rafmacalaba/data-use-mentions-extended huggingface dataset:
Source: Own estimates based on Turkey 2016 HBS. Note: The figure shows the Gini coefficient for each income concept described in Figure 1. M.I. refers to before and after fiscal interventions, Disposable income refers to after direct taxes and transfers, Consumable Income refers to after indirect taxes and Final Income refers to after in-kind (education and health) transfers.

Figure 3 presents the overall impact of fiscal policy on inequality in peer countries to benchmark Turkey’s performance. For comparability, in all cases the baseline scenario considers market income plus pensions as starting point, treating old-age pensions as deferred income. Qualitatively speaking, income inequality falls in all countries after taxes and transfers, which implies that there is fiscal redistribution in all these economies. The differences are in the magnitudes. The highest absolute decrease in the Gini is observed in Argentina, at 0.18 Gini points, and the smallest drop is found in Russia, at 0.06. Among peer countries, Turkey shows a below median performance, close to Mexico and just ahead of Chile and Russia. The differential performance with comparator countries is driven by the inequality-increasing impacts of indirect taxes, first, and the relatively moderate inequality-decreasing impacts of direct transfers and taxes, second.

**Figure 3. Gini coefficient before and after fiscal policy in Turkey and peer countries**

<!-- Start of picture text --> Market Income plus Pensions (M.I. P.) Disposable Income<br>Consumable Income Final Income<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Argentina Brazil Chile (2013) Mexico Croatia Poland Russia USA Turkey<br>(2012) (2009) (2014) (2014) (2014) (2014) (2016) (2016)<br><!-- End of picture text --
|Private Preschools|-0.002|-0.000|0.005***|-0.002|
||(0.003)|(0.000)|(0.001)|(0.003)|
|Eligible Child|-0.037**|-0.007***|0.010***|0.045***|
||(0.014)|(0.002)|(0.004)|(0.014)|
|FDR q-value|0.693|0.693|0.002|0.693|
|Observations|36,287|37,223|37,223|37,223|
|Mean|0.446|0.008|0.038|0.491|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dummy for having main activity as indicated in column heading on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q- values for the interaction coefficient of preschool density and eligible child are computed over all 4 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows. Main activities are only asked pertaining to survey years and not to historical employment.

74
# 5. **Fiscal Policy** and StabilLuation

Balance of payments stabilization problems have been a constant theme for countries in SSA since the mid-19709. Two lessons for fiscal policy seem important. The first is the importance of control over the public sector deficit. The second is the importance of redirecting government expenditures so as to shift the burden of stabilization away from aggregate demand contraction. We discuss these in turn.

Deficits cannot be financed externally in crisis periods, and domestic borrowing **is** in any case from the baiking system. Public sector deficits therefore mean increases in domestic credit and the monetary base. Since stabilization of pr'ces (including the exchange rate) and/or the balance of payments requires control over the growth of the nominal monetary base relative to the demand for it. controlling the public sector deficit **is** a potentially important input. The basic insight here is simply the monetary approach to the balance of payments (or the exchange rate), and in fact control of government expenditures and the overall deficit are standard features of IMP stabilization packages applied throughout the developing world.<sup>31</sup> A number of observations are suggested by the particulars of the SSA case.

First, much attention has been devoted to the need for countries in SSA to move towards more market-related exchange rates (e.g., World Bank, 1981). In practice, this has meant experimentation with foreign exchange auctions and 31 A survey of 94 Fund adjustment programs supported by Stand-By Arrangements or the Extended Fund Facility between 1980 and 1984 found that 91 percent included restraint of government current expenditure. LimLts on bank credit to the central government were included in 53 percent of the programs, and measures to improve or reform tax administration in 55 percent (ItM, 1986).

29
December 2018 were currently working, according to family members. Graduation from college in the relevant academic year (reported in table (5) column 4) is negatively associated with migration without any control variables included in the regression but is so strongly associated with province and urban locations that after controlling for these factors there is no association with households having a migrant member. The large majority of college graduates are concentrated in urban areas, and especially in Tashkent, where relatively few international migrants are from.

The survey data also allow for a reasonably clear profile of current migrants. More than 80 percent of migrants are men (varying between 80-89 percent depending on the source). Migrants are younger than the overall population with an average age of slightly over 33 years, in comparison to more than 38 years in the rest of the population in the baseline survey data. Adult migrants are substantially more likely to be unmarried than other adults (27 percent, in comparison to 22 percent of non-migrants). Nearly 60 percent of current migrants received a vocational degree as their highest level of education compared to about 45 percent among non-migrants. A similar share of migrants and non-migrants completed upper secondary (29 percent). However, only about 4 percent of migrants completed a bachelor’s degree or more, whereas about 11 percent of non-migrants had completed one. A remarkable difference between migrants and non-migrants is their higher propensity to speak Russian: more than 53 percent of migrants are described as Russian-speakers, while only 32 percent of the remaining population is.

The Russian Federation is by far the largest destination country for international migrants from Uzbekistan. Before 2015, regional cooperation agreements permitting visa-free entry and waiving certain registration requirements facilitated this migration (Ajwad et. al., 2014). However, changes in migration law in Russia beginning in 2015 has significantly reduced the ease with which migrants from outside of the Eurasian Union are able to legally migrate to Russia. Nonetheless, the country remains the dominant destination, accounting for more than 75 percent of current migrants
0.002|-0.004|0.017|0.031|0.006|
||(0.036)|(0.043)|(0.046)|(0.034)|(0.046)|(0.022)|(0.030)|(0.031)|
|Divorced dummy|0.258***|-0.002|-0.031***|-0.006|-0.027|-0.003|-0.012|-0.019|
||(0.019)|(0.017)|(0.008)|(0.025)|(0.030)|(0.017)|(0.022)|(0.023)|
|Observations|21,629|15,368|46,118|8,934|5,595|19,940|11,801|11,696|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011. _Note:_ Marginal effects of the labor force participation are evaluated at the mean of the independent variables. Standard errors are in parentheses. Never married is an omitted category for marital status. Standard errors are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.
90

|**Country**|**P**|<sup>**a**</sup>**Diversification**|**Conservation**|
|---|---|---|---|
||||conservation measures, and designing<br>systems and helping secure sources of funds<br>as well as assessing the project. Shell’s<br>survey of drivers in Mar 2010 found that<br>93% of Thai drivers were willing to change<br>behavior to save automotive fuel, and 80%<br>proactively practiced fuel-saving habits.|
|Togo|√|<br>No information.|No information.|
|Tunisia||**_Alternative energy targets_**: RE share of power generation 4% by<br>2011, 16% by 2016, and 40% by 2030; specific capacity installation<br>targets for wind and solar water heaters by 2011; RE capacity targets<br>for 2016 and 2030.|<br>RCREEE member. There is a substantial<br>body of law governing EE, including<br>provisions for mandatory audits, incentives<br>for investment in EE improvement, and<br>establishment of an EE fund. National<br>Agency of Energy Conservation established<br>in 1985. It set up an energy-saving program<br>for the period 2008–11.|
|Turkey||**_Alternative energy targets_**: RE share of primary energy 30% by<br>2023.<br>Turkey in 2010 was second only to China in the installed capacity of<br>solar water heaters. Turkey’s automotive LPG market in 2011 was<br>second only to that in the Republic of Korea. A law on the use of RE<br>for power generation was adopted in 2005.|<br> <
(2013), we assume a minor degree of learning-by-doing externality in the traded good sector ( _ρYT_ = _ρzT_ = 0 _._ 10). Also as in Berg _et al._ (2010), investment adjustment costs are set to _κN_ = _κT_ = 25.

- _Households preferences._ The coefficient of risk aversion _σ_ = 2 _._ 94 implies an intertemporal elasticity of substitution of 0.34, which is the average LIC estimate according to Ogaki _et al._ (1996). We assume a low Frisch labor elasticity of 0 _._ 10 ( _ψ_ = 10), similar to the estimate of wage elasticity of working in rural Malawi - see Goldberg (2013). The labor mobility parameter _ρ_ is set to 1 as in Horvath (2000), and the elasticity of substitution between traded and nontraded goods is _χ_ = 0 _._ 44, following Stockman and Tesar (1995). To capture limited access to international capital markets, we set _η_ = 1 as in Buffie _et al._ (2012).

- _Measure of intertemporal optimizing households_ . Since a large proportion of households in LICs are liquidity constrained, we pick _ω_ = 0 _._ 40, implying that 60 percent of households are rule-of-thumb. Depending on the degree of financial development of a country, the measure of intertemporal optimizing households can be lower than 40 percent in some SSA countries. Based on data collected in 2011, Demirguc-Kunt and Klapper (2012) report that on average only 24 percent of the adults in SSA countries have an account in a formal institution.

- _Mining_ . The royalty tax rate _τ_<sup>_O_</sup> is made time-varying to match the projections of natural resource revenues for Kenya in Subsection 5.1.

- _Tax rates_ . Consistently with data collected by the International Bureau of Fiscal Documentation in 2005-06, the steady-state taxes on consumption, labor and capital are chosen so that _τ_<sup>_C_</sup> = 0 _._
- Start of picture text --> Upper middle income Lower middle income Low income Upper middle income Lower middle income Low income<br>45 45<br>40 40<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10<br>10<br>5<br>5<br>0<br>0<br>0 50 100 150 200<br>-2 0 2 4 6 8<br>General government gross debt - 2021 forecast (% of GDP)<br>Real GDP Growth - 2021 forecast (% of GDP)<br>(in percentage points) (in percentage points)<br>Consolidated Distance to Break Point (CDBP) Consolidated Distance to Break Point (CDBP)<br><!-- End of picture text --> Source: Own calculations using data from Fitch (up to third quarter 2020), the Global Economic Prospects (2021), and the World Bank Macro-Fiscal Model Data Base available at https://mtimodelling.worldbank.org/highfreqmonitor/dashboard.html. Note: All figures report the percentage point increase in the non-performing loan ratio at the country level (the Consolidated Distance from Break Point - CDBP) that wipes out capital buffers for banks representing at least 20 percent of the banking system assets (Y axes). Higher values denote higher capacity to absorb NPLs increases. Panel A shows the country distribution of the CDBP across World Bank developing regions (EAP: East-Asia Pacific; ECA: Europe and Central Asia; LAC: Latin America and the Caribbean; MENA: Middle East and North Africa; SAR: South Asia; SSA: Sub-Saharan Africa). Panel B plots the CDBP against the Economist Intelligence Unit (EIU) banking risk indicator. Panel C plots CDB against the real GDP growth. Lebanon is not included in the graph because of a too-low value for the 2021 real GDP
# 4 Empirical Strategy

We begin by estimating the impacts of offering NEP-B and NEP-I on parental behaviors and child development, what is usually referred to as the intention-to-treat parameter (ITT). Impacts on child outcomes are estimated using data at the child level, while impacts on parental outcomes such as behaviors, beliefs and well-being are estimated using data at the household level.

We use the following specification: where Y is the outcome of interest at endline, which varies by child / household i, in health center, c. NEP is an indicator which takes value 1 if the caregiver was invited to participate in NEP-B, and NEP is an indicator which takes value 1 if the caregiver was invited to NEP-I. X is a set of control variables including children’s attributes such as sex and age, household characteristics such as family structure, household’s per capita income, caregiver’s education, and the outcome of interest at baseline. Γ are health center fixed effects which capture unobservable differences in program quality. Coefficients β and β7 are the ITT parameters of interest.

Since participation in NEP was voluntary, we complement the ITT analysis with the estimation of instrumental variables (IV) estimates of the impacts of NEP. If these impacts are heterogeneous in the population, these estimates are usually interpreted as Local Average Treatment Effects (LATE). The first and second stage equations for this estimator are: where random assignment dummies NEP and NEP are used as instrumental variables for participation in each treatment arm, T and T (Basico and Intensivo, respectively).

## 5 Results

This section presents the estimated impacts of NEP-B and NEP-I on child outcomes and parenting attributes. Because the endline survey was conducted between 30 to 36 months after the end of the interventions for households in our sample, these can be interpreted as medium-term effects of the program. In this section we focus on the simplest specification where we only control for health center fixed effects. In tables A1-A4 in Appendix 2 we present specifications that include age, gender
In many countries, central banks or bank superintendences operate public credit registries (PCRs). PCRs are databases with detailed information of the credits granted by financial intermediaries<sup>6</sup> created to support bank regulation and supervision. On occasions they also participate actively in the credit information market by providing data to creditors (namely banks) and credit bureaus.<sup>7</sup> The degree of interaction and involvement with other CRS<sup>8</sup> players (e.g. financial institutions, other credit providers and credit bureaus) varies from country to country. It heavily depends on the institutional and legal arrangements that support their operation and on the idiosyncrasy of the local credit markets.

Besides its role in improving creditors’ risk assessment and in facilitating access to finance, credit information is also an important tool for bank supervisors and regulators as watchdogs of financial stability. One of the many lessons this crisis has painstakingly taught is the paramount importance that supervisors regularly and thoroughly monitor banks’ risk exposure (i.e. at least on a quarterly basis). Being able to evaluate the credit risk of borrowers in the financial system gives important information to monitor the quality of banks’ portfolios and to shape the prudential regulation.

This paper is about the importance of the information in PCRs for supporting and improving banking sector regulation and supervision. Bank on-site examinations and off-site monitoring can be more efficient and effective when supervisory authorities have direct access to credit data, and hence can avoid relying exclusively on bank internal databases and regulatory reports. The fact that PCR databases contain individual credit information on borrowers makes it possible to implement advanced techniques that measure banks’ credit risk exposure. It also allows optimizing the prudential regulation ensuring that provisioning and capital requirements are properly calibrated to cover expected and unexpected losses respectively.

PCRs will play a role in the adoption of the new International Regulatory Framework for Banks (Basel III). The foundations laid down in Basel II remain – to a large extent –
IPRs because of PTA provisions, it must extend this treatment to all WTO members. That is, it cannot discriminate between rights-holders from PTA members and others. Indeed, it makes little practical sense for nations to discriminate across the origins of applications for intellectual property protection. Thus, in principle, rights-holders from countries not party to a PTA are affected legally under the same terms as their counterparts from member countries. This fact suggests that the effects of elevated intellectual property norms in PTAs spread beyond the agreements’ members _de jure_ , though it may not prevent _de facto_ discrimination. This insight suggests that the effects of such standards may not display much diversion.

In this paper we study the effects of membership in enforceable IP-related PTAs, negotiated both with strong _demandeur_ countries (the United States and EU/EFTA) as well as with other countries (such as Japan, South Korea, and Australia), on exports and imports of goods that intensively use intellectual property, accounting for levels of economic development. We estimate the impacts on member nations’ aggregate and bilateral trade in IP-intensive sectors, using a difference-in-differences approach comparing various definitions of treatment agreements. We adopt rigorous econometric specifications to deal with endogenous selection into such agreements. In general, we find that the trade effects are modest. However, there is evidence of a trade-expanding impact on specific IP-intensive sectors, such as pharmaceuticals, chemicals, and information technology products, particularly in higher-income emerging countries. We also find evidence that developing countries that join such agreements see significant reductions in their trade in goods that are not IP-intensive, relative to countries that do not join them. These findings imply that strong IPRs chapters may exert a sorting effect, shifting trade from industries that are less sensitive to IPRs protection to those that are more dependent on it.

The remainder of the paper is organized as follows. Section 2 provides background on the development of PTAs with strong intellectual-property chapters and overviews their scope and coverage, relying on World Bank data. Section 3 discusses the empirical framework and presents
# 4. Conclusions

Without a thorough understanding of the economy-wide costs of infrastructure disruptions, policy makers are ill-equipped for identifying, prioritizing and implementing adequate investments and reforms for enhancing the reliability, quality, and resilience of their infrastructure systems.

Disruption of infrastructure services affect firms in every economy around the world. However, disruption frequencies vary widely across as well as within economies. Whether or not a firm will have to cope with blackouts, water shortages, logistical obstacles or loss of telecommunication ultimately depends on the interaction of country-level as well as firm-level exposure and resilience factors. The quality of infrastructure is generally linked closely to the level of economic development, proxied by GDP per capita in this paper and by the quality of the institution in Kornejew, Rentschler, & Hallegatte (2019). For transport, water, electricity, and infrastructure more generally, more developed countries tend to have fewer disruptions and higher perceived infrastructure reliability. However, the data shows that there exist significant differences in infrastructure quality between countries with the same level of development, what is explained in part by firm-level factors. On average, larger firms tend to experience less water disruptions and blackouts while at the same time many outages cause firms to invest in own back-up generation. Exposure to disruptions is also highly dependent on the sector in which the firm operate, further suggesting that effective measures are taken to shield critical infrastructure to prevent outageinflicted damage from spreading further. Finally, concerning natural hazards, extreme precipitation and storms are positively associated with water disruptions, while transport networks are affected by storms.

Reliable and efficient infrastructure systems are crucial to enable firms to follow long-term planning, and to maximize their capacity, without the need to invest excessively in expensive back-up or contingency technology. A thorough analysis of electricity network systems shows that, in the 138 countries covered by the Enterprise Surveys, sales losses are worth US$81.6 billion due to power outages (lower bound estimate). The total annual blackout duration reported by firms is estimated to increase sales losses by 0
effects are the significant uptick in public lower-secondary education, and a fall in uppersecondary education, between 2009 and 2010. In no case is there a significant change in enrollment rates between 2007, 2008, and 2009. We conclude that the recession did not have a measurable effect on the level of enrollments.

**Table 6.  Measuring the  Impact of the 2008-09 Recession on School Enrollment Rates**

|||Enrollment|Change in age-|specific enrollm|ent rate comp|ared to|
|---|---|---|---|---|---|---|
|||rate (%)|2007:Q1||||
|Pre-school|Total|34.5||4.1|3.3|3.7|
||||||_0.97_|_0.95_|
||Private|8.8||0.3|-0.2|0.0|
||||||_0.82_|_0.89_|
||Public|25.7||3.9|3.5|3.7|
||||||_0.90_|_0.97_|
|Primary school|Total|99.3|0.0|0.0|0.0|-0.1|
|||||_0.82_|_0.88_|_0.74_|
||Private|16.5|-1.4|-1.4|-1.4|-3.7|
|||||_0.65_|_0.99_|_0.00_|
||Public|82.8|1.4|1.3|1.4|3.7|
|||||_0.70_|_0.99_|_0.00_|
|Lower secondary|Total|97