rafmacalaba/data-use-mentions-extended huggingface dataset:
comparing it to actual and trimmed market prices as well as, subject to data availability, the NMV,<sup>27</sup> assess the difference between different that a shift to market-based valuation would make.

Table 5 rows 4-7 report, respectively the mean actual price, the predicted price from the regression model, the average prices computed using a 10-observation threshold per administrative unit and the NMV. For ease of interpretation, rows 8-10 provide the difference between the NMV and the regional average, the predicted and the actual price. For the entire sample, the predicted price of US$ 994/ha is 24% above the NMV, below the actual price (32.8%).

National figures conceal considerable inter-regional variation: predicted land values using our model are close to the NMV in the East and the South but 24.8%, 22.9%, and 80.2% above it in the North, Center, and West, respectively. Using the NMV as a basis for valuing or taxing land would ignore recent interregional shifts in economic opportunity and, for example, impose an unjustified tax burden on regions negatively affected by the war while making it impossible for owners in the West to leverage the full value of their land as collateral for investment.

# **4.3 Potential refinements and extensions**

Several refinements in terms of adding additional parcel characteristics could potentially be considered. First, we used the admittedly coarse share of built-up area from sentinel imagery as a measure of built area. A digital address or building register or commercially available building footprints could be possible alternatives. Second, geographical characteristics including elevation, slope, and north and east exposure vary little across parcels used for commercial agriculture in Ukraine that are mostly flat and located at low altitudes but will need to be considered if land for personal farming is included. Third, no information was available on access to irrigation facilities as responsible authorities are still in the process of establishing a registry of public irrigation assets. Cadastral
- **Customs transactions data** From Madagascar customs we obtained administrative data tracking imports at the transaction level for the period January 2015-November 2018. For each import declaration, the data covers the HS 8-digit products included (designated as items), their source country, the dates/times of registration, assessment, and clearance, the broker, the importing firm, and, the customs inspector assigned to handle the declaration. For each item, the data contain information on both the initially declared and the finally registered import value, weight, and taxes paid (tariff and value added tax as well as exemptions). These variables enable us to evaluate inspector modifications of value, weight, and tax liabilities. In addition, for each declaration we can track any modifications made to the IT system’s initial inspector assignment by the customs port manager.<sup>14</sup> This will allow us to disentangle the role of IT department staff from that of the customs port manager in generating deviations from official rules in inspector assignment.

- **Fraud records** Fraud records were provided by the Legal Department ( _Service des Affaires Juridiques et du Contentieux_ ). For each declaration, we know both whether and if so what type(s) of fraud was detected and the amount of taxes recovered (if any). Information on whether and how much inspectors modified tax yield is important for assessing the role of inspectors.

- **Risk management data** From the customs risk management unit we received for each import declaration information on the initial and finally-used clearance channel (documentary control/yellow channel, physical inspection/red channel or no inspection/blue channel). From GasyNet we received the risk score assigned to each import declaration (related to the risk of non-compliance with customs regulations ranging from 1 to 9) and valuation advice in case it was issued.

- **Container weight measurement data** We obtained from the company in charge of managing Toamasina’s container terminal - Madagascar
education) during the 2018/19 recession.

This section is divided into three subsections. The first provides macroeconomic context and a broad overview of the changes in sectoral growth by period. The second documents the extent to which the less developed provinces grew more quickly than the other provinces, in each sector and within each period. To do this, we estimate absolute convergence at different periods across the business cycle. The third section documents the relationship between sectoral growth and remoteness during different periods. Together, this section demonstrates that employing a spatial perspective to analyze official sub-national data provides insight into the relationship between business cycles and spatial inequality in Türkiye during the first two decades of the twenty-first century.

## **2.1 The Macroeconomic Context**

In what follows, we use official government data to document the relationships between macroeconomic events and changes in economic activity across provinces. Türkiye is governed as a single entity by a central government, but it is divided into 81 provinces for administrative purposes. Each province is divided into districts, totaling 922. Below districts are towns and villages. The provinces are often grouped into 7 regions and 21 subregions for economic and 5
in the poverty headcount from Market Income (pre-fiscal income) to Consumable Income (Market Income after taxes and transfers, excluding in-kind benefits from health and education). We include 58 countries in total.

**Globally, total poverty change due to the combination of taxes and transfers modeled ranges from the worst case of an 8.9 percentage point increase in Armenia to the best case of an 8.4 percentage point decrease in Spain.** The fiscal system in the West Bank and Gaza increases the poverty rate at the $5.5 2011 PPP poverty line by 6.0 percentage points. Note, however, that this result can vary substantially depending on the poverty line used. At the $3.20 and $1.90 2011 PPP per day international poverty lines, the combination of taxes and transfers in the West Bank and Gaza is poverty- _reducing_ by 1.2 and 0.8 percentage points respectively.

Figure 8.2 Total poverty impact at the $5.50 line is severe relative to other CEQ assessments Percentage point change in poverty rate, 2011PPP international poverty lines (changes from Market to Consumable Income)

# **Figure 8-2 Total poverty impact at the $5.50 line is severe relative to countries in other CEQ assessments**

Percentage point change in poverty rate, 2011 PPP international poverty line (changes from Market to Consumable Income) <!-- Start of picture text --> 10<br>8<br>West Bank and Gaza: 6.0 percentage point increase in poverty rate<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>High incomeHigh income Upper middle incomeUpper middle income Lower mLower m idd le incomele income Low Low incom ee<br>Spain Mauritius United States Panama Chile Uruguay Romania Argentina Venezuela Iraq Ecuador Jordan Thailand Paraguay Russia Iran Malaysia Colombia
.0314|0.175<br>0.0006|
|_Distribution of Students_|_by Fa_|_culty (%)_|||||
|Agriculture|5776|0.0571|0.232|8016|0.0586|0.235<br>0.7101|
|Education|5776|0.254|0.436|8016|0.246|0.431<br>0.2653|
|Health Sciences|5776|0.0703|0.256|8016|0.0750|0.263<br>0.2969|
|Humanities|5776|0.115|0.319|8016|0.130|0.337<br>0.0052|
|Law|5776|0.0452|0.208|8016|0.0485|0.215<br>0.3610|
|Science & Technology|5776|0.144|0.351|8016|0.124|0.329<br>0.0006|
|Social Sciences|5776|0.315|0.465|8016|0.318|0.466<br>0.7142|

_Source_ : Own calculations using NUL students’ records data for 2010, 2011, 2012, 2013, and 2014. _Notes_ : OWM is the overall weighted mean, calculated as the weighted sum of final scores of core, pre-requisite, and/or elective subject courses that make a maximum of 36 credit hours, where the weights are each course’s credit hours divided by 36. The N for some variables is smaller due to missing values.

11
Charlotte Watts. 2009. “How Does Economic Empowerment Affect Women’s Risk of Intimate Partner Violence in Low and Middle Income Countries? A Systematic Review of Published Evidence.” _Journal of International Development_ 21 (5): 577–602. https://doi.org/10.1002/jid.1500.

Waiselfisz, Julio Jacobo. 2015. “Mapa da Violência: Homicídio de mulheres no Brasil.” Faculdade Latino-Americana de Ciências Sociais, Rio de Janeiro. http://www.onumulheres.org.br/wpcontent/uploads/2016/04/MapaViolencia_2015_mulheres.pdf.

WHO (World Health Organization). 2013. “Global and Regional Estimates of Violence against Women: Prevalence and Health Effects of Intimate Partner Violence and Non-Partner Sexual Violence.” WHO, Geneva.

25
0.457<br>0.457<br>Robustness check<br>No<br>1st order<br>polynomial<br>polynomial<br>State fxed efects<br>x<br>x<br>District characteristics<br>x<br>x<br>Individual characteristics<br>x<br>x<br>_Notes:_ Data are from the 2015-16 National Family Health<br>estimates using the fraction of female leaders in a district <br>indicator for using a modern contraception method in Pan<br>the bandwidth for the defnition of close elections. In colu<br>for women’s age fxed efects instead of controlling for wom<br>(99th percentile). In column (10), we add political party <br>seats for SC/ST population. All regressions include state <br>men. Individual controls include age, years of schooling, an<br>female population, the share of urban population, and the <br>are clustered at the district level. ***, **, and * denote sig|

34
developing countries than the modest effects suggested by earlier findings.

This paper presents **the** results of two studies of family background **offacts** on student achievement that, taken together, address the shortcomings of past research. We argue, first, that attention ahould be given to a greater variety of student background variables that may affect achievement by mediating, supplemknting or substituting for conventional background variables. In particular, we are interested in perceptions held by children that may motivate higher school performance. The first study, **an** analysis of longitudinal data from Thailand, explores the effects of **several** such motivational variables in explaining how family background influences student achievement over **time.** Second, we raise the question of whether the influence zfi stadent background on achievement can be adequately tested in the Third World by relying on conventional measures of class, borrowed from industrialized societies. Culturally specific indicators may show distinctly greater influence over student achievement than such imported indicators as parents' occupational status and formal educational attainment. The second study, an analysis of crossectional data from Malawi, examines how estimates of student background effects on learning can be improved by utilizing material indicators of class that are more relevant to local conditions.

The paper is organized as follows. In this section, we review the literature on family effects on student achievement in developing countries. In the second section, we present Study 1 on Thailand, and in the third section we present Study 2 on Malawi. In the final section, we discuss our findings in relation to educational policy.

- 2-
# _4.1. Effects of Changes in Market Conditions_

Amid concerns about increased indebtedness since the GFC, several studies analyze how different shocks could impact the balance sheet of emerging market firms. One of the first studies on this issue was performed by the IMF in 2014 (IMF, 2014). The IMF conducted a scenario analysis to test the resilience of emerging market firms to shocks to earnings, borrowing costs, and exchange rate depreciations. Debt-at-risk is used as the outcome variable. Using data for 2012, the analysis concludes that under an adverse scenario debt-at-risk in emerging economies would almost double, from 19 to 35 percent. This scenario analysis was re-run in 2015 and in 2016 with similar results (Chow, 2015; IMF, 2016).

In 2017, the United States Board of Governors of the Federal Reserve (henceforth, Federal Reserve) conducted a similar analysis with two improvements (Beltrán et al., 2017). First, the study qualifies the levels of risk by comparing resulting values of debt-at-risk with those during the AFC. Second, the study assesses the relative importance of different shocks. This study models the effect of shocks to earnings, interest rates, and the exchange rate on debt-at-risk of nonfinancial corporates assuming large but plausible shocks, based on previous crisis episodes. In particular, the authors assume a fall in earnings of 20 percent, a rise of 100 basis points in the interest rate paid by firms, and a 20 percent depreciation of the exchange rate. This study finds that if the three shocks would occur simultaneously, the debt-at-risk in emerging economies in 2016 would rise from 25 to 41 percent. Excluding China, debt-at-risk would increase from 15 to 34 percent. Although high, these values would still be below those on the eve of the AFC when debt-at-risk stood at 48 percent. Nevertheless, in several large emerging economies the share of debt-at-risk would get near or, even, surpass 48 percent. This would be the case of Indonesia (62 percent), Mexico (53 percent),
enhancing the overall impact on poverty, underscoring the need for comprehensive approaches to addressing financial hardship and income disparities.

**Figure 9. Simulated Performance and Poverty and Inequality Impacts of the MSA Scheme under Policy Scenario 1**

- **a. Simulated Coverage (% of decile in each decile)           b. Simulated Benefit incidence (% benefits allocated to each decile)**

<!-- Start of picture text --> 32.3<br>1<br>2<br>3<br>5.6<br>4<br>0.5 0.1<br>5<br>80.9<br> 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text -->

- **c. Simulated Generosity (benefits as % of MYPP) d.Simulated Inequality, poverty, and poverty gap impact**

<!-- Start of picture text --> 30.1<br>Marginal Contributions to<br>0.00<br>Inequality<br>Change in Poverty Gap 0.84<br>Marginal Contributions to<br>0.28<br>Poverty<br>0.4 0.0 0.0<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by taking the difference between the inequality (or poverty) indicator without the transfer and with it. The marginal contribution is calculated by taking the difference between the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**The changes in the
Policy Research Working Paper 8574

# **Abstract**

This paper evaluates the impact of market-oriented structural reforms, in particular labor market policies, social assistance programs, and trade liberalization on long run unemployment, wage inequality, and the distribution of employment across sectors in a small open economy with search frictions and idiosyncratic productivity shocks. The paper builds a search and matching model of a labor market with a large informal sector and estimates the model using Colombian household-level data. Changes in labor taxes may have sizable aggregate, compositional, and distributional effects if workers associate high payroll taxes with more valuable and efficient social security services. The higher is the valuation of the services, the higher is the reduction in the log-wage gap. An expansion of public health insurance to informal sector workers has minor aggregate and distributional effects. Changes in relative prices that negatively affect the relative profitability of the formal sector have quite sizable aggregate effects, producing more long-run unemployment and informality, and increasing unemployment duration.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The author may be contacted at mrobayo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the
4

# **II. The Role of External Factors on the Regional Slowdown during 1998-1999**

A noticeable feature of regional output growth in 1998 and 1999 was the magnitude and persistence of the slowdown. Casual examination of quarterly GDP growth rates (Fig. 1) shows that the recent slowdown is more persistent than that experienced during the Tequila episode<sup>3</sup> . Regional GDP annual growth rates fell from 5.4% in 1997 to 2.3% in 1998 and to an expected -0.3% in 1999 (Table 1). In 1998 and 1999 the eight largest Latin American economies experienced decelerations ranging from -13.5 to -2.8 percentage points. The 5.7% regional slowdown experienced between 1997 and 1999 is only comparable, in magnitude, to the initial deceleration registered in 1982.

<!-- Start of picture text --> Figure 1<br><!-- End of picture text --> <!-- Start of picture text --> Latin America Big 8*: Quarterly Gross Domestic Product<br>(Neighted y-o-y growth rate)<br>-2 a<br>911 913 921 923 931 933 941 943 951 953 961 963 971 973 981 983 991<br>Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Pe- and Venezuela.<br><!-- End of picture text --> The widespread nature of the slowdown strongly suggests that common factors were at play, besides the particular domestic circumstances of each country. In this section we seek to quantify the relative importance of external factors vis-a vis the internal ones accounting for the regional slowdown during 1998-1999.

> 3Comparisons with the eighties using quarterly GDP data was not possible due to non-existent data. A more thorough examination of business cycles in Latin America during the eighties and the nineties, probably using monthly industrial output could be an interesting task, but falls beyond the scope of this paper
Most of the Caribbean sugar industries will need to be restructured, and some will need to be closed. If this is not done, they face ongoing losses that will increase as preferences erode. Since most of the sugar industries of the region are state-owned enterprises, the governments must ultimately pay for the losses. Sugar industries in a few countries in the region, such as Guyana and Belize, are expected to survive with lower preferences. And some portion of other sugar industries in the region could be viable if properly restructured to include only the best sugar producing lands and factories. In Jamaica, for example, two private sector estates are profitable and could survive with lower preferences while much of the state-owned industry could not survive, even now, without government subsidies and would probably not be able to adjust to lower preferences. The Jamaican government has announced that it will close two of its stateowned sugar factories (F.O. Lichts 2005).

Sugar policies often adversely affect consumers. Many Caribbean countries have regulated sugar prices or high tariffs that increase the cost of sugar to consumers. For example, Jamaican consumers paid more than double the retail prices of consumers in Belize, Dominican Republic and Guyana (Figure 2), and slightly higher retail sugar prices than consumers in the European Union in 2002.

**Figure 2. Retail Sugar Prices, 2002, US$s per Lb., Selected Countries**

<!-- Start of picture text -->
1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Belize Dominican Rep. Guyana Jamaica Trinidad &<br>Tobago<br><!-- End of picture text -->

Source: International Sugar Organization 2003.

Government officials of Caribbean countries have been reluctant to close or rationalize their sugar industries because of the economic and social impact of
Globally, more teachers will be needed. Teacher shortages are a global problem, with an estimated need for 69 million more teachers to meet SDG4 by 2030. This includes a need for 24% more primary teachers. The need for primary teachers varies by region, with Sub-Saharan Africa and South Asia accounting for the majority of the need in developing countries. In 2016, Southeast Asia and Oceania were estimated to need 3 million and 43,000 more primary teachers, respectively (Sources: UIS 2016, International Task Force on Teachers for Education 2030, 2021). The main driver behind the need for additional teachers is the rapid growth in student numbers, as well as countries’ desire to avoid having large class sizes with detrimental impacts on student learning (e.g. see Finn and Achilles 1999).

Teachers with better skills and knowledge will be needed. Different data sources suggest wide differences in the qualifications that primary teachers have. For instance, surveys conducted in six East Asian countries (Cambodia, Lao PDR, Malaysia, Myanmar, the Philippines, and Vietnam) reveal that the academic credentials of primary education teachers (Grade 5) range widely. All or the majority of children in the Philippines (100 percent), Malaysia (89 percent), and Myanmar (88 percent) had a teacher with at least a bachelor’s degree from a university, while this figure was 68 percent in Vietnam. However, in Lao PDR, on average less than 1 percent of children had a teacher who held this academic credential, and the majority of its students were taught by teachers with a post-secondary level credential, but not a bachelor’s degree. In Cambodia, approximately 60 percent of students had a teacher with secondary education or lower academic credentials. This situation reflects national policies that have lower requirements for teacher

> 3 Covered countries (alphabetical order in each subregion): Cambodia, China, Indonesia, Lao PDR, Malaysia, Mongolia, Myanmar, the Philippines, Thailand, Timor-Leste, and Vietnam in