rafmacalaba/datause-encoder-data huggingface dataset:
23 attainment of men (7.0 years). In Argentina, working women have 9.4 years of schooling compared to 8.8 years for men and only 7.8 years for non-working women. The disparity between female and male length of schooling is even greater in the less advanced countries of the region and between urban and rural areas." Providing more education to women appears to be a promising direction social policy can move in. In terms of simple arithmetic, average female education will increase more, and in a more cost-effective way, if many illiterate women attend primary school than if a few secondary school graduates attend a 4-year university course. In this respect, the high rates of return to female university education when education entered the earnings fur.ctions as a spline variable rather than in a continuous variable representing years of schooling, need be qualified accordingly.<sup>52</sup> First, the most qualified workers, especially females, find employment in the public sector, and our estimates may simply reflect this. Second, and more importantly, the earnings functions that are estimated in the conventional econometric form are based on the explicit assumption that the only cost of education is foregone earnings during the period of studies, which amounts to saying that education is a free good in terms of other costs. This is clearly unrealistic and the difference between the returns to primary and university education is not necessarily so great as to justify the public provision of more tertiary education at the expense of lower levels of education.53 Third, and finally, the pro-rich distributional effects of the emphasis on tertiary education, rather than basic education, in developing countries have been widely documented.<sup>4</sup> With respect to other determinants of female participation, the econometric results showed that family characteristics are important determinants for a woman to participate or not to participate in the labor force. Single women are more likely to participate than married women. Among the married, tulose with many and young children are less likely to participate than those with fewer or older children. Though some of these "characteristics" are endogenous (for example, women decide about whether to work and whether to have a family and children), policies which affect family size can be beneficial. One such measure is improving women's understanding, especially in rural or relatively poor areas, of how to avoid unwanted pregnancies "See individual country studies in Psacharopoulos and Tzannatos (1991).

2 nThis finding is quite common in developing countries (Haque, 1984; Khan and Irfan, 1985).

"<sup>3</sup> For estimates of the cost-efficiency of investment on different levels and types of education see Adelman (1975); Colclough (1982); Mingat and Tan (1988); Psacharopoulos (1977, 1985); Lockheed (1988).

'The unintentional distributional effects of public expenditure on education have been shown by among others Ribich (1968); Selowsky (1979); Stromquist (1986); Tzannatos (1991).
national headcounts makes the counter-intuitive social judgement that someone who is poor in a poor country may not be poor if their wellbeing were assessed in a richer country.

# _Database of National Poverty Lines_

The most recent expressions of global relative poverty lines (Ravallion and Chen, 2011; Chen and Ravallion, 2013), and similarly the $1.90 international poverty line (Ferreira et al.

2016), are estimated from a set of national poverty lines compiled by RCS (2009). The compilation of these national poverty lines by RCS, and previously by Ravallion, Datt and van de Walle (1991), has been foundational to the work on measuring global absolute and relative poverty. More recently, Jolliffe and Prydz (2016) offer a compilation of national poverty lines with significantly better temporal and geographic coverage and improved harmonization (and thus improved comparability), compared with previous data sets.

In this paper, we draw from the Jolliffe-Prydz data and use a sample of 699 national poverty lines covering 107 countries.<sup>11</sup> The sample of national poverty lines is compiled by taking officially reported poverty headcount (HC) rates at national poverty lines and identifying the unique value on the harmonized wellbeing vector in PovcalNet for the same year that

**·** corresponds to the reported national poverty headcount.<sup>12</sup> In other words, if F( ) is the

> 11 The sample of 699 poverty lines is the same as the non-OECD country sample in Jolliffe and Prydz (2016). The 104 countries in our sub-sample of lines close to 2011 drops three countries for which the closest line is prior to 2000.

> 12 The national poverty headcount rates come from the World Development Indicators (WDI, <u>http://data.worldbank.org/data-catalog/world-development-indicators) database. The WDI is the primary</u> World Bank collection of development indicators, compiled from officially-recognized international sources. PovcalNet is  a research tool for poverty measurement that allows analysts to specify parameter values such as the global poverty line, and then estimate the number of poor people in the world based on their assumptions. For more details, see: http://iresearch.worldbank.org/PovcalNet/index.htm.

9
**Table 5. Determinants of Program Duration across Communities 1997-2000 (pooled OLS)**

|Model|(1)|(2)|(3)|
|---|---|---|---|
|**_Health Status_**||||
|Average Height-for-Age|-|-0.87<br>(0.90)|-|
|Proportion of Children Stunted (%)|-|-|4.61*<br>(2.64)|
|**_Sociol-Economic Status_**||||
|Average Per-Capita Expenditure|-0.91|-0.7|-0.47|
||(1.16)|(1.19)|(1.20)|
|Farm Household Share (%)|-3.01|-2.72|-2.73|
||(2.77)|(2.80)|(2.76)|
|Share of Households with Male Head (%)|1.43|1.28|1.35|
||(2.12)|(2.10)|(2.10)|
|**_Physical Infrastructure_**||||
|Availability of Asphalt Road (Yes=1)|2.1|2.02|2.07|
||(1.77)|(1.76)|(1.76)|
|Proportion of Land with Semi-Tech Irrigation (%)|-4.25|-4.18|-4.7|
||(4.02)|(3.96)|(3.88)|
|Access to piped water (yes=1)|0.12|0.15|0.15|
||(1.39)|(1.38)|(1.38)|
|**_Remoteness of Community_**||||
|Distance to District Capital (km)|0.04*|0.04*|0.04*|
||(0.02)|(0.02)|(0.02)|
|Distance to Bus Station (km)|-0.14|-0.14|-0.15|
||(0.10)|(0.10)|(0.10)|
|Public Transportation in Community (Yes=1)|-4.30***|-4.30***|-4.39***|
||(1.53)|(1.52)|(1.52)|
|Urban Community (Yes=1)|2.61*|2.75*|2.78*|
||(1.56)|(1.56)|(1.55)|
|District Fixed-Effects|Yes|Yes|Yes|
|R-Squared|0.24|0.25|0.25|
|F-Test (economic status) (p-value)|0.79|0.59|0.53|
||(0.50)|(0.62)|(0.66)|
|F-Test (infrastructure) (p-value)|0.80|0.78|0.90|
||(0.49)|(0.51)|(0.44)|
|F-Test (remoteness of community) (p-value)|3.18|3.18|3.26|
||(0.01)|(0.01)|(0.01)|

Notes: Dependent variable is number of months the program was in the community between 1998-2000. Independent variables are community averages of 1997 covariates. Standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5% and 1%.

31
delayed prior to being shipped reduces trade by at least 1 percent. Nordas, Pinali, and Geloso Grosso (2006) analyze the relation between time for import and export procedures, logistics services, and international trade, and find that time delays result in lower trade volumes and can reduce the probability that firms will enter export markets for time-sensitive products. Clark, Dollar, and Micco (2004) explain variations in trade costs due to port efficiency on bilateral trade flows. They show that improving port efficiency reduces shipping costs a lot. They find that improving port efficiency from the 25th to the 75th percentile can reduce shipping costs by 12 percent.

Eifert et al. (2005) explore firm-level data on total factor productivity for about 3,000 firms in Africa over the period 2000–04. They show that a weak business environment is reflected in disproportionately high indirect costs, thereby lowering the return to labor in production and reducing demand for labor and real wages.

# **3. Constructing Trade Facilitation Indicators Using Factor Analysis**

In the context of trade facilitation indicators, several approaches have been used to construct aggregate indicators from primary variables. The simplest approach is to average primary variables into an aggregate indicator; the underlying assumption is that the relative importance of each primary variable is proportional to its weight in the indicators. Principal component analysis is another way of reducing the multidimensionality of the data, by transforming the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie in the first coordinate or principal component, the second greatest variance on the second coordinate, and so on. Francois and Manchin (2007) use principal component analysis to construct institutions and infrastructure indicators based on the first two components of each aspect.

Unlike principal component analysis, factor analysis proposes an explicit underlying model that attempts to “explain” correlations among a set of m observed variables (X1, X2,..., Xm) through the linear combination of a few latent (unobserved) random factors (Fs). In the case of a single factor, F, the underlying model is defined as: 9
24 Jappelli, T. and M. Pagano (1995): “Saving, Growth, and Liquidity Constraints”, Quarterly Journal ofEconomics 109: 83-109.

Kiviet, J.F. (1995): “On Bias, Inconsistency, and Efficiency of various Estimators in Dynamic Panel Data Models”, Journal ofEconometrics 68: 53-78.

Levine, R., N. Loayza, and T. Beck (2000): “Financial Intermediation and Growth: Causality and Causes”, Journal ofMonetary Economics, forthcoming.

Loayza, N., H. Lopez, K. Schmidt-Hebbel, and L. Servén (1998a): “The World Saving Database,” World Bank manuscript, The World Bank, Washington, DC.

Loayza, N., H. Lopez, K. Schmidt-Hebbel, and L. Servén (1998b): “Saving in the World: Facts, Trends, Correlations,” manuscript presented at the World Bank Conference on Saving in The World: Puzzles and Policies, The World Bank, Washington, DC, Sep. 16-18.

Lopez, H., K. Schmidt-Hebbel and L. Servén (2000): “How Effective is Fiscal Policy in Raising National Saving?” Review ofEconomics and Statistics, this issue.

Maravall, A. and Planas, C. (1999: “Estimation Error and the Specification of Unobserved Component Models,” Journal ofEconometrics 92, p. 325-353.

Masson, P., T. Bayoumi and H. Samiei (1995): “Saving Behavior in Industrial and Developing Countries,” IMF manuscript.

Modigliani, F. “Saving in Developing Countries: Income, Growth and Other Factors”, Pacific Basin Capital Markets Research 3: 23-35.

Schmidt-Hebbel, K. and L. Servén (1997): “Saving across the World: Puzzles and Policies,” World Bank Discussion Paper No. 354.

Schmidt-Hebbel, K. and L. Servén (1999): The Economics of Saving and Growth: Theory, Evidence, and Implications for Policy. Cambridge: Cambridge University Press.

White, H. (1982): “Instrumental Variables Regression with Independent Observations,” Econometrica 50: 483-499.

Ziliak, J. (1997): “Efficient Estimation With Panel Data When Instruments Are Predetermined: An Empirical Comparison of Moment-Condition Estimators,” Journal of Business and Economic Statistics 15 (4), p. 419-431.
# **Figure 1 Ratio of Primary to Manufacturing Commodity Price Indices (2010 = 100)**

<!-- Start of picture text --> 180<br> 160<br> 140<br>Agriculture<br> 120<br> 100<br> 80<br> 60<br>Metals<br> 40<br> 20<br>Energy<br> ‐<br>1948 1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 2008 2013<br>Source : World Bank<br><!-- End of picture text -->

**Figure 2**

# **Nominal Agricultural and Manufacturing Price Indices (2010 = 100)**

<!-- Start of picture text --> 140<br> 120<br> 100<br>Manufacture<br> 80<br> 60<br>Agriculture<br> 40<br> 20<br> ‐<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012<br>Source : World Bank<br><!-- End of picture text --> _23_
28

# Annex Bs Statistical Annex.

# Notation for Tables 8.1 and B.2

# Tabl& l l

# a) **and** b)

- I aggregate investment

Y gross domestic product (GDP) T total taxes C private consumption Gna non-interest current public expenditure (national accounts) O non-interest current public expenditure (fiscal accounts) (K-Z)na trade deficit (national accounts) Erbop beginning of period commercial exchange rate Eravg average comercial exchange rate LIBOR foreign interest rate (2/3 of LIBOR plus 1/3 US CD rate) EcBal economic fiscal deficit

- Ipu public investment

- GDPd GDP deflator

is nominal interest domestic public debt iDpun nominal interest on foreign public debt iBf nominal interest on domestic federal debt iDpufn nominal interest on foreign federal debt inf CPI inflation (december to December) inf-avg CPI inflation (average compared to beginning of period)

## C)

- Dpr private foreign debt F private foreign assets

- CACC current account balance

- lFo interest retained abroad (official)

- WR workers remittances

- int-exp interest received from abroad (official)

iDn net foreign interest paid (excluding interest on private assets) US-WPI wholesale price index of US

- (M-X) trade deficit (SOP accounts)

iD-g gross external interest paid D total foreign debt Dpu public foreign debt d) DD domestic debt CBD-BS commercial bank deposits at Bank of Mexico Cetes Cetes interest rate (90 days to 1984, 28 days thereafter) CPI consumer price index MBASE monetary base iB domestic interest on public debt

# Table 8.2.

(1 ef GDP) Sp/Y private saving Sg/Y public saving
products that open a large merchandizing market (Santagata 2002).<sup>8</sup> In addition, a growing concentration signals opportunity, which helps attract the best talent as individuals with ideas or relevant skills migrate in from other locations.

# **_I. Creative Cities in the Context of A National Innovation System_**

What then drives innovation in creative cities? Traditional thinking focuses more on internal factors, such as the capabilities and processes within firms. Recent research shows that the external environment for innovation or a creative milieu is at least as important (Kresl and Singh 1999, Porter 1998, Porter and Stern 2001). Creative milieu is similar to what historians have termed as a “moral temperature” allowing a particular kind of talent to develop in one place at one time (Hall 2000). In periods of intense creativity historically (mainly in the arts), a group of people have acquired a set of common characteristics, a kind of accumulated culture and style of life. Major breaks also seem to come through clashes between generations. A creative milieu, a notion similar to that of the innovative milieu, has four key features: information transmitted among people, knowledge or the storage of information, competence in certain activities, and creation of something new out of these three activities ( _ibid_ ).

Beyond the local environment, the vitality of innovation in a place also is shaped by a national innovation system – a complex network of agents, policies and institutions supporting the process of technical advance and spanning all industries (Crow and Bozeman 1998, Nelson and Rosenberg 1993, Porter and Stern 2001). This system, specifically, includes a nation’s IP protection system, its universities and its research laboratories. More broadly, it may also include many other subsystems and processes, such as norms of competition and a nation’s financial and monetary policies. Research shows that the aggregate level of R&D spending, the effectiveness of IP protection, openness to competition and the intensity of spending on higher education are particularly important determinants of innovative output (Porter and Stern 2001).<sup>9</sup>

> 8 For the software cluster, Bangalore’s huge pool of software engineers is attracting attention for services that include E-commerce, customer relationship management, application service providers, and information technology (IT) enabled services.  A McKinsey study notes that IT-enabled services could generate annual revenues of $17 billion in India by 2008 and create as many as 1 million new jobs (cited in Reason 2001).

> 9 The importance of national innovation systems is showcased in, for instance, Denmark and Finland.  They have made major gains in wireless technology since the mid-1980s by substantially increasing their R&D workforce and investment and emphasizing policies that support open competition and strong IP protection.  New centers of innovation also are emerging outside of the western hemisphere, in Singapore, Taiwan (China), Korea and Israel (Porter and Stern 2001).

4
<!-- Start of picture text --> Figure 1. Food prices<br>(Nominal $ Index, Jan 2000=100, world export value weights)<br>250<br>200<br>150<br>100<br>50<br>0<br>Jan-00 Jan-02 Jan-04 Jan-06 Jan-08<br>Source: DECPG<br><!-- End of picture text -->

**The increase in food commodities prices was led by grains (Figure 2) which began sustained price increases in 2005 despite a record global crop** in the 2004/05 crop year<sup>9</sup> that was 10.2 percent larger than the average of the three previous years and a nearrecord crop in 2005/06 that was still 8.9 percent larger. Global stocks of grain increased in 2004/05 but declined in 2005/06 as demand increased more than production. From January 2005 until June 2008, maize prices almost tripled, wheat prices increased 127 percent and rice prices increased 170 percent. The increase in grain prices was followed by increases in fats & oils prices in mid-2006, and that also followed a record 2004/05 global oilseed crop that was 13 percent larger than in the previous year and an even larger crop in 2005/06. Fats

& oils prices have shown similar **Figure 2. Food price sub-indices** increases to grains, _(Nominal $ Index, 2000=100, world export weights)_ with palm oil prices 350 Fats & Oils up 200 percent from 300 ~~Grains~~ January 2005 until <u>Other</u> 250 June 2008, soybean oil prices up 192 200 percent, and other 150 vegetable oils prices 100 increasing by similar 50 amounts. Other foods prices (sugar, 0 citrus, bananas, Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 shrimp and meats) Source: DECPG increased 48 percent from January 2005 to June 2008.

> 9 Crop years begin with harvest and continue until the next harvest.

3
regional poverty line selected, we find that approximately 8.0 percent to 12.0 percent of individuals qualified as extremely poor in 2013, while approximately 25.0 percent to 33.0 percent qualified as moderately poor during the same year. Finally, we present an innovative decomposition exercise which shows the effect that the new underlying data of subnational poverty lines, the new PPP values, and the change of time period have on the poverty headcount.

The rest of the document is organized as follows. Section 2 introduces several stylized facts related to the estimation of comparable poverty lines. Section 3 brings together the various sources of information used, highlighting the potential of the official poverty lines data set as well as the recent 2011 PPP-ICP. Section 4 presents the methodology and the results, including the proposal for international poverty lines for LAC and a simple simulation exercise of the potential effects of the introduction of these new reference lines on poverty rates in LAC using a harmonized set of household surveys under the SEDLAC project. Section 5 presents our conclusion.

# **2 Counting the poor: Why do we need to update the regional methodology in LAC?**

Given the economic development of most of the countries in LAC, all nationally-defined official poverty lines in the region are greater than $2 per person per day, and the share of population under the Global Extreme Poverty Line frequently used for international poverty comparisons in the developing world (i.e., $1.25 per person per day) is relatively low in most economies in the region. Therefore, researchers and international organizations have used higher lines for poverty comparisons in LAC. The World Bank has used a $2.5 and $4 per person per day for extreme and moderate poverty in 2005 PPPs, respectively. In this section we argue that there are several important reasons for considering the update of these two regional lines for poverty comparisons in LAC.

First, the ICP has recently released new internationally comparable 2011 PPP values, increasing coverage, both globally and within countries, and introducing certain changes to the methodology used to calculate PPP values in 2005. The new 2011 ICP data have solved some of the well-known overestimation of the 2005 PPPs in Asia, Western Asia, and Africa (Ferreira et al. 2015; Deaton and Aten 2014). Moreover, Central American countries, including the Dominican Republic and Haiti, did not participate in the 2005 ICP round; their PPP values were obtained from 5
O.A.M.D.G.

households: there is evidence that absolute wealth levels attract certain types of crime.<sup>29</sup> However, the households in our sample come from the lowest strata of the Colombian income distribution and are unlikely to be targeted specifically because of their asset constellation. Further, there is no evidence that specific portfolio shares of certain assets attract crime. Third, we have to take into account that displacement and migration influence the household portfolios observed in a region. It is conceivable that people adjust their portfolio in order to migrate, and still hold a skewed portfolio once they reached the peaceful area. We therefore correct for potential sample selection by including the Inverted Mills Ratios calculated from the underlying SISBEN register.

For the actual choice of an econometric specification, we propose using various approaches to test the sensitivity of our predictions. Translating our model directly into a specification would suggest testing simultaneous OLS regressions of valued asset stocks on violence variables and controls. Given that many assets will be debt financed, it would also make sense to include the demand for debt as a third regression. We test this approach below in section A). Further, our model included a message about the share of fixed vs. mobile assets in the overall portfolio, which we examine through a Tobit in section B). Finally, our bi-variate analyses revealed an interesting dynamic at the very poorest fringe of the population. The mere participation in the two kinds of assets appeared to respond to violence. We therefore test this in section C) below.

# **A) Valued asset demands**

We estimate the absolute asset demands, and the demand for debt (mortgage + cash debt) with a seemingly unrelated regression model. A seemingly unrelated regression (SUR) system is a set of regressions which seem to be unrelated, but which have

> 29 Gaviria and Pages (1999) and Gaviria and Vélez (2001).

28
WPS3894

# **LABOR MARKET DEVELOPMENTS * DURING ECONOMIC TRANSITION**

## **Jan Rutkowski**

### World Bank Policy Research Working Paper 3894, April 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*This paper was prepared as a background paper for the 2005 ECA Labor Market Study. I would like to thank Mamta Murthi for helpful comments, and Barbara Ziolkowska for preparing  the paper for publication.
# **Chart 4B: Average income level of world exports of sophisticated products, 19902006 (excluding China)**

<!-- Start of picture text --> 26000<br>above median<br>25500<br>above 75th pctile<br>25000<br>24500<br>24000<br>23500<br>23000<br>1990 1995 2000 2002 2004 2005 2006<br>Income level (per capita GDP, $ PPP)<br><!-- End of picture text --> See note to Chart 4A above.

17