rafmacalaba/datause-encoder-data huggingface dataset:
attempt to ensure that it has the same purchasing power in every country. The specific PPP conversion factors used for this exercise are those for private consumption, which come from the International Comparison Program (ICP).<sup>4</sup> Over the last few decades, the ICP has at various times collected price data on a large number of goods and services and in a large number of countries, with each assessment providing new information on how price levels compare across countries. Each release of new PPP data has led both to revisions of the international poverty line, and to re-assessments of the relative differences in wellbeing across countries and regions. The first international poverty line that was based on a sample of national poverty lines was set at $1.01 using 1985 PPPs, by Ravallion, Datt and van de Walle (RDW, 1991) and used in the 1990 World Development Report (World Bank, 1990). Chen and Ravallion (2001) later updated this to $1.08 per day, using the 1993 PPPs. With the release of the 2005 PPPs and a new set of national poverty lines, Ravallion, Chen and Sangraula (RCS, 2009) proposed a new poverty line of $1.25 per day, which is the line that has been used by the World Bank to measure poverty since 2009. Each of these updates has also been associated with revisions in the number and geographical composition of global poverty, often in ways that can be jarring and cast into doubt the reliability of the global poverty estimates.<sup>5</sup> The 2011 PPP conversion factors, released by the ICP in 2014, once again led to substantial revisions in relative price levels, the magnitude of which varies widely across countries.

Inevitably, incorporating the new information on relative price levels that is contained in these conversion factors has implications for the measurement of global poverty and, more specifically, for the definition of the international poverty line. In considering how best to update the line to take account of this new information, we have been guided by three central principles. First, international comparisons of living standards should be based on the most recent reliable information on relative price levels, unless there is sufficient reason to suspect that it is less accurate than previous estimates. Second, we have sought to minimize changes to the real value of a poverty line that has become an important component of a system of international goals and targets, so as to avoid “shifting the goalposts”. We have therefore sought to keep the definition of the line unchanged, and its new value as close as possible to that of the $1.25 line (in 2005 PPPs)

> 4 For more details on the ICP, see www.worldbank.org/data/ICP 5 Deaton (2010) is one example of a critique of this approach. See also Reddy and Pogge (2008).

3
24 Deaton, A., J. Ruiz-Castillo, and D. Thomas (1989), “The influence of Household Composition on Household Expenditure Patterns”, _Journal of Political Economy_ , **97(1)** :179-200.

Desta, M., G. Haddis, and S. Atakelt (2006), _Female-Headed Households and Livelihood Intervention in Four Selected Weredas in Tigray, Ethiopia_ , Report No. **44** , Drylands Coordination Group, Addis Ababa.

Federal Democratic Republic of Ethiopia (2001), _Report on The 1999/2000 Household Income, Consumption, and Expenditure_ , Addis Ababa: General Statistical Authority.

Gibson, J. and S. Rozelle (2004), “Is it Better to Be a Boy? A Disaggregated Outlay Equivalent Analysis of Gender Bias in Papua New Guinea”, _Journal of Development Studies_ , **40(4)** : 115-136.

Gong, X, A. von Soest, and P. Zhang (2005), “The Effects of Gender of Children on Expenditure Patterns in Rural China: A Semiparametric Analysis”, _Journal of Applied Econometrics_ , **20** : 509-527.

Gorman, W.M. (1976), ‘Tricks With Utility Functions’, in (ed.s) Artis M. and A.R. Nobay, _Essays in Economic Analysis_ , pp. 211-43, Cambridge: Cambridge University Press.

Gronau, R. (1988),’Consumption Technology and the Intra-family Distribution of Resources: Adult Equivalent Scales Reexamined’, _Journal of Political Economy_ , **69(6)** : 1183-1205.

Haddinot, J. and L. Haddad (1995), “Does Female Income-Share Influence Household Expenditure? Evidence from Cote D’Ivoire”, _Oxford Bulletin of Economics and Statistics_ , **57(1)** : 77-96.

Kebede, B (2008), “Intrahousehold Allocation in Rural Ethiopia: A Demand System Approach”, _Review of Income and Wealth_ , **54(1)** : 1-26.

Koohi-Kamali, F. (2007), “Robust Detection of Child Gender Bias from Adults Consumption Patterns: Tests Based on an Iranian Budget Survey”, paper presented at MENA chief economist series, World Bank.

Kynch, J, and A.K. Sen (1983), “Indian women: well-being and Survival”, _Cambridge Journal of Economics_ , **7(3/4)** : 363-380.

Lanjouw, P. and M. Ravallion (1993),”Are Larger Households Really Poorer?”, _Policy Research Department_ , Washington: World Bank.
Table 7: Impacts on community land cover management index and on household participation in land cover maintenance activities and community work (log no. days)

||(1)<br>**Full**|(2)<br>**2011-2012**|(3)<br>**2013-2014**|
|---|---|---|---|
|||**Panel A: Community survey**||
|Land management index|0.130***|0.178***|0.117***|
||(0.029)|(0.061)|(0.035)|
|Control Mean|0.273|0.247|0.292|
|Control SD|0.217|0.223|0.211|
|Observations|862|357|505|
|||**Panel B: Household survey**||
|Land cover work|0.613***|0.963***|0.474***|
||(0.140)|(0.220)|(0.179)|
|Control Mean|1.869|1.628|2.034|
|Paid land cover work|0.461***|0.553***|0.481***|
||(0.104)|(0.169)|(0.142)|
|Control Mean|0.418|0.302|0.498|
|Unpaid land cover work|0.260**|0.586***|0.092|
||(0.127)|(0.199)|(0.169)|
|Control Mean|1.558|1.401|1.666|
|Community work|0.183|0.250|0.042|
||(0.113)|(0.179)|(0.148)|
|Control Mean|1.968|1.882|2.028|
|Obs|8050|3342|4708|

> 1 Regressions include the threshold dummy, the first and second order polynomials of the re-centered point score and their interactions with the threshold dummy and state fixed effects. For the community level analysis, robust standard errors are presented in parentheses. For the household level analysis, standard errors were clustered at the community level. *** p _<_ .01; ** p _<_ .05; * p _<_ .10

> 2 The sample for the household level analysis includes only observations for which all four variables had non-missing values. This was done to ensure results were fully comparable.

39
-10

-

# Table 1

# Distortions, Production, Demand Structure, and Elasticities in the Auto and Steel Industries

||Autos|Steel|
|---|---|---|
|Distortions|||
|Wage Premium (0)<br>|27.0<br>|26.0|
|Premium Rate or, Intports|31.8|7.0 b/|
|Production and Demand a/|||
|Gross Output<br>|124.2|57.5|
|Employment (1,000 man-years)<br>|536.5|531.0|
|Domestic Final Demand Sales<br>|111.0|0.0|
|Intermediate Sales|8.3|56.1|
|Exports|4.9|1.4|
|Imports<br>|||
|Intermediates<br>|2.3|12.7|
|Final Demand|30.3|0.0|
|Elasticities and Parameters c/|||
|Capital-Labor Substitution<br>|0.8|1.0|
|Import-Domestic Substitution Elasticity d/<br>|1.9|3.0|
|Composite Final Demand<br>|0.8|n.a.|
|Export-Domestic Transformation Elasticity d/|2.9|2.9|
|Calibrated Conjecture (D)|0.72|n.a.|
|Derived Elasticity of Demand (Ed)<br>|1.37|n.a.|
|Cost Disadvantage Ratio (CDR)|0.11|0.04|

Notes: n.a. - not applicable.

- a/ 1984 US $ billion.

- b/ Premium rate resulting from rationing steel to 85 percent of the volume of steel imports in 1984.

- c/ The sources for demand and supply price elasticities are detailed in de Melo and Tarr (199Ob).

- d/ The selected CES (CET) functions imply that the corresponding substitution (transformation) elasticities are compensated import demand (export supply) price elasticities.
worker pay at previous employers in 2011-2016, which we use to estimate worker effects in order to infer ability. The worker effects allow us to measure the quality of workers’ skills at each firm as well as the relative quality of top managers versus other workers.

Table A7 in the Appendix provides descriptive statistics on worker and firm attributes for firms with management z-scores above and below the median. To proxy for worker ability, we consider estimates of individual effects from AKM models using data for the period 2011-2016. We use a similar approach to estimate managers’ ability. Furthermore, we consider a direct measure of firm management skills, namely the share of a firm’s managers holding a university degree.

Managers are identified in two alternative ways. Our main approach is to identify managers directly by _occupational category_ in the data (using the categories CEO and other top managers). Although allowing for more precise identification, the downside of this approach is a loss of observations due to missing data. We therefore complement it by an alternative identification strategy which is in line with previous literature (Juhn, McCue, Monti, and Pierce, 2018), namely to identify managers by their position in the firm’s wage distribution. In this vein, we classify the _five percent highest earners_ in the firm as top managers.<sup>14</sup> The set of firm-level attributes is composed of firm size, the share of foreign and state capital, firm age, the percentage of female employees, export status and export share. The summary statistics reported in Table A7 reveal that firms with higher management z-scores tend to be larger, to have a greater share of foreign and privately-owned capital, and are slightly more likely to be exporters and tend to employ a larger share of female workers. Turning to worker and manager attributes, the statistics reveal that firms with above-median z-scores tend to have a higher share of workers and managers with a degree, as well as larger average estimates of employee and managers’ ability (as revealed by person effects from AKM models). The relationship between z-scores and observable manager characteristics is further explored in Figure A3 in the Appendix. In this figure we show the distribution of z-scores across firms with high- and low-skilled managers, respectively, where the former (latter) are defined as firms with a share of managers with a degree above (below) the median. These distributions are shown when managers are identified according to occupational category (Panel A) and when managers are identified as the top five percent earners in the firm (Panel B). In both cases, we see that the z-score distribution of firms with high-skilled managers lies systematically to the right of the corresponding distribution of firms with low-skilled managers. This pattern is

> 14We have also used a stricter threshold of one percent highest earners. The results are very similar and are available upon request.

14
<!-- Start of picture text --> BSA a<br>a 5S)<br>a C<br> |0-0.05 oe<br>|_| 0.05 - 0.1 qe<br>(| 0.1-0.2 aA<br>| 10.2-0.3 :<br>[7 0.3 - 0.4<br>[0.4<br>lm 0.6 - 0.61 LN.oo<br>aie<br>t<br>er<br>A<br>f QO<br><!-- End of picture text -->
variation across time and products identify the effects of NTMs on the performance of firms more cleanly than in the case of tariffs. However, it is also plausible to conjecture that the government may indeed use some NTMs to protect domestic producers from import competition, which would threaten the validity of the analysis. We cannot rule out this hypothesis, as we do not observe the real motives of the Indonesian government in setting NTMs. Hence, in the Section 3.1 we discuss several ways in which we address potential threats to the identification.

# _2.2. “Virtuous”_ vs. _“Protectionist” NTMs_

NTMs are highly heterogeneous measures with different aims and likely different effects. To guide our analysis, we make one central distinction between NTMs. We ask whether they are likely to breach a key principle of WTO rules, i.e. not restricting trade unnecessarily (Cadot et al., 2012). While NTMs’ _raison d’etre_ is to increase domestic welfare by addressing market failures, they can also distort trade unnecessarily in the process of fulfilling a legitimate policy objective, or even without fulfilling one.

Establishing whether a measure is strictly necessary to achieve a “legitimate policy objectives” is challenging, as policy-makers may have different definitions - even within the same country - of what “legitimate” means. One hint on its “legitimacy” is given by how widespread the application of the measure is across countries. An NTM that is used by most countries is more likely to be necessary than one that is used only by a handful of countries.

We focus on OECD countries, which tend to have more transparent trade policy, and compute the share of OECD countries that apply each of the 2-digit NTMs applied by Indonesia during the period of our analysis. We take data from UNCTAD’s _TRAINS_ dataset on NTMs applied by 34 OECD countries in 2015.

We restrict the analysis to the most common groups of applied NTMs in Indonesia, i.e. SPS, TBT and INSP. We focus in particular on SPS to identify possible “virtuous” NTMs, for two reasons. First, SPS measures address threats to the health and safety of consumers stemming from high-risk products, including food and medical products. The legitimacy of such first-order policy objectives may be more difficult to challenge than that of other NTM categories. Second, the application of SPS on agricultural products and drugs makes them a less suitable instrument to protect the manufacturing sector than 7
|**Table 3. Ward variables**|||||||
|---|---|---|---|---|---|---|
|**Isolation**|**Unit**|**Mean**|**Median**|**St.dev.**|**Min.**|**Max.**|
|Urban population within 2 hours travel time|thousands|128.0|0.0|218.0|0.0|795.0|
|Population density in the district|per sqkm|383|185|483|2|1692|
|**Prices**|||||||
|House rental price|US$/month|9.75|7.25|8.35|0.27|37.94|
|Median rice price in ward|US$/Kg|0.44|0.44|0.14|0.12|1.04|
|<br>Median wage rate in ward|US$/day|0.75|0.48|1.01|0.00|12.35|
|<br>**Inequality**|||||||
|Inequality in household consumption|Gini coef.|0.290|0.282|0.083|0.083|0.609|
|<br>Inequality in per capita consumption|Gini coef.|0.257|0.246|0.082|0.091|0.509|
|<br>**Indices of consumption variety**|||||||
|<br>Herfindahl index for food products|Index|0.12|0.09|0.07|0.03|0.49|
|<br>Herfindahl index for non-food products|Index|0.14|0.14|0.03|0.09|0.42|
|<br>Herfindahl index for durable goods|Index|0.44|0.44|0.16|0.15|0.85|
|<br>**Rainfall**|||||||
|Average rainfall in ward|mm|1702|1459|612|1039|3431|
|<br>Standard deviation of rainfall in ward|mm|411|366|197|176|903|
|**Maoist Insurgency as reported in 2000**|||||||
|<br>In a most affected district||2.5%|||||
|In an affected district||9.9%|||||
|In a little affected district||20.4%|||||
|Not affected||67.2%|||||
|Number of wards||274|||||
32

**Figure 13  Market Rate and Minimal Interest Rate Requirement**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>10Y TBY MIR BVG<br>1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> Data-Source: SNB for the yields on 10-year zero-coupon bonds for Switzerland (10Y TBY)

# **_3.2.1 Comparison of Returns on Investment_**

Portfolios of pension funds may also take advantage of potentially higher returns in stock markets by including risky assets. Figure 14 depicts the annual returns of the Swiss Performance Index (SPI), the Morgan Stanley Capital International Index (MSCI) for Switzerland, a bond index from Pictet<sup>31</sup> and the MIR. As riskier assets are associated with higher volatilities, the returns of the SPI and the MSCI index fluctuate more than the bond index.

> 31Bonds: Pictet Sub-index "Domestic Bond Index" with coupon interest payments incorporated; the index is based on a sufficiently representative random sample covering 5 different classes of bond issuer according to their market capitalization.
# **V. Regression Results**

In both cities, we use the log of house rent as the dependent variable. For the main policy variable of interest, water availability, we use the number of days per week water is available in the house through a direct connection. The estimation results show the expected patterns in terms of signs and significance. For Bhopal, positive and very significant coefficients are observed for Size, Baths, Rooms, Floor, Walls, Roof, and Electricity (see Table AA1). Water availability measured through DPW is positive and significant. Among additional water sources different from an individual water connection, only the presence of a tube well has a negative and significant effect while the coefficient for Rain is positive and significant. Indicator variables are included for every Ward to account for neighborhood characteristics for which data are not available. Some of these variables remain significant even after introducing the spatial lag into the model. The coefficient estimated for the spatial lag is above 0.24 and very significant in all cases. The LM statistic for remaining spatial autocorrelation in the LAG model suggests the presence of remaining spatial autocorrelation of unspecified form. Therefore, following the approach suggested in Anselin and Lozano-Gracia (2007a), it is appropriate to use the Kelejian-Prucha Heteroskedasiticity and Autocorrelation Consistent estimator (HAC) (Kelejian and Prucha 2007). We employ three alternative kernels (epanechnikov, bisquare, and triangular) to further assess the robustness of our findings. We also compare the results to estimated standard errors that only correct for unspecified heteroskedasticity (White 1980). For the standard errors and confidence intervals using two standard deviations shown in Table AA1 we find that the largest changes in standard errors are seen when we go from classical to the White correction, with a much smaller effect for the spatial adjustment.

Estimates for Bangalore are shown in Table AA2. We observe a positive and very significant coefficient estimate for water availability measured through DPW. House characteristics have positive and significant effects as expected; the coefficient of number of bathrooms, however, loses its significance when going from the Classical to the HAC standard errors. The bathroom variable is significant at the 1% level when looking at the classical standard errors but only significant at 5% when moving to the more appropriate HAC standard errors. Other sources of water availability that show negative and significant results are fountain and community tube well. The coefficient for the spatial lag is also above 0.24 and very significant as is the case for Bhopal. The LM statistic for the lag model confirms the presence of remaining spatial
|_g_<br>σ|2.13–<br>3.06|0.47–<br>1.20|2.89|5.33|6.20|1.59|
|---|---|---|---|---|---|---|
|_a_<br>ρ|0.95|0.97||||0.84|
|_a_<br>σ|0.17–<br>0.54|0.63–<br>0.78|0.68|0.73|0.58|0.32|

Source: Aguiar and Gopinath (2007), Naoussi and Tripier (2013), authors’ analysis outlined in the Consumption Volatility and Permanent versus Transitory Income Shocks section

**Table 4** Business Cycle Stylised Facts for the Indian Economy in the Preand Post-Reform Period

This table reports the changes in business cycle facts for the Indian economy from the pre-reform to the post-reform periods. The span of the analysis is 1951– 2009.

||Pre-r|eform pe|riod (195|1–91)|Post-re|form per|iod (199|2–2009)|
|---|---|---|---|---|---|---|---|---|
||Std.|Rel.|Cont.|First|Std.|Rel.|Cont.|First|
||dev.|std.<br>dev.|cor.|ord.<br>auto<br>corr.|dev.|std.<br>dev.|cor.|ord.<br>auto<br>corr.|
|RealGDP|2.25|1.00|1.00|0.056|1.93|1.00|1.00|0.714|
|Pvt. Cons.|1.86|0.83|0.70|0.038|1.99|1.04|0.92|0.605|
|Investment|5.26|2.34|0.19|0.510|5.18|2.69|0.76|0.607|

_Source_ : National Accounts Statistics, Labour Bureau, authors’ estimates outlined in the Case Study section 43
# II

motivated by a few stylized correlations; and, we have an impressive array of cross-country empirical studies, each advertising a few stylized correlations. wlat we have not had is a sufficiently intensive study of the fragility of frequently used stylized correlations or a systematic empirical competition among competing theoretical models of growth.

# D. Measurement

This subsection discusses statisti-al and conceptual issues associated with measuring and constructing the data used in cross-country growth studies. The subsection is divided into two parts. The first part examines both the general implications of measurement error and specific biases associated with cross-country regressions. The second part involves a rather lengthy discussion of the statistical and conceptual differences between using data constructed by the International Comparisons Project [see: Summers and Heston (1988) and Kravis and Lipsey (1990)] or own currency price data available from the International Monetary Fund and the World Bank. This section may be skipped by readers not interested in a detailed discussion of the data.

# 1. Measurement Error. Index Problems, and Data Availability

A troubling problem with cross-country studies of growth is the measurement of the underlying data. Measurement error generally biases coefficient estimates. Although data problems plague much empirical work, measurement problems may be particularly important in cross-country growth studies because the data collection processes in many countries are poor. Furthermore, the accuracy of data collection may be correlated with factors
Commission's new regulation are expect to be affected when this regulation comes into force.

# V. **Simulations**

This section provides results on how trade flows between Africa and Europe would differ under conditions in which (1) a standard developed using CODEX guidelines were imposed or (2) the European harmonized standard is imposed on African exports. The predicted trade flow in value under both scenarios is computed for each EU and African country for products analyzed in the previous section. An upper and a lower bounds for change in trade flow are imposed in order for the result to reflect the non-negative export and the capacity constraints on exports; i.e., trade flow will not increase or decrease by more than 100 percent.

As noted, CODEX does not set standards on aflatoxin B I alone. In order to establish a baseline estimate for an international standard, we assume the 9 ppb for the studied products based on the standard on the level of total aflatoxins contamination (15 ppb). Composition of aflatoxin B 1 in all aflatoxins in food can vary across products and samples. The variation is normally between 50 to 70 percent. We assume, therefore, that 60 percent of all aflatoxins are in fact aflatoxin B 1 by adopting the average of these two bounds. Given this assumption, aflatoxin BI contamination should be below 9 ppb in order to sustain total aflatoxins below a 15 ppb level.

Cereals exhibit a significant difference between the two scenarios. As shown in Table 6, the predicted loss of cereals trade flow under the Commission's new standard is US$ 177 millions<sup>6</sup> , or 59 percent lower than the value of EU-Africa cereal trade in 1998. The predicted total trade flow under the Commission's new standard US$ 120 millions or 15
<!-- Start of picture text --> ds<br>BARRANQUILLA<br>40268 an 2S<br>eo Cae<br>SEEERS<br>( wwiSitalSRR aePiety (NauAo2Se: cUCUTAMi<br>MEDELLIN WigsCSss ra? aN mwa<br>166566 Be agie ae)<br>peRK SN,ERGs @<br>SE Ce<br>ae BUG este: Venezuelans 2018<br>: ee fF) reso<br>as Sy 251-500<br>etmy ee<br>INS i 2001-10000<br>| 10000+<br>4]<br><!-- End of picture text -->

<!-- Start of picture text --> ire}<br>+<br>0ra 0<br>&Ss<br>“7 Ts se<br>- - uc<br>wer<br>o<br>2012-2013. «2014S 2015 2016=S 2017S 2018 ~—S 2019<br>year<br>—=- Venezuelan inflows ©—— Cumulative Venezuelan inflows<br><!-- End of picture text -->