rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4.4. The percentage of population that changed residence in the preceding five years**

|**Country**|**Percent who moved**|
|---|---|
|Canada|47.6|
|Australia|47.1|
|United States|46.4|
|New Zealand|45.3|
|Turkey*|40.1|
|HongKong|38.3|
|Switzerland|36.0|
|Israel|29.8|
|Puerto Rico|29.6|
|Japan|22.6|
|Austria|20.1|
|Spain|18.5|

* Mobility rate for Turkey is only for intra city Ankara (Baharoglu, D. 1993).

Source: Long, L (1991) Residential mobility differences among developed countries.

# **4.2.8. Implications for the urban poor**

The earlier analysis has shown that poor urban households are less likely to have access to infrastructure and energy services and are more likely to have unreliable services when provided than is true for upper income households. Poor urban households are also less likely to have an indoor toilet and are more likely to use dirty fuels for cooking and heating (Sections 4.2.2-4.2.4).

**The poor live under somewhat more crowded conditions.** Although transition countries are generally well provided with housing space on a per capita basis, analysis of the data from the household surveys shows that lower income households have less space per capita than do high income households.  Figure 4.31 below shows the ratio of housing space in the top quintile to that in the bottom quintile in capital cities and other urban areas.  Values above one show places where the top quintile households have more housing space than the bottom quintile households.  In every case the bottom quintile lives under more crowded conditions, except in Georgia where the poorer households are very slightly better off.  In all but four countries (Hungary, Albania, Romania and Azerbaijan), the disparity in housing provision for rich and poor households is greater in secondary cities than in the capital, as is true in other countries in the world.

The above pattern suggests that although the region as a whole is relatively well housed, there are substantial disparities in housing space among income groups and that those disparities are generally more pronounced in secondary cities. As housing costs continue to increase and housing markets further develop, it is likely that the poor will live under more and more crowded conditions.<sup>42</sup>

> 42 Crowding is to some degree a relative concept since there is no agreed international standard as to how much floor space is acceptable.

49
markets.<sup>21</sup> Finally, the results in Table 3 show that the fi rm’s human capital - measured by the incidence of on-the-job training and the percentage of the workforce with more than secondary education - is positively related with the propensity to adopt new technology.<sup>22</sup> These results are in line with the idea that a more quali fi ed workforce improves the fi rm’s absorptive capability and reduces the costs of adopting or creating new technologies (Cohen and Levinthal, 1989). Table 3 also shows that public-owned fi rms are less likely to adopt new technology, although the effect is not statistically signi fi cant. This fi nding could re fl ect the fact that public-owned fi rms tend to operate in more protected markets and thus have smaller incentives to innovate.

In sum, the main results in this section suggest an important role of trade and FDI for technological innovations at the fi rm level. The technological advantage of exporters could be the result of their access to more and better knowledge through the interactions with foreign buyers or it could simply re fl ect a higher pressure to innovate driven by the strong competitive pressures felt in foreign markets.<sup>23</sup> Similarly, the technological advantages of importers could re fl ect a process of reverse engineering of higher quality foreign inputs. This mechanism would allow fi rms to learn about the embodied technological knowledge which may not be available domestically (e.g., Grossman and Helpman, 1991; Keller, 2005). Minority foreign-owned fi rms are also more likely than domestic-owned fi rms to adopt new technology while majority foreign-owned fi rms are less likely.<sup>24</sup> This evidence sheds some doubts on the dynamism imparted by the technology that multinational parents transfer to their fully-owned or majority-owned subsidiaries in developing countries. This fi nding could be suggesting that parent multinationals transfer more mature technologies less prone to

> 21Cohen (1995) also fi nds an innovation advantage for larger fi rms in developed countries. Cohen and Klepper (1996) develop a model where larger fi rms have more incentives to innovate since they face lower average fi xed (innovation) costs per unit of output.

> 22A positive correlation between innovation or technology adoption and human capital is documented at the aggregate level by Comin and Hobjn (2004), Lederman and Maloney (2003), and Keller (2005).

> 23Exporters may also bene fi t from scale economies in innovation due to their access to larger foreign markets. Hobday (1997) provides evidence for the electronics industry that the large size of export orders received by fi rms in Asian countries played an important role in encouraging them to undertake R&D.

> 24Our fi ndings seem to differ from those in Criscuolo et al. (2005) for the UK, where foreign-owned fi rms are more innovative (in the sense of being more likely to introduce new processes or products) and those in Vishwasrao and Bosshardt (2001) for India, where foreign-owned fi rms are more likely to adopt foreign technology than domestic-owned fi rms.

14
However, the plant performance in several cases was suboptimal and lower than expectations; for example, the Huilai project sold in the 2003 wind power auction achieved a capacity factor of only 16.9 percent in its first year of operation – nearly 25 percent lower than the estimates from the pre-auction feasibility studies (Pengfei, 2008). While these downside risks are typical of the wind generation activity, the fact that companies had bid very narrow margins in the Chinese auctions in the first place resulted in lower profits. Ultimately, this led several of the auctioned projects to be delayed or cancelled.

The offshore wind projects auctioned in 2011 have been also facing important challenges largely due to the lack of coordination between the auction organizer and the State Oceanic Administration which is responsible for the management of the sea areas (conflicting uses of delimited sea zones reserved for offshore wind projects have resulted in bureaucratic hurdles, ultimately requiring modifications to the siting of original projects). All four projects are currently severely delayed, and the short track record of offshore wind velocity measurements also contributes to the uncertainty regarding the profitability of projects. These unresolved issues have likely been the main reason for no other offshore wind auctions being organized since the first one.

Offshore wind is currently in a less mature stage of development, and it is likely that further bidding processes will be organized before a national FIT is set for this technology. A similar cycle can be expected to be followed by solar CSP.

# _Development of the renewable energy industry_

Auctioning of concessions has played an important role in China’s scale-up of renewable energy, particularly as a price-discovery mechanism for the determination of feed-in tariffs.

As illustrated in Figure 14, the FIT levels established by the Chinese government<sup>26</sup> have been comparable to the average Chinese auctioned prices, and slightly slanted towards the upper end of the price bids. This pricing strategy is more conductive to rapid capacity expansions, since there will be a greater number of potential investors that will be interested in the opportunity; and it is coherent with the Chinese ambitious renewable energy goals.

> 26 In 2009, the Chinese government established FITs for wind power developments varying by geographical region, in the range between 0.51 and 0.61 yuan/kWh. In 2011, a nationwide FIT for solar power equal to 1.0 yuan/kWh was established – two years later, this tariff was modified to differentiate among geographical regions, varying between 0.9 and 1.0 yuan/kWh.

29
Figure 1: GDP<sup>Growth, Producer wage, New taxes</sup> <!-- Start of picture text --> 20-<br>150<br>-50<br>88 89 90 91 92 93 94 95<br>| Real GP growth E3 Series2 oDPW87: PPI deflated wage a TDPW87: DPW87 +tax wedge|<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 3: Gasoline versus Ethanol Consumption in Brazil (million litres)<br>30,000<br>Ethanol consumption Gasoline consumption<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>Source: Using CONAB data<br><!-- End of picture text --> Brazilian ethanol policies are also important factors affecting the sugar/ethanol markets and can be classified into four categories. First, Brazil has a mandate for anhydrous ethanol mixed with gasoline, requiring 18 to 25 percent of the total fuel mixture to be anhydrous ethanol, depending on supply-demand conditions (it has recently been raised back to 25 percent but was reduced from 25 percent to 20 percent in October 2011). Second, E100 sales enjoy a tax exemption that is greater than what is needed to compensate for the fewer kilometers obtained relative to a liter of the gasoline-anhydrous fuel mixture. The tax on anhydrous ethanol is even lower, although as we show later, consumers only see the E25 price. Third, the government has often in the past, and again recently, held the price of gasoline below world gasoline prices, which fluctuate with world crude oil prices, to avoid adverse effects on inflation. Fourth, the federal government has recently eliminated the fuel tax. We show that in theory each of these policies has an ambiguous impact on ethanol market prices, but empirically we determine that a 5
adjusted quarterly until returning to the long-run equilibrium relationship, all other things being equal. Otherwise, the existence of a relationship would be spurious and no meaningful interpretation would exist on the statistical model results. Second, an important assumption for the consistency of the autoregressive distributed lag model (ARDL) is that the resulting residual of the error-correction is serially uncorrelated and that the explanatory variables can be treated as exogenous. Such conditions can be fulfilled by including the ARDL (p,q) lags for the dependent (p) and independent variables (q) in the error-correction form. Third, the relative size of timeseries (T) and sample size (N) is important because the use of the dynamic panel technique helps avoid biases in the average estimators by resolving the issue of heterogeneity. Through this estimation approach, the multiplicity between macroeconomic variables—the BOPNFA, in the (1) formula below—and stock indexes can be presented while solving for the traditional panel model association problem. This research utilizes the following panel PMG model: _SMI = μit +λi SMI t-n + βt GDPPC it-n + βt FX it-n+ βt MMIR it-n+ βt HICP it-n+ βt IPI it-n + βt BOPNFA it-n + εit_ (1) In the (1) formula above, _β_ term is the long-run parameter coefficient, _λ_ is scalar value vector, _i_ represents countries, _t_ refers to time, _μ_ refers to the constant value, _n_ represents time periods, and _ε_ refers to error disturbances. An optimal number of time lags was determined by the results guidance of Schwarz Bayesian information criterion (SBIC). This research paper introduces, for the first time, coverage of the model relationship between the selected set of macroeconomic indicators and the level of stock exchange indexes (SMI). The selected macroeconomic indicators include gross domestic product per capita (GDPPC) in euros, inflation (HICP) in nominal value, the levels of industrial production (IPI) indexes, the local currency exchange rate (FX) with the United States dollar (US$) in nominal value, the money market interest rate (MMIR) in nominal value, and the balance of payments net financial account (BOPNFA) in euros. The proxy for the regulated exchange-traded stock prices in Bosnian and Herzegovinian (B&H), Serbia, North Macedonia, Croatia, and Slovenia are the respective equity indexes: BATX, BELEX15, MBI10, CROBEX, and SBITOP. This research tracks quarterly data in an approximate full economic cycle 12 | P a g e
innovation does not lead to job losses and that it generates greater demand for a more qualified labor force.

In the same vein, Crespi and Tacsir (2012) present a comparative analysis for these four Latin American countries showing that product innovations are an important source of firm‐ level employment growth due to demand enlargement. Process innovation accounts for a small share of the changes observed in employment, inducing small or zero displacement effects. The last result can be explained by the absence of productivity gains that would lead to a reduction in employment, or the combined effect of productivity gains (displacement effect), which induce a demand enlargement through market competition (creation effect). Results are similar for small and large firms. They also find some evidence of skill bias of product innovation for high‐tech sectors.

Crespi, Tacsir and Vargas (2016) examine the determinants of technological innovation and its impact on firm labor productivity. They use the World Bank Enterprise Survey (WBES) database for 17 Latin American countries in 2010. They find that the decision to invest in innovation (R&D) is strongly correlated with firm size and firm capabilities (human capital) and is significantly and positively affected by public support. They also find that an increase in R&D spending affects positively the probability of innovating. Most of the relationship between expenditure and innovation is through product innovation rather than process innovation. Finally, the effects of innovation on productivity are positive and large. Total factor productivity of innovative firms is 50 % higher than that of non‐innovative firms. These results confirm the previous findings by Crespi and Zuñiga (2012) for an earlier period and using micro‐data from innovation surveys.

# **3. Data**

Our empirical analysis is based on the _Encuesta Nacional de Dinámica de Empleo e Innovación_ (ENDEI, National Survey of Employment Dynamics and Innovation), a firm survey ran in 2013 where manufacturing firms with 10 or more employees provided information about the period 2010‐2012 and thus works as a retroactive panel. The ENDEI provides annual information on employment by worker type, average wage, value added, sales, expenditure in R&D and 8
- 19 -

evolution of international prices, or tax ratio, which depends in part on the initial structure of production). This effect of the environment is even more obvious in the case of indicators meant to represent a strategy, for example in favor of agriculture, through rate of growth of agricultural production, or a strategy of openness to the outside world, through rate of growth of exports. It is not enough to establish a positive relationship between the rate of growth of CDP and that of agricultural production or that of exports in order to justify a policy favorable to agriculture or an openness policy. That entails the risk that a situation will be regarded as resulting from a policy when in ' ct it results in part from the starting condition4.

Moreover, certain environmental variables that significantly affect the growth rate undoubtedly do so by reason of the economic policy reactions that they usually -- even though avoidably - generate. This is true of the instability of export receipts in particular (Demeocq and Guillaumont 1985).

Separate treatment has to be given to the purely political factors, whether that of the more or less democratic nature of the political regime (taken into account by, for example, Venieris and Gupta 1983 and Kormendi and Meguire 1985) or that of political instability (coups d'etat, war, violence, etc.) (Wheeler 1984, Venieris and Gupta 1983). These factors can be regarded as either environmental variables or economic policy variables, depending on the purpose of the analysis: in an analysis of the efficacy of economic policy as an intervention technique, they form part of the environment (Wheeler); in an analysis of development strategies they can be deemed to result from human choices and must therefore be treated as policy variables.

Generally speaking, it seems that the political conclusions to be drawn from cross-sectional analyses would be more reliable if greater attention were paid to the nature of the environmental variables and their linkage with autonomous economic policy decisions.

# 2.1.2 Measurement and international comparability of variables

Regardless of the nature of the independent variables, the cross-sectional studies reveal substantial difficulties in measuring them on a comparable basis in different countries. These difficulties have been overcome in a variety of ways and more or less well.

The variable measured is very often no more than an approximation ("'roxy") of the initial concept. A good approximate variable has to be correlated with the variable it replaces yet in such a way that the percentage of error is not itself a function of the growth of the product one seeks to explain. Many approximate variables no doubt fall victim to this risk. Thus, gross investment is often used in place of variation in capital for lack of a measurement of capital: the error introduced concerns the depreciation rate which can itself be a function of the CDP growth rate (thus, a period of rapid growth can
**Policy Research Working Paper Series**

|**Title**|**Aulhor**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2429 Corruption, Composition of Capital<br>Flows, and Currency Crises|Shang-Jin Wei|August 2000|H. Sladovich<br>37698|
|WPS2430 Financial Structure and Bank<br>Profitability|Asli DemirgCsq-Kunt<br>Harry Huizinga|August 2000|K.Labrie<br>31001|
|WPS2431 Inside the Crisis: An Empirical<br>Analysis of Banking Systems in<br>Distress|Asli Demirguc-Kunt<br>Enrica Detragiache<br>Poonam Gupta|August 2000|K.Labrie<br>31001|
|WPS2432 Funding Growth in Bank-Based and<br>Market-Based Financial Systems:<br>Evidence from Firm-Level Data|Asli Demirgui-Kunt<br>Vojislav Maksimovic|August 2000|K. Labrie<br>31001|
|WPS2433 External Interventions and the<br>Duration of Civil Wars|Ibrahim A. Elbadawi<br>Nicholas Sambanis|September 2000|H. Sladovich<br>37698|
|WPS2434 Socioeconornic inequalities in Child<br>Malnutrition in the Developing World|Adam Wagstaff<br>Naoko Watanabe|September 2000|A. Maranon<br>38009|
|WPS2435 The Evolution of Poverty during the<br>Crisis in Indonesia, 1996-99|Asep Suryahadi<br>Sudarno Sumarto<br>Yusuf Suharso<br>Lant Pritchett|September 2000|P. Sader<br>33902|
|WPS2436 Safety Nets and Safety Ropes:<br>Who Benefited from Two Indonesian<br>Crisis Programs-the "Poor" or the<br>"Shocked?"|Sudarno Sumarto<br>Asep Suryahadi<br>Lant Pritchett|September 2000|P. Sader<br>33902|
|WPS2437 Quantifying Vulnerability to Poverty:<br>A Proposed Measure, Applied to<br>Indonesia|Lant Pritchett<br>Asep Suryahadi<br>Sudarno Sumarto|September 2000|P. Sader<br>33902|
|WPS2438 Measurements of Poverty in<br>Indonesia: 1996, 1999, and Beyond|Menno Pradhan<br>Asep Suryahadi<br>Sudarno Sumarto<br>Lant Pritchett|September 2000|P. Sader<br>33902|
|WPS2439 State-Community Synergies in<br>Development: Laying the Basis for<br>Collective Action|Monica Das Gupta<br>Helene Grandvoinnet<br>Mattia Romani|September 2000|M. Das Gupta<br>31983|
|WPS2440 Lessons from Uganda on Strategies<br>to Fight Poverty|John Mackinnon<br>Ritva Reinikka|September 2000|H. Sladovich<br>37698|
|WPS2441 Controlling the Fiscal Costs of<br>Banking Crises|Patrick Honohan<br>Daniela Klingebiel|September 2000|A. Yaptenco<br>38526|
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- Larson, D. F., Otsuka, K., Matsumoto, T., and Kilic, T. (2014). “Should African rural development strategies depend on smallholder farms? An exploration of the inverse-productivity hypothesis.” _Agricultural Economics_ , 45, pp. 355–367.

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29
Table A.2: Summary statistics cross-sectional model

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|
|---|---|---|---|---|
|change in CPIA cluster A and B|0.046|0.661|-1.731|1.819|
|log of cumulative loans 1996-2008|.8286652|.7132909|0|2.30259|
|log of cumulative conditions 1996-2008|2.091848|1.694547|0|4.82831|
|CPIA A and B 1996|3.699|0.856|1|5.231|
|average annual GDP per capita growth|4.770|8.168|-2.486|82.035|
|log of GDP per capita 1996|6.834|1.168|4.191|9.031|
|ethnic fractionalization|0.473|0.252|0|0.930|
|Political Rights 1996|3.738|2.06|1|7|
|change in Political Rights|-0.079|1.312|-5|3|
|average annual aid over GDP|0.035|0.045|0|0.277|
|log of population 1996|15.588|1.958|10.618|20.92|
|average fraction of votes with G-7|0.478|0.203|0.113|0.943|

32