rafmacalaba/datause-encoder-data huggingface dataset:
three randomly drawn hypothetical electricity consumption levels, which were identical in each survey. The three levels roughly correspond to SE4All’s multi‐tier access definition<sup>9</sup> and could be provided by different electricity access technologies. The three scenarios are displayed in Table 4.

Respondents were asked a dichotomous choice question (“Are you willing to pay each month amount X?”) followed by a bidding game in order to elicit the highest accepted price. In order to reduce the hypothetical bias, respondents were reminded of their budget constraint and were told that their response would in no way influence the price at which the electricity service would actually be offered.

**Figure 2: WTP bidding sequence**

<!-- Start of picture text --> How much?<br>+2,000 yes<br> ̈ ____________<br>+1,500 ̈ yes ________ RWF RWF<br>+1,000 ̈ yes ________ RWF no no [STOP]<br>yes ________ no [STOP]<br>Initial priceHarga awal RWF<br>[STOP]<br>-1,000<br>_______ RWF no ________ RWF ̈ yes -1,500 ̈ yes [STOP]<br>no<br>________ RWF no -2,000 ̈ yes [STOP]<br>________<br>RWF How much?<br>no<br>____________<br>RWF<br><!-- End of picture text --> _Note: 1000 RWF=0.17 USD_ _Source:_ Rwanda (2011) household questionnaire The bidding sequence is illustrated in Figure 2, which is taken from the original questionnaire employed in Rwanda.<sup>10</sup> The full question can be found in the Appendix.

We use three alternative initial price bids in order to test for anchoring effects. The starting price was randomly drawn for each respondent. The starting prices were

> 9 Unfortunately, the consumption levels do not exactly mirror the different SE4All access tiers because they had not yet been defined at the time of the data collection in 2010 and 2011.

> 10 In the Senegal survey, the same incremental amounts were used, albeit with different starting bids and, of course, a different currency. In the Burkina Faso baseline survey, the incremental amounts were +/1,000 CFAF, +/- 2,000 CFAF, +/- 4,000 CFAF.

18
# **Does Higher Openness Cause More Real Exchange Rate Volatility?**<sup>*****</sup>

## **César Calderón**<sup>a</sup> , **Megumi Kubota**<sup>b,**</sup>

> a _The World Bank, USA_

> b _Department of Economics, University of York, UK_

**First Version:** March 2007 **This Version:** March 2008

**JEL Classification:** F31, F41, C23

**Key Words:** Real Exchange Rate Volatility, Openness, Panel Data

> * We are grateful to Eduardo Cavallo, Menzie Chinn, Maurizio Habib, Miguel Leon-Ledesma, Gian-Maria Milesi-Ferretti, Peter N. Smith and Michael Wickens as well as participants in the Second Open Macroeconomy and Development Conference at the Université de la Mediteranée and the 2008 Royal Economic Society Annual Conference for their comments and suggestions. This paper initially circulated with the title “Are real exchange rate fluctuations more volatile in more open economies?” The usual disclaimer applies. The views expressed in this paper are those of the author, and do not necessarily reflect those of the World Bank or its Boards of Directors.

> ** Corresponding author. University of York, Department of Economics, Heslington, York YO 10 5DD, UK Tel. + +44 (0) 7940389164; fax: +44 (0)1904 433759. E-mail address: <u>mk540@york.ac.uk</u> (M. Kubota)
compilation of Household Budget Surveys. Eurostat publishes consumption means from household budget surveys, which are available in five-year intervals between 1988 and 2015. The Eurostat compilation provides consumption data for all 28 EU Member States and also for Montenegro, the Former Yugoslav of Republic of Macedonia, Turkey and Norway.<sup>7</sup> For the United States, we supplement our database with annual data from 1981-2015 from the Consumer Expenditure Survey.<sup>8</sup> To facilitate comparisons with per capita national accounts data, we express all survey values (consumption and income) in annualized per capita basis. In cases where household survey consumption data are only available at the household level or in per adult equivalent terms, we adjust them to express in per capita terms. By converting all survey means to per capita terms, we align survey means with national accounts data.

Our comparisons of the gaps in levels in Section 3 are based on converting all data to local currency units. To accomplish this, we combine per capita mean consumption or income (expressed in 2011 PPP values) from each national survey with (i) inflation measures, (ii) currency conversion rates (in the case of currency devaluations and change of national currency) and (iii) the PPP values from PovcalNet that were used to convert the national survey data into 2011 PPP US dollars.<sup>9</sup> For the analysis in Section 4, where we examine how potential adjustments to account for the gaps between household surveys and national accounts affects global poverty and inequality measurement, we extract detailed information on the Lorenz curves for each welfare vector from PovcalNet. We are then able to construct national and global

> 7 We obtain the Eurostat consumption data from the Eurostat Household Budget Survey Database available at <u>https://ec.europa.eu/eurostat/web/household-budget-surveys/database</u> (accessed on November 5, 2020). Eurostat provides survey means per adult equivalent in constant currency. We use available data about household structure from the corresponding surveys, as well as deflators and exchange rates to recover per capita means in current local currency units. With this data we can also recover estimates in 2011 PPPs, using the methods applied in PovcalNet.

> 8 <u>https://www.bls.gov/cex/, accessed on March 28, 2017.</u>

> 9 The data from PovcalNet used for this version of the paper was accessed March 28, 2021. The ancillary data on deflators and exchange dates is available at <u>http://iresearch.worldbank.org/PovcalNet/data.aspx.</u> PovcalNet has data for 168 economies, of which Argentina and Taiwan are not included in our compilation due to partial national geographic representativeness of the surveys.

7
insignificance. Among the districts, misperception appears to be most prevalent in _Comilla_ , and significantly lower in _Munshiganj_ .

The third column of Table 2 presents the determinants of the farmers’ _overuse_ of pesticides. Unfortunately, misperception did not yield the expected sign, however, it was not a significant determinant of overuse.<sup>11</sup> Among the individual characteristics, _income_ and _ownership_ influence the propensity to overuse. For pesticide toxicity class, farmers with higher proportions of _highly hazardous_ (WHO Ia & Ib) pesticides are less likely to overuse, and farmers with higher proportions of _moderately hazardous_ (WHO II) _pesticides_ are more likely to overuse. Neither training in safe-handling nor use of IPM techniques has the expected effect. However, the application of pesticides revealed strong crop patterns. The incidence of overuse is less likely in the case of _potato_ , _rice_ , _cabbage_ , _sugarcane_ and _mango_ as compared to _eggplant_ and _beans_ . In addition, even after controlling for individual circumstances and crops, we find a strong locational pattern. Overuse is more prevalent in the districts of _Chapainawabganj, Chittagong, Comilla, Jessore, Narshingdi, Rajshahi_ and _Rangpur_ than in _Bogra_ , _Mymensingh_ , _Munshiganj_ and _Kishoreganj_ (excluded).

# **6.  Summary and Conclusions**

Empirical evidence on pesticide application in developing countries is rather thin. Drawing on a new survey conducted in Bangladesh, this paper is an attempt to address this gap. In our survey, 47% of the farmers were found to have overused pesticides, with an average overuse rate of 3.4 kg per growing season. To model pesticide overuse, we used a trivariate probit framework with health effects and misperception of pesticide risk as endogenous dummy variables.

The results, to some extent, are consistent with our expectations. The variable found to be significantly associated with the probability of a farmers’ health impairment from pesticides is: _amount of pesticide applied_ . Among the determinants of probability of

> 11 One possible explanation could be that with prolonged use over time, pesticide resistance necessitates greater use to achieve similar outcomes.

14
Orissa, for example, score higher than the median student in more than 42 of 46 countries tested in 2003.

Faced with similar results on learning, defenders of the quality of education in Indian schools often point to the large number of globally competitive Indians. We perform the following thought experiment: Suppose that these two states represent India (more on this below). Could the country’s size combined with the large variance in scores explain how divergent beliefs can be sustained by the same data? As it turns out, in absolute terms, India has just under half the number of 14-year olds who pass the advanced international benchmark as the United States—100 thousand compared to 250 thousand—and roughly the same number who pass the intermediate international benchmark. Indeed, India has more top achievers than any European country tested, which, although not surprising given India’s size, helps explain India’s visible position on the academic stage. But another view is also sustainable. The average child scores far below any reasonable curricular standard and a large minority in these two states fails completely. If the results form these two states hold more generally, over 18 million 14-year olds in India are either not enrolled or are failing the lowest international benchmark if enrolled. That number is 22 times the number of failing children in the United States and more than any other country tested.

Beyond providing illustrative results for India, this paper is about the building blocks for research on learning and learning inequality in low-income countries where data on internationally comparable tests are typically absent. This requires 1) techniques to place individual students on a single comparable achievement metric and 2) methods to calculate other population quantities, such as the fraction of children passing particular criterion-referenced thresholds or the 5th to 95th percentile achievement spread. Clarifying what is required for comparable measures of learning and learning dispersion allows the research to focus on substantive rather than statistical issues, without worrying about whether results are driven by measurement tools and differing methodologies.

To preview the methodology, independent tests can be linked to the TIMSS achievement distribution provided at least one question is drawn from the TIMSS item bank to fix the free parameters. The primary methodological difficulty arises because “knowledge” or “achievement” is inferred from the data rather than directly observed. Since individual knowledge is measured 3
Chart 3 Characteristics of Rate Cycles: January 1970–May 2024 <!-- Start of picture text --> Duration Number of Rate Changes<br>150 30<br>100 20<br>50 10<br>0 0<br>Tightening Easing Tightening Easing<br>Average Pace Initial Velocity<br>4 3<br>2 0<br>0 -3<br>-2 -6<br>Tightening Easing Tightening Easing<br>Amplitude (total rate change)<br>16<br>8<br>Range (10 - 90 percentile)<br>0 Mean<br>Median<br>-8<br>-16<br>Tightening Easing<br>Months<br>Rate change (in pp)<br>Number<br>Rate change over first 6 months (in pp)<br>Total rate change (in pp)<br><!-- End of picture text --> Sources: Calculated based on the rate cycles defined in the text, with data from January 1970 through May 2024. Notes: The number and pace of rate adjustments only include "in-sync" rate adjustments, i.e., the rate increases for tightening phases and decreases for easing phases. Initial velocity and amplitude are the total changes in rates (in any direction) over the first six months of the phase or the entire phase, respectively. Members of the euro area are included as individual countries through 1998, and then the euro area is included from 1999.

A closer look at the statistics other than the means and medians, as well as individual economy information in Table 1, provide additional insights on these patterns. Although the durations of the shortest tightening and easing phase are similar (around 25 months), the longest easing phase (at 156 months for Japan) is much longer than the longest tightening phase (at 98 months for Spain). The fastest initial velocity for easing phases (rate declines of 25 percentage points in Ireland) is four times larger than that for tightening phases (6 percentage points in Greece)—and both are substantially faster than for other economies, partly reflecting higher inflation and nominal policy rates in these countries at the start of the sample. Tightening phases have a higher maximum amplitude, number of rate adjustments, and average pace than easing phases.

16
<!-- Start of picture text --> Figure 3: Comparisons of Distributions of Real Wages by groups, Costa Rica _1989 --1995<br>age < 25 yrs. primary blue collar<br>Cu. Cu C<br>CO .0 CD<br>0 2 4 6 8 0 2 4 6 8 0 2 4 6 8<br>logwage logwage logwage<br>25=< age <45 secondary white collar<br>.0 .D<br>Cu Cu C<br>~0 2 6 80 2. 6 80 2 4 6 8<br>0 2 4 6 8 0 2 4 6 8 0 2 4 6 8<br>logwage logwage logwage<br>age >=45 higher education professionals<br>.0 .0<br>.0 .0.<br>0L ... 0.<br>0 2 4 6 0 2 4 6 80 . 2<br>logwage logwage logwage<br><!-- End of picture text --> _35_
<!-- Start of picture text --> Figure 2.2 Total installed cost for utility-scale solar PV projects and the global weighted average,<br>2010-2018<br>YO. 000<br>9.000 ——g $s SSsSsSsSsSsSSSS<br>8000 : a<br>————= ———__@______,<br>Ct a a<br>~ 6000 Wu) (%) A n .<br>B ¢ i) 4 i & : ‘<br>eo o ¢) _§ @ @<br>oy i ' oe 2 6<br>5 percent (7 2569 i gs fe) Wen HN<br>ww Q )) hol 2323 f, an | ene<br>1000 ; 8 Us Ne) Rey 1309 ig<br>mms Weighted average ey > uy \y wys =<br>9 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Capacity MW “1 @10 = @w 6 >300<br><!-- End of picture text -->
**Table S8. Sensitivity analyses considering transfers across the study period.** Effect of the IST intervention at 12 and 24 months follow-up on health outcomes for study children. Results presented (i) for all children with either 12 or 24 months follow-up measurements of the outcome (unadjusted) with children who transferred schools excluded and (ii) for those with baseline measurements of each outcome and accounting for age, sex and stratification effects as the primary pre-specified analysis with children who transferred schools excluded.

|**Outcome**|**(5**|**Control**<br>**0 schools)**<br>**n (%)**<sup>**a**</sup>|**In**<br>**(5**|**tervention**<br>**1 schools)**<br>**n (%)**<sup>**a**</sup>|**Risk ratio**<sup>**b**</sup><br>**(95% CI)**|**p-value**|**Cluster-size;**<br>**range (average)**|
|---|---|---|---|---|---|---|---|
|**12 month follow-up**|N=2439||N=2574|||||
|**Prevalence of anemia**<sup>**c**</sup>||||||||
|Unadjusted|2117|827 (39.1%)|2255|906 (40.2%)|1.03 (0.91,1.16)|0.640|15-54 (43.3)|
|Adjusted|2023|780 (38.6%)|2106|847 (40.2%)|1.02 (0.93,1.13)|0.670|15-54 (40.9)|
|**Prevalence of****_P.falciparum_**||||||||
|Unadjusted|2078|300 (14.4%)|2235|243 (10.9%)|0.76 (0.49,1.19)|0.234|11-54 (42.7)|
|Adjusted<sup>d</sup>|2078|300 (14.4%)|2235|243 (10.9%)|0.72 (0.46,1.11)|0.139|11-54 (42.7)|
|**24 months follow-up**|N=2362||N=2417|||||
|**Prevalence of anemia**<sup>**c**</sup>||||||||
|Unadjusted|1929|770 (39.9%)|1999|843 (42.2%)|1.06 (0.91,1.22)|0.463|15-52 (39.3)|
|Adjusted|1845|728 (39.5%)|1862|780 (41.9%)|1.01 (0.90,1.12)|0.920|14-52 (37.1)|
|**Prevalence of****_P.falciparum_**||||||||
|Unadjusted|1908|162 (8.5%)|1972|239 (12.2%)|1.42 (0.83,2.43)|0.206|15-52 (38.8)|
|Adjusted<sup>d</sup>|1908|162 (8.5%)|1972|239 (12.2%)|1.49 (0.86,2.57)|0.154|15-52 (38.8)|

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**Table 4. Pooled continuous time intensity matrix 1983-2002**

||**O.L.F.**|**U**|**S.E.**|**I**|**F**|
|---|---|---|---|---|---|
|**O.L.F.**|(0.1015)|0.0343|0.0314|0.0244|0.0114|
||_0.00002_|_0.00001_|_0.00001_|_0.00001_|_0.00001_|
|**U**|0.4385|(0.8883)|0.1186|0.2360|0.0952|
||_0.00015_|_0.00021_|_0.00009_|_0.00013_|_0.00008_|
|**S.E.**|0.0953|0.0220|(0.2434)|0.0956|0.0305|
||_0.00003_|_0.00002_|_0.00005_|_0.00003_|_0.00002_|
|**I**|0.1028|0.0598|0.1421|(0.5142)|0.2094|
||_0.00004_|_0.00004_|_0.00005_|_0.00009_|_0.00006_|
|**F**|0.0187|0.0139|0.0139|0.0491|(0.0956)|
||_0.00001_|_0.00001_|_0.00001_|_0.00001_|_0.00002_|
|**Duration**|9.8532|1.1257|4.1081|1.9450|10.4560|
||_0.00082_|_0.00013_|_0.00038_|_0.00016_|_0.00099_|

Note: Pooled instantaneous transition matrix computed using monthly data from the Monthly Labor Survey (PME) from Jan 1983 to Dec 2002 following the procedure by Geweke et al. (1986) outlined in Annex 1. Computations are based on 10.000 Monte Carlo replications. Standard errors are reported in italics. OLF=Out of the Labor Force, U=Unemployment rate, I=Informal Salaried, SE=Informal Self-employed, F=Formal Sector.

40
**Figure A.1:** Domestic government health expenditure as a percent of total government expenditure, by income group

<!-- Start of picture text --> 2000 2004 2008 2012 2016<br>High income Upper middle income<br>Lower middle income Low income<br>Comparison of residuals from regression of HCI on GDP per capita (y-axis) and residuals<br>from regression of social spending as a share of GDP on GDP per capita (ρ = 0.37)<br>SVN<br>KAZ<br>RUSSYC<br>VNM<br>GEO<br>ARM<br>IDN<br>KIR<br>CHN TLSNAMZWE<br>ETH MOZ ZAFSWZ LSO<br>LBR<br>TCD<br>-5 0 5 10 15<br>Residuals from HC spending as share of GDP ~ GDP pc<br>Residuals Fitted values<br>14<br>12<br>10<br>8<br>6<br>Domestic gov health expenditure (% of total gov exp)<br>.4<br>.2<br>0<br>-.2<br>Residuals from HCI ~ GDP pc<br>-.4<br><!-- End of picture text -->

**Figure A.2:** Comparison of residuals from regression of HCI on GDP per capita (y-axis) and residuals from regression of social spending as a share of GDP on GDP per capita (ρ = 0.37)

28
To discuss the relative merits of state and private ownership we thus review the fundamental literature on ownership, discuss the main drivers of political decision making and draw some conclusions on what role state ownership or more generally public governance does and/or should play in regulating economic activity. Section I introduces the issues. Section II reviews the broader conceptual framework. Section III presents our views.

# **Section I**

SOEs exhibit a significant lower productive efficiency (as distinct from allocative efficiency) in comparison with privately owned counterparts.<sup>3</sup> The main causes have been traced back to a general lack of accountability,<sup>4</sup> leading to a) a lack of managerial and employee incentives to efficiency b) problems of competence or corruption by state authorities c) the use of SOEs for political purposes, in favor of favored constituencies Boardman and Vining (1992) also concluded that SOEs may undermine the existence of private competitors.

There are striking parallels between the governance problems caused by state ownership or by diffuse private ownership. The free-rider problem applies to taxpayers even more so than to dispersed shareholders; moral hazard may be enhanced under state ownership since the powers of government are greater than private management and thus harder to control (Perotti, 1994); and limited disclosure is probably a greater problem in

> 3 We will not review empirical findings on the efficiency of privatization across countries. Good surveys are found in for instance in World Bank (1995), Megginson et al (1994), McKenzie and Mookherjee (2002), and Boubakri and Cosset (1999).

> 4 We mean accountability to citizens, not investors. While SOEs are incorporated firms, they have no scrutinizing equity market investors; nor do lenders play a disciplining role, as SOE debt is perceived as a public obligation.

- 4 -
Yeung 2011). We thus rely on a large number of robustness checks, and try to offer a coherent argument to tie various findings together.

# **Base results**

Table 6 presents the base results. Columns (1) and (2) use firm-level government facilitation variables directly; but our preferred results are in columns (3) and (4), because they use city-industry average values of the facilitation variables which, as pointed out earlier, are less subject to endogeneity concerns. Columns (1) and (3) report the labor productivity regressions. Columns (2) and (4) report TFP regressions: log labor productivity remains the dependent variable, but those models also control for industry dummies and their interaction with log capital-labor ratio and log labor. For columns (3) and (4), we cluster the standard errors at the city-industry level to avoid overstating the precision of our estimates (Moulton 1990).

The results consistently show that government facilitation is associated with higher productivity. In general, the results for labor productivity and TFP are qualitatively similar, though magnitudes for the facilitation variables are smaller in the TFP results, especially for Loan Help. This makes sense since Loan Help likely would affect capital intensity, and part of the effect of Loan Help on labor productivity is due to changes in capital intensity. Interestingly, the coefficients for the city-industry average government facilitation are much larger than for the firm-level government facilitation. This indicates that firm-level government facilitation is negatively correlated with determinants of productivity, and government facilitation is similar to a compensatory program in which the government picks lower productivity (but presumably high-potential) firms when allocating limited government facilitation resources. The magnitudes are large: in model 4, increasing local Loan Help by one standard deviation would increase TFP by 16 log points, and increasing local Info Facilitation by one standard deviation would increase TFP by 12 log points.

Since the results based on labor productivity and TFP tend to be qualitatively similar, and those based on TFP perhaps are more defensible (because TFP is net of input changes), we focus on TFP results in the rest of this paper. But the results in general are similar when using labor productivity. Since our focus is on government facilitation, we just briefly note here that the coefficients for our control variables indicate that total factor productivity tends to be higher in larger, younger firms, non-state (including foreign) firms, and in richer regions.

12
- 24 -

from the assumed by policy makers, the degree of shifting, particularly across jurisdictions, also suggests potential serious distortions in resource allocation.

Reductions in tax burdens for commercial and industrial property are generally made at the behest of central governments, as part of sectoral promotion strategies; or by a local government, to attract new industry. To achieve these objectives, property taxes must constitute a significant part of the affected industry's costs, and the tax break must be significant and sustained. In most cases it is neit'her. Business tax breaks are therefore ineffective in meeting their allocative objectives, but potentially very costly in terms of foregone revenue.

# B. Property Tax Administration and Institutional Arrangements

The performance and fairness of property taxation depend, to a great extent, on how well it is administered. The property tax is difficult to administer cost effectively. It involves a large number of individual taxpaying units, each yielding a relatively small amount of revenue.

The job is particularly difficult in LDCs. Due to rapid urban growth (and often high inflation) the tax base is constantly changing. Skill levels in the taxing authority are generally low. In addition, the sources of information used to facilitate property tax administration in developed countries are less useful in LDCs. Records of property transactions, title information, and building permits often omit large segments of the urban property market.