rafmacalaba/datause-encoder-data huggingface dataset:
(Dickey and Fuller 1981) in the last column jointly tests for a unit root and a deterministic trend. The test statistics for all the variables except mechanical patents are less than the critical value, indicating that there are no deterministic trends.

Table 1: **Testing the Variables for** Order of Integration

|Variable Name and Abbreviation|AR order<br>DF Tests|CRDW Tests|D-F 0, Tests|
|---|---|---|---|
|LOG RATIO MACHINERY/LABOUR (M/L)|1<br>-2.40|0.07|3.07|
|A LOG RATIO MACHINERY/LABOUR|-5.66|1.75||
|LOG RATIO PRICE MACHINERY/LABOUR (PM/PL)|1<br>-1.36|0.18|1.37|
|A LOG RATIO PRICE LABOUR/MACHINERY|-7.48|2.29||
|LOG<br>RATIO<br>FERTILIZER/LAND<br>(F/A)|l<br>_-179|_0.03_|_2.24_|
|A LOG RATIO FERTILIZER/LAND|-5.94|1.85||
|LOG RATIO PRICE FERTILIZER/LAND<br>(PF/PA)|I<br>-1.22|_0.03_|1.26|
|A LOG RATIO PRICE FERTILIZER/LAND|-5.93|1.85||
|LOG RATIO PRICE LAND/LABOUR<br>(PA/PL)|I<br>-_I.41|0.12|2.48|
|A LOG RATIO PRICE LAND/LABOUR|-6.76|2.11||
|LOG OF REAL R&D EXPENDITURES (RD)|I<br>-1.07|0.04|1.63|
|A LOG OF REAL R&D EXPENDITURES|-5.32|1.42||
|LOG OF REAL EXTENSION EXPENDITURES (EXT)|1<br>-2.07|0.12|4.28|
|A LOG OF REAL EXTENSION EXPENDITURES|-7.57|1.97||
|LOG OF FARM SIZE (SIZE)|I<br>-0.37|0.02|1.68|
|**A LOG**OF FARM SIZE|_-5.15_|1.65||
|LOG OF CHEMICAL PATENTS (CHP)|I<br>-2.68|0.11|6.45|
|A LOG OF CHEMICAL PATENTS|-8.46|2.54||
|LOG OF MECHANICAL PATENTS (MEP)|1<br>-3.81|0.66|13.50|
|A LOG OF MECHANICAL PATENTS|-6.26|1.95||
|**Critical values**|**AU variables**<br>**-2.90**|**0.67**|**6.73**|

Apart from establishing that all but one of the variables have the same time series properties and may cointegrate, these results indicate that for South Africa, the induced innovation hypothesis should be formulated in the original manner (Hayami and Ruttan, 1985), with factor ratios being a function of factor price ratios. This is contrary to Tiffin and Dawson (1995) who suggest modifying the hypothesis to make _changes_ in factor ratios a function of factor price ratios. This result comes from finding that for Hayami and Ruttan's (1985) US data, the factor ratios are 1(1), while the price ratios are 1(2).

9
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What about those that demand information? Consumers and citizens<sup>willonlydemand</sup> information if it is perceived as useful and will only pay for it if they cannot get it otherwise.<sup>2</sup> Similarly for private business. Countries are often cited to have or not have a "culture"<sup>of</sup> openness. In other words citizens either do not see value in having certain<sup>kindsof information</sup> being made public or, put another way, do not have strong enough<sup>incentivestopressure</sup> governments or private agents to make such information available. Or sometimes<sup>theydonot</sup> have strong enough coalitions to support their desire for greater openness and/or<sup>the transactions</sup> cost of forming coalitions is too high.

This paper extends the empirical work on information and economic and political markets. It examines how the availability of information may affect<sup>governance. Specifically,it</sup> looks at (a) how the availability of basic economic data affects governance<sup>and (b) how the legal</sup> framework governing access to information might affect the quality of governance.<sup>Iask,is</sup> better knowledge about economic performance in terms of the timely<sup>availabilityof economic</sup> data associated with better economic and political outcomes? Second,<sup>I examinehow restrictions</sup> on the use of information can affect the quality of governance. In<sup>particular,how restrictionson</sup> the media may affect information flows and therefore governance.<sup>Massmediaprovide</sup> consumers and producers with information that they use to make<sup>decisionsineconomicand</sup> political markets. The specific restriction I consider is the presence of<sup>a Freedomof Information</sup> Act or Law (FOIA). FOIs determine the modalities by which citizens or<sup>privatebodiescan</sup> obtain information which resides with public entities.

It is clear how economic data helps economic markets function<sup>better.Investors,</sup> consumers and producers can make better business decisions by better<sup>assessingmarket</sup> conditions for their products. For example, price and inflation data<sup>help determineconsumers'</sup>

> **2** Information being a public good suffers from the classical problems.

4
Jovanovic (1982), who proposed a Bayesian model of noisy selection in which efficient firms grow and survive, whereas inefficient ones decline and fail. Jovanovic’s model of entrepreneurial learning is perfectly consistent with a world in which founders are quite heterogeneous in terms of both general and specific characteristics, entry mistakes can easily occur, entry can be originated by a “try and see” bet and early failures are rather common (see previous sections; Hopenhayn 1992; Ericson and Pakes 1995).

If entrepreneurial learning is crucial and entry is often tentative, both spinoffs (entrepreneurs leaving a mother firm to found a new business) and “serial entrepreneurs” (founders who have previously run other businesses) may have an advantage compared with “de novo” entrepreneurs.<sup>11</sup> For example, Hirakawa, Muendler, and Rauch (2010), using microdata from Brazil over the 1995–2001 period, found that spinoffs are characterized by larger entry sizes and lower exit rates than are new firms that are not generated by a parent company. Similarly, the role of past experience and path-dependence is confirmed by the fact that serial entrepreneurs are more likely to replicate the success of their past companies than single venture entrepreneurs or serial entrepreneurs who failed in their prior business (Gompers et al. 2006).

Empirical studies on DCs provide support for the importance of entrepreneurial learning for the post-entry performances of newborn firms either by observing the direct effect between experience and survival (Parker 1997) or by showing that in contexts characterized by a substantial absence of learning opportunities, the average survival is quite short (Barr 1998). McPherson (1996) found a positive relationship between annual employment growth and the previous experience of the founder in similar economic activities for entrepreneurial firms in Swaziland and Botswana, whereas Vijverberg (1991) and Goedhuys and Sleuwaegen (2000), in studies of Côte d’Ivoire, found that job experience 14
# Table 8. EFFECT OF IE ON GAP

|||DID|-OLS|||DID|-RE|||DID|-FE||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**T (quarters with IE==1)**|-0.073***|-0.061***|-0.074***|-0.061***|-0.065**|-0.038|-0.066***|-0.037|-0.066**|-0.034|-0.067**|-0.034|
||(-8.17)|(-5.72)|(-8.28)|(-5.73)|(-2.56)|(-1.48)|(-2.60)|(-1.45)|(-2.43)|(-1.29)|(-2.47)|(-1.27)|
|**D (project with IE==1)**|-0.0098|0.0056|-0.0094|0.0055|-0.0090|-0.0080|-0.0079|-0.0083|||||
||(-1.43)|(0.46)|(-1.37)|(0.45)|(-0.51)|(-0.33)|(-0.44)|(-0.34)|||||
|**_TTL switch dummies_**|Y|N|Y|N|Y|N|Y|N|Y|N|Y|N|
|**_TTL UPI dummies_**|N|Y|N|Y|N|Y|N|Y|N|Y|N|Y|
|**_Polynomial of sector averages_**|N|N|Y|Y|N|N|Y|Y|N|N|Y|Y|
|**_Observations_**|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|
|**_Outcome mean_**|0.148|0.148|0.148|0.148|0.148|0.148|0.148|0.148|0.148|0.148|0.148|0.148|
|**_Effect in %_**|49.3%|41.2%|50.0%|41.2%|43.9%|25.7%|44.6%|25.0%|44.6%|23.0%|45.3%|23.0%|

_Note:_ Standard errors are clustered (sector and quarter level). T-stats are in parenthesis. All specifications include fixed effects for quarters of project life cycle, sector, fiscal year when project became effective and country.

Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent 35
27 Table 5: Health facilities used in provider analysis

|||‘Untreated’|‘Treated’|Total|
|---|---|---|---|---|
|County hospitals|Off common support|1,284|112|1,396|
||On common support|253|97|350|
||Total|1,537|209|1,746|
|Central THCs|Off common support|3,109|259|3,368|
||On common support|813|283|1,096|
||Total|3,922|542|4,464|
|General THCs|Off common support|9,273|800|10,073|
||On common support|2,936|1,031|3,967|
||Total|12,209|1,831|14,040|
<!-- Start of picture text --> (jo)<br>N<br>=<br>n<br>><br>S<br>=<br>~—<br>wn<br>S<br>&<br>{))<br>To<br>o (oo)<br>oe<br>[o)<br>ne}<br>O<br>fb)<br>n<br>oO<br>dD)<br>Ts)<br>g<br>i?<br>oO<br>AGO BEN CIV CMR COD COG COM CPV GAB GHA GIN GMB GNB GNQ KEN LBR MDG MOZ MRT MUS NAM NGA SEN SLE SOM SSA STP SYC TGO TZA ZAF<br>ME Baseline MERE With port and border improvements<br><!-- End of picture text -->
population in the first quintile of LYD 243 per capita would be about LYD 2 billion. In case targeting the first quintile is not possible, extending a transfer of LYD 243 per person per year to the entire population—sufficient to maintain poverty at 8.5 percent—would reduce the net gains to the budget from subsidy reform _and_ cash transfers to about LYD 1 billion per year.

**Figure 8: Poverty Impact of Cash Transfers to First Quintile under Energy Subsidy Reform S2**

<!-- Start of picture text --> Initial level of poverty: 8.475 percent<br>20<br>Required transfer to maintain poverty at pre-reform level: 242.995 LYD per capita per year<br>15<br>10<br>5<br>0<br>0 200 400 600<br>Level of individual transfer (LYD per capita per year)<br>Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.<br><!-- End of picture text --> Energy price increases would also be expected to reduce consumption (Table 26).<sup>23</sup> Based on our assumptions, a 30 percent reduction in energy subsidies would reduce the quantities of energy products consumed by 46 percent for electricity, 52.7 percent for kerosene, and 40 percent for gasoline and diesel. The estimated impact on quantities would also vary across quintiles. For kerosene, for example, the impact would be greater for richer households, but for other products such as diesel and gas GPL the impact would be the greatest for the second quintile.

> 23 These results are entirely dependent on the choice we made regarding the point elasticity at market price and the shape of the demand curve. Underlying our analysis are demand curves that depict the same elasticity for all households but differ in elasticity across products, with the difference depending on the gap between market price and subsidized price. For energy products, we assumed a point elasticity of -0.5 at the free market price. This estimate and a linear demand curve function are then used to estimate the point elasticity at the subsidized price.

29
Policy Research Working Paper 10433

# **Abstract**

This paper studies the relationship between participation in global value chains, worker routine task intensity, and within-country wage inequality. It uses unique survey data from 47 countries across the development spectrum to calculate worker-level, country-specific routine task intensity and combines them with sectoral measures of backward and forward global value chains participation. Higher global value chains participation is associated with more routine-intensive work, specifically among offshorable occupations, especially in countries at lower development levels. The results by broad sectors contrast sharply: higher global value chains participation is linked to a higher routine task intensity in offshorable occupations in the industry but a lower routine task intensity in non-offshorable occupations in business services. Higher worker-level routine task intensity is strongly associated with lower wages, so global value chains participation indirectly widens the within-country wage inequality through this routine task intensity channel. At the same time, global value chains participation directly contributes to reduced wage inequality, except for the richest countries. Overall, this analysis finds that global value chains participation reduces wage inequality in most low- and middle-income countries that receive offshored jobs but widens wage inequality in high-income countries that offshore jobs.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at piotr.lewandowski@ibs.org.pl (corresponding author), karol.madon@ibs.org.pl and dwinkler2@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
The equation was estimated for only Ruens in 1987/88 and the Vaalharts area in 1990/91. These surveys included information which allows for specification of the variables in equation 1, with some adjustments to capture the specific set of circumstances particular to the local situation. The specification of P/K and OP in equation I is straight forward from the available data. Most of the land is owned by the operator, with the results that OW is correlated with OP. To avoid estimation problems, OW was specified as the percentage of land owned by the operator. Due to the situation in South African commercial agriculture, H was specified not as the number of household workers, but as remuneration of management (including the owner and household members, and people other than that of the owner which benefit from revenue sharing arrangements)25 expressed as a percentage of other (hired) labor costs. Lastly, Z was specified as the fixed land improvements per unit area, because most of the other land quality characteristics were already considered when adjusting farm size for land quality differences.

In addition, the estimation for Vaalharts was done both with and without inclusion of managerial ability (M) as independent variable in equation 1. The argument for inclusion is similar to that for incorporating differences in land quality. The estimation of equation I assumes that all factors not specified are fixed and similar for all farms. Factors which differ between farms, and may have an influence on the results (such as managerial ability), should therefore be accounted for explicitly. In this respect the intuition is that the coefficient of M should be positive, as better managers should get a higher return on their investment.

Table 11 shows the results of the regression analyses which were used to determine the relationship between profit (net of family labor) per unit of investment, and the other variables.

> **25** This approach in essence broadens the concept of family members engaging in and benefiting from supervision tasks. Only a relatively small number of farms (16%) had a value other than zero for H. 37
port authorities to attract traffic (basically through tariffs), coupled with the difficulty in adjusting inputs (mainly quasi-fixed inputs), which implies that the likely desirable orientation of the distance function is an output-orientation.

Empirically, the paper has shown that the restructuring and the substantial reforms introduced not only changed the conditions for the development of port activities subject to regulation but also led to significant improvements in technological change. Technical efficiency has however not improved in a similar way and has in fact changed little on average. There is however a significant movement of the efficiency within ports over time as a result of reforms.

These results are particularly relevant in practice because a third wave of reforms has just been implemented and many more changes are expected to come from forthcoming European Union guidelines for the liberalization of port activities with potentially strong influence for container traffic. The results provided here however show that island and mainland ports are likely to be influenced differently if the past is considered to be a valid indication of the future reaction of port authorities to reform.

# **References**

Baird, A. (1995) “Privatization of Trust Ports in the United Kingdom: Review and Analysis of the First Sales”, _Transport Policy_ , 2: 135-143.

Baños Pino, J., Coto Millán, P. and Rodríguez Álvarez, A. (1999) “Allocative Efficiency and Over-capitalization: an Application”, _International Journal of Transport Economics_ , XXVI (2): 181-199.

Battese, G.E. and Coelli, T.J. (1995) “A Model for Technical Inefficiency Effects in a Stochastic Frontier Production for Panel Data”, _Empirical Economics_ , 20: 325-33.

Battese, G.E., and Coelli, T.J. (1988) “Prediction of Firm-Level Technical Efficiencies with a Generalized Frontier Production Function and Panel Data”, _Journal of Econometrics_ , 38: 387-399.

Bennathan, E. and Walters, A.A. (1979) _Port Pricing and Investment Policy for Developing Countries_ , Oxford University Press.

Bonilla, M., Medal, A., Casasús, T. and Sala, R. (2002) “The Traffic in Spanish Ports: an Efficiency Analysis”, _International Journal of Economics Transport_ , XXIX (2): 215-230.

Chang, S. (1978) “Production Function and Capacity Utilization of the Port of Mobile”, _Maritime Policy and Management_ , 5: 297-305.

Coelli, T. and Perelman, S. (1999) “A Comparison of Parametric and Non-Parametric Distance Functions: with Application to European Railways”, _European Journal of Operational Research,_ 117: 326-339.

Coelli, T. and Perelman, S. (2000) “Technical Efficiency of European Railways: a Distance Function Approach”, _Applied Economics_ , 32: 1967-1976.

18
**Table 11 Private Sector Participation in R&D in 2001**

||Private<br>Financing|Private<br>Performance|
|---|---|---|
|US<br>|68.3%<br>|74.4%<br>|
|Denmark<br>|63.4%|62.5%|
|Finland|70.8%<br>|66.0%|
|Ireland|66.0%<sup>1</sup><br>|68.5%|
|EU|56.2%<sup>1</sup><br>|64.5%|
|Slovenia|55.0%<sup>2</sup>|58.3%|
|Czech Rep.|52.5%|60.2%|
|Hungary|44.3%<br>|44%<br>|
|Turkey|42.9%<sup>1</sup>|33.4%<sup>1</sup>|
|Poland|30.8%|35.8%|
|Slovakia|56.1%|67.3%|
|Estonia|24.3%|23%|
|Lithuania|12.1%<br>|21.5%|
|Bulgaria|21.6%<sup>1</sup>|22.6%|
|**Latvia**|15,7%<br>|40%|
|Romania<br>|74.4%<sup>2</sup>|61.2%|
|_1 in 2000_<br>|||
|_2 in 1999_|||

_Source_ : OECD, 2002 ; Eurostat 2003

- The existing R&D system has been successful in competing for grants under the 5<sup>th</sup> Framework program and expects to be successful in the 6<sup>th</sup> Framework as well. But this is primarily contract research, led by industries and research teams from outside Latvia.  Not surprisingly, these R&D programs generate few scientific or economic spillovers to Latvian industry.

One key challenge for public policy is to break this cycle of decline. The following recommendations are designed to improve the efficiency of R&D spending in Latvia – to align it more closely with stated government priorities and to help Latvian research institutes foster more productive linkages with both international research institutes and, perhaps more importantly, with international corporations. Subsequent sections will discuss technology commercialization and technology upgrading of Latvian enterprises. Each of the recommendations in this and the following sections should be eligible for financing either with EU structural funds or World Bank funds, or a combination of both.

# **<mark>PRIORITIES</mark>**

<mark>The recommendations described below are presented and organized around the three themes: (i) the production of knowledge; (ii) the commercialization of knowledge; and (iii) the absorption and diffusion of knowledge produced elsewhere.</mark> <mark>However, it is also possible to organize the recommendations in terms of priorities: what should be done first, what should be done second, and what can wait for later. This organizing principle would generate the following hierarchy:</mark> 47
Table 1 presents the key summary statistics of the merged RWI and weather dataset for South Asia. On average, households in urban South Asia have higher relative wealth than rural households, with more wealth variation in urban areas. South Asia’s average maximum temperature is about 30 degrees Celsius, and it is almost 31 degrees in urban areas. The region is also exposed to temperatures above 35 degrees Celsius, a commonly used threshold for extreme heat, for about 15 percent of the year (57 days per year) in urban areas and 18 percent of the year (67 days per year) in rural areas.

Households with lower wealth are more exposed to higher temperatures, in both urban and rural areas in South Asia (table 2). The estimates are similar with and without state fixed effects. The magnitude of the estimated relationship can be illustrated by comparing locations at the mean of the temperature distribution (30 degrees Celsius) to those at its top tercile (34 degrees Celsius). In urban South Asia, within the same state or province, the RWI is 0.5 standard deviations lower in locations with an average temperature of 34 degrees Celsius than in locations with an average temperature of 30 degrees Celsius. This difference is approximately half the gap between urban and rural areas and is statistically significant. In rural South Asia, the RWI is 0.3 standard deviations lower in locations with an average maximum temperature of 34 degrees Celsius than in locations with 30 degree Celsius. This is approximately the difference between the 5<sup>th</sup> and 10<sup>th</sup> percentile in rural areas or the gap between Vasant Vihar and Hauz Khas in New Delhi. While small, this difference is statistically significant. The relationship between RWI and heat is imprecisely estimated at temperatures above 35 degrees Celsius, possibly because some of the major cities in South Asia with higher RWI like Chennai and Karachi have high average temperatures and rural areas with high average temperatures and higher RWI are in more industrialized states like Gujarat.

Similarly, regressing the RWI on the 5-year average temperature or a count of the number of days above 35 degrees Celcius yields qualitatively similar results in urban areas (table 2). In rural areas, the relationship is less robust when alternative measures of heat are used. This may be due to the larger variation in temperature in rural areas or the non-linearities in the relationship in rural areas. Nonetheless, the results are broadly consistent with findings from recent reviews: as in the rest of the world, the poor are more exposed to heat in South Asia (Hallegatte et al. 2016; Hallegatte et al. 2016; Triyana et al. 2024).

9