rafmacalaba/datause-encoder-data huggingface dataset:
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_

### **2. Indices of the Real Exchange Rate**

First we present definitions of indices measuring the real exchange rate. Then we present four indices, and compare them to each other.

#### **Defining the Real Exchange Rate**

We relied upon Sebastian Edward’s definitions<sup>1</sup> of the real effective exchange rate (REER). The concept requires a trade partner. So, we have bilateral rates (BREER) between two specific trading partners, and the multilateral real effective exchange rate (MREER), which includes a representative set of trade partners.

Starting with a “purchasing power parity” (PPP) measure of the real exchange rate, built using consumer price indices (CPI), for the bilateral real effective exchange rate in time period t we have:

## f (1) BREERt = (Et x CPIt )/CPIt

##### d

Where Et is the number of CFA or local currency units per unit of foreign currency, the foreign CPI appears in the numerator, and the CPI for Cote d’Ivoire (the local economy) appears in the denominator.

The multilateral REER is a weighted average of bilateral rates. It is built using trade weights, α it, which sum to unity in time period t, obtained by in our case from IMF _Direction of Trade Statistics_ . In time period t, for i = 1 to n “representative” trading partners, we have:

# i i d (2) MREERt  = Σ i [α it (Et<sup>x CPI</sup> t )/CPIt ]

Edward’s formulation of the REER and the MREER differs from that commonly used by the International Monetary Fund, and we apologize for any confusion. The IMF measure is essentially the inverse of the measure used here. Also, the IMF uses geometric rather than arithmetic weighting when calculating the MREER.

#### **Indices Of The Real Exchange Rate**

We looked at the bilateral real exchange rate between the Cote d’Ivoire and France. France is the former ruling colonial power and was historically Cote d’Ivoire’s largest trading partner; the CFA franc was pegged to the French Franc. For the multilateral real effective exchange rate, we selected twelve of the largest trading partners in recent years, ensuring that we had eight of the top import origins and eight of the top export destinations across a span of recent years. Rather than using a single year upon which to

> 1 See Edwards 1988.

2
advanced technologies -- for fear that they will be imitated by local rivals. The existence of stronger IPR could induce multinationals to deploy these technologies because they now have a legal remedy against imitation. Using highly disaggregated data on international patenting available from the European Patent Office, we can actually track the filing of patent applications in sensitive, highly advanced technological areas. While data on royalty payments provide us, at least in principle, with measures of the economic value of technology deployed by affiliates, highly disaggregated patent data allow us, in principle, to track the technological content of the underlying technology flows.

The international economics literature has established that changes in the IPR environment will affect the trade-off between licensing, local production, and exports. It is possible to embed IPR regime changes within a larger model in which the level of investment in a country and exports from production facilities located outside the country are also endogenously determined. The imposition of more structure holds the promise of providing further insight on the global welfare impact of recent changes in IPR systems. Recent contributions to the theory of the multinational firm provide us with a useful foundation on which to build.<sup>39</sup>

> 39  See Markusen (2000) and, for a treatment that combines empirics and theory, Carr, Markusen, and Maskus (2001).

28
Bouma, M.J. and H.J. Kaay, 1996. The EI Niño Southern Oscillation and the Historic Malaria Epidemics on the Indian Subcontinent and Sri Lanka: An Early Warning System for Future Epidemics? Tropical Medicine & International Health 1:86–96.

- Braun, L.N., W. Grabs, and B. Rana, 1993. Application of a Conceptual Precipitation-Runoff Model in the Langtang Kfaola Basin, Nepal Himalaya. Proceedings of the Kathmandu Symposium, November 1992. IAHS publ. no. 218. pp. 221-237.

- Brohan, P., J.J. Kennedy, I. Harris, S.F.B. Tett, and P.D. Jones, 2006. Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850. Journal of Geophysical Research 111: D12106, doi:10.1029/2005JD006548.

- Bookhagen, B., 2010. Appearance of extreme monsoonal rainfall events and their impact on erosion in the Himalaya. Geomatics, Natural Hazards and Risk, 1(1), 37-50.

- Bush, K.F., H. Frumkin, S. Rani Kotha, R.C. Dhiman, J. Eisenberg, D. Sur, R. Rood, S. Batterman, A. Joseph, and C. Gronlund, 2011. The Impact of Climate Change on Public Health in India: Future Research Directions. Epidemiology 22: S21,

doi:0.1097/01.ede.0000391722.16123.9b.

- Caesar, J., L.V. Alexander, B. Trewin, K. Tse ring, L. Sorany, V. Vuniyayawa, N. Keosavang, A. Shimana, M.M. Htay, and J. Karmacharya, 2011. Changes in Temperature and Precipitation Extremes over the Indo Pacific Region from 1971 to 2005. International Journal of Climatology 31: 791-801.

- Campbell, A., V. Kapos, J.P.W. Scharlemann, P. Bubb, A. Chenery, L. Coad, B. Dickson, N.

- Doswald, M.S.I. Khan, and F. Kershaw, 2009. Review of the Literature on the Links Between Biodiversity and Climate Change: Impacts, Adaptation and Mitigation.

- Secretariat of the Convention on Biological Diversity, Montreal. Technical Series. by the Secretariat of the Convention on, p. 124.

- Central Electricity Authority (CEA), 2011. Status of Hydroelectric Potential Development.

- Available at: http://www.cea.nic.in/reports/hydro/he_potentialstatus_region.pdf. Accessed 12 June 2011.

- Challinor, A.J., F. Ewert, S. Arnold, E. Simelton, and E. Fraser, 2009. Crops and Climate Change: Progress, Trends, and Challenges in Simulating Impacts and Informing Adaptation. Journal of Experimental Botany 60: 2775-2789.

30
Policy Research Working Paper 8402

# **Abstract**

Returns to investment in education based on human capital theory have been estimated systematically since the 1950s. In the 60-plus year history of such estimates, there have been several compilations in the literature. This paper reviews and highlights the latest trends and patterns based on a database of 1,120 estimates in 139 countries. The review shows that the private average global rate of return to one extra year of schooling is about 9 percent a year and very stable over decades. Private returns to higher education have increased over time, raising issues of financing and equity. Social returns to schooling remain high, above 10 percent at the secondary and higher education levels. Women continue to experience higher average rates of return to schooling, showing that girls’ education remains a priority. Returns are higher in low-income countries. Those employed in the private sector of the economy enjoy higher returns than those in the public sector, lending support to the productive value of education.

This paper is a product of the Education 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://econ.worldbank.org. The authors may be contacted at hpatrinos@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 Research Support Team
Table 7: Comparison with other studies

|**(1) Marginal impact of**<br>**expected public debt on**|basis point<br>per 1 percent|Forecast data|Countries/Group||
|---|---|---|---|---|
|**government bondyields**|||||
|This study|0.9-2.0(the share of foreign investors is 20 percent)<br>2.8-3.8(the share of foreign investors is 30 percent)<br>4.2-6.2(the share of foreign investors is 40 percent)|IMF, OECD, EC, EIU|11 AEs and 14 EMs||
|Engen and Hubbard(2005)|2.8|CBO|U.S||
|Laubach(2009)|3 to 4.4|CBO|U.S||
|Traum and Yang(2015)|3|DEGE implied data|U.S||
|Arslanalp and Poghosyan(2016)|2.7 to 2.8|IMF|22 AEs||
|Gruber and Kamin(2012)|0.4 to 1.3|OECD|19 OECD countries||
|Ichiue and Shimizu(2015)|0.7 to 0.9|OECD|10 OECD countries||
|Jaramillo and Weber (2013)|4 to 6|EIU|26 EMs||
|**(2) Marginal impact of**|basis point|Forecast data|Countries|Investor type|
|**the share of foreign investors**|per 1 percent||||
|**ongovernment bondyields**|||||
|This study|-11.8 to -8.2|IMF, OECD, EC, EIU|11 AEs and 14 EMs|Foreign Private|
|This study|-17.9 to -14.5|IMF, OECD, EC, EIU|11 AEs and 14 EMs|Foreign Offcial|
|Arslanalp and Poghosyan(2016)|-11.5 to -6.9|IMF|22 AEs|Foreign Private|
|Arslanalp and Poghosyan(2016)|-6.9 to -5.5|IMF|22 AEs|Foreign Offcial|
|Ebeke and Lu (2015)|-10.6 to -6.5|Actual data|13 EMs|Foreign|
|**(3) Estimated tipping points**|Threshold value of debt|Forecast data|Countries|Investor type|
|**for the impact of the increase in**|to GDP ratio||||
|**foreign investors**|||||
|This study (Baseline)|59 percent (Expected gross public debt)<br>when the share of foreign investors is 30 percent<br>91 percent (Expected gross public debt)|IMF, OECD, EC, EIU<br>IMF, OECD, EC, EIU|11 AEs and 14 EMs<br>11 AEs and 14 EMs|Foreign Private<br>Foreign Private|
||when the share of foreign investors is 15 percent||||
|Ebeke and Lu(2015)|90 percent (Lagged external debt)|Actual data|13 EMs|Foreign|
|Ebeke and Lu (2015)|21.5percent(Lagged short-term debt)|Actual data|13 EMs|Foreign|
|Notes: AEs=advanced economies<br>and EMs=emerging markets.|||||
the estimates. In terms of the proposed QLME method, both the markup and productivity effects are estimated unbiasedly. Note that the estimator is asymptotically consistent in the numerical estimation. For small _T_ and small _N_ the variance in the estimator increases. In addition, the estimator is able to pick up the production function specification - _γ_ is much larger than zero.

<u>1</u> Table 3 summarizes the results when the elasticity of substitution is _σ_<sup>_CES_</sup> = 1 _−ρ_<sup>=2.</sup> In this case, the ACF and DLW methods are unable to estimate the true coefficient of regulation on markups and productivity, respectively. In addition, the presence of correlation between error terms worsens the DLW significantly for markups. In contrast, the QLME method is able to pick up the true coefficient in the majority of cases. The small sample problem in _T_ makes inference hard in all cases. _T_ = 5 is deliberately chosen to be small to test the strength and weaknesses of the results. The small _T_ makes inference hard and thus researchers should be cautious when interpreting results on small firm-level panels.

In addition to estimating the impact of regulation on productivity and markups, researchers are typically interested to compare market power between industries, either by using the markup in levels or its dispersion within firms in a given sector. Because of the absence of price information and other problems related to endogeneity as discussed in section 2.2, we expect that any estimate on the markup level would be biased (see Bond et al. (2020)). Figure 1 presents the simulated markup distribution derived using a Cobb-Douglas production function. We compare it with the distribution of the estimated markups using the DLW and the QMLE methods. The results suggest that none of the methods can recover the precise level of markups. The DLW method is biased upwards relative to the QMLE method. Research that attempts to explain how the markup level changes over time using sales revenue data will be inaccurate. However, the distribution is similar when centered. With enough variation in the data, one would be able to estimate the impact of regulation on the change in markups, implying that policy inference is still possible and hence represents a work-around to the problem raised in Bond et al. (2020).

25
- 23 -

and address of the property; (ii) the name and address of the owner or claimant; (iii) data on the physical characteristics of the lot (approximate dimensions, steepness, susceptibility to flooding); and (iv) data on the current use and physical characteristics of the structures (approximate exterior dimensions, and details of external and interior building materials, plumbing and wiring).

# Calculation of Unit Costs

4.17 New unit cost tables for land and buildings are then calculated. As is common in Brazil, estimates of the unit value of land are determined for different neighborhoods by a committee, appointed by the mayor, of landowners and real estate agents. These committees do not use evidence from actual market transaction data in any systematic way, but instead rely on their general knowledge of the local real estate market. Unit costs for buildings are based on data published in the Brazilian trade journal _"Revlsta de Construcao,"_ as modified by the CIATA project supervisor. Depreciation tables are also prepared by the project supervisor.

# Preparation of Assessment Roll

4.18 Individual property valuations are calculated by applying the unit cost tables to the characteristics of each property. A trial assessment roll is then prepared for the consideration of the mayor. The mayor may, at this time, modify either the unit cost tables or individual valuations as he deems appropriate. (Mayors generally exercise this option.) Once the mayor is satisfied, a final assessment roll is prepared, listing properties by identification number.

# Printing of bills

4.19 Based on the assessment roll approved by the mayor, the nominal tax rate and any subsequent inflation adjustments authorized by the municipality, individual property tax bills are then printed and turned over to the municipal treasurer for delivery. Where records are processed manually or in batch, CIATA will also produce a tax roll with space to record payments against each account.

# C. Duration. Technology and Scope

4.20 CIATA administrators estimate the duration of a typical project, from mobilization to the printing of bills--at about five mouths--in a municipality of 5,000 property units. In a municipality of 3,000, a project normally takes three months. At the peak of data gathering, survey teams working in pairs average 15 property units per day. The scope and duration of individual CIATA projects will, however, vary according to preproject conditions. If existing reference maps are ade.quate, new ones will not be prepared. If existing property records are reliable, the data-gather phase will be relatively short.

4.21 CIATA's use of computers has evolved with changes in computer technology. Early CIATA projects were designed to be administered manually, or in batch on mainframe computers at SERPRO's central computer facilities.
Under our most conservative estimate, where the marginal cost of finance is regressed on lagged values of the explanatory variables, a 1 p.p increase in government shareholding reduces SOEs’ cost of finance by 0.01 percent. Additionally, in line with previous empirical evidence for Europe (Cusolito and Didier, 2020), we find that large and mature firms face a lower cost of finance than small and young ones.

An important feature of our empirical results is the positive elasticity between a firm’s physical productivity and idiosyncratic financial distortion. Such a property dictates that the nature of the underlying financial distortion is not just randomly creating dispersion in marginal returns to debt and equity but misallocating financial resources away from productive firms and into less productive ones. This is also a pervasive feature of idiosyncratic distortions in the real-misallocation literature and is particularly detrimental to Total Factor Productivity.<sup>14</sup> Table 4: Financial Premium (Tax) of Bureaucrats in Business

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||(OLS)|(OLS)|(OLS)|(OLS)|(OLS)|(IV)|
|State Ownership|-0.0004***<br>(0.0001)|-0.0004***<br>(0.0001)|-0.0004***<br>(0.0001)|-0.0006***<br>(0.0001)|-0.0008***<br>(0.0000)|-0.0001**<br>(0.0001)|
|(Publicly Listed=1) x State Ownership||-0.0004|-0.0004|-0.0002|-0.0008**|0.0014**|
|||(0.0006)|(0.0006)|(0.0007)|(0.0004)|(0.0007)|
|Age|||-0.0378***<br>(0.0090)|-0.0594***<br>(0.0099)|0.0128***<br>(0.0035)|-0.0210**<br>(0.0098)|
|Log(Total Assets)||||-0.3284***<br>(0.0009)|-0.5358***<br>(0.0003)|-0.1577***<br>(0.0011)|
|Log(TFPQ)|||||0.4019***<br>(0.0001)|0.0638***<br>(0.0005)|
|Observations|5896499|5896499|5896499|5896499|5896499|3917199|
|Country fxed efects|Y|Y|Y|Y|Y|Y|
|Firm fxed efects|Y|Y|Y|Y|Y|Y|
|Industry-time fxed efects|Y|Y|Y|Y|Y|Y|

Standard errors in parentheses. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 _._ While we highlight the estimates from the specification with lagged explanatory variables, it is worth noting that the estimates for contemporaneous regressors are statistically significant and notably higher in magnitude than the preferred specification. According to the proposed finance allocation theory, firms’ current characteristics determine their

> 14As shown in Hsieh and Klenow (2014), the productivity-dependence of idiosyncratic distortions discourage firms’ investments in innovation, magnifying the detrimental effect of these distortions on aggregate productivity.

21
# 1 Introduction

Increasing domestic and international mobility has magnied the devastating consequences of infectious diseases: more than 11,000 deaths from Ebola, 440,000-1,300,000 cases of Zika infections in 13 countries, and most recently, more than 125,048 infections and 4,613 deaths from COVID-19<sup>1</sup> across 117 countries ( Bogoch et al. 2016, WHO 2020a, WHO 2020b). Negative externalities from mobility are also relevant for long-standing diseases that we are aiming to eliminate. For example, Venezuela, the rst country certied by the World Health Organization (WHO) for eliminating malaria in its most populated areas in 1961, experienced a dramatic resurgence of the disease in 2016 in part due to migrant workers in the mining region becoming sick, traveling home, and spreading the disease to their home villages and cities (Casey 2016). This paper uses a case study of malaria in Senegal to demonstrate how to harness big data to causally estimate the size of this externality of movement and apply the results towards more eective policy targeting. The methods can be applied more broadly to inform policies related to mitigating spread of infectious diseases.

The economics literature identies malaria eradication as having important impacts on adult income and consumption (Bleakley 2010, Cutler et al. 2010, Venkataramani 2012), real estate wealth (Hong 2011), longer term health including chronic disease and disability (Hong 2013), test scores and educational attainment (Barofsky, Anekwe, and Chase 2015, Lucas 2010, Venkataramani 2012).<sup>2</sup> Ninety-nine countries have been certied by the WHO as malaria free; however, Sub-Saharan Africa, which accounted for 93% of all malaria deaths in 2018, has only had a single successful case of elimination (World Malaria Report 2019). While previous work has studied malaria prevention/treatment in short-term settings, focusing on the pricing of malaria interventions (Jessica Cohen and Dupas 2010, Jessica Cohen, Dupas, and Schaner 2015, Dupas 2014, Laxminarayan et al. 2010, Tarozzi et al. 2014) as well as the adoption of preventative or anti-malarial treatment (Adhvaryu 2014, Apouey, Picone, and

> 1Data as of March 12, 2020

> 2See Currie and Vogl (2013) and Apouey, Picone, and Wilde (2018) for summaries of the literature.

1
results in Columns 1 – 3, and results with recipient fixed effects in Columns 4 – 7. All regressions include year fixed effects. A causal interpretation of this result suggests that a policy improvement leads to additional aid. It is important to emphasize that all the regressions control for a one-year lag in the aid level, which means that our coefficient on policy captures _additional_ aid that a recipient receives from a policy improvement. Some regressions include a squared term, which is also statistically significant, implying a non-linear inverted U-shape relationship. The FE results estimate a turning point at a CPIA of 4.4. Only a very few countries in our sample have a CPIA measure that is larger than 4.4. The CPIA at the 95th percentile equals 4.38. Notice also that our fixed effect regression suggests that democratization is associated with more aid. Our democracy measure is taken from Acemoglu, Naidu, Restrepo, and Robinson (forthcoming) and updated in accordance with their methodology. Table 2 repeats the analysis for the years 1985 to 2014 using data from the PennWorld Tables for GDP and population, which cover a longer time-span. We obtain a very similar result.

We consider the results presented in Tables 2 and 3 as evidence that the “contest for aid” works. A recipient improving policies attracts a larger amount of aid. Figure 2 shows the partial point estimate for the CPIA conditional on the level of the CPIA. This figure reveals that there is a statistically significant relationship between the CPIA and the amount of aid given to a recipient for countries that have a CPIA rating below 2.7. Thus, incentives seem to work particularly for countries with low policy levels. Quantitatively, the estimates suggest that a CPIA measure change from 1.5 to 2 for a recipient is associated with about a 13% increase in aid.

With these findings, the question arises whether the “contest for aid” improves policies in recipient countries as our model suggests. Clearly, if improving policies is associated with more aid and if recipients are interested in attracting more aid, then this contest for aid should provide recipients with an incentive to improve policies. The incentive effect of policy-selective aid is a challenging notion to investigate empirically, however, as the relationship between policy-selective aid and policy includes both selection effects – as seen in Tables 2 and 3 – and incentive effects.

To identify the incentive effects we make use of our game theoretic model that suggests it is the policy-selectivity of the global aid budget that affects incentives and not the aid budget received by a given recipient. The model shows that if the budget of the policy-selective donor increases, then incentives to improve policies strengthen to the extent that a larger budget makes it individually rational 15