rafmacalaba/datause-encoder-data huggingface dataset:
- First, Latvia’s existing innovation and R&D policies which place public institutions at the center of the technology development process should gradually be replaced with policies that place industrial firms at the center of this process.

- Second, technology policy should recognize that a dense network of interactions and linkages – between enterprises and knowledge sources on the one hand and between enterprises and customers on the other – are critical aspects of the technology development process.  A key objective of public policy should be to <u>foster these linkages, interactions and feedback processes.  As Latvia joins the EU, it will have unparalleled access to dynamic industrial and research partners. The task for Latvia is finding ways to exploit these potential linkages to the fullest.</u>

- Third, as the diagram above suggests, technology policy should not be limited to <u>promoting R&D.  A much broader focus is needed, with a stress on technology</u> **<u>creation</u>** <u>, including both R&D and design and engineering skills, technology</u> **<u>acquisition</u>** <u>, and technology</u> **<u>use</u>** <u>.   These are all vital dimensions of technology</u> development.  Indeed, the non-R&D dimensions of technology development may <u>be especially important for Latvia</u> since most Latvian industries are not engaged in R&D, are far from the technological frontier, and do not require cutting edge R&D to improve their competitive standing.  For these firms, assistance in honing skills related to technology acquisition and use may be much more relevant than additional public R&D funding.

Put differently, the current R&D system is simply not a viable or effective instrument for creating a modern, innovative economy. For example:

- The Latvian innovation system shrank dramatically since the early days of transition.  With 17,000 researchers, Latvia was a major center of R&D in the former Soviet Union.  Most of these scientists and engineers were engaged in defense related activities.  But as defense orders dried up, the number of research personnel declined dramatically.  Unfortunately, shrinkage is not the same as structural reform.

**Table 7 Changes in R&D Personnel in Latvia, 1989-1999**

|**Employed in Science**|1990|1993|1996|1999|2000|
|---|---|---|---|---|---|
|Total number of employed persons|30,700|8,536|4,744|4,301|4,280|
|Total number of researchers|17,700|3,999|2,839|2,626|2,590|
|Researchers with degrees|3,710|1,977|1,491|1,492|1,495|

_Source:_ European Trend Chart on Innovation, 2001

- Latvia’s remaining innovation system is small, with only about 4000 personnel. Therefore, to generate a critical mass of R&D with this small base, Latvia will need to focus its R&D efforts in a few key areas.

- R&D personnel are ageing and not being replaced by new, younger cadres.  This is true both of R&D personnel as well as university professors in scientific disciplines.

43
<!-- Start of picture text --> Figure 1. Inflation in Israel<br>Annual rate. X<br>400<br>300<br>200_<br>100<br>O Mm<br>1949 1952 1955 1958 l981 1964 1t97 1970 1973 1970 1979 198 1985<br><!-- End of picture text --> associations; the communal sector, the kibbutzim, will generally not be discussed at this time. There are 420 moshavim in Israel with, on the average, 66 farm families per moshav. Seventy seven are "veteran" moshavim established before the creation of the State of Israel and the rest are "young"--established after 1948. The moshavim are again organized in 9 regional and 3 nation wide supply cooperatives (termed "purchasing organizations" in the Israeli parlance; for short, we refer to all of them as "regionals") and in several "movements," each associated with a political party. The movements represent the moshavim in the political arena and attempt to set standards and guide the moshavim in cooperative life-style.

Financial cooperation has been reduced drastically in reaction to the current crisis. But some of the moshavim and a few of the regionals, those with a stronger cooperative basis, are still active in credit intermediation--perhaps in a modified mode of operation. In the paper, we survey financial cooperation as it was before the crisis, and discuss the crisis itself separately. Other sections are devoted to the analysis of credit supply to agriculture and to the role of public intervention in the credit market, both in increasing and strengthening 3
# **Intergenerational Mobility around the World**<sup>*****</sup>

Roy van der Weide<sup>†</sup> Christoph Lakner<sup>‡</sup> Daniel Gerszon Mahler<sup>§</sup> Ambar Narayan<sup>¶</sup> Rakesh Ramasubbaiah<sup>‖</sup> Originally published in the <u>Policy Research Working Paper Series on</u> _June 2021._ This version is updated on _August 2021._ To obtain the originally published version, please email prwp@worldbank.org.

Keywords: Intergenerational mobility, Inequality, Education, Poverty. JEL codes: D63, I24, J62, O15.

> * The authors would like to thank Alexandru Cojocaru, Miles Corak, Francisco H. G. Ferreira, Berk Özler, Silvia Redaelli, Bob Rijkers, Carolina Sánchez-Paramo, and Stefan Thewissen for their generous comments and inputs. The research presented in this paper featured in the report titled “Fair Progress? Economic Mobility Across Generations Around the World” (Narayan et al., 2018). The Global Database on Intergenerational Mobility compiled for this study can be accessed at <u>http://www.worldbank.org/en/topic/poverty/brief/what-is-the-global-database-onintergenerational-mobility-gdim. The estimates are also included in the Equal Chances database at http://www.equalchances.org/web/index.php. We gratefully acknowledge financial support from the UK government</u> through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme.

> † Development Economics Research Group, World Bank (rwanderweide@worldbank.org) (corresponding author).

> ‡ Development Economics Data Group, World Bank.

> § Development Economics Data Group, World Bank.

> ¶ Poverty and Equity Global Practice, World Bank.

> ‖ Development Economics Research Group, World Bank.
Policy Research Working Paper 5463

# **Abstract**

Energy poverty is a frequently used term among energy specialists, but unfortunately the concept is rather loosely defined. Several existing approaches measure energy poverty by defining an energy poverty line as the minimum quantity of physical energy needed to perform such basic tasks as cooking and lighting. This paper proposes an alternative measure that is based on energy demand. The energy poverty line is defined as the threshold point at which energy consumption begins to rise with increases in household income. This approach was applied to cross-sectional data from a comprehensive 2005 household survey representative of both urban and rural India. The findings suggest that in rural areas some 57 percent of households are energy poor, versus 22 percent that are income poor. For urban areas the energy poverty rate is 28 percent compared with 20 percent that are income poor. Policies to reduce energy poverty would include support for rural electrification, the promotion of more modern cooking fuels, and encouraging greater adoption of improved biomass stoves. A combination of these programs would play a significant role in reducing energy poverty in rural India.

This paper—a product of the Agriculture and Rural Development Team, Development Research Group—is part of a larger effort in the department to understand the determinants of rural energy poverty and its influence in rural income and poverty. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhandker@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
by `outdetect` , with x indicating the target variable (say, consumption) and y indicating the transformed variable (say the log-consumption).

**Table 1. Transformations to approximate normality**

|**Description**|**Formula for****_y_**|**Source**|
|---|---|---|
|**Natural log**|y= ln (x)|1|
|**Log base 10**|y= log10(x+ a)witha= max[0, −(min(x) −0.001)]|2|
|**Box-Cox**|y={<br>x<sup>λ</sup>−1<br>λ<br>if λ≠0<br>ln(x)<br>if λ= 0<br>|3|
|**Yeo-Johnson**|y=<br>{<br> <br> <br> <br> <br>x<sup>λ</sup>−1<br>λ<br>if x≥0, λ≠0<br>log(x+ 1)<br>if x≥0, λ= 0<br>−[(−x+ 1)<sup>2−λ</sup>−1]<br>2 −λ<br>if x< 0, λ≠2<br>−log(−x+1)<br>ifx< 0, λ= 2|4|
|**Inverse**<br>**hyperbolic sine**|y = ln (x+ √x<sup>2</sup> + 1<br>)|5|
|**Square root**|y= √x|6|

Sources: [1,2 and 6] any math textbook; [3] Box and Cox (1964); [4] Yeo and Johnson (2000); [5] Friedline et al. (2014).

Each of the transformations in Table 1 has properties that fit different needs. For example, while the natural log may only be applied to strictly positive variables, log10 (x+ a) takes care of negative and zero values, too. Similarly, while the use of Box-Cox is limited to strictly positive variables, the Yeo-Johnson and the inverse hyperbolic transformations apply to all variables and perform relatively better in the presence of highly skewed distributions (which makes them particularly suitable for the analysis household wealth data). A general criterion to select the “best” transformation is goodness-of-fit: the transformation that provides the best approximation to a Normal distribution for the specific variable and data set in use will be the best choice for that particular context. `outdetect` allows the user to maximize goodness-of-fit relying on the Pearson chisquared test (Snedecor and Cochran, 1989). As pointed out by Peterson and Cavanaugh (2019), the Pearson statistic, P, divided by its degrees of freedom, df, converges to 1 when the data approaches a Gaussian distribution: Pdf⁄ can be interpreted as a measure of how close a distribution is to normality, and used to rank transformations according to how successful they are in normalizing the data.

7
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variables from the second wave of the LiK 2011 survey. Specifically, we use information from the 2012 module called “Local Budget” designed to collect information about awareness and participation of individuals in local budget processes (the questions in the module are illustrated in Table A.1 in the Appendix). On the individual level we use information about a person’s background, such as age, gender, ethnicity, education, labor market status, social capital, and subjective indicators, such as trust and political preferences. At the household level, we use data on household demographics, income, assets ownership, and location. We use the community level data pertaining to population size, locational and infrastructure characteristics.<sup>6</sup>

# **_Empirical methods_**

Descriptive analysis includes frequencies and means of the various indicators pertaining to the research questions of the study. We exploit both descriptive methods and regression models to investigate the relationship of dependent variables with various factors at the individual, household, and community levels. As responses to the questions on awareness and participation in local budget processes are given in several categories or in “Yes” and “No” responses, we respectively exploit ordered and binary logit regression models – the most suitable tools for such cases. The analysis also re-groups the multiple responses to binary responses (zero or one) to utilize binary logit models.

# **4. DATA**

# **_Sample_**

This paper analyzes rural households and individuals from the LiK survey.<sup>7</sup> In total, we exploit information from 5,145 adult members aged 18 and older, originating from 1,671 households (Table 1). The population, gender and ethnicity structure of respondents seem to be in concordance with the national level indicators.

# **_Individual, household and community characteristics_**

We exploit a rich set of individual, household and community characteristics available from the LiK survey: (i) objective characteristics, such as age, gender, ethnicity, education, labor market status; and (ii) individual attitudes and preferences, such as trust, political preferences, and social links. At the household level, we mainly look at demographics and wealth indicators, such as aggregate income, land, and livestock. We also look at the reliability of public services, represented by electricity provision. The same methodology is followed in analyzing community data, where we look at

> 6 Because in the LiK survey for 2012 the community characteristics were not collected, we use the community level information from the LiK 2011 survey.

> 7 While we do not analyze involvement of population in budget processes in urban areas, Table A.2 gives a comparison of responses of rural and urban residents.

6
the type of standards the World Bank database captures, since they mostly relate to testing and product specifications. CEN introduced EN 20105 in 1992, and amended some parts of it in 1994. It is therefore considered to be in force at all times during the database sample period (1995-2003). Since EN 20105 implements ISO standard 105, it is coded as being harmonized with ISO standards. The standard applies to all textiles, and is therefore mapped to all HS 2- and 4-digit codes that cover that product category, namely Chapters 50-63. As this example makes clear, individual standards often have very wide product coverage.<sup>10</sup> It is for this reason that in what follows, we aggregate the data so as to distinguish amongst three “sub-sectors” rather than 14 HS Chapters. Those sub-sectors are clothing (HS 61-63), fabrics (HS 56-60), and fibers (HS 50-55).

Tables 3-7 and Figures 1-5 contain some basic descriptive results from World Bank database. A number of trends are apparent over the sample period (1995-2003). First, EU standards are distributed unevenly across sub-sectors (Table 3 and Figure 1). Clothing accounts for only a modest fraction of the overall number of harmonized standards, a little over 10%. The main standardization activity has been in relation to fibers and fabrics, each of which account for around 45% of the total number of harmonized standards. This division is reasonably constant throughout the sample period.

Meanwhile, Table 4 and Figure 2 show that aggregating across sub-sectors, the share of internationally harmonized EU standards increased between 1995 and 1999, before falling for the remainder of the period. The overall movement involved is not large, however: the extreme shares are 45% and 56%. A more stark contrast appears when we compare the experiences of the

> 10 The correspondence table in Moenius (2000) between ICS and SITC categories discloses the same dynamic.

- 11 -
protecting industrial-country textile and apparel industries from low-income country imports was dismantled under an agreement worked out as part of the Uruguay Round of the GATT that entered into force in January 1995. Relaxation of the quota was to be phased in over 10 years, with the last quotas eliminated completely in January 2005, but most of the liberalization was deferred until the end of the period. Thus, the period 1995 to 2005 was largely a period of _anticipation_ of a trade liberalization.<sup>14</sup> We examine this episode with data on US manufacturing workers who were between 22 and 64 years old from the IPUMS-CPS database between 1981 and 2010. (See King et al. (2010)). The sample size is 267,281 workers over all years. We observe workers’ wage, industry, occupation, age, gender, education, and relation to the head of household. Figure 6 shows the evolution of textile and apparel wages relative to other manufacturing wages (in other words, the average textile-and-apparel sector wage divided by the average non-textileand-apparel manufacturing wage). The vertical lines in the figure mark the anticipation period. Clearly, wages in the sector increased sharply during the anticipation period and fell sharply afterward, as predicted by the model. Figure 7 shows the evolution of the share of textile and apparel employment within manufacturing. Clearly, the anticipation period saw a rapid movement of workers out of textiles and apparel, from around 8% to below 4%, and this movement was not simply the continuation of a pre-existing trend.

A natural question is whether or not these trends are driven by compositional effects. For example, it could be that the least-educated workers leave the sector first, driving up average wages without raising the wage for any one worker. This would be quite different from the anticipation rent we have discussed. To answer this question, we purge the compositional effects from wages and labor allocations using the following Mincer wage and logit regressions: 14Considerable background on the end of the Multifiber Arrangement together with some empirical analysis can be found in Evans and Harrigan (2005) and Harrigan and Barrows (2009).

38
24 Table III.1: Regional Pattern of Federal Guarantees (percentage shares)

|Region|number of<br>value of<br>approved projects *)|total stocks<br>**)|
|---|---|---|
|Africa|30.2<br>29.6|9.9|
|Latin America|33.7<br>32.0|47.3|
|Asia<br>Europe***)|22.8<br>27.9<br>13.3<br>10.5|12.7<br>30.1|
|Total<br>Memo items:<br>Total<br>(number/<br>DM billions)|100.0<br>100.0<br>2557<br>9.3|100.0<br>29.2|

*) as end of 1988.- **) as end of 1987.- ***) incl. Spain. Note: Country classification might deviate. Sources: Treuarbeit (1989), Deutsche Bundesbank

The German guarantee scheme has proven not to be quantitatively restrictive in the past, as no application has been turned down because of budget limitations. As it is at least potentially subsidized by the government, although the actual subsidy might be low, it is only justified, if it causes additional FDI in developing countries. In the following analysis it will be tested wiether the amount of federal guarantees had a significantly positive effect on German FDI outflows to developing countries.

# III.2 The Effect of Guarantees on FDI Qutflows

In a recent study on the determinants of German FDI in developing countries it was found that German FDI inflows to developing countries did not react to a decrease in the rating of a country's creditworthiness, whereas FDI inflows to developing countries in general were significantly negatively affected (see
**Table 3.  Estimates of the Impact of Business-as-Usual Survey Approach on** **<u>Labor Outcomes of All Adults Aged 18-64</u>**

|||**Within c**<br>**agricult**|<br>**rop**<br>**ure:**|<br>**Livestock**|<br>**Non-far**<br>**enterprise (N**|**m**<br>**FE):**|**Wage**|**_Ganyu_**|
|---|---|---|---|---|---|---|---|---|
||**A**|**A l 1**|**All**||||||
||**ny crop**<br>**work**<br>**(all HH)**|**t east**<br>**crop mainly**<br>**for sale**|**crops**<br>**for HH**<br>**own use**||**Ownership/**<br>**management**|**Helping/**<br>**supporting**|||
||**(1)**|**(2a)**|**(2b)**|**(3)**|**(4a)**|**(4b)**|**(5)**|**(6)**|
|**Total sample**|||||||||
|Participation (7d)|-0.015|0.007|0.017|-0.032 ***|-0.035 ***|-0.011 ***|-0.011|0.007|
||[-0.95]|[0.29]|[0.75]|[-2.93]|[-4.56]|[-2.69]|[-1.56]|[0.81]|
|R-squared|0.25|0.15|0.18|0.10|0.07|0.02|0.21|0.11|
|Participation (12m)|-0.013|-|-|-0.044 ***|-0.057 ***|-0.032 ***|-0.017 **|-0.004|
||[-1.09]|||[-3.38]|[-6.85]|[-5.15]|[-2.22]|[-0.39]|
|R-squared|0.38|-|-|0.16|0.08|0.04|0.23|0.22|
|_Observations_|_21,810_|_6,935_|_6,935_|_21,810_|_21,810_|_21,810_|_21,810_|_21,810_|
|Weekly hours (cond.)|-0.023|-|-|-0.145 ***|0.018|-0.169 *|-0.001|0.044|
||[-0.58]|||[-3.21]|[0.40]|[-1.67]|[-0.02]|[1.11]|
|R-squared|0.27|-|-|0.29|0.28|0.31|0.27|0.21|
|_Observations_|_7,026_|-|-|_3,745_|_2,215_|_718_|_2,291_|_4,249_|
|Annual hours (cond.)|-|-|-|-|-|-|0.069<br>[1.21]|0.177 ***<br>[4.14]|
|R-squared|-|-|-|-|-|-|0.25|0.18|
|Annual earnings|||||||||
|<br>(cond.)|-|-|-|-|-|-|0.070|0.134 ***|
||||||||[0.91]|[2.79]|
|R-squared|-|-|-|-|-|-|0.44|0.22|
|_Observations_|<br>-|-|-|-|-|-|_2,680_|_8,971_|
|**Notes:**|||||||||

The results are from linear probability models for binary outcomes, and from linear regressions for hours and earnings. Coefficients represent the impact of the business-as-usual survey approach, originating from regressions that also include the aforementioned control variables, and district, month and enumerator fixed effects. The regressions are weighted by the response weight, and t-statistics accounting for clustering at the enumeration-area level are presented in brackets. Significant differences are indicated with * (p<0.1), ** (p<0.05) and *** (p<0.01).

15