rafmacalaba/data-use-mentions-extended huggingface dataset:
estimated by applying the long-run elasticities to the actual values of the macroeconomic fundamentals. The Hodrick-Prescott (HP) filter<sup>30</sup> is applied to the predicted values of the estimated equation in order to abstract from short-term random disturbances. The (smoothed) fitted values of the REER are measures of real exchange rate equilibrium.

The log-deviation of the actual REER from the estimated BEER measures the degree of overvaluation. According to the baseline specification, the peso is overvalued by 25% (21% applying the HP filter). A much larger overvaluation of approximately 114% (110% applying the HP filter) is obtained using the inflation-adjusted REER measure. Robustness checks suggest that the former underestimates the exchange rate misalignment whereas the latter largely overestimates it. This large discrepancy in measuring exchange rate misalignment highlights sensitivity of results to the mismeasurement of the REER. Contrasting results across the two measures, we detect a substantial decoupling of the misalignment according to the adjusted and unadjusted REER. Whereas relatively little misalignment is evidenced by the unadjusted REER, the adjusted-REER measure points to substantial overvaluation of the peso.

Additionally considering the adjusted measures that correct for potential mismeasurement (underestimation) of the CPI-PPI ratio reduces estimates of overvaluation substantially. A smaller overvaluation is estimated according to the adjusted REER towards more plausible values (e.g., compared to parallel exchange rate

> al, 1997, among others), where the fundamentals are insignificant (we eliminate one by one those that are least significant), or where coefficient signs are inconsistent with theoretical predictions. 30 We use a smoothing parameter of 129,600 consistent with monthly data.

17
# 3.2 Consumption Expenditures

As noted, the consumption expenditure recall period for the 2014 HIES covers the halfyear before the onset of the Ebola epidemic in June 2014, whereas the consumption expenditure recall period for the 2016 HIES (based on the restricted sample of households interviewed in the first semester) covers the half-year starting from January 2016, during which time Liberia was virtually Ebola-free (see figure 1). While the data represent repeated cross-sections of households, for our welfare measure (per capita consumption), we have variation over time, i.e., just before and just after the epidemic. Presumably, household consumption in the first half of 2016 reflects any adverse income shock due to disrupted rice production in 2015.

We estimate an EA fixed effect regression of the form: where W��� is household welfare (varying over two points in time _t_ ), γ�� is an EA fixed effect, λ�� is a dummy for 2016 that multiplies all the time invariant covariates: the Ebola prevalence variable E� introduced in equation (1), the interaction of Ebola prevalence with a measure of community resilience R�� – i.e., the share of agricultural land in the district planted to rice – and a vector of household demographic characteristics including a constant term.

4. Results

# 4.1. Labor Use and Rice Production

Table 1 shows that within the counties that were severely affected by Ebola, use of hired/kuu labor was lower in districts with a particularly high Ebola incidence. While the 13
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.001)|(0.002)|(0.001)|(0.000)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**var0308_all_DB**|0.010***<br>(0.000)||||||0.012***<br>(0.002)||||||
|**var0308_Busin_Oper.**||0.0051***||||||0.0095***|||||
|||(0.000)||||||(0.001)|||||
|**var0308_Permit**|||-0.031||||||-0.0341***||||
||||(0.004)||||||(0.004)||||
|**var0308_Tax**||||0.014***||||||0.0206***|||
|||||(0.000)||||||(0.004)|||
|**var0308_Trade**|||||0.006||||||0.0068***||
||||||(0.004)||||||
job search models from a survey of unemployed workers in germany,” _The Quarterly Journal of Economics_ , 137, 1181–1232.

- DellaVigna, S., A. Lindner, B. Reizer, and J. F. Schmieder (2017): “Referencedependent job search: Evidence from Hungary,” _The Quarterly Journal of Economics_ , 132, 1969–2018.

- DellaVigna, S. and M. D. Paserman (2005): “Job search and impatience,” _Journal of Labor Economics_ , 23, 527–588.

- Donovan, K., W. J. Lu, T. Schoellman, et al. (2020): “Labor market dynamics and development,” Tech. rep., JSTOR.

- Dubin, J. A. and D. Rivers (1993): “Experimental estimates of the impact of wage subsidies,” _Journal of Econometrics_ , 56, 219–242.

- Faberman, R. J., A. I. Mueller, A. S ̧ahin, and G. Topa (2022): “Job search behavior among the employed and non-employed,” _Econometrica_ , 90, 1743–1779.

- Falk, A., A. Becker, T. Dohmen, B. Enke, D. Huffman, and U. Sunde (2018): “Global Evidence on Economic Preferences,” _The Quarterly Journal of Economics_ , 133, 1645–1692.

42
approach meant that she could not include NUTS2 fixed effects but only country fixed effects. This leaves her results more susceptible to confounding differences in trend growth within countries, a problem we avoid with the inclusion of subnational (NUTS2) fixed effects. Finally, Coelho uses EU fund commitments as her instrument, which tend to predict levels of spending rather than changes.<sup>12</sup> In contrast, in our paper _changes_ in our instrument are a strong predictor of _changes_ in spending in most specifications.

Durand and Espinoza (2021) use a similar adjusted EU-fund data and IV approach, though they only examine effects at the country level.<sup>13</sup> In contrast with Durand and Espinoza (2021), we produce estimates at the NUTS2 level, which improves the sample size and accuracy of the estimates (though reduces the length of the sample). However, even when we use country-level estimation, our results are still quite different, with those authors finding large (>1) and significant GDP multipliers, whereas we find small (<1) or insignificant multipliers. The different results seem to be mostly explained by the treatment of 2009 (when Europe suffered a severe recession): Durand and Espinoza remove it whereas we control for the average depth of the recession in Europe. When we replicate Durand and Espinoza’s all-Europe results, we get a similarly large IV multiplier of about 1.26 (they get 1.21), but this drops to 0.79 when 2009 is included in the sample (see Section 4.1 for a discussion). While both their approach and ours are reasonable, the fact that a multiplier above one hinges on the modeling assumptions of a single year suggests that that finding should be used with caution in policy discussions.<sup>14</sup> Canova and Pappa (2022) also estimate the effect of EU funds on economic growth at the subnational and national levels, using similar adjusted EU-fund data to ours
studying the role of education systems and institutions in determining student performance across countries [Wössmann, 2016]. Many recent papers use PISA data and have looked at this issue through the lens of autonomy [Hanushek et al., 2013, Wössmann et al., 2007], competition [West and Wössmann, 2010], student tracking [Hanushek and Wössmann, 2006, Ruhose and Schwerdt, 2016], external exams [Wössmann, 2005], and instructional time [Lavy, 2015]. Our PISA-based index enables researchers to consider school management in such studies.

Our second contribution—a framework to explain why management matters in schools—relates to the literature in personnel economics exploring incentives and selection. These channels have featured in prior work seeking to explain the performance of private sector employees [Bender et al., 2018, Cornwell et al., 2019, Lazear, 2000], public sector employees [Finan et al., 2017, Prendergast, 2007] and politicians [Besley, 2004, 2006, Gagliarducci and Nannicini, 2013, Martinez-Bravo, 2014]. Most closely related is Lazear [2003], who emphasises the potential selection margin of teacher performance pay, albeit without fully working up a formal model.<sup>3</sup> A selection margin also features in the dynamic occupational model of Rothstein [2015] and the Roy model of Biasi [2019]. We study a wider range of management practices (beyond just performance pay) and provide an intuitive decomposition of the impact of these practices on student learning into incentives and selection.

The remainder of this paper is organized as follows. In Section 2, we set out our approach to measure management practices in schools, illustrating with the construction and validation of PISA-based and Prova Brasil-based management indices. In Section 3, we describe our theoretical framework, its testable predictions, a series of corroborative descriptive analyses from across Latin America, and the policy implications of these results. Section 4 concludes.

> 3See also Dohmen and Falk [
# **1. Introduction**

The Zambian economy has enjoyed rapid economic expansion throughout the last decade, following a long period of low growth and declining per capita income since independence in 1964, with growth cycles driven by international commodity-price fluctuations (World Bank, 2011). After 1996 the economy began to grow again, albeit slowly, as a result of a relatively stable macroeconomic environment and improved macroeconomic policies. The economy grew at a faster pace in the 2000s following the privatization of the mining industry and a return to fiscal discipline and low inflation. However, real GDP per capita in 2010 was still equivalent to just 69% percent of GDP per capita in 1965. In addition, higher recent growth rates have not translated into higher living standards, and Zambia‘s rank in the 2011 UN Human Development Index was 150 out of the 169 countries assessed.

The recent period of sustained economic growth also seems to have had a limited impact on poverty reduction. This is due to the fact that growth has been narrowly focused on capital-intensive industries (especially mining and construction) and on the service sectors (tourism and banking), and has had little impact on employment or wages in the labor-intensive rural economy, where the majority of the poor and very poor are employed (World Bank 2011). However, the moderate reduction in overall poverty observed in the past decade has been accompanied by more substantial improvements in social indicators, though at an uneven pace. There has been continued progress in education indicators, including rising school enrollment rates at the primary and secondary levels, with more modest improvements observed in the youth literacy rate (see Table 1). However, significant challenges still remain, including overcrowded classrooms and limited access to education in rural areas. There have also been some improvements in health outcomes, including a reduction in the under-5 mortality rate and a decline in maternal mortality rates, but Zambia is still not likely to meet the 2015 Millennium Development Goals (MDGs) for the health sector. The prevalence of
resource abundance can contribute to more broad-based development only if well-governed institutions to manage these resources exist (Oechslin 2010). This is borne out by empirical evidence both across countries (Cabrales and Hauk 2011) and within more specific country contexts where resource booms may have fuelled widespread rent-seeking and corruption (Bhattacharyya and Hodler 2010) or even violence (Angrist and Kugler 2008) rather than economic development.

To better understand this phenomenon and its potential impact, an empirical analysis of the factors driving transnational land acquisition is needed. To this end, we constructed a global database with country-level information on both foreign demand for land and implemented projects as documented in international and local press reports. We complement it with country-specific assessments of the amount of potentially suitable land and other relevant variables. We then use bilateral investment relationships from the database to estimate gravity models that can help identify determinants of foreign land acquisition. Results confirm the central role of agro-ecological potential as a pull factor but suggest that, in contrast to what is found for foreign investment more generally, rule of law and good governance have no effect on the number of land-related investment. Moreover, and counter-intuitively, we find that countries where governance of the land sector and tenure security are weak have been most attractive for investors. This finding, which resonates with concerns articulated by parts of civil society, suggests that, to minimize the risk that such investments fail to produce benefits for local populations , the micro-level and project-based approach that has dominated the global debate so far will need to be complemented with an emphasis and determined action to improve land governance, transparency and global monitoring.

The paper is organized as follows. Section 2 puts recent land demand into broader context, highlighting the importance of governance in attracting investments. It draws on an analysis of how foreign direct investment (FDI) is treated in the macro-literature to suggest a methodological approach, and outlines how we address specific data needs. Section 3 presents our cross-sectional data on land demand, outlines the econometric approach, and briefly discusses relevant descriptive
<sup>2</sup> What seems to be the case is that as official requirements become more burdensome, some firms are able to cope much better than others. For example, an increase in official time from 77 to 601 days for construction licenses is associated for some firms with an actual increase of just 3 days whereas for others it meant 130 extra days. The authors also find that for relatively simple regulations there is an association between official and actual time taken. It seems, when the law is simple, people follow it. When it gets complicated, firms find ways around it.

We assess whether corruption as reported by the ES can help explain the difference between official and actual time taken. The ES ask firms for each the three permits and the electricity connection that we look at “whether an informal gift or payment was expected or requested”. For each country we constructed a percentage of firms that answered with “yes”. We then correlated this percentage with the official and actual time required to get the permit or connection. We controlled for country characteristics (GDP level and GDP growth, the level of crime and the ranking on the DB enforcement of contract indicator) and for firm characteristics (ownership - privately or state-owned and proxies for the quality of the firm (share of export in sales and experience of top managers).

> 2 Part of this may be due to the fact that firms asked how much time it takes to obtain an operating license may not have recent actual experience with this.

7
.

Some growth accounting studies for Guatemala also exist. Results differ and no firm conclusions can be drawn. The main discrepancies stem from the assumed or estimated factor shares, distinct time periods and, in particular, from data issues. Most studies rely on international data sets. They make no adjustments for the quality of inputs and are not concerned too much about data problems in the light of the civil strife. In general, studies tend to find that the role of total factor productivity growth was moderate and decreasing for recent periods. (with the exception of Bailén 2001, see for example Bosworth et al. 1996, Edwards 2000, Gregorio 1992, Loayza et al. 2002, Morán and Valle 2002, Nehru and Dhareshwar 1994, Segovia and Lardé 2002, Prera 1999, and World Bank 1996).

Particularly interesting is the work of Sakellariou (1995) who claims to use the Lucas (1988) model of endogenous growth. While analyzing microdata from the 1989 household survey, Sakellariou tests external effects of education on wage differentials. Unfortunately, the study suffers from a limited number of industry categories and human capital variables. Consequently, the regressions turn out to be statistically insignificant and strong conclusions cannot be drawn. However, Sakellariou goes as far as finding that the analysis does not reject the hypothesis that external effects of human capital investment could be present in Guatemala.

# **5.3 Schooling and Growth by Education Level**

Using education data by levels may be preferable for a number of reasons. In particular, the growth impact of different forms of educational capital may vary. Columns 1-6 in Table 6 present the results of the production function augmented for human capital. The education level of the labor force enters separately into the estimation. The share of the labor force with primary, secondary and tertiary education is used here as the relevant unit. It may be argued that _average years of schooling_ by level of education should be used instead of labor force _participation_ . In any case, with the given data
. Higher interest rates and uncertainty would tend to crowd out productivity-enhancing private investment and weigh on output growth. Crowding-out of private investment due to high public debt is documented in Huang, Pagano, and Panizza (2020), and with data for China, and Panizza, Huang, and Varghese (2018), with firm- and industry-level data across 69 countries.

# 4. Understanding debt booms

The costs associated with debt are often amplified by financial crises triggered by rapid debt accumulation as discussed in the previous section. This section presents an empirical analysis of the evolution of macroeconomic outcomes and fiscal positions during rapid government debt accumulation episodes—“debt booms”—over the past five decades. Many countries have been experiencing debt booms over the past decade, making the current global debt wave the largest, fastest, and most synchronized one of the past five decades. We also compare the features of the ongoing debt booms with those of the past episodes.

A debt boom is defined as a period during which the government debt-to-GDP ratio rises from trough to peak of the episode by more than the maximum of ten-year rolling standard deviations during the episode. This identification approach for debt booms follows Kose et al.

12
share). In addition, it is also postulated that non-economic factors, such as geographical location and former colonial relationship, may explain the currency weights.

The size of the currency area formed by an anchor country is calculated by assuming that a G-5 country constitutes its own currency area; by decomposing every non-G-5 country into G-5 currency areas, based on the country's estimated G-5 currency weights; and by aggregating over all decomposed G-5 currency areas. For each developing country, the volume of trade with each G-5 currency area is computed according to this principle, and using bilateral trade data (average exports plus imports for the 1990-96 period) obtained from IMF, Direction of Trade Statistics."<sup>3</sup> In explaining the estimated G-5 currency weights, we have used a set of dummy variables that represent the country's geographical location and the past (or present) colonial status. First, 5 regional dummies are introduced: Africa, East Asia, Europe, the Middle East, and Latin America. South Asia is the remaining regional dummy that is excluded to avoid linear dependence in regressor variables. Second, 3 colonial dummies are introduced: the French colony, the UK colony, and the former Soviet Union Republic dummy. Only when a country was a French or UK colony in year 1950 or was part of the forner Soviet Union at the time of its breakup, is the colonial dummy used.'<sup>4</sup> " For example, the estimated G-5 currency weights in the basket for the Thai baht are 0.82 for the US dollar (USD), 0.11 for the Japanese yen (JY), 0.05 for the Deutsche mark (DM), and 0.02 for the UK pound sterling (UKP). This means that 82 percent of Thailand belongs to the USD area, 11 percent to the JY area, 5 percent to the DM area, and 2 percent to the UKP area. Hence, any country that
) (see Table 3 bottom panel). Since the CES 2014/15 does not include actual consumption data, we rely on the estimates by Newhouse and Vyas (2019). In our prediction, we use the Health SCS 2014/15 applied to a consumption model estimated over CES 2011/12. In this comparison, we thus compare predictions across different surveys, while the earlier backcasts compared actual and predicted poverty in the same survey (various rounds of the CES). The 2014/15 CES and SCS, both official nationally representative surveys, show broadly similar socio-economic indicators (Tables 2 and 3). Across all four models, our national poverty estimates (between 16.8 and 20.93 percent) are consistently higher than the estimates from Newhouse and Vyas (2019) (14.6 percent), although the confidence intervals for our estimates in models 2 and 4 would include Newhouse and Vyas’ estimates. These differences are mostly driven by rural areas, while for urban areas models 1, 2 and 4 are not very different from Newhouse and Vyas (2019). There is thus an interesting contrast between the three validation tests: While our model underpredicts poverty in 2004/05 and 2009/10, it overpredicts in 2014/15. Again, this is likely because coefficients estimated using 2011 data are applied to earlier data, while in reality the coefficients may vary over time.

> 15 For an overview of these kinds of validation methods, see James et al. (2013). Similarly, microsimulation exercises are validated by “back-casting” reforms that occurred in the past, for example see Figari et al. (2015).

10
_T = c hi/a (_ **_1/i)_** Thus, we have an adjustment cost function which is convex in the hiring rate, h, but also depends on the employment level, _1._ This dependence of the "training and hiring costs" on the employment level is the only difference to the previous model formulation. It is, however, straightforward to incorporate the more general adjustment cost function into the efficiency wage model resulting into three steady state equations in the three unknowns _w_ * ,p _*_ , *. We plan to conduct quantitative policy analysis for such an extended model with a general matching technology in the future. The efficiency wage model developed in the last sections can be thought of as the special case in which a = 1 since then we have T = _c h,_ that is, an adjustment cost function which only depends on the hiring rate. Incidentally, this function is linear, a property we assume in the quantitative section 4.

# **3. Microeconomic Data Analysis**

This section uses micro-economic data from Mexico to estimate the quit-rate function and to test the model's predictions about the determinants of quitting behavior. The quit-rate function estimated here is used in Section 4 for macroeconomic policy analysis.

# _a) Data Description_

The National Urban Employment Survey (ENEU) conducts extensive quarterly household interviews in the 16 major metropolitan areas and is available from 1987 to 1993. The sample is selected to be geographically and socio-economically representative. The statistical agency (INEGI) expanded it significantly over the period by adding municipalities, however, we include only those present in every year of the survey to prevent changes in composition. The questionnaire is extensive in its coverage of participation in the labor market, wages, hours worked, etc. that are traditionally found in such employment surveys. INEGI's treatment of sample design, collection, and data cleaning is careful. Surveys and documentation of methodology are available on request.

The ENEU is structured