rafmacalaba/data-use-mentions-extended huggingface dataset:
899 points ([3] in Table 6), respectively. When both variables are jointly included in the specification, a one-point increase in both trade and financial openness reduces borrowing costs by 0.51 and 0.0635, respectively in the same regression ([5] in Table 6). The interaction term between data transparency and trade openness is negative and statistically significant when using the IMF data transparency index ([6] in Table 6). The coefficient estimate suggests that a one-point increase in this interaction reduces borrowing costs by 0.434 points. The interaction between IMF data transparency and financial openness does not show any significant results. GDP growth reduces sovereign bond spreads while the VIX index increases spreads robustly. GDP per capita also reduces bond spreads. Higher CPI inflation, higher global market volatility and lower US bond yield increase bond spreads, and hence raise the cost of borrowing.

In Table 6 we find robust results with the World Bank data transparency variable and, additionally, we find some significant results when decomposing the World Bank data transparency index into its methodology, periodicity and timeliness, and data source components. Next, we introduce the lag of trade and financial openness variables as instrumental variables, to control for endogeneity, and include those interactions with data transparency indicators in Table 7.

Table 7 shows that the results from the IV estimations are robust and consistent with the findings from previous regressions. GDP growth, for instance, reduces sovereign bond spreads while the VIX index increases spreads robustly. GDP per capita also reduces bond spreads. Higher CPI inflation, higher global market volatility and lower US bond yield increase bond spreads, and 12
# **2. Experimental design and descriptive statistics**

# **2.1. Experimental design**

The data come from the Tanzania Methodological Survey Experiment on Household Consumption Measurement, which was conducted from April to July 2022 by the Tanzania National Bureau of Statistics, with technical support from the World Bank Living Standards Measurement Study (LSMS) program. Informed by the power calculations based on the past rounds of the Tanzania National Panel Survey (TZNPS) and the Household Budget Survey (HBS), the experiment spanned 143 enumeration areas (EAs) across Mainland Tanzania and Zanzibar, including both urban and rural areas. In each sampled EA, 25 households were selected at random from a fresh household listing that was conducted, out of which five sampled households were assigned at random to one of five survey treatment arms.

We analyze three survey treatment arms that are most relevant for our study.<sup>3</sup> Treatment Arm 1 (TA 1) administered the standard TZNPS household questionnaire that provides observed consumption and poverty estimates and that permits the estimation of all imputation models presented in Dang _et al._ (forthcoming), whose Tanzania-specific portions of the research relied on the data from the previous rounds of the TZNPS. Table A.1 in Appendix A shows each of the models and their predictors. The TA 1 sample consists of 711 households.

Treatment Arm 2 (TA 2) administered a light questionnaire that includes:

- (1) “Core modules” that only include the questions necessary for computing the predictors for

a data-modest subset of models that are presented in Dang _et al._ (forthcoming) - specifically

> 3 The two additional treatment arms that are not discussed/used in this paper were (a) the sample that was subject to a 14-day diary for data collection on food consumption, following the HBS 2017/18 methodology, and otherwise identical non-food consumption expenditure modules vis-à-vis T1; and (b) the sample that was subject to a modified version of T1 questionnaire
# Unraveling a Secret: Vietnam’s Outstanding Performance on the PISA Test

## Suhas D. Parandekar Elisabeth K. Sedmik

Global Practice for Education, The World Bank

**Keywords:** PISA; Vietnam; Oaxaca-Blinder Decomposition; Fryer-Levitt; Economics of Education.

**JEL Classification Numbers:** I21 (Analysis of Education); I28 (Government Policy); Z18 (Public Policy).

This paper has been written using open source software: R for the econometric analysis and graphics and LaTeX for typesetting. Thanks to all who make free software possible and to OECD for making the PISA data freely and easily available to anyone. The R and Latex code used in writing this paper is freely available for download at http://github.com/zagamog/PISA PAPER. The authors would like to thank World Bank colleagues Amer Hasan, Marguerite Clarke, and Thanh Thi Mai for reading earlier versions of the paper and providing helpful feedback. Errors and omissions are the responsibility of the authors only.
for female heads than for male heads in the categories of non-farm as a main job (29 to 26%) and non-farm alone (44 to 37%). In Ghana, less surprisingly, female heads experienced greater reductions in poverty than men in all categories and greater reductions were found for female heads with some connection to non-farm activities. For those female heads in agriculture only, poverty dropped by 31%, and for men it dropped by only 18%. For female heads in non-farm only, poverty fell by 37% and for men, it fell by 32%. In the combination of agriculture and non-farm, poverty for female heads fell by 35% also, and for males it fell by only 14%.

# **5 Rural Employment and Income Changes in Uganda and Ghana**

To better understand the underlying changes behind poverty reduction in non-farm activities, we examine participation and income data each in a couple of different ways. Since the participation data shows trends at the individual level, we get the clearest picture of what is happening by gender from participation. We first take a snapshot look at the different types of industries comprising non-farm activities and how these differ by gender. We then look in more detail at how participation in the broader categories of agriculture, wage work, and self-employment differ by gender and family role. Then we turn to the income data.

# _5.1 Sector Participation_

Tables 6 and 7 show industry participation trends for main and secondary activities. Agriculture is clearly the main activity for both genders in both countries, but what is nicely depicted is how important it is to look at secondary activities. Among secondary poverty, or whether by contrast, the poor face too many barriers to participation in non-farm activities.

12
and b) some arguments are echoed and endorsed by authoritative voices while others only enjoy the support of outsiders or mavericks (Crawford 2004). States and national governments control access to the deliberative forums in which national development agendas are articulated; states and national governments also enjoy more prestige, authority, and legitimacy than international civil servants. In other words, states control political salience.

Certain development goals, such as educational attainment and social exclusion, are, in the first approximation, the outcome of national policies. For these, MDGs 2.0 could state that each country’s government is responsible for formulating, deliberating about, and implementing national targets. Some countries might wish to draw on, or request, the advice of the international development community; but in many countries such requests could jeopardize the perceived national “ownership” of the development targets. (The development goals that involve the global agenda, such as those currently included in MDG 8, would be formulated at the international level). These “national MDGs 2.0” might be more circumspect, in some cases, than what the international community might have endorsed. But from the point of view of political mobilization, gains in political salience from this approach would likely exceed the losses from little ambition. National processes in the formulation, deliberation, adoption, and implementation of MDGs 2.0 might create important focal points for social and political mobilization. Accountability for development outcomes still arises almost entirely from domestic mobilization, and national MDG targets might play a role in generating it.<sup>6</sup>

> 6 It is worth noting that the formulation of the post-2015 agenda is shaping up to be much more participatory and consultative than the process that led to the MDGs. There are expected to be 70-100 national consultations, open meetings involving international and interdisciplinary perspectives, spaces for online input from ordinary people, a global survey of poor people, and inter-governmental working groups on the Sustainable Development Goals (“The Post-2015 Development Agenda,”
# **4.5 Choosing the right instruments**

The validity of the conditional LATE approach—and to a lesser extend of the narrow bounds approach—crucially depends on the choice of instruments. We adopt two strategies for this choice. First, we use an _ad hoc_ strategy in which we select a number of variables at the village and at the household level likely to affect the share of _a-compliers_ (within the group of compliers). For instance, we include the province fixed effects under the expectation that some provinces may have provided, before the beginning of the intervention, better preschool infrastructure than others without necessarily affecting the treatment effects. Using the same logic we include population size and land area of the villages<sup>35</sup> and whether the village has a primary and/or a secondary school. In a second specification within this same strategy, we add caregiver level variables (education and poverty level as well as caregiver cognitive test scores).

In a second strategy, we improve the transparency of the variable selection by implementing a machine learning algorithm to determine which variables should be included in equation (10) as X instruments to predict ASP attendance. We leverage the large baseline dataset composed of variables at the province, village, household, caregiver and child levels. Our objective is to predict the probability that a child will be enrolled in an ASP using baseline variables following the procedure implemented in Belloni et al. (2013) and Chernozhukov et al. (2018). We start the procedure by selecting all covariates available at baseline (1354 in our case). We exclude free-text variables, variables with very low response rate,<sup>36</sup> and variables without variation, which results in 224 eligible variables. We then create dummy indicators for categorical variables, compute the square of each covariate, and then remove perfectly collinear variables.<sup>37</sup> We use the resulting 493 baseline variables to predict ASP enrollment status at follow-up
this threshold. This phenomenon, known as ’bunching’ in the literature, has been a subject of investigation by Saez (2010). However, recent research by Mascagni and Molla (2018) reveals that there is no noticeable bunching effect below the VAT registration threshold.

Subsequently, we examine the gendered distribution of taxpayers within the categories of ’A’ (potential VAT registrants) and ’B.’ The actual disparity in registering for Category A is relatively minor, with 81% of businesses owned by men registering for category A and 79% representing women. Notably, the difference is not statistically significant, as indicated in Table 8.

Table 8: Share of Individuals by Gender and Tax Category

||Men|Women|Diferences|
|---|---|---|---|
|Share of Category A TP|0.81|0.79|0.016|
||0.01|0.02|0.881|
|Share of Category B TP|0.19|0.21|-0.016|
||0.01|0.02|-0.881|
|Number of Observations|1,654|666|.|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 We report standard errors below the proportion estimate and z-statistics below the difference.

# _4.1.4. Industrial Classification_

Examining industrial classification is vital, as it may have a close connection with gender. Our administrative data provides the industrial classification of businesses, based on the Ethiopian Standard Industrial Classification Rev. 1.<sup>8</sup> The summary statistics reveal that women-owned businesses represent a significant share of the taxpayers in specific sectors, including hotels and

> 8It is important to note that, within our dataset, approximately 30% of the taxpayers did not disclose their
related to initial unemployment, and (3) negatively related to the initial share of employment in manufacturing. Racial composition and segregation are not correlated with later city population growth. Government expenditures (except for sanitation) are also not associated with subsequent growth. However, per capita government debt is positively correlated with later growth.<sup>1</sup> In a long run analysis, Beeson et al. (2001) examine the location and growth of the U.S. population using county-level census data from 1840 and 1990. They showed access to transportation networks, either natural (oceans) or produced (railroads), was an important source of growth over the period.<sup>2</sup> In addition, industry mix (share of employment in commerce and manufacturing), educational infrastructure, and weather have promoted population growth.

In a recent paper for developing countries, Au and Henderson (2004) took a slightly different approach. They modeled and estimated net urban agglomeration economies for cities in China, which can be postulated by inverted-U shapes of net output or value-added per worker against city employment. They found urban agglomeration benefits are high – real incomes per worker rise sharply with increases in city size from a low level, level out nearer the peak, and then decline very slowly past the peak. The inverted-U shifts with industrial composition across the urban hierarchy of cities. Larger peak sizes are for more service oriented cities, but smaller for intensive manufacturing cities. In addition, (domestic) market potential and accumulated FDI per worker have significant and beneficial effects on city productivity, measured by value-added per

> 1 They attributed this correlation to higher expected growth which made it cheaper to borrow, or government invest heavily in infrastructure to serve that growth.

> 2 Transportation network is represented by a group of dummy variables indicating ocean, mountain, confluence of two rivers, railroads, and canals.

7
# **1. Introduction**

Political disagreements between the Ethiopian federal government and the Tigray regional state ensued into full-scale war on November 4, 2020, with the armed forces of the Tigray region on one side and the federal army and its allied forces from Amhara region and neighboring Eritrea on the other. The conflict played out in most parts of Tigray while also spilling over into parts of Amhara and Afar regions. As a direct consequence of the war, a deep humanitarian crisis continues to unfold, with widespread loss of life, displacement of people, property damages, and disruptions to economic livelihoods documented in the press.<sup>1</sup> In November 2021, the United Nations estimated that about 7 million people in Tigray, Amhara and Afar regions have been directly affected, with 2.4 million internally displaced persons in dire need of food assistance.<sup>2</sup> As the conflict continues, these figures are rising. A more recent assessment by the World Food Programme shows that across the three conflict-affected regions (Tigray, Amhara and Afar) more than 9 million people need humanitarian food assistance and 83 percent of people in Tigray are food insecure (WFP, 2022).<sup>3</sup> Despite these high-level assessments and anecdotal accounts, to date there has not been any micro-level assessment of the welfare impacts of the armed conflict on affected households. Using a unique High-Frequency Phone Survey (HFPS) dataset, we provide the first empirical evidence of the (near real-time) impacts of the conflict on food security, access to food markets and disruptions to livelihood activities. We also evaluate the distributional patterns of these effects across economic sectors, geography and household characteristics.

Much of the earliest work on the adverse impacts of violent conflict is cross-country in nature, examining macro-level relationships between conflict and economic growth and development outcomes. Most of these cross-country studies find significant negative impacts of conflicts on macro-level economic outcomes (Barro, 1991; Gupta et al., 2004
India (48). Brazil’s position in the ranking is influenced by measures adopted by Argentina, its main partner in the Mercosur. If measures adopted by Argentina (26) and Uruguay (1) were to be excluded, then Brazil would be ranked 14<sup>th</sup> among the exporting countries most affected by antidumping measures.

The productive sectors most affected by antidumping measures between 1995 and 2002 are: metals (31.6% of the total), chemical products (18.6%), and plastics (12.2%). The same trend holds true in Brazil, where the chemical industry, followed by the metal and plastic sectors, accounts for 73.4% of all the measures adopted in that period.

Lastly, according to Zanardi (2003), who evaluated more comprehensively the use of antidumping measures — based on data from the semi-annual notifications submitted by WTO Member countries and on a specific survey for non-Member countries between 1981 and 2001 — considering all the antidumping investigations initiated by the European Union, the United States, Canada and Australia, the proportion of final affirmative determinations were, respectively, 73.7%, 59.3%, 58.4% and 41%. In the countries that started resorting to this instrument in the 1990s (such as India, South Korea, Mexico and Turkey), affirmative determinations amount to 71.9%, 65.1%, 65% and 52.5%, respectively. In Brazil, almost half of the proceedings (49.6%) end up with a final affirmative determination.

Brazil is ranked 15<sup>th</sup> in safeguard measures notified in the same period —having resorted to them only twice (toys and coconut) — lagging behind the United States (12), Czechoslovakia (10), Jordan (9), Chile (8), and Venezuela (6). Safeguard actions have increased substantially since 1998, when they rose
# _Selected policy suggestions_

There is a vast literature discussing policy recommendations that nurture the benefits and mitigate the negative effects of migration. Before concluding, we briefly summarize a few selected policy suggestions such as integrating migration and development policies (both in South and North), facilitating the flow on remittances, improving recruitment practices, and facilitating international labor mobility.

Migration should be incorporated both in the development cooperation strategies in the North, as well as in the national poverty reduction strategies in the South (Clemens 2010). Although as of yet rare in practice (UNDP 2009, p. 82), there are some cases of successful strategies for cooperative efforts between the sending and the receiving countries to manage migration (Martin _et al._ 2002).<sup>27</sup> Areas of cooperation include the drivers of migration in the source country, networks that move people across borders, and integrating the legal migrants into their destination countries.

While many developing countries have large stocks of immigrants, very few of them have explicit policies on how to deal with immigration or the capacity to manage their borders effectively. Similarly to the destination countries in the North, there are calls in developing countries to focus on attracting the highly skilled migrants, discourage irregular migration, and reports of growing xenophobia (Lucas 2006). Empowering the sending and receiving countries in the South to manage international mobility will benefit the migrants, the host societies and the country of origin.

Statistics on migration and remittances are often of poor quality, especially in developing countries. Few statistics measure migration flows and data are even scarcer when it comes to transit, circular, or irregular migration (World Bank 2010b, p. 25-26). Official estimates of remittance flows are usually gross underestimates of the true volumes, since a large portion of remittances are sent through unofficial channels.<sup>28</sup>

> 27 For instance, Mali and France initiated annual bilateral consultations at the ministerial level to discuss the integration of Malians who want to remain
to July 2007, yielding a total of about 260,000 observations.

Unfortunately, neither the middlemen, societies nor boat owners keep track of the prices paid by the exporting and processing companies. Since frozen fish is sold internationally, however, the exchange rate and the international price of Indian fish should be relatively good proxies for the actual price boat owners receive for their catches.

We therefore collected price data from one of the largest fish markets in Europe. Spain has the largest fish trading companies in the continent and receives the majority of the fish that is later redistributed to the rest of Europe. The largest Spanish fish market, Mercabarna, provided us with all available information on Indian fish imports. Because they only keep records of fish species that are consistently traded for at least a year and with significant total sales, they provided us with records on the average price at which frozen Indian calamari is sold to retailers, which corresponds to the largest share of Indian sea product traded in the

> 14See, for example, Robertson et al. (1990), Tesch (1989), Barlow et al. (1986), Hastings (1981), Entright (1975), and Zucker (1978).

7
# VIII. **NONMEDICAL CONSUMPTION**

VHI may also have an impact on nonmedical consumption by reducing the uncertainty associated with future medical expenses, encouraging households to reduce savings. Some items of nonmedical consumption—such as food consumption, and expenditures on water and sanitation—may have effects on health outcomes, especially among children.

We examine the impacts of VHI on aggregate per capita household nonmedical consumption, as well as its impact on various sub-aggregates of household consumption, including food consumption, and expenditures on electricity, water, garbage collection and water. Aggregate consumption includes the imputed use value associated with ownership of consumer durables and other assets, and as is standard in LSMS surveys, consumption includes expenditures on services, as well as the value of home-produced food (Glewwe, Gragnolati and Zaman 2000). The focus on per capita _household_ consumption makes sense if households share resources, so that health insurance coverage of one household member reduces the financial risk faced by _all_ household members. Clearly, the risk faced by the household is reduced by more if several household members are insured, rather than just one member. We therefore experimented with three different methods of health insurance coverage at the household level. The first was to classify a household as insured if every member is insured. However, since less than 4% of households fall into this category, this approach was rejected. The second was to define a household as insured if _the majority_ of household members are insured. The third was to define a household as insured if _at least one_ member is insured.

The double-difference results in Table 10 suggest that VHI coverage increases nonmedical consumption. The impact is larger in the case where coverage is defined as the majority of members being covered than in the case where coverage is defined as one or more members being covered. Whichever definition of coverage is used, the effect on nonmedical consumption is larger in absolute size than the effect on medical consumption (the latter is not significantly different from zero at the household level). This result