rafmacalaba/datause-encoder-data huggingface dataset:
To see how well the changes over time in price levels can be explained, I have assembled a data file of the price level index (PPP for GDP over exchange rate) and GDP per capita at PPP for the 2005 and 1993 ICP rounds for all countries (developed or developing). These are the World Bank’s estimates, rather than Penn World Tables (PWT).<sup>7</sup> I will also study the changes in PPPs between 1985 (using PWT) and 2005.

The following section summarizes the arguments as to why one might expect the PPP changes to be predictable. Section 3 describes the empirical models to be estimated while section 4 presents the results. Section 5 tests for whether the 2005 PPP for China—the country that has clearly received the most attention in the debates surrounding the release of the results of the 2005 ICP—is consistent with the pattern seen across countries. Section 6 looks at the implications for extrapolating PPPs between ICP rounds. Section 7 concludes by summarizing the paper’s findings and drawing some implications for future analyses of ICP data.

# **2. What might account for the observed changes in PPPs between ICP rounds?**

Some of the country-specific factors that influence price levels (or, equivalently, real exchange rates) can be treated as time–invariant between ICP rounds.<sup>8</sup> By focusing instead on the changes in PPPs, the following analysis will eliminate the influence of all additivelyseparable error components stemming from such country-specific factors.

An important clue to why PPPs change over time can be found in the very same reason PPPs were developed. It has long been recognized that international comparisons of GDP at market exchange rates are deceptive about the differences in real income, given that some commodities are not internationally traded, notably most services. Without trade, there is no mechanism for assuring price parity across borders. The most common economic explanation of why the PPP would differ systematically from the nominal exchange rate is the BalassaSamuelson model (outlined independently by Balassa, 1964, and Samuelson, 1964).<sup>9</sup> This assumes a competitive market economy in which all factors of production are fully employed

> 7 There are methodological differences between the World Bank’s PPPs and those in PWT; see Deaton and Heston (2010) for a useful overview of those differences.

> 8 For example, Clague (1985) shows that natural resource endowments will influence the price level index at a given level of GDP per capita. However, such endowments can be treated as country-level fixed effects for the present purposes.

> 9 An alternative explanation was proposed by Bhagwati (1984) based on factor endowments, leading (labor-intensive) services to be cheaper in poor countries.

5
The basic characteristics of the sampled households generally match the nationally representative data from the Household Budget Survey (2006/07) (results not presented here). Household interviews were conducted over a 12-month period, but because of small samples, we do not explore the survey assignment effects across seasons (such as harvest time with peak labor demand and dry seasons with low demand). The random assignment of households is validated when examining a set of household characteristics (Table 2). With the exception of household size and acres owned (driven by a handful of very large households with many children in one group), household traits are not statistically different across survey assignment. Of a total of 96 pairwise comparisons, eight are statistically significant.

Turning to individuals, we classify people on the basis of the survey assignment that they actually received in order to measure their labor statistics. An individual’s actual survey assignment is the result of the initial assignment of their household among one of the four survey assignments, whether the individual is selected to be a respondent or a self-report, and whether the self-report/proxy assignment is realized. In the case of self-report modules, up to two persons over age 10 are randomly selected to selfreport. If persons randomly selected to self-report are unavailable, an alternative person is selected at random. In the case of proxy assignment, one person in the household over the age of 15 is selected to self-report and to proxy report on up to two random household members. Thus, in the proxy assignment, one household member actually self-reports in addition to reporting on other household members. Therefore, the number of self-reports should be about half the number of proxy reports for households in the proxy assignment. In total there will be more self-reports than proxy reports. Appendix 2 shows the sample sizes by planned assignment and by 18
||Observations|TFP<br>Non<br>parametric|R usingG<br>Translog|NR<br>Cobb-<br>Douglas|TFPRusin<br>Translog|gACF<br>Cobb-<br>Douglas|TFP<br>Non<br>parametric|Q usingG<br>Translog|NR<br>Cobb-<br>Douglas|TFPQ usin<br>Translog|gACF<br>Cobb-<br>Douglas|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Chile|22,151|0.010***|0.011***|0.015***|0.013***|0.013***|0.030***|0.032***|0.036***|0.034***|0.034***|
|Colombia|61,001|0.003|0.005*|0.009|0.013**|0.011*|~0.004|~0.001|0.002|0.006|0.005|
|||(0.003)|(0.003)|(0.006)|(0.005)|(0.006)|(0.010)|(0.010)|=(0.011)|(0.012)|(0.012)|
|Indonesia|169.899|0.022***|0.028***|0.041***|0.042***<br>|—0.043***|-0.011|0.004|<br>0.008|0.009|0.01|
|||(0.006)|(0.007)|(0.011)|(0.009)|<br>(0.009|(0.016)|(0.017)|(0.019)|(0.018)|(0.018)|
|Ethiopia|7,356|0.023|-0.029|0.047|-0.059|-0.065|-0.061**=|-0.068**|-0.085**|-0.098"*|-0.103**|
|||(0.019)|(0.023)|(0.038)|(0.038)|(0.043)|<br>(0.027)|<br>(0.030)|(0.042)|(0.042)|(0.046)|

Notes: See Table 1. This table presents results with alternative functional forms and techniques for estimating TFPR and TFPQ (Foster et al). The results reported in table 1 using the GNE methodology with non-parametric functional form are repeated here for comparison sake. TFPQ a la De Loecker et al. is estimated only using the non-parametric functional form.
# Job Creation and Labor Reform in Latin America<sup>∗</sup>

David S. Kaplan<sup>†</sup> Enterprise Analysis Unit The World Bank September 2008

> ∗I gratefully acknowledge helpful comments from Mohammad Amin, Penelope Brook, Simeon Djankov, Veselin Kuntchev, Vikram Pathania, and Augusto de la Torre, as well as seminar participants at the World Bank. Joshua Wimpey provided excellent research assistance. All remaining errors are mine.

> †MailStop F4P-400, 1818 H Street, NW, Washington, DC 20433. Email: dkaplan@worldbank.org.

1
**Figure 5:  Decomposition of Changes in Poverty,1994-2002**

<!-- Start of picture text --> 3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>-3%<br>-4%<br>-5% total poverty change<br>due to grow th<br>-6% due to inequality<br>1994-1998 1998-2002 1994-2002<br><!-- End of picture text --> Note: The 2002/1994 change is approx. a sum of 1998 –2002 and 1994-1998. _Source:_ Staff calculations based on GUS data..

For further illustrative purposes, one can look at the consumption level and its dynamics at different parts of the consumption distribution. It is interesting to compare mean incomes/consumption across the ranked population. Inverting the cumulative distribution of consumption at a p-th percentile, one can infer consumption levels at this percentile. This line – depicted in the left panel of Figure 6 – is called a quintile function, or, sometimes, a Pen’s parade (Pen, 1971). For example, individuals at the 70th percentile had in 2002 a consumption level of almost PLN750 per month.

The left panel of Figure 6 presents three curves for three different years (1994, 1998, and 2002). Between 1994 and 1998 the line shifted upward almost in parallel, implying a rise in consumption across the entire population. However, between 1998 and 2002 the line generally shifted downward (except for the higher percentiles). The magnitude of this decline was clearly greater for the lower percentiles. The consumption level of the poor returned to, or fell below, its 1994 level. The right panel in Figure 6 presents the same story but from a dynamic perspective. The period 1995-98 was generally favorable for all the consumption groups,<sup>8</sup> but in 1999 an acrossthe-board deterioration emerged, with only the top quintiles maintaining a barely positive level of consumption growth (except in 2001).

> 8 There could be two explanations for the 1997 performance of the lower quintiles IN 1997: a tightening of social benefits eligibility, and massive flooding in the summer.

8
Note that, in a repeated cross‐section setting, y�,�� is unobserved because individuals are not followed across years. Thus, the unexplained part of changes in earnings will only be possible to estimate for aggregate, anonymous quantiles of the distribution.

# **3. DATA AND DESCRIPTIVE STATISTICS**

# **3.1 DATA SOURCES**

Most of the empirical work done on labor markets in Europe uses the harmonized EU‐LFS quarterly labor force survey. This survey represents an invaluable data source for labor economists. However, public access microdata do not include information on earnings of workers. We thus use household surveys harmonized by the Luxembourg Income Study (LIS) center. These surveys include information on both employment characteristics and earnings of individuals, allowing to carry out the decomposition analysis detailed before. LIS harmonizes different household surveys to a common standard to assure comparability. In this work we use the German Socio‐Economic Panel editions of 1994 and 2013, the Household Budget Survey of 1992 and the EU‐SILC (Statistics and Income Living Conditions) edition of 2013 for Poland, and the Household Budget Survey of 1990 and the EU‐SILC edition of 2013 for Spain<sup>6</sup> .

The main variables of interest of our analysis are occupations and labor related earnings. With respect to occupations, we classify them into three categories based on their most intensive task, using O*NET task content information: 1) routine task intensive jobs; 2) non‐routine, manual task intensive jobs; 3) non‐ routine, cognitive task intensive jobs. In Appendix 2 we provide a detailed description of how we construct this classification. With respect to labor related earnings, we use annual earnings coming from wage employment. Due to the limitations that household surveys usually have in correctly capturing self‐ employed income, we exclude self‐employed from our analysis. Self‐employed represent between 9% and 15% of the total employment in the countries included in our work. Moreover, we restrict our sample to individuals aged between 18 and 64.

Appendix 1 provides more details on the surveys used and the construction of the variables relevant to our analysis.

> 6 The analysis is also done for Georgia, the Kyrgyz Republic, the Russian Federation and Turkey. See Appendix 4 for data sources and analysis.

15
- 34 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

population density in a city is below its upper limit for its urban carrying capacity.<sup>2</sup> In this scenario, the city can increase its population by infill of available spaces within the urban area, or via an increase in population density, before the occupation of new areas. When the population density has reached its maximum or saturation point, the only option left is growth on new adjacent areas. We analyzed if the urban growth is a consequence of saturation by comparing the highest population densities reached in the new urban areas in 2000 and 2010, with the population density reached in the respective date in the urban extent area for the initial date (1996). We built an indicator of saturation for each period as shown in Table 5, using population figures for the last date in each period (i.e., 2000 for the 1996 to 2000 period, and 2010 for the 2000 to 2010 period):

> 2 For a comprehensive discussion on urban carrying capacity, see Li and Lian (2012).
high elasticity of the financial repression tax base to the tax rate, would manifest itself at very high rates of inflation-as we argued above in section 2.2.

Finally, to illustrate the extent to which domestic interest rates do not reflect subsequent exchange-rate changes we plot in figure 10 the cross-country correlations between interest-rate differentials (dollar interest rates on foreign debt minus domestic currency interest rate on domestic debt) and exchange rate changes (percent increase in the price of the dollar in domestic currency terms). If domestic interest rates were allowed to reflect expectations about exchange changes, and these expectations were on average correct, this correlation should be negative, and close to -1. We find instead that over many years in the 1970s the correlation is positive and rather large. It turns negative, and close to -1 in thc 1980s, in part because of the inclusion of an outlier, Brazil, whose experience is discussed above.

# **5 Summary and Concluding Remarks**

rhis paper has presented an exploratory analysis of the theoreti,al underpinnings and the empirical relevance of the phenomenon of financial repression from a public-finance perspective. We have shown that financial repression should not be resorted to when countries face no constraints in the use of different forms of taxation. However, whenever there are costs of administering certain types of taxes or whenever income distribution becomes an objective of the government, an implicit tax on domestic financial markets may be part of an optimal taxation program. We also argued that there should be complementarities between the financialrepression tax and the inflation tax. Throughout, we relied on explicit open economy assumptions, and stressed the interactions of capital controls and financial repression.

Our empirical investigation suggests that the revenue from financial repression can be quite substantial: the unweighted crosscountry average is about 2 percent of GDP and 9 percent of total government revenue (excluding the revenue from financial repression), but varies significantly across countries. Hence, reforms 19
# Table 6. Bond Maturity OLS Regression Results

|Dependent Variable: Maturity of B|ond Tranche||||||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|VIX|-2.951**<br>(1.245)|-3.076*<br>(1.749)|||||||
|RISK|||-3.301***<br>(0.983)|-3.201***<br>(1.157)|||||
|FED|||||1.186<br>(1.824)|2.822**<br>(1.385)|||
|LIBOR|||||||-1.364*|-1.176|
||||||||(0.738)|(0.735)|
|GDPPC|-2.046**||-1.988**||-2.031**||-2.213*||
||(0.836)||(0.847)||(0.844)||(1.121)||
|GROWTH|0.0968||0.0996||0.0929||0.0974||
||(0.0652)||(0.0657)||(0.0658)||(0.0763)||
|EXT|-0.0355*||-0.0351*||-0.0346*||-0.0419*||
||(0.0185)||(0.0187)||(0.0186)||(0.0229)||
|CA|-0.0553||-0.0549||-0.0563||-0.0273||
||(0.0714)||(0.0719)||(0.0721)||(0.0654)||
|PCRED|0.0528*||0.0542*||0.0532||0.0609||
||(0.0316)||(0.0320)||(0.0324)||(0.0387)||
|Log of Size of Bond Issued|0.524**|0.717**|0.527**|0.713**|0.525**|0.722**|0.445*|0.573|
||(0.250)|(0.320)|(0.246)|(0.317)|(0.253)|(0.311)|(0.238)|(0.352)|
|UST10Y|0.00287|-0.361|0.0999|-0.339|0.160|-0.299|-0.0613|-0.425|
||(0.265)|(0.533)|(0.257)|(0.501)|(0.301)|(0.519)|(0.261)|(0.537)|
|Bond Tranche fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|No|Yes|No|Yes|No|Yes|No|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-period fixed effects|No|Yes|No|Yes|No|Yes|No|Yes|
|Includes China issuance|Yes|No|Yes|No|Yes|No|Yes|No|
|Observations|6,749|3,684|6,749|3,684|6,749|3,684|6,406|3,347|
|R-squared|0.393|0.298|0.393|0.298|0.391|0.297|0.401|0.301|
|No. of Countries|64|71|64|71|64|71|64|71|
|No. of Industries|198|203|198|203|198|203|197|202|
|No. of Bonds|6144|3268|6144|3268|6144|3268|5840|2969|

Robust standard errors clustered on the country-industry level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 34
Figure 9: Net international reserves as equivalent to months of import cover, 2008–14 <!-- Start of picture text --> 3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>2008 2009 2010 2011 2012 2013 Sep-14 Dec-14<br><!-- End of picture text --> Source: Data from Bank of Ghana.

A significant and related policy action was Ghana’s issuance of a US$1 billion sovereign bond in October 2014.<sup>5</sup> This had the stated objective of investing in infrastructure. However, given the precarious position of the country’s foreign exchange reserves, the sovereign bond proceeds— supported by proceeds from an annual US$1.7 billion cocoa-syndicated loan, which Ghana borrows annually to purchase cocoa—were instead used to shore up foreign exchange reserves (BOG 2014). Whereas Ghana’s net international reserves amounted to some US$950 million in September 2014, these measures helped boost net international reserves to US$3.2 billion (2.2 months of import cover) by the end of the year.

In response to the deterioration in Ghana’s external payments position, the exchange rate of the Ghanaian cedi also depreciated sharply following the onset of oil production and in line with the government’s expansionary fiscal and monetary policy stance. In 2014 the cedi depreciated by some 31 per cent against the US dollar. The depreciation continued with a loss of 11 per cent in the first quarter of 2015 (Figure 10).

> 5 See Cbonds (no date) for some history of sovereign bonds in Ghana.

21
|Sequence vs. Control|0.574***<br>(0.131)|||
|---|---|---|---|
|Low experience|-0.301|-0.113|-0.752***|
||(0.208)|(0.256)|(0.199)|
|(Sequence vs. Control)*Low experience|-0.333<br>(0.282)|||
|Coaches vs. Control||0.570***<br>(0.147)||
|(Coaches vs. Control)*Low experience||0.288<br>(0.335)||
|Coaches vs. Sequence|||-0.175<br>(0.141)|
|(Coaches vs. Sequence)*Low experience|||0.824**<br>(0.333)|
|Observations|1,042|1,072|1,102|

_Source_ : Author's analysis based on experimental data generated for this study.

_Note_ : ** significant at 5 percent; *** significant at 1 percent. Robust standard errors in parentheses clustered at school level. Controls: (a) students’ characteristics (gender, age, nationality, parent’s education, if the student missed, at most, one class per month, if the student has internet in his/her home), (b) teacher characteristics (gender, age, general teaching experience [in years], if she/he has postgraduate certificates), and (c) school characteristics (school size, seventh size, seventh repetition rate, FEPBA score in Language, school district, or location). Low experience represents a dummy variable equal to 1 if the teacher has less than two years of experience in teaching Science and zero otherwise. An interaction term between treatment status and the dichotomous variable of low experience is included. All the regressions exclude the classrooms where the Human Body unit was not taught.

4
-36-

# TABLE A-2 PUBLIC SECTOR EMPLOYMENT

(in percent of total employment)

|||PUB|UC SECTOR E|MPLOY|MENT||
|---|---|---|---|---|---|---|
|LATIN AMERICA||General Civilian|Govemrnme<br>E|nt<br>mnploy|ment||
|AND|Govemment|Administration|Social Se|ctom|Total||
|THE CARIBBEAN|<br>Central<br>Govemment|Non Central<br>Govemment|Education|Health|General Civilian<br>Govemment|Armed<br>Forces|
|Antigua & Barbuda|||||||
|Argentina|2.6|8.3|2.4|1.0|14.3|0.6|
|<br>Bahamas|7.3|0.0|3.9|3.4|14.6|0.0|
|Barbados|9.9||2.8|3.9||0.0|
|Belize||||||1.7|
|Bermuda|||||||
|Bolivia|3.2|0.4|2.9|0.8|7.3|1.2|
|Brazil||||||0.4|
|Chile|0.9|0.5|0.2|1.0|<br>2.5|1.9|
|Colombia|0.3|1.5|2.3|0.5|4.6|1.1|
|Costa Rica|||||||
|Dominican Republic|||||||
|Ecuador|5.5|1.3|3.3|0.9|11.1|1.7|
|El Salvador|1.9||1.6|1.0|4.5|1.5|
|Guatemala||||||1.2|
|Guyana|3.9|1.0|0.2|0.8|5.6|0.5|
|<br>Honduras|3.2|0.4|2.0|0.3|5.9|1.1|
|Jamaica||||||0.3|
|Mexico|||||||
|Nicaragua|1.6|||1.7|3.2|1 .0|
|<br>Paraguay|_<br>9.1||||9.1|1.3|
|Peru|49||||4.9|1.6|
|Suriname||||||1.4|
|Trinidad and Tobago||||||0.5|
|Uruguay|5.9|2.9|5.3|2.0|16.0|2.0|
|Venezuela|3.3|1.5|2.4|0.4|7.6|1.1|
|_AVERAGE|3.6|1.8-|2.5|1.1i|8.9|1.0|

Coumn **aveagas** encompass only those countries for which the informatIon is complete.
income levels. Finance can more specifically help by distributing opportunities fairer. There is evidence that finance matters especially for poor households and smaller firms. Cross-country studies on the link between finance and poverty include Beck, DemirgüçKunt and Levine 2004a. Controlling for reverse causality, they find that financial development causes less income inequality. Clarke, Xu, Zou, 2002 also find that inequality decreases as finance develops, and, since the more concentrated income the higher poverty, finance thus helps reduce poverty.

Honohan (2004a and 2004b) specifically shows that financial depth explains poverty (number of people with income less than $1 or $2 a day). But, he also finds that across countries the degree of microfinance penetration, often thought to be specifically useful for the poor, has no special effects on poverty (see, however, Morduch and Hayley, 2002). Specific ways in which financial sector development has been found to help reduce poverty is by alleviating credit constraints that reduce child labor and increase education, including the opportunity cost of foregone child labor services, and by insuring against shocks (see Morduch, 2003 for the important of micro-insurance for poverty). More generally, with one or two exceptions, it is arguably that direct access of poor people to financial services can strongly affect the attainability of each of the Millennium Development Goals (MDGs)<sup>1</sup> , including those that chiefly require upgrading of public services in health and education, etc, as these also require poor households to be able to afford these services, which in turn is facilitated by their access to finance Second, as economic production is changing and countries are liberalizing their real economies, it has become clearer that the degree of financial development importantly influences the ability of countries, firms and individuals to make use of (new) growth opportunities. The fact that finance matters for firms’ growth opportunities is especially so for SMEs. Beck, Demirgüç-Kunt and Levine, 2004b show that, while large SME sectors are characteristic of successful economies, SMEs do not “cause” growth, nor do SMEs alleviate poverty or decrease income inequality. Rather they show that the overall business environment  ease of firm entry and exit, sound property rights, and proper

> 1 See IMF/World Bank 2004 on the MDG and progress in achieving them.

4