rafmacalaba/data-use-mentions-extended huggingface dataset:
many people it serves, and (c) the _type of facility_ , since ‘higher-level’ facility types offer a wider range of health services and have more and better trained staff. The intuition is that parishes have better health access if their residents can travel shorter distances to facilities that serve fewer people and offer more (and better) services. To combine these three factors, for each parish, we calculated the distance and population served to the nearest facility of each type.<sup>14</sup> We then rescaled and transformed these measures (since shorter distances and fewer people indicate better access), averaged across the five facility types, and standardized it.

Fourth, since health access is based only on the number of _new_ facilities, it does not capture improvements to existing facilities (e.g., more providers, equipment, and medications). Although we do not have data on these improvements during our study period, we proxy for healthcare _quality_ by including a measure of _Health Utilization_ . Unlike the previous measures, which are constructed at the parish-year level, health utilization is an individual-level measure. It is derived from Demographic and Health Surveys (DHS) of over 30,000 Ugandan children and their households in 2006, 2011, and 2016. Our standardized utilization measure averages child health services (e.g., vaccinations, deworming, iron supplements), their mothers’ maternal health services (e.g., tetanus injections before and during pregnancy, antenatal visits, delivery by health professionals), and household measures (e.g., insecticide-treated mosquito nets). Reassuringly, _Health Utilization_ is positively correlated with _Health Access_ . For more details on how both variables are constructed and validated, see SI Section S1.5.

Fifth, to create a measure of _Road Density_ for 2011, 2016, and 2020, we use two data sources: the Global Roads Open Access Data Set gathered by the NASA Socioeconomic Data and Applications Center from 2010, and the World Food Programmes
5- Estimate the expected mean of the natural log. per capita income (ln I�I ı ), the residuals i and using estimation of step 4. eê uu�6- Sum ooii i and and estimate the expected total variance following equation 7: 2 ooii i � 0 1 2 i 3 i 4 i i . eê uu� ooii ii ii �uu� + eê = ππ + ππ NN + ππ XX NN + ππ XX + ππ tt + θθ Identify the significant interactions parameters to keep them in the estimation of the final equation 7. 7- Predict the expected variance ( 2 i ) using estimation of step 6 including only significant interactions. σσ�uuooii+ee � ln P ı 8- Compute the probability of being vulnerable to poverty as i ~~(~~ ). 2 −lnIIPPI ~~�~~<sup>σσ�</sup> VV<sup>�</sup> = ΦΦ uuooii+eei 9- Classify the households as vulnerable and non-vulnerable using the threshold of 0.29.

10.4- Classify the households as vulnerable and non-vulnerable using the threshold of 0.29. 11- Classify the households as PI those vulnerable households from step 9 with ln I�I i 12- Classify the households as RI those vulnerable households from step 9 with ln I�I i < ln IIPP. ln I <mark>14- <13?></mark> �I i Classify the households that under steps 9 and 10.4 are vulnerable as CRI if their We interpret these households as vulnerable to poverty due to climate or natural > ln IIPP. disaster risk. These are the households that are not PI but can be pushed into poverty due to CR. > ln IIPP.

# **3 Data**

As we mentioned, we exploit five years of survey data per country to expand the variability of data, specifically in small countries such as the Dominican Republic, El Salvador,
compliance and expands this literature in two ways.<sup>7</sup> First, unlike many experiments that give a generic nudge or reminder to taxpayers, this paper (specifically, experiment one) leverages a significant real investment in the identification capacity of the government. Second, whereas most papers in the literature can only study the immediate impact of a notice, this paper is able to show that the impact of identification and penalty information on compliance persists over three years.<sup>8</sup> The rest of the paper proceeds as follows. Section 2 provides background information on Liberia and the administration of property taxes, while section 3 outlines a simple conceptual framework for thinking about these three aspects of tax capacity on tax compliance. Sections 4 and 5 describe the design of the two experiments and present the corresponding results. Section 6 concludes and provides policy implications.

# **2 Context**

Liberia has low compliance rates for real estate taxes (as well as other tax categories). Prolonged civil conflict in the 1990s and early 2000s led to the erosion of state capacity across several sectors, including tax administration. In 2018, the real estate tax register contained fewer than 7,000 properties in Greater Monrovia, the largest urban metropolis in Liberia, with over 1 million residents. Even among properties on the tax register, timely tax collection remains a challenge. After taxpayers enter the tax base in one year, over 60 percent of them do not pay taxes in the subsequent year (according to the author’s analysis of administrative tax data from 2015 to 2018). Many property owners who pay are motivated by the desire to obtain a tax clearance needed for administrative purposes in another sector, such as to bid for a contract with the government or to import goods at the port.

Several factors related to weak identification and collection capacity contribute to the low real estate tax compliance. In the absence of a cadaster that outlines all the properties in the city, the LRA adds properties
shown to get the word out to firms. In other countries the outreach was less intensive and slower – in one country there were only two branch offices for the program throughout the country, neither particularly noticeable, and the marketing materials produced were not visually very appealing.

Developing strong awareness campaigns can be a challenge. Despite one of the projects investing considerably in a large launch and communications campaign, a survey of 209 businesses conducted one year after the launch found that none of the sampled businesses had heard of the program. Of those contacted, 39% were interested, and ultimately only 6% were both interested and eligible to participate, highlighting the need for targeted awareness campaigns when eligibility criteria are restrictive (Figure 2).

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advanced chipset makers such as TSMC and Samsung, eroding market shares of Huawei and HiSilicon. In 2021, seven supercomputers manufacturers were added to the blacklist: Tianjin Phytium Information Technology, Sunway Microelectronics, Shanghai Center for High-Performance Integrated Circuit Design, and the National Supercomputer Centers of Jinan, Shenzhen, Wuxi, Zhengzhou.

We match the Entity List to the PATSTAT dataset using entity names. To identify the affected corporations, we match the exact entities on the Entity List, as well as their 7
therefore, jobseekers plan to migrate to megacities. Lastly, 1.7% of the job postings are in foreign countries.<sup>11</sup>

**Figure 1. Geographical distribution of jobseekers, employers, and job postings**

_Source_ : Authors’ calculation using the data from Rozee.pk. _Note_ : (1) We refer to Lahore, Karachi, and Islamabad/Rawalpindi as first-tier cities and Faisalabad, Gujranwala, Hyderabad, Multan, Peshawar, and Quetta as second-tier cities. (2) Job locations are multiple for a substantial number of job postings. Thus, the sum of the percentages for job postings is greater than 100%.

Comparing Rozee.pk users and the entire labor market participants based on the Labor Force – Survey 2014 15 and Enterprise Survey 2013 shows notable differences between them. First, the proportion of female workers is smaller in the online job portal than the entire labor market. Women constitute 21% of jobseekers in the portal but 25% of the entire labor force (Table 1). This may be because many female workers are agricultural and household workers, who mostly do not use Rozee.pk. Second, jobseekers in the online job portal are much younger (mean age is 26) than the entire labor force in Pakistan (mean age is 35). In the portal, 83% of all the jobseekers are aged 30 and below while only 44% of the entire labor force in the economy is aged 30 and below. The age distribution of the online job market is concentrated between 20 and 30 years old: the proportion of the 20- to 30-year-old labor force is 77% in the online job market but only 33% in the entire labor market. The composition of jobseekers by education in Rozee.pk is significantly different from the entire labor market of Pakistan. Of jobseekers at Rozee.pk, 86% have postsecondary degrees, and 32% have master’s degrees or a Ph.D. By contrast, among
two-thirds of discount days are 30 days or less, while 27% are between 30 and 60 days, and 9% are more than 60 days. Some terms seem very common; 20% of discount days are 10 days, 20% are 30 days, and 16% are 60 days. The most common spreads of net days less discount days are 1 day (34% of contracts with discounts), 30 days (29% of contracts), and 20 days (16% of contracts), with the majority (or 63%) of contracts having a spread of net days less discount days equal to or less than 20 days. The mean spread of net days less discount days equals 17 and the mean ratio of discount to net days is 63%.

> 8 The largest contract in our dataset is for a large, diversified U.S. retailer.

> 9 This is a comparable figure to that obtained using SSBF survey data on U.S. firms, indicating that 20% of firms that use trade credit are offered an early payment discount from their suppliers.

9
Figure 7: School attendance and Secondary School or Higher Education Completion <!-- Start of picture text --> Secondary School Completion School attendance<br>.4 1<br>2008<br>2014<br>95% CI<br>.8<br>.3<br>.6<br>.2<br>.4<br>.1<br>.2<br>2008<br>2014<br>95% CI<br>0 0<br>5 10 15 17 20 24 25 5 10 15 17 20 24 25<br>Age Age<br>Proportion attending school<br>Proportion with secondary or higher education<br><!-- End of picture text --> _Source_ : Own calculations using the 2008 and 2014 Lesotho Demographic Health Survey data. _Notes_ : Household survey weights have been used to get the age-specific proportions.

increased. Therefore, without an increase in its capacity, the university may have admitted the best students from a larger pool of qualifying students compared to the pre-FPE period. This would overstate the real impact of the FPE program on achievement. Second, it could be that the pre- and post-FPE cohorts that got admitted into the university differ by household wealth. Specifically, the post-FPE cohort could be poorer than the pre-FPE cohort, which would likely attenuate the FPE program effect. Therefore, I use a nationally representative data from the 2008 (pre-FPE period) and 2014 (post-FPE period) Lesotho Demographic Health Surveys (LDHS) in order to make sense of who actually got into university pre- and post-FPE.

The left panel of figure 7 shows the proportion of individuals who have completed secondary or higher education by age, pre- and post-FPE, and the right panel of the figure shows the enrollment rates by age, pre- and post-FPE. In 2014, the first age-appropriate FPE cohort to enter the university was aged 20 years old. We can see from the
are routine 9-5 arrangements. This offers some evidence that work that is viewed as more prestigious may entice workers to sacrifice some flexibility in favor of professional development.

Interestingly, other characteristics of respondents’ current jobs, or attributes that they may value such as close interaction with colleagues or working in international teams does not seem to affect the preference for the hours of work.

# 4. Discussion and Conclusions

This paper has attempted to understand the valuations of and trade-offs between flexibility and income among workers in standard employment and digital freelancers, in the context of Malaysia, a fast growing East Asian economy that is experiencing a sharp rise of the gig economy and digital freelancing. Analyzing the results of a unique survey experiment in which respondents are asked to choose between jobs characterized by standard working conditions, and more flexible alternatives, we draw important lessons for management practices and employment policies globally.

First, we find considerable heterogeneity in preferences for flexibility with respect to the location and hours of work among freelancers as well as standard employees. While most freelancers tend to prefer working from home for two to five days per week, a sizable share (around 40 percent) of those in standard employment would also like not to have to work from an office all five days of the week, even if that came at a cost of 10 to 20 percent of their income. Equally, around 40 percent of current freelancers would like to work from a fixed office if that translates into higher earnings.

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meaningful comparison, the same discount rate must be applied to compute the NPV of the new bonds and the old (holdout) bonds. Both old and new bonds face the risk of another default in the future and they both benefit from the debt relief effect of the restructuring.

Haircuts are computed on a bond-by-bond basis. Because of the heterogeneous nature of the debt renegotiations, it is not possible to simplify the calculations by relying on a “representative bond.” Here we use information on a total of 1,134 defaulted sovereign bonds.<sup>25</sup> To compute aggregate haircuts for each restructuring event, we build a weighted average haircut across restructured bonds and use amounts outstanding for weighting purposes. To choose the discount rate , we follow Sturzenegger and Zettelmeyer (2006) and Cruces and Trebesch ii (2013) and use the “exit yield,” which is the secondary market yield of the new bonds that start trading tt rr after the restructuring. This rate reflects the expected risk of a future default on the new obligations, taking into account the success (or failure) of the restructuring that was just implemented. as well as existing liquidity conditions in that market, an issue we take up in Section 4. Whenever possible, we use the secondary market yield of country _i_ in the month after the exit from default, using the bond pricing data summarized in Section 4. For 32 debt restructurings, no market yield data were available, mostly in small countries and low-income countries with no liquid bonds trading in London and New York. In these cases, we use a “worst yield” approach, by using the highest bond yield observable among non-defaulted sovereigns in London or New York at that point in time as a proxy for the country’s own exit yield. Appendix C2 provides further details and shows robustness checks when using alternative discount rates. Among other checks, we apply a 10% flat rate to all deals, as well as a “risk-free” lower bound rate,
**_Figure 2:_** _Share of shopping trips in Bogota and Buenos Aires, by type of travel chain and trip start time (%)_

<!-- Start of picture text --> a. Bogota<br>45%<br>Round trips Chained trips Reduction potential<br>40%<br>35%<br>30%<br>25% 26.1%<br>20%<br>15%<br>6.2% 13.2%<br>10%<br>3.7%<br>5%<br>0%<br>6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23<br>Hour of trip start<br>b. Buenos Aires<br>45%<br>41.3%<br>40%<br>35%<br>30% 27.6%<br>25%<br>18.3% 20.2%<br>20%<br>15%<br>10% 7.4%<br>0.7%<br>5%<br>0%<br>6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23<br>Hour of trip start<br>Source: Team’s estimate based on EDM Bogota 2019 and ENMODO 2018<br>Percentage of shopping trips<br>Percentage of shopping trips<br><!-- End of picture text --> Shopping trips have a higher weight in short trips, reaching 26 percent for trips under 10 minutes in Bogota and 25 percent in Buenos Aires. Only 10 percent of trips that last 30-40 minutes and less than 5 percent among trips lasting over an hour in Bogota are shopping trips. In Buenos Aires, similarly, the weight of shopping trips falls rapidly with
where ∆ _Ni,t−_ 1991 denotes overall population growth in ward _i_ between 1991 and _t_ and ∆ _Ni,t_<sup>_B_</sup> _−_ 1991<sup>denotesblackpopulationgrowth.14Ourcontrolsinclude:populationgroups,</sup> population density, education, the gender ratio, employment and income ( **_Xi,_ 1991** ) and province-level fixed effects ( _δp_ ) (see Table 2). The errors are clustered at the municipality level.<sup>15</sup> The ward level is the lowest geographical level that can be tracked consistently over time and municipalities are the lowest level of local government. Distance is defined as the distance to the nearest homeland measured from centroid to centroid. Since no measure of domestic migration is available in the census data, black population growth conditional on fixed effects and covariates is used as a proxy for black migration.<sup>16</sup> A dummy variable for Cape Town is also included as the municipality is a special case in terms of location, politics and demographics and hence migration patterns. The Western Cape was the only province where the African National Congress did not come first in the general elections in 1994. Until today, it has not achieved the political dominance in the province or the municipality of Cape Town that it has in the rest of the country. In terms of demographics, there is a much higher white and especially colored population in Cape Town, more than anywhere else in the country. Most importantly, there is a

> 14∆ _Ni,t B −_ 1991<sup>isdefinedasabsolutegrowthoftheblackpopulationfrom1991toyeartdividedbythe</sup> overall population in t, where t corresponds to 1996, 2001 or 2011. ∆ _Ni,t−_ 1991 is defined similarly using overall instead of black absolute population growth.

> 15Using Conley (1999) standard errors to account for spatial correlation yields similar results.

> 16As has been used previously as a proxy for migration status, see for example Czaika and Kis-Katos (
have a double status of "regionmunicipality" from the analysis. Given their weight in the total fiscal flows, such exclusion may change substantially statistical characteristics of the sample and influence conclusions of the analysis.

(d) Compared to the original work by Lavrov (1 996a), we tried to develop a more comprehensive statistical analysis of the data, including cross-regional variation of main parameters and factor analysis of decentralization.

(e) Finally, we have used the data to explore the links between decentralization and regional economic performance, which provides an opportunity to compare some conventional predictions of the decentralization theory with actual numbers.

Also, we've had an opportunity to incorporate the 1996 budget outcomes into the database. Given considerable stabilization efforts in Russia in 1995 such an extension may provide important additional information. Data on 1996 reflect some initial post-stabilization fiscal realities, which could be different from the trends observed during the inflationary phase of 1992-95.

All data on the federal budget is based on the reports from the Ministry of Finance with some adjustments done by the staff of the World Bank (Le Houerou, 1995; World Bank, 1998) to incorporate major types of off-budget operations of the central government. As usual in this kind of analysis, we do not consider here main extra-budgetary funds (such as the Pension fund) which operate outside of the traditional government budget.

An important caveat relates to the quality of the data, that was affected by various incentives of local governments to underreport their budget revenues and hide them using all kinds of extrabudgetary accounts (World Bank, 1998). Still, we believe that the data quality is sufficient for adequate evaluation of prevailing trends in the subnational budget system.
0.0294***|
||(0.000101)|(0.000132)|(0.000384)|(0.000989)|
|Temporary|0.00796|-0.00490|-0.0372***|-0.0325***|
||(0.0138)|(0.0247)|(0.00688)|(0.00540)|
|Firm size|0.0242***|0.0368***|0.0588***|0.0172***|
||(0.000224)|(0.000498)|(0.00265)|(0.00166)|
|Individual-Year|Yes|Yes|Yes|Yes|
|Observations|20453784|24511458|7462266|57445724|
|R-squared|0.914|0.919|0.940|0.940|

_Note:_ Innovative firms is measured by the share of PoTec workers in total employment for firm _f_ in year ** _t_ . Heteroskedasticity robust standard errors clustered at the firm level are reported in parenthesis. ***, and * respectively indicate 0.01, 0.05 and 0.1 levels of significance.

# **4.4 Innovation premium by skill level**

The literature on skill-biased technical change has emphasized the role of technology in increasing labor polarization and depressing the incomes of low-skilled workers (Acemoglu and Autor, 2011). This empirical literature suggests that the returns on high skill workers should be larger on average than for other skills. One exception to this evidence is Aghion et al. (2019), who develop a model where low and medium-skilled workers that possess soft skills that are complementary to high skill workers in innovative firms. The authors using data from the United Kingdom find empirical support to the hypothesis of large complementarities, and a larger wage premium associated with working in an innovative firm for low and
|(0.017)|(0.024)|||
|treatment x post x Goal|0.057**<br>(0.028)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 1. For each outcome, the coefficients from a single regression are presented in two sub-rows, with estimates of _β_ 1 in column 1 and _β_ 2 in column 2, labeled treatment x post, and the estimate of _γ_ 1 in the second of the two rows, labeled treatment x post x Goal. Column 3 presents the outcome mean at endline among females in ELA only communities who were not assigned to the _Goal_ treatment, and column 4 shows the number of observations in the model. All specifications include controls for highest grade attended, whether the female’s household owns the house she lives in, whether the female talks to her mom about sexual reproductive health topics, age of the female, and club fixed effects. IPV often (in last year) index is generated by taking the unweighted mean of the three indicators for each frequency set after they have been standardized to the mean and standard deviation among females in ELA only communities who were not assigned to the _Goal_ treatment at baseline and endline separately. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Female baseline and endline data, balanced panel.

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