rafmacalaba/data-use-mentions-extended huggingface dataset:
)).

After estimation, a fundamentals vulnerability index is generated. Figure 5 illustrates build-up of public and private debt vulnerabilities within each country using the proposed index. It is important to note that an increase in each of the variables used in the index point to an increase in vulnerability, such that the index has the same interpretation and such that the weights are positive (i.e., an increase in the index implies an increase in one of the variables - e.g., larger foreign denominated debt issuance will contribute to an increase in the index).

The black dots in Figure 5 represent the debt vulnerability indices by country while the red lines are the country trends of these indices (i.e. by construction the average is roughly zero). The figure summarizes vulnerability since 2002, while the making of each country’s index takes the full sample size (which differs by country) into account.<sup>3</sup> A positive value indicates that a country is now more vulnerable than, on average, in the past. As an example, Argentina’s index has been rising steadily since 2012 and is above its trend. Using this measure, ex post, one would have inferred rising vulnerabilities faced by Argentina and using the decomposition chart in the Appendix, that most of the risk was emanating from upcoming amortization. Other countries in the sample (e.g., Brazil and Belarus) also have rising vulnerabilities, however, have not defaulted (in Brazil’s case, probably because foreign debt is a very small share of its large public sector debt).

> 3See Appendix E for the same chart but for the resilience indicator.

9
therefore to infer with confidence that our results are not driven by spatial sorting biases in the 2001 dataset.

Table 17 shows the impact of violence on school attainment in 2007 for the sample of nonmigrant individuals. The coefficients in columns 1-3 do not significantly differ from estimates in table 10. Our results of the effects of the 1999 violence on school completion in 2007 do not seem therefore to be driven by a process of spatial sorting (i.e. are not driven by those that migrated). Columns 4-6 of table 17 show slightly weaker results than those in table 11 and 12, but the main coefficients do not change substantially.

# **6. Conclusions**

The aim of this paper was to examine the effects of the conflict in Timor Leste on educational outcomes among boys and girls exposed to the violence that took place in 1999 following the withdrawal of Indonesian troops from the territory. We analyzed the short-term impact on primary school attendance and grade deficits in 2001, and the longer-term impact on school completion for cohorts of children of primary school age in 1999 observed in 2007. We also analyzed the impact of peaks of violence in the 1970s and 1980s on schooling outcomes in 2007 (among those that were of primary school age at the time of the various violent events), as well as the overall impact of the conflict. This has enabled us for the first time to compare the impact of violent conflict on educational outcomes in the short, medium and long terms.

The conflict in Timor Leste has led to considerable adverse impacts on educational outcomes among children exposed to the violence, boys in particular. We find that the impact of the conflict on girls‘ education is statistically insignificant in most cases, and positive in some cases. Because girls‘ educational outcomes were catching-up to boys‘ education before 1999, we interpret this result as suggesting that the catching up process continued once the 1999 conflict was over. The 25 years of violent conflict had, in contrast, a clear negative impact on the education of
in order to guide both research and policy design.

This paper reports on an ongoing research program intended to help fill this void. Using data derived from direct observations, unannounced visits, and tests, from primary schools in seven Sub-Saharan African countries—Kenya, Nigeria, Mozambique, Senegal, Tanzania, Togo, and Uganda—which together represent close to 40 percent of the region’s total population, we answer four questions: (1) How much time do teachers actually spend teaching? (2) Do teachers have the relevant subject content knowledge to teach basic and higher-order language and mathematics skills? (3) Do teachers have the pedagogical knowledge and skills

> 1 See for example Rockoff (2004); Rivkin et al. (2005); Aaronson et al.  (2007); Metzler and Woessmann (2012); Chetty et al. (2014); and Das and Bau (2016) for evidence based on quasi-experimental data. These findings are supported by a growing experimental literature reviewed in, for example, Kremer, Brannen, and Glennerster (2013); Glewwe and Muralidharan (2015); Bruns, Filmer and Patrinos (2014) and Evans and Popova (2016), showing that traditional educational inputs have little impact on test scores but incentivizing teacher effort and supporting specific aspects of pedagogy do.

2
. That is, the effect of the BH insurgency on women’s experiences of IPV, as measured by the change in IPV between BH affected areas and non-BH areas before and after the BH insurgency. In other words, ββ it estimates the effect of the presence of the BH insurgency on IPV in the BH affected areas.<sup>21</sup>

# 4.6 Estimation Bias

In standard DD models, there are two common causes of estimator bias. First, selection bias across time and groups (Stuart et al. 2014). Second, using data from repeated cross sections as opposed to longitudinal data can cause estimates to be biased due to terms that must remain unchanged to ensure before-after comparability (Blundell and Dias 2009). For our model, selection bias across time could occur if the composition of the sample in the BH and non-BH affected areas changes in the period before and during the BH insurgency due to the BH insurgency. Alternatively, selection bias across groups could occur if the composition of the sample changes across the BH and non-BH affected areas. Population movements to and from the BH affected area because of the operations of law enforcement, forced returns and flight from the conflict could affect the composition of women in the BH affected area before and after the BH insurgency. This could cause selection biases to occur both across groups and time.

We address these potential sources of bias as follows. First, as discussed in Section 4.1, we exclude from our analysis observations from the 2008 NDHS from areas not surveyed in the 2013 NDHS on the account of the BH insurgency to ensure before-after comparability. Second, we employ a

> 21 We also check if the intensity of exposure to BH may affect the likelihood of increased IPV beyond residing in an area where the BH insurgency is present, i.e., we determine the marginal effect of BH events in the BH affected area, by accounting for the number of BH events in the affected area within the study period. However
contributors account for approximately 25 percent of the total labor force.<sup>14</sup> The participation rate has a negligible impact on the later calculations and is based on a historical mean value.

Although a broad approach may limit the precision of calculations, the regressions in sections 5 and 6 show that the variable has a high explanatory power on growth. Moreover, as can be seen from Figure 3, the estimated values give a more reasonable picture than the data from official sources. Notice that the _level_ of the economically active population, but not its growth rate, is basically in line with ILO or INE calculations. In 1980s, when the civil war had already taken genocide proportions, the labor force dropped dramatically by about 660,000.<sup>15</sup> For recent years, the estimate for the economically active population derived from IGSS statistics comes close to INE data.

# **4.3 Physical Capital Stock**

Internationally, the Perpetual Inventory Method (PIM) is a common way to estimate capital stock, but there are uncertainties associated with the calculation. In general, due to the lack of information about the initial capital stock, questionable validity of assumptions about the rate of depreciation, and lack of information about the utilization of capital, estimates should be taken with care. With these reservations in mind, the PIM was used to construct the physical capital stock for Guatemala. The following paragraphs present two distinct calculations, one with aggregated and another with disaggregated investment data.

# **_Estimate with Aggregated Investment Data_**

The physical capital stock that is used throughout the subsequent analyses is computed using the PIM with aggregated investment data. The procedure argues that the stock of capital is the accumulation of the stream of past investments: where _K_ is the capital stock, _I_ gross fixed capital formation, _δ K_ the annual depreciation rate of the capital stock, and _t_ an index for time. The initial value of the capital-output ratio for 1950 is taken from the
Table 2: Country-pair correlation regressions

||(1)<br>GDPD|(2)<br>GDPD|
|---|---|---|
|log index davax tot|-0.126<sup>_∗∗∗_</sup><br>(0.016)||
|log index davax fn||-0.048<sup>_∗∗_</sup><br>(0.022)|
|log index davax int||0.061<sup>_∗∗∗_</sup><br>(0.025)|
|log index DVARF||-0.137<sup>_∗∗∗_</sup><br>(0.021)|
|N|25692|25403|
|R2|0.575|0.575|
|Time windows|2|2|
|Country pairs|14823|14648|
|Time FE|YES|YES|
|CP FE|YES|YES|

Standard errors clustered by country-pair in parentheses Note: observations with non-overlapping 10-year windows. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01 positive and significant association between bilateral inflation correlation and crosscountry production, as proxied either by total gross trade in intermediate inputs, or by value-added trade in intermediate inputs that are ultimately absorbed by the trade partner. The negative association between re-exported import and domestic inflation reveals that inflation dynamics and the association between two countries’ inflation does not only depend on how much they trade, but also on who is the ultimate consumer for those trade flows. Table 3 shows that the same results hold for the correlation of CPI inflation.

Table 4 comes back to the use of gross trade indicators but this time constructed using data from Borin and Mancini (2019) based on EORA data. The goal of separating
about efficiency-enhancing transfers is constrained by potential tenants' endowment of assets and other means of production (bullocks).

Thus, while land rental markets improve the allocation of resources in the presence of factor market distortions by bringing land to imperfectly or non-tradable factors of production (experience, family labor, animal power), the gains are constrained by endowments of potential transactors. In addition, there is evidence of fixed transaction costs which preclude some poor households that would desire only relatively minor adjustments, from entering the tenancy market. Similarly, data from India suggest the prevalence of imperfect adjustment whereby, on average, farmers realize only about 75% of the desired level of land transactions (Skoufias 1995). The latter study also indicates that the adjustment effected by the land rental market is asymmetric for net in-renters and out-renters; consistent with the view that market power depends on relative scarcity of factors; in this environment of land scarcity, it is easier to rent out than to rent in.

What, then, are the magnitudes of the productivity effects that are brought about by the operation of land rental markets? A comparison can be performed between sharecropped and owned (or cash-rented) plots for the same household (in order to control for unobservable farmer attributes) allows to estimate the loss due to the second-best nature of sharecropping. Bell (1976) was the first to conduct such an analysis, finding that farmers indeed exert less effort on tenanted plots. Applying the same methodology, Shaban (1987) found that, on average, tenancy was associated with a 32 percent lower output; but the difference was only 16 percent once adjustments were made for differences in land quality. Inputs of family labor and draft animals were significantly lower on sharecropped plots than on owned parcels. No statistically significant differences in productivity were found between owned plots and plots rented on a fixed-rent basis, confirming that fixed-rent contracts induce higher productivity. Other studies yield results that point in the same direction (Sen 1981).

The productivity loss entailed in sharecropping can be
dates, for _Bolsa Familia_ , and in Uruguay, where voters do not reward parliament or local councils, just the President and the incumbent government. A different dimension of heterogeneity is highlighted by Larreguy, Marshall and Trucco (2015), who find that central government leaders gained in popularity from a land titling program, but local leaders lost support, since it undermined clientelist strategies. Partisan differences are also important to the theoretical framework of Imai, King and Velasco Rivera (2016), who argue that in cases where programs are supported by both (or all) relevant parties, such as Mexico’s Seguro Popular health reform and Progresa CCT program, there is no logical reason why voters would reward specific politicians (i.e. incumbents) rather than out-of-power parties who also support the program.

In Tanzania, the ruling party, CCM, has held power at the central level continuously since independence, and the vast majority of local (village-level) governments are also controlled by CCM. The ITN distribution was predominantly an initiative of the central government and international donors (although local leaders participated in implementation), so if voters are “correctly” attributing credit where it is due, there should be a reduced treatment effect in opposition controlled localities, since the opposition had no power in the central government or the Ministry of Health, and an increased treatment effect in CCM-controlled settings.

The National Panel Survey collected information on local political leaders, such as their bureaucratic or elected post and their partisan affiliation. This enables an examination of the interaction of treatment with villages where a) all recorded village level officials are from the ruling party (this represents 43% of villages), and b) villages where more than 50 percent of village officials listed on the community leader roster are from opposition parties (14% of villages). For these models, attention is restricted to village level leaders, since information about the partisan composition of higher levels of the political system (i.e. ward councils) is not available in
exceptions are the short term interest rate for Brunei, trade for China and Indonesia, GDP growth for Lao PDR and Thailand, real effective exchange rate for Malaysia. This is a very desirable result as it confirms the suitability of a GVAR model for this region. Based on the eigenvalues<sup>4</sup> , the model is also found to be stable.

# **3.3 Time varying weights**

As mentioned in section 2, trade weights are calculated for each country in order to create the country specific foreign variables. These trade weights reflect the proportion of a country’s trade with other countries in the sample. Yearly bilateral trade flow data among each of the countries are used in order to create these weights. Use of time varying trade weights (instead of fixed trade weights) in this GVAR model means there are trade weights for each country for each year in the sample. This large trade weight matrix gives insights on how trade flow fluctuated between countries over the years. Figure(1) shows the trade weights of China for the 10 ASEAN countries over the sample period (1994-2016). Trade weights here represent the relative importance of China in terms of trade for these countries compared to other countries in the sample (i.e. rest of the ASEAN countries + Japan).

It is evident from the figure that for almost all the ASEAN countries, the importance of China as a trading partner has increased over the time. While the trade weights of China increased most sharply for Vietnam, the weakest increase is displayed by Brunei. As a trading partner, China’s importance is greatest for Vietnam, Cambodia and Myanmar at the end of the sample period. Myanmar shows the most fluctuation in terms trading relation with China. In 1994, China was their most important trading partner among all the ASEAN countries, but the weight decreased until 2000 and started to rise again after the creation for ACFTA in 2010. In short, the graphs in general show an upward
% of public health spending. Slightly less than one-fifth of aid (excluding actions relating to debt and donor administrative costs) is targeted toward education or health.

# **Hypothesis tests for no fungibility and full fungibility**

As discussed in section I, the presence of off-budget aid in the donor-based measure of sectoral aid ( _SAIDit_ ) decreases the estimate of _β_ , thereby overstating the true degree of fungibility. For a correct assessment of fungibility, it is necessary to distinguish between on- and off-budget sectoral aid. Consequently, I also estimate models that partition education and health disbursements into the four prefix codes: where _IP_ represents investment projects, _SP_ denotes sector program aid, _TC_ represents technical cooperation, and _ONM_ denotes other (no mark) aid.

SP aid should primarily be on-budget aid because, by definition, program aid involves a government-togovernment transfer of resources. In contrast, TC is a good proxy for off-budget aid. The costs of providing 11
livestock. Higher temperatures tend to encourage small farmers to shift from cropping to livestock; the returns to both activities fall, but the decline is greater for crops. On the other hand, large livestock producers, who are typically more specialized in beef cattle which are less well adapted to warmer climates, experience sharper losses at higher temperatures.

In wrapping up this survey of a decade of Ricardian analysis in developing countries, Mendelsohn (2009, 16-17) concludes that: The studies generally confirm the hypothesis that tropical and subtropical agriculture in developing countries is more climate sensitive than temperate agriculture. Even marginal warming causes damages in Africa and Latin America to crops. Crops are also sensitive to changes in precipitation. In semi-arid locations, increased rainfall is beneficial. However, in very wet places, increased rainfall can be harmful. If climate scenarios turn out to be relatively hot and dry, they will cause a lot of damage to farms in low latitude countries. However, if climate scenarios turn out to be relatively mild and wet, there will be only modest damages and maybe even beneficial effects. The magnitude of the damage depends greatly on the climate scenario.

The hedonic approach relies on two key assumptions. First, there is long run equilibrium in factor markets (especially land). Second, there are no adjustment costs such that land rents fully reflect the value of climate at any given location. Given these assumptions, the hedonic approach typically utilizes cross-section data to estimate long-run relationships, which are thus sensitive to omitted variable bias. Schlenker, Hanemann and Fisher (2005) examine this issue in considerable detail in the context of the Mendelsohn, Nordhaus and Shaw (1994) findings of warmer temperatures leading to higher economic returns in the US. These authors show that this finding is a consequence of the omission of a variable indicating whether or not the land is irrigated. Since irrigated land tends to be very productive, and the value of pre-existing water rights (and low prices for water) tend to be capitalized into land values,
7|5.9|1.7|

Note: Gross benefits, excluding incidence of contributions. Source: Nuñez (2009) using 2008 ECV data.

46. **Early childhood programs are well-targeted to the poor, even if small in fiscal terms.** The benefit is progressive with 32.4 percent received by the poorest quintile and only 3.1 percent of the subsidy received by the richest quintile. The cash equivalent of the three main programs, _Primera Infancia_ , ICBF (financed through payroll taxes of formal workers), and a school feeding program, would hypothetically reduce the Gini coefficient by 0.41 and poverty by 0.58 points. The subsidy represents 7.0 percent of the household incomes of the poorest quintile.<sup>25</sup>

> 25 Other relevant social protection initiatives in recent years include the expansion of _Red Juntos_ which tries to integrate more effectively programs targeted to the extreme poor.  Unfortunately, an incidence analysis of this program is not available.

17
in regression [2] of Table 2 when controlling for IMF data transparency (SDDS). Our overall results support that countries with higher income per capita tend to exhibit lower spreads. Data transparency interacted with institutional quality (proxied by the ICRG index) reduces sovereign bond spreads significantly while data transparency interacted with public debt increases spreads.

We calculate the benefits of investing in data transparency across Sub-Saharan African countries using the coefficients from regression [4] in Table 2. If the level of data transparency in SubSaharan Africa (measured by the average of the statistical capacity index of the World Bank) improves to that of the top quartile of the middle-income country group, sovereign bond spreads could decrease by 47.99 percentage points. Data transparency, therefore, reduces spreads by 119 basis points and will improve an additional 36 basis points when combined with institutional improvement (as measured by increases in the ICRG index). The total benefits are 155 basis points from improving both data transparency and ICRG together in 2018. The stock of Eurobonds issued in the Africa region by the end of 2018 is about US$82.3 billion and the average spread for the region is 405 basis points in the same year. Therefore, Sub-Saharan African countries could have saved US$9.8 billion in 2018 if they had the level of data transparency of Thailand. The total dollar amount saved from improving both data transparency and institutional quality could have reached US$12.8 billion in 2018.

CPI inflation seems to be another significant pull factor. Higher inflation increases the sovereign spreads (Table 2). Therefore, inflation stabilization and adequate management of inflation expectations by the central bank are important to reduce spreads. Primary balance to GDP and current account to GDP unfortunately did not show any significance in our regressions. Some push factors such as global market volatility, as proxied by the VIX, and the US Treasury bond yield matter: the coefficients of global market volatility are positive and significant while the coefficients of US Treasury bond yield are negative and