also declare fewer than 10 employees on average throughout the year. In Figure A4, we show that the level of employment matters for the bunching behavior. The figure presents a heatmap, where lighter colors indicate bins (intervals of employment and turnover) where there are more taxpayers. First, note the clear bunching pattern around the €300,000 level: the bright colors to the left of the threshold immediately disappear to the right of it. But note also that this behavior only exists for firms with fewer than 10 employees: for those above, there seems to be no bunching in turnover, consistent with the fact that larger firms do not benefit from the 5% tax rate, regardless of their turnover level. There is also some evidence that firms bunch below the 10 employee level, with a slight concentration of firms below that level. Since the vast majority of firms are much smaller than that, however, it is harder to precisely estimate the importance of that behavior.

**Economic sectors.** In Figure A5 we document that bunching behavior is widespread but not equal across economic sectors. We observe particularly large responses in construction, wholesale, real estate and other services. Potentially surprising, we actually see no bunching in retail, and restaurant and hotels—sectors that are often seen as at-risk for misreporting taxes.

11
The separations observed in the kernel density estimates presented in Figures 2 and 3 confirm the necessity to match merge the BEEPS data with the Amadeus observations on country, sector, sub-national location, and firm size. However, it is important to reiterate here that strong conclusions at the international level cannot be derived from direct cross-country comparisons because of data requirements and varying sample attrition across countries. For example, given the limited data sources available for Serbia, firms that have the prerequisite data on production function variables in all four years of the panel exhibit very high log TFP levels, resulting in a distribution much higher than those of the other countries. Nonetheless, even though sample biases may exist between countries, the basic test in this paper examines withinindustry differences across countries and will not be affected unless there are systematic biases in sub-national location-size-year groups within industries in each country.

# **_3.2. Identification Strategy_**

The analysis exploits cross-cell (defined by country, sub-national location, and firm size) variation in the changes of the business environment variables across time to determine their effect on firm-level productivity. Estimated using ordinary least squares (OLS) with White correction for heteroskedasticity, the full regression model is a first-differenced equation in firm characteristics with two-year changes whose main regressors of interest are lagged two-year changes in business environment indicators and is formally specified as follows:  ln _TFPit_     1  _INFRASTRUCTUREts_  ,1 _l_ , _c_   2  _FINANCEts_  ,1 _l_ , _c_   3  _GOVERNANCEts_  ,1 _l_ , _c_ where  ln _TFPi_ , _t_ is the change in the logarithm of TFP of manufacturing establishment _i_ from 2002 to 2004, estimated by the semiparametric estimation technique developed by Levinsohn and Petrin (2003);  _INFRASTRUCTURE ts_  ,1 _l_ , _c_ ,  _FINANCE ts_  ,1 _l_ , _c_ ,  _GOVERNANCE ts_  ,1 _l_ , _c_ **,**  _LABOR_ MARKETt_  _s_ ,1 _l_ , _c_ **,** and  _LABOR_ QUALITYt_  _s_ ,1 _l_ , _c_ are the changes from 2001 to 2003 in respective business environment indicators for groups of firm size _s_ , location _l_ , and country _c_ ;  ln _TFPi_ , _t_  1 is the change in the logarithm of TFP from 2001 to 2003;  _Z in_ is a vector of logarithmic changes in firm characteristics from 2002 to 2004 that include the number of 13
**Figure 1: Countries with Higher Per-Capita Income Are More Similar to top-20 U.S. Schools in Topics Covered**

<!-- Start of picture text --> 7 8 9 10<br>Log GNI per capita (PPP)<br>.5<br>.4<br>.3<br>.2<br>Average Jaccard Measures<br>.1<br>0<br><!-- End of picture text --> Note: regression best fit line shown. Slope is 0.039, p-value is 0.010. Similarity to top-20 U.S. schools of a course in a developing country is assessed as the average over the Jaccard similarity coefficient of this course with each of the 16 courses offered in the top-20 U.S. economics departments.

24
<!-- Start of picture text --> Asia: fast convergers<br>1?<br>2<br>Ld<br>5 ©<br><!-- End of picture text --> <!-- Start of picture text --> o 4<br>*<br>1960 1980 2000 2020<br>Year<br>—— Cambodia ——— China<br>—— India ——— Indonesia<br>vieman<br><!-- End of picture text --> <!-- Start of picture text --> Asia: slower convergers<br>V<br>2<br>2<br>3<br>5<br><!-- End of picture text --> <!-- Start of picture text --> o<br>6”<br>bs<br>1960 1980 2000 2020<br>Year<br>—— Bangladesh ——— SriLanka<br>—— Mongolia —— Pakistan<br>—— Philippines<br><!-- End of picture text --> <!-- Start of picture text --> Africa: fast convergers<br>=<br>~<br>5 i<br><!-- End of picture text --> <!-- Start of picture text --> $s<br>a4<br>*<br>1960 1980 :oe 2000 2020<br>—— Ghana —— Liberia<br>——— SieraLeone 9 ————— South Africa<br><!-- End of picture text --> <!-- Start of picture text --> Africa: slower convergers<br>2?<br>&<br>go<br><!-- End of picture text --> <!-- Start of picture text --> fe<br>7<br>1960 1980 2000 2020<br>Lea<br>—— Cameroon —— Cote d'ivoire<br>—— Kenya —— Morocco<br>——— Republic of Congo<br><!-- End of picture text --> <!-- Start of picture text --> — a<br>—— 7<br><!-- End of picture text -->
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largely valid; the overidentifying restriction test cannot be rejected no matter which transport connectivity variables are used.

|**Table 5 Instru**|**mental Varia**|**ble Regressi**|**on Estim**|**ation**|||||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)||(4)||(5)||(6)||
|ln_RAI_|0.028<br>(0.402)||||||||||
|ln_RDDEN_||0.163<br>(1.277)|||||||||
|ln_GDRD_|||–0.214<br>(0.370)|<br>|||||||
|ln_TCMK_|||||–0.240<br>(0.141)|*<br>|||||
|ln_TCPT_|||||||–0.028<br>(1.678)||<br>||
|ln_MAI_|||||||||0.405<br>(0.194)|**<br>|
|ln_N15_|0.817 ***|0.783 **|0.870|***|0.773|***|0.824|***|0.763|***|
||(0.135)|(0.323)|(0.101)||(0.076)||(0.096)||(0.077)||
|_POPDEN_|0.2500|0.2270|0.2160||0.3050|***|0.2500||0.2680|***|
||(0.1400)|(0.1890)|(0.1310)||(0.1080)||(0.1590)||(0.0891)||
|_INFO_|–0.006|–0.003|–0.014||0.001||–0.004||0.003||
||(0.027)|(0.035)|(0.029)||(0.025)||(0.029)||(0.026)||
|_UNEMP_|–0.004|–0.005|–0.015||–0.008||–0.005||–0.005||
||(0.022)|(0.014)|(0.024)||(0.014)||(0.020)||(0.014)||
|_EDU_|0.021|0.025|0.004||0.019||0.027||0.017||
||(0.096)|(0.114)|(0.094)||(0.091)||(0.095)||(0.091)||
|Constant|–13.420 **|–13.940 *|–11.390|**|–13.250|***|–13.630|***|–12.610|***|
||(5.964)|(8.199)|(5.605)||(4.776)||(5.008)||(4.803)||
|Observations|136|136|136||134||134||134||
|R-squared|0.64|0.631|0.627||0.653||0.641||0.658||
|Wald chi<sup>2</sup>|1247.7|1184.7|1160.7||1669.1||1191.9||1774.8||
|Number of<br>|||||||||||
|region dummy|||||||||||
|variables|5|5|5||5||5||5||
|Exogeneity test<sup>1</sup>|||||||||||
|<br>Chi<sup>2</sup>test|||||||||||
|statistic|0.000|0.071|0.321||1.152||0.106||2.775|*|
|P-value|(0.990)|(0.790)|(0.571)||(0.283)||(0.744)||(0.096)||
|Overidentifying r|<br>estriction test<sup>1</sup>||||||||||
|Chi<sup>2</sup>test|||||||||||
|statistic|3.999|3.943|3.695||3.096||4.065||2.909||
|P-value|(0.135)|(0.139)|(0.158)||(0.213)||(0.131)||(0.234)||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively.

1 The instrumental variable estimation is performed using three instruments in logarithm: _SLOP_ , _ELEV_ , and _DIST_ .
Figure 7: Foreign Demand Shocks: Event Study and Dynamic Treatment Effects <!-- Start of picture text --> 2013 2014 2015 2016 2017 2018 2019<br>2013 2014 2015 2016 2017 2018 2019<br>MNCs Domestic Firms<br>Non MNC Exporters Dynamic Treatment Effects<br>(a) Event Study Coefficients (b) Dynamic Treatments<br>.08<br>.08<br>.06<br>.06<br>.04 .04<br>.02 .02<br>0 0<br>Log Employment -.02 Log Employment -.02<br>-.04<br>-.04<br>-.06<br>-.06<br>-.08<br>-.08<br><!-- End of picture text --> _Notes:_ The sample is restricted to the manufacturing sector only. The left figure plots event study analysis results from equation (1) where the dependent variable is the log of employment. There are three groups in the left panel: (i) Korean firms with no foreign affiliates and no exporting during the period 2013~2016 (in green); (ii) Korean firms with no foreign affiliates and exporting during the period 2013~2016 (in red); (iii) Korean firms with foreign affiliates during the period 2013~2016 (in blue). The right figure plots dynamic treatment effects results where the dependent variable is the log of employment. Coefficients along with 95% confidence intervals are illustrated. Standard errors are clustered at the industry level.

Figure 7 shows the event study (panel (a)) and dynamic treatment effects (panel (b)) results. In panel (a), for the first group (in green) and the second group (in red), the estimated coefficients are all statistically insignificant throughout the sample period. In contrast, the MNCs group (in blue) shows a downward employment trend after the labor market policy change. In panel (b), we estimate equation (4) with dynamic coefficients. Although the 2017 lag indicator is estimated to be positive and statistically significant, all other coefficient estimates are statistically insignificant, lending further support to the previous result (i.e., a negligible role of foreign demand shocks).

# **7 Conclusion**

As the role of MNCs in the economy grows, it is increasingly important to reassess the effects of not only trade policies but also other domestic policies from a transnational perspective. Still, the focus of trade researchers tends to be limited to trade policy, and labor studies rarely consider the prevalence of MNCs in their evaluation of domestic labor policy. This paper addresses one of such unexplored links between the two research areas by investigating how MNCs adjust their employment differently from domestic firms in 32
However, some of these children might be able to pick up nutritional intake given relative better access to solid food. The evidence suggests that women lack knowledge of quality feeding for their children even among better-off households. The high fertility rate and close birth gaps also make it difficult to give infants sufficient exclusive breastfeeding in the first six months.

Figure 4. The Trend of Stunting, Wasting, Underweight Rates with Age <!-- Start of picture text --> 0 20 40 60 0 20 40 60<br>Age in month Age in month<br>Male Female Male Female<br>0 20 40 60<br>Age in month<br>Male Female<br>.6 .3<br>.5 .25<br>.4 .2<br>Stunting status .3 Wasting status .15<br>.2 .1<br>.3<br>.25<br>.2<br>.15<br>Underweight status<br>.1<br><!-- End of picture text --> Data source: HIES 2009-2010 Figure 5. The Rate of Stunting Rate and Age Across Five Wealth Quintiles 8
|-|Mode of transportation to school|48|27|
|---|---|---|---|
|Reaso|ns for not consultinghealth care|||
|-|Distance to the facility|89|54|
|-|Lack of transportation|19|18|
|Inform|ation on health care utilization|||
|-|Distance/time to health care facility|24|16|
|-|Transportation to health care facility|17|12|
|Health|expenditure on transportation|39|30|
|Transp|ort related externalities - Road Safety|||
|-|Road crash fatality|12|10|
|-|Road crash injury|45|36|
|Owner|shipof vehicles|||
|-|Car/truck|295|111|
|-|Motorcycle/scooter|261|100|
|-|Bicycle|233|93|
|-|Boat|130|74|
|-|Animal-drawn cart|111|68|

The Demographic and Health Survey (DHS) is the flagship program of the United States Agency for International Development (USAID) mandated to produce household survey data on topics such as population, health, and nutrition. This program operates in more than 90 countries and emphasizes the statistical capacity building of NSOs in the design and implementation of surveys, as well as data analysis. In the most recent phase of DHS surveys, the household questionnaire collects the information on household ownership of different types of vehicles including car or truck, motorcycle or motor scooter, bicycle, boat with a motor, and animal-drawn cart.

In addition, information of road crash fatalities and injuries are collected in many DHS surveys (e.g., Ethiopia, Cambodia, etc.) and the health module of national population censuses and other surveys. Traffic injuries are part of broader transport issues, as lack of access to safe transport is faced by many LMICs, which has implications in participation in economic activities. While tracking progress towards SDG 3.6 of reducing traffic deaths and serious injuries poses a measurement challenge in most LMICs due to large discrepancies between reported official statistics and estimates from global health measurement studies, censuses and household surveys can fill the information gaps in traffic-related injuries.

The Household Budget Surveys (HBSs) are nationally representative household surveys with a focus on consumption expenditure. For most low- and middle-income countries, HBS is the main data source for poverty estimation and consumer price index weights calculation. Some HBSs, such as Kosovo’s and the Republic of Yemen’s, only collect the expenditure evidence, while some HBSs with additional modules, such as Albania’s, can capture more information on transport such as the vehicle ownership and number of cars in the household through the standalone section of transport and communication.

16