rafmacalaba/data-use-mentions-extended huggingface dataset:
. The section begins with direct and indirect taxes and subsequently with the subsidies and social spending of the Colombian system.

## i. The Colombian government's revenues

The revenue structure of the Colombian government is in Table II-1. The total government revenue considering taxes paid by residents and non-residents amounted to 225,889 billion pesos, which is equivalent to 24.5 percent of GDP in 2017. When exclusively viewing taxes paid by residents, collections total 205,056 billion pesos, equal to 22.3 percent of GDP in 2017 (see column 2, Table II-1). Revenue from the payment of taxes was the largest revenue earner. These collections accounted for 16.6 percent of GDP in 2017. The analysis in this study focuses on the central taxes such as income tax, value-added tax (VAT), and consumption tax. Income taxes are equivalent to 6.2 percent of GDP (see column 2, Table II1), while revenue from social security contributions is equivalent to 5.4 percent of GDP (see column 2, Table II-1). Thus, government revenues considered in this paper represent 11.6 percent of GDP, that is to say, 52.1 percent of total general government revenues in 2017. As mentioned earlier, corporate taxes paid could not be considered because of the difficulty in allocating their impact to a specific household. Similarly, taxes paid by non-residents were also not included because the household survey fails to capture them.

Column 6 of Table II-1 presents the ratio between the total collections obtained with the household survey, and the one reported in the administrative data. This ratio is different from 100 percent since the survey only captures taxes paid by households and administrative data consider not only household 4
Figure 22. Average monthly labor income of men 15+ by state of origin <!-- Start of picture text --> Men who migrated Men who did not migrate Percent difference<br>20,000 140<br>120<br>100<br>15,000<br>80<br>60<br>10,000<br>40<br>20<br>5,000 0<br>-20<br>0 -40<br>State of origin<br>Mexican pesos Percent difference<br>chs tla yuc nay coa gro pue agc oax gto ver tab col qro mor mex cam mich hgo zac jal slp tam dgo qroo nl sin cdmx bcs son chh bc<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1.

**Homeownership significantly differs among women who migrated and those who did not migrate. The same hold for men.** Regarding dwelling tenure, only 40.8 percent of women who migrated internally five years ago own the house they inhabit against 73.6 percent of those who did not migrate (Figure 23). There are no significant differences compared to men considering their migration status.

**Women and men who migrated tend to live in localities most populated.** Almost 60 percent of women who migrated reside in urban localities with 100,000 or more people versus 48.8 of their counterparts who did not migrate. On the contrary, 11.7 percent of women who migrated live in rural localities with less than 2,500 people versus 20.4 of those who did not migrate (Figure 24). A similar pattern is observed among men.

Figure 23. Percentage of women and men who own the house they inhabit by internal migration status <!-- Start of picture text --> 80 73.5 73.6<br>
”, _OED Working Paper Series No. 11_ , Washington, DC: World Bank.

- Evans, Peter and James Rauch (1997). “Bureaucratic Structure and Economic Performance in Less Developed Countries”, _IRIS Working Paper_ 175, Maryland: IRIS.

- Fiszbein, Ariel (1997). “ The Emergence of Local Capacity: Lessons from Colombia”, _World Development_ 25 (7), pp.1029-1043.

- Goel, Rajeev K. and Michael A. Nelson (1998). “Corruption and Government Size: A Disaggregated Analysis”, _Public Choice_ 97(1-2), pp. 107-120.

- Grier, Robin (1997). “The Effect of Religion on Economic Development: A Cross National Study of 63 Former Colonies”, _Kyklos_ 1, pp.47-62

- Huther, Jeff and Anwar Shah (1998). “A Simple Measure of Good Governance and Its Application to the Debate on Fiscal Decentralization”, _World Bank Policy Research Working Paper Series No. 1498_ ,  Washington, DC: World Bank.

- Huther, Jeff and Anwar Shah (2000). “Anti-corruption Programs and Policies: A Framework for Evaluation”, _World Bank Policy Research Working Paper Series No. 2501_ , Washington, DC: World Bank.

- Isham, Jonathan, Daniel Kaufmann, and Lant H. Pritchett (1998). “Civil Liberties, Democracy, and the Performance of Government Projects”, _The World Bank Economic Review_ 11(2), pp.219-242.

26
cognitive and social-emotional skills and document ITT impacts of 0.16 SD on a skills index. The magnitudes of these estimates are consistent with other early childhood interventions in the literature (see, for example, Bos et al. 2024; Berlinski et al. 2009). Our qualitative surveys suggest that the close proximity of the preschool centers to the families’ residence and to the primary school may be a key mechanism behind increased enrollment in preschool and primary education. Moreover, 3
# **C Pre-trend checks**

The key identifying assumption of our difference-in-differences strategy is that, in the absence of the ULGSP, outcomes in treated and reference units would have evolved similarly. While this parallel trends assumption is not directly testable, in this section we use nighttime light (NTL) data as proxy for economic activity and assess whether treatment and reference units presented similar trends before our first round of interviews.

In order to cover a longer time period before the intervention, we use the recently harmonized NTL dataset developed by Li et al. (2020), covering the 1992-2018 period. This is a synthetic dataset that uses the original Defense Meteorological Satellite Program (DMSP) data for 19922013 and calibrate data from the Visible Infrared Imaging Radiometer Suite (VIIRS) for the period 2012-2018. Following recommendations from the authors, in our main specification we drop any pixels with NTL value lower than 7.<sup>30</sup> We present results in Figure C1. In panel A we present average NTL, with 95% CI, for ULGSP and comparison wards included in our survey sample. Consistent with the fact that ULGSP was targeted at more urban districts, the average nighlights are higher for those wards when compared to comparison. The trends before the first assessment, however, do not suggest differential pre-trends. We test this formally using a difference-in-differences model of the following form: where ntlwy are measures of nighlight in ward _w_ in year _y_ , γw and θy are ward and year fixedeffects, and the coefficients βy measure the differential nighlights in ULGSP wards in each period. We allow for differential linear trends by region, included in the time-varying vector Xwy. Since treatment is defined at the LGA(district)-level, we cluster standard errors at that level. We use the entire sample period 1992-2018, but pool all years before 2000 in one coefficient.

We present the βy coefficients in panel B of Figure C1. While results are somewhat noisy, they do not suggest a differential trend between ULGSP
standard deviation over a 5-year period, _sd_temperature_ . Alternatively, mean temperatures, _temperaturecelsius_ , are available in the data set as well. Both have shown to be negatively correlated with economic growth in the past (e.g., Burke et al., 2015; Kotz et al., 2021) but external validity about future causal impact of climate change is difficult to establish, due to non-linearities and the fact that countries are differently affected by global warming (e.g., Ortiz-Bobea et al., 2021).

Our dataset further includes the variables _disaster_aff_ and _disaster_death_ , which capture the number of people affected by or deceased from natural disasters, respectively, per million inhabitants. The data come from the Emergency Events Database (EM-DAT).<sup>16</sup> Since GDP does not take destruction into account and disasters are usually followed by reconstruction, which boosts GDP, the negative economic consequences of natural disasters are usually not captured in cross-country growth regressions. We therefore do not include those variables in our regression models.

To account for positive and negative shocks due to changes in international price levels, we include **terms of trade changes** , _dltot_ , calculated as changes in the log of the export price level relative to the import price level ( _pl_x/pl_m_ ), both taken from PWT10.0.

We further include the log of **government consumption** as a share of GDP, _lkg_ , calculated as the log of csh_g from PWT10.0, into our model. This variable is supposed to capture growth-reducing effects through either distortionary taxation (Afonso and Furceri, 2010), or public debt issuance. Note that the negative association with growth is motivated by the fact that we already control for the positive effects that government consumption may have on growth, e.g., through education or infrastructure spending. As our model describes long-run growth, it is also important not to conflate the short-term positive stimulus effect that increased government consumption can have during economic downturns
# 1. Introduction

There are serious concerns about debt transparency in low-income countries (LIDCs)<sup>1</sup> given the current context of rising public debt levels, the emerging role of non-traditional lenders, the increased borrowing by non-central government entities and the frequent use of complex debt instruments. The Debt Service Suspension Initiative (DSSI) introduced by the G20 countries in 2020 to aid LIDCs coping with the fiscal impact of COVID-19, has further highlighted the importance of debt transparency.

Debt transparency, defined as the availability of comprehensive, detailed, timely, and consistent public sector debt data (WB, IMF 2020c), is key to allow (i) policy makers to make informed decisions; (ii) creditors and rating agencies to fully understand borrowers’ debt sustainability; and (iii) citizens to hold their governments accountable.

Improvements in accountability within debt management practices are strongly enhanced by investments in debt data transparency; this helps bring about substantial long-term benefits, and ultimately reduces the cost of external borrowing (Kubota and Zeufack 2020). Conversely, low levels of debt data disclosure may lead to debt mispricing and/or significant fiscal and debt roll-over risks (WB 2020). Despite this, governments, especially those with limited dependence on market financing and weak governance, may have short-term incentives to under-report. Recent studies have focused on trying to quantify “hidden debt” (Horn, Reinhart, and Trebesch 2019; WB, IMF 2020a).

Current research on key factors that may influence countries’ level of transparency is limited. As yet, few studies have tried to explain why “notwithstanding decades of multilateral effort, the goal of making information about public debt accessible and intelligible to the public, remains elusive” (Gelpern 2018). Another gap in the literature concerns divergences across debt statistics sources; even though the existence of different instrument and sectoral coverage in public debt statistics is well documented (Dippelsman et al, 2012; Seiferling, 2020), there is no quantification of the magnitude of mismatches
At the same time that the size of the pie shrunk, its distribution became markedly **more** unequal. The only statistics available are based on income and on the family budget surveys, which **are** characterized by marked methodological shortcomings. Nonetheless, **even a casual** comparison suggests that the extent of open income inequality has become quite large during the transition (Milanovic, Forthcoming; Klugman and Braithwaite, 1998; Koen 1996, Commander, Tolstopiatenko, and Yemstov 1997).

A further complication stems from the prevalence of arrears in wages and pensions in the FSU countries, and the irregular nature of even formal state-sector employment due to forced administrative leave without pay and reduced working hours. Since the breakup of the FSU, wage and cash transfer arrears have become common as countries have grappled with the introduction of stabilization programs and fiscal austerity. Budgetary sequestration in Russia, Armenia, and Kyrgyz Republic resulted in long delays for wages in the "budgetary sphere" (health, education, government/administration, the Armed Forces, law enforcement, etc.) and for cash transfers, including pensions and child allowances.

It is difficult to accurately measure "official" or "registered" income given the prevalence of arrears, but it is practically impossible to quantify the informal sector in transition economies. In the FSU countries, households have been extremely reluctant to **report** most of their income, even to survey researchers, much less to the tax authorities. (Koen and Gavrilenko, 1994). Most studies suggest that the informal sector is around 40 **percent** of official GDP (KaufTmann and Kaliberda, 1996). In this study, total reported **household** income as a percent of total household expenditures varied from less than 50 43
Fig. 3 Innovation rates (% of firms) Fig. 4 Investment in intangibles (% of firms) <!-- Start of picture text --> 50 16<br>14<br>40<br>12<br>10<br>30<br>8<br>20 6<br>4<br>10<br>2<br>0<br>0<br><!-- End of picture text --> _Source:_ the authors’ calculations based on EBRD-EIB-WB Enterprise Survey. Innovative firms are defined as those introducing new or improved products, services or processes, or spending on research and development activities.

_Source:_ the authors’ calculations based on EBRD-EIB-WB Enterprise Survey. Intangible assets include trademarks, copyrights, patents, licenses, service contracts and franchise agreements.

Private sector development is lagging in MENA. On average, during the 2014-2019 period, the contribution to GDP growth of manufacturing and selected services sectors – which proxy the private sector in the Enterprise Surveys – was 44% in the region, ranging from 37% in Lebanon to 50% in the West Bank and Gaza (Figure 5).<sup>6</sup> This is in line with the average of lower-middle income and upper-middle incomes economies (44% and 45%). For example, although Morocco registered the highest total growth in the region for the 2017-2019 period, more than half of it was driven by other sectors. The employment dynamics using Enterprise Surveys are in line with value added growth at the macroeconomic level, especially in Egypt, Morocco and Tunisia (Figure 6). In Lebanon, the private sector’s contraction is reflected in the decrease in the employment at the firm level.

The significant presence of SOEs in MENA discourages the entry of private companies and hinders innovation. SOEs are typically subject to limited competition, weak oversight and soft budget constraints (Olugbade et al., 2021). As governments try to
# **DEVELOPING COUNTRY TRADE POLICIES AND MARKET ACCESS ISSUES:**

# **1990-2012**

## **Constantine Michalopoulos and Francis Ng**

**Keywords:** Trade policies, merchandise and service trade, market access issues, tariffs and nontariff measures, trade remedies, developing countries

**JEL Classification:** F01, F10, F13, F15, F43

## **Sector Board:** PREM

Constantine Michalopoulos is a former director at the World Bank and at present a visiting scholar at the School of Advanced International Studies of Johns Hopkins University. Email: <u>c.michalopoulos@comcast.net.</u> Francis Ng is a senior economist of Trade and International Integration Team (DECTI) of Development Research Group in the World Bank. Email: <u>fng@worldbank.org.</u> The authors would like to thank Mariem Malouche and Jose-Daniel Reyes of International Trade Department (PRMTR) for their help in obtaining information from the UNCTAD/World Bank NTM database about non-tariff measures in a number of developing countries. The valuable comments and useful suggestions from Chad Bown and anonymous referees on an earlier version of this paper are also greatly appreciated. The authors are responsible for any remaining errors. The findings, interpretations, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the World Bank and its affiliated organizations.
income countries separately.

<u>Income.</u> The income measure used is annual GDP per capita provided by the World Bank for 194 countries for the period of 1996-2016. The values are in U.S. dollars of 2011 in PPP terms to allow for comparisons across countries. The average GDP per capita in our sample is almost USD 18,000 and the median is around USD 10,500. When splitting the sample between low and lower-middle income countries and higher-middle and high income countries, we use the World Bank income categories.

<u>Insurance markets. The measure of insurance market development, that is, the main dependent variable,</u> is life and non-life insurance written premiums as percent of GDP, or insurance penetration (IP). The data are collected by AXCO, a British company that provides extensive and detailed information on insurance markets across the globe. AXCO gets the data from the insurance supervisor in each country, or from the insurance association in situations where the supervisor does not provide data, or the data are outdated. Non-life insurance includes motor, machinery, bonds and health insurance, among others. See Section OA1 of the Online Appendix for the all the insurance classes available in the AXCO data.

Insurance penetration data are available for 180 countries, spanning the years of 1996 to 2016. Insurance premiums add up to a relatively small fraction of the GDP. As seen in Table 1, the mean of total (i.e. adding life and non-life) insurance penetration is 2.9% of GDP and most of the countries in our sample have IP lower than 5% of GDP (75<sup>th</sup> percentile is 4.1%). Total insurance penetration in higher-middle and high income countries is almost 4 times higher than total IP in low and lower-middle income countries (see OA Table 1).

7
# **Figure 2: Relative Incidence of main transfers in Mongolia (2016)**

<!-- Start of picture text --> Income Taxes VAT Excise Tax SSC<br>Pension Child Allowance SA Mortgage Subsidies<br>Electricity Subsidies Heating Subsidies Education In‐Kind Health In‐Kind<br>250.0% 250.0%<br>200.0% 200.0%<br>150.0% 150.0%<br>100.0% 100.0%<br>50.0% 50.0%<br>0.0% 0.0%<br>‐50.0% ‐50.0%<br>1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> <u>Source: Authors elaboration using HSES 2016</u> <u>Notes: Relative incidence refers average tax or transfer in each decile as a percentage of the average pre-tax-transfer</u> income in the same decile. Horizontal axis stands for deciles of market income (i.e., before tax-and-transfers income). Line stands for relative incidence of total net benefits.

21 MONGOLIA Distributional impact of taxes and transfers / November 2018
opened between May 2017 and May 2018 and operated from their opening onwards. Summary statistics Table A1 (collected after various months of implementation through the endline survey described in Section 4) reveals the effectiveness of the program components 1 and 2. First, each preschool had on average 4 instructors and 75-79 children enrolled in 2018 and 2019, respectively. An average of 47 children attended the preschools a small effective student-teacher ratio. Most were built daily, implying fairly preschools with a playground (94%) and provided educational games for children (76%). Most preschools also had notebooks or writing paper (56%), pens and pencils (55%), and drawing books or cards (54%). They also had leisure games (56%) and storybooks for children (57%). Instructors had, on average, 8.6 years of schooling, and for most of them, this was their only job. The preschools were open for three hours a day for ten months every year. However, component 3 was not implemented as planned. Although the plan was to hold monthly parenting education sessions, they were held every three months on average.

In terms of delivery modality, the DICIPE program was delivered through a partnership between the Ministry of Education and Human Development (MINEDH) and thirdparty providers. By the time of the program launch, the MINEDH’s implementation capacity was constrained by a parallel rapid expansion in access to primary education and its almost no experience with ECD programs. Fortunately, there was substantial local capacity to implement ECD programs within international and local NGOs. In this sense, the delivery of the ECD package was operated by third-party providers, which were competitively selected and supervised by MINEDH.

# **3 Experimental Design**

The experimental design started with the selection of eligible communities. First, three provinces (out of five provinces participating in the overall DICIPE program) and the two districts within each province—Macomia and Chiúre (Cabo Delgado); Eráti and Memba
expand regression (2) in order to gauge the relative roles of effort, performance shocks, and ability. Adding effort (column 3), we explain an additional 30 percent of the variation in classes completed. As expected, we explain all the variation in classes completed when we add the shock as well (column 4), with a coefficient on log ability equal to our point estimate for _α_ . Further, in column 4 effort and the shock account for 47 and 52 percent of the variation in classes completed, respectively, leaving a mere 1 percent explained by log ability. In other words, in our model almost the whole variation in classes completed is due to effort and shocks.

Consistent with estimates from the exogenous effort model, when effort is not controlled for in these regressions the coefficient on ability is overestimated by about 75 percent. This large upward bias should be taken into account not only when estimating achievement regressions with administrative datasets, which typically do not include effort measures, but also when designing policy. By ignoring or underestimating effort’s role, policy might place too much weight on a student trait—ability—that cannot be changed rather than on a student choice —effort—that could, in principle be changed.

# **7.5.4 Policy implications**

The previous findings elicit two important questions. The first is whether policies could actually raise the number of classes completed. Combining equations (3) and (12), we arrive at the effort 29