rafmacalaba/datause-encoder-data huggingface dataset:
# **PRICE ELASTICITY OF NONRESIDENTIAL DEMAND FOR ENERGY**

## **IN SOUTH EASTERN EUROPE**

Atsushi Iimi<sup>¶</sup> Finance, Economics and Urban Development (FEU) The World Bank 1818 H Street N.W. Washington D.C. 20433 Tel: 202-473-4698 Fax: 202-522-3481 E-mail: aiimi@worldbank.org _JEL classification_ : Q41; Q48; P28; N74.

_Keywords_ : Energy demand; technical efficiency; infrastructure quality; micro econometrics; Balkan states.

> ¶ I am most grateful to Mohammad Amin, Antonio Estache, Marianne Fay, Mohinder Gulati, Bjorn Hamso, Peter Johansen, Charles Kenny, Shahidur Khandker, Lucio Monari, Marisela Montoliu Munoz, Hoa B. Nguyen, Orhan Niksic, Catalin Pauna, Rita Ramalho, Jasneet Singh, Maria Vagliasindi and other Bank colleagues as well as many seminar participants for their insightful comments on an earlier version of this paper.
rate of the developing world in 2030 to be in the range of 6.3 percent and 8.9 percent while holding within-country inequality constant and assuming country-specific population and economic growth rates. Although our projected poverty rates are at the high end of Lanjouw et al. (2013)’s projections, we share a similar view that maintaining the pace of economic growth in the recent past would not be sufficient to achieve three percent poverty rate by 2030.

Figure 5 separates contributions of each factor to the global poverty rate. A comparison of poverty rates between scenario (i) and (ii) illustrates the impact of introducing country-specific population growth. As mentioned above, it is limited in terms of percentage points, but this is likely due to the fact that the poverty rate before introducing country-specific population growth is as low as 2.1 percent. In fact, the percent change between (i) and (ii) is 33 percent, which is substantial. A comparison between (ii) and (iii) clearly shows that the biggest contributor for the very high poverty projection of 2030 is a large variation in country-specific growth rates. Introduction of country-specific growth rates increases the global poverty rate by three-fold and nearly 6 percentage points.<sup>12</sup> Finally, the impact of considering the withincountry inequality effect is minimal.

**Table 4. Projected Extreme Poverty Rate (%) in 2030**

|**Assumptions of**<br>|**Survey mean**|**Uniform (**|**4.7%)**|**Countr**|**y-specific**|
|---|---|---|---|---|---|
|**growth rates**||||||
||**Population**|**Uniform**||**Country-specifi**|**c**|
|**Scena**|**rios**|**(i)**|**(ii)**|**(iii)**|**(iv)**|
||EAP|0.17|0.17|1.13|0.78|
||ECA|0.09|0.09|0.24|0.35|
|Developing|LAC|2.18|2.21|3.21|2.68|
|countries as of<br>|MNA|0.13|0.13|1.76|1.73|
|1990|SAR|0.45|0.45|7.87|8.92|
||SSA|13.80|14.29|34.98|35.00|
||Total|2.44|3.20|9.77|9.91|
|Developed coun|tries as of 1990|0.00|0.00|0.00|0.00|
|Wo|rld|2.08|2.78|8.51|8.63|

Source: Authors' estimations based on population projections of 2030 from World Bank Data Group; PovcalNet (as of October 2013)

> 12 We also projected poverty rate in 2030 assuming uniform population growth rate and country-specific economic growth rate. Under this scenario (not shown in Table 4), the projected poverty rate for the world in 2030 is 6.7 percent, significantly higher than 2.08 percent in scenario (i). This underscores the significant effect on future poverty projections of the large variation in country-specific economic growth rates.

14
there are even cases of further losses (Jack et al. 2023; Gambi and de Witte 2023). During the school closures, online tutoring programs were shown to reduce learning loss significantly in several randomized controlled trials (see, for example, Carlana and La Ferrara 2021; Gortazar et al. 2022). A large-scale randomized trial testing low-technology interventions – SMS messages and phone calls – with parents to support their child in Botswana improved learning by 0.12 standard deviation (Angrist et al. 2022). Even low-cost, low- technology interventions can recover learning losses in some cases (Angrist et al. 2023), but more intense instruction will be needed in others (Crawfurd et al. 2023). High dosage tutoring was shown to be effective even before the pandemic; online tutoring variants are just as effective and much less costly (Guryan and Ludwig 2023).

# **Comparisons**

There are only two other international comparisons using global achievement data, both using PIRLS (Jakubowski et al. 2023; Kennedy and Strietholt 2023). For PIRLS, losses are about a year’s worth of learning. But using PISA, we find seven months’ worth of losses. This could be because more time has passed since the pandemic: PIRLS compared results over time to 2021, while PISA looked at results up to 2022. Also, PISA students are much older: 15 years compared to an average age of 10 for PIRLS. Studies investigating student self-regulation skills have shown that in general older students are better able to seek assistance, set goals, plan, monitor, and organize their learning. Hence, it may be that younger students and boys may be more strongly impacted by the school closures compared to older students (Schuurman et al. 2023).

In addition to the global studies, there are several national studies. Review of such studies find an average loss of 0.10 to 0.19 SD (Betthäuser et al. 2023; Di Pietro 2023; Donnelly and Patrinos 2021; Hammerstein et al. 2021; Patrinos et al. 2023; Storey and Zhang 2021; Zierer 2021). These losses are roughly to one-half school years’ worth of learning. A causal 20
<!-- Start of picture text --> f3 ><br>yy x a<br>3 ge y x Te ><br>ee ag<br>nS<br>* ee, 8’ 2. 3<br>iv . gut aft o : ow<br>"s bad O247 Geda ~5 *te, - ** -<br>~~, * ee ><br>+m a” ee<br>CEN tel ole SE PT<br>ae “ °- OF Ted ~@ & ‘:<br>ws,a ma, .89 + @ .% rs<br>“7% aio ny at .<br>te” aotrain °v ~~,we x -<br>. ad UT Aan “4. « *<br>” oe” an? pet. +<br>} a> shy *, .<br>Panes Poon, . Z as > & k<br>mS ~ “Aime<br>eo ny ¢ “e sy<br>Pie -<br>% > ¢ -.<br>Legend . uf<br>* <0.2% - * aan<br>@e 0.2%0.6% - 0.6% yee<br>@ 1.1% - 2.51.1 % Ls WM“<br>@ = .y<br>2.5% -14% {5 ‘ he ‘<br>Palestinian buildup x os Br ET<br>0 5 10 20 30 40<br>ees Kilometers<br><!-- End of picture text -->
21

# Source Country Goncentration

Over time, as FDI accumulates, a single source country may come to dominate the stock of FDI in a given recipient country. A relatively high degree of source-country concentration can create greater nationalistic sensitivities about FDI and vulnerability to shifts in the FDI policies of the home governments and corporations. Among the eleven countries, there is considerable variance in the source-country concentration ratios presented in table 3-5. Four of the host countries exhibit ratios of 60 percent or more: Kenya, where the British colonial legacy is still apparent; Colombia and Mexico, where the proximity to the United States is a factor; and Indonesia, where proximity to Japan is significant. The concentration ratios exceed 50 percent for two other countries-Nigeria (United Kingdom, 54.5 percent) and Korea (Japan, 54.9 percent).

Table 3-4. Source Country FDI Stock Concentration Ratios in Eleven Host Countries

|Host<br>country|Perceng|Source<br>Country|Yeara|
|---|---|---|---|
|Argentina|39.7|U.S.|1976|
|Brazil|31.4|U.S.|1985|
|Colombia|67.0b|U.S.|1986|
|Mexico|66.0|U.S.|1984|
|Indonesiac|60.0|Japan|1983|
|Korea, Rep. of|54.9<br>|Japan|1986|
|Malaysia|n.a.<sup>0</sup>|Singapore|1987|
|Thailand|30.2|U.S.|1984|
|Kenya|67.0|U.K|1972|
|Nigeria|54.5|U.K.|1986|

# n.a. Not available

- a. Latest year available in Becsky, Lee, and Ordu (1990).

- b. Rounded to nearest percent in original source.

- c. Based on U.K. figure for 1987 (estimated) against total for 1984 in original table, Singapore's share would be greater than 18.5 percent.

- d. Estimates from the country study for total FDI. Smaller estimates of 42.1 percent and 48.2 percent in Table 5.5 are for BKPM jurisdiction FDI projects only. Using data in Table 5.3, the figure would be 54.3 percent.

- Source: Becsky, Lee, and Ordu (1990), tables 2-7, 3-6, 3-11, 3-15, 4-4, 5-7, 5-11, 6-4, 6-8, 7-5, and 7-11.
Policy ReseaRch WoRking PaPeR 4443

# **Abstract**

A firm’s absorptive capacity, human capital and linkages with knowledge institutions have been shown to increase the firm’s probability of innovating in OECD economies. Despite its importance for national- and firm-level competitiveness, few papers examine the impact of the same variables for firms innovation in Latin America. This paper investigates the link between firm innovation and its absorption capacity as proxied by the presence of a R&D department, the firm’s human capital, and its interaction with research centers and universities. We analyze the case of Chilean and Colombian manufacturing firms using data from innovation surveys. A probit regression model is applied to identify the determinants of innovation activity. We find that collaboration with university and research institutions is associated with an increase in the probability of introducing a new product in Chilean and Colombian firms of 29 and 44 percent, respectively, and it can increase up to 58 percent in the case of Colombian firms interacting with research centers. Moreover, firms whose employees have a higher level of education, or whose managers/supervisors have a higher (perceived) level of knowledge, are more likely to innovate. Although the estimates could be affected by biases and suffer from shortcomings in data, the findings suggest that policies and incentives to increase firm-level human capital and industry-university linkages are important to increase innovation in Latin America.

This paper—a product of the Human Development Sector Unit, Latin American and Caribbean Region—is part of a larger effort in the region to support client country's improve economic competitiveness through investments and policy reforms of tertiary education and the innovation system. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmarotta@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
# **8 Conclusions**

We conducted a correspondence study using an online job platform in Malaysia. We tested for ethnic discrimination, gender discrimination and the value of signaling soft skills in the labor market. Unlike many correspondence studies, the data allow us to observe different stages in the hiring process. We observe if the employer rejects an application, visits the profile of a candidate, number of times the profile is visited, if they contact them and if they offer an interview. Uniquely, we observe competition in the labor market on both the demand and supply sides.

We do not find evidence of gender discrimination in the hiring process. Malaysia’s observed differential wages and labor force participation rates by gender do not seem to be associated with discrimination or human capital accumulation. More research is needed to determine why women in the Malaysian labor market have lower employment rates and wages.

We find that Indian and Malay sounding name profiles are discriminated against in comparison to Chinese-sounding name profiles. There is discrimination along all the hiring process variables we observe. Malay and Indian candidates are 8 and 9 percentage points less likely to receive an interview offer relative to a Chinese candidate. Discrimination for both ethnicities is also present in other outcomes. They are at least 10 percentage points more likely to be rejected for the position they apply for, even when the profile was not thoroughly examined. Their profiles are visited less often and they are less likely to be contacted by the employer. Ethnic discrimination varies by company characteristics. Small companies seem to examine job applications of Malay and Indian sounding names more carefully, as measured by profile visits. However, this does not translate into higher contact rates or interview offers. Jobs that offer high salaries have fewer visits to the profiles of the discriminated groups. Companies with low average processing time reject more applications of Indian and Malay sounding names.

The results of this paper are consistent with the model proposed by Bartoˇs et al. (2016) where employers decide to put differential levels of effort to collect information of candidates according to their ethnicity. Companies in Malaysia reject applications at different rates based on the ethnicity of the applicant, companies also visit profiles differentially according to ethnicity, and they take more days to contact applicants who have names of a discriminated ethnicity, when they do so. Bertrand 35
<!-- Start of picture text --> Investor clout (i)<br>1 eocccccscccccccecscscsces possessseennnnnnnnnnannnsacnnnnnnnnnaasssssetse gy<br>“ dM<br>—>0 ;<br>H<br>dL i<br>— +9<br>» UM = 1¢ dé<br>& H<br>i/a 1<br>Government effectiveness (0)<br><!-- End of picture text --> <!-- Start of picture text --> Investor clout (1)<br>1 waccceccccecccscccsccccccccencceecccecaceececes serrenenennnennnnrnenneeegy<br>da ° . :<br>dL i<br>=z >0 i<br>lt —>0:dLo<br>dé<br>‘ i<br>i/a 1<br>Government effectiveness (0)<br><!-- End of picture text --> <!-- Start of picture text --> Investor clout (2)<br>1 ey,<br>ap |<br>de i<br>i<br>dLc i<br>7 >0<br>EP =< pf<br>© :<br>i/a 1<br>Government effectiveness (0)<br><!-- End of picture text -->
specific forms of information derived from gender-specific migration networks (Davis and Winters 2001); to respond actively to underlying economic motivations even when they move as “associational migrants”, and to exhibit distinct gradients with respect to education and other measures of socio-economic status (Kanaiaupuni 2000). Thus, female migration – while embedded in household and family strategies and interests – might well retain its own economic rationale and respond to its own particular incentives. These various incentives relate to human, financial and social capital factors whose impact on migration we investigate in the next section.

# **3. Hypotheses on the causes of female migration**

The preceding discussion suggests a number of potential hypotheses to explore in the context of Albania. From both economic and non-economic perspectives, gender differences in the link between migration and education suggest different migration opportunities and constraints on men and women. Evidence from Mexico suggests that higher education levels raise the odds of female migration just as they lower the odds of male migration (Kanaiaupuni 2000). One explanation is that women, relative to men, typically face more discrimination in local labor markets, and this raises their incentive to migrate towards more egalitarian labor markets. What has not been clearly shown is if and how this educational differential by gender evolves as migration becomes normative. This leads to our first two hypotheses: first, that female migration from Albania is more strongly associated with education than male migration and second, that this difference should strengthen over time as women are more able to take advantage of economic opportunities. This tendency for females to seek better labor employment abroad may be heightened by increasing gender inequality in the post-communist Albanian labor force over time – a pattern which is also found in other post-Communist states (Einhorn 1993) – and which limits women’s access to higher status occupations and generates “new forms of marginalization” (Calloni 2002; UNDP 2003).

Crises have the potential to alter gender relations at both the household and societal levels (Peteet 1991). Under the assumption that female actions are more tightly bounded by household decisions we expect that female migration decisions will be more sensitive to constraints and incentives faced by households. This leads us to our third hypothesis: that female migration behavior will respond more strongly than male behavior to household-level income or health shocks and that this differential response will weaken over time, presumably a signal of women’s 5
- 16 -

the major of which were the subsidies, countervailing-duties code and the antidumping code. The codes are elaborations of the initial GATT specifications thiit were intended to limit when and how a country could impose an import restriction. The logic here, as in the original GATT, is that if the circumstances under which a country may impede trade are specified, the frequency with which it does so will be less. The formulators of this approach hoped that codification would make national procedures to impose trade restrictions the sa-a in one country as in another, and that as precedents built up each national government would have effective means for handling pressures for protection. Petitioners would be assured that their cases were decided on the same grounds as anyone else's (and that these grounds were the right grounds) and would thus be brought toward accepting a possibly negative decision.

As to effects, all reviewers agree that the subsidies code had had little effect, either on the extent of subsidization or on the propensity of importing countries to take countervailing actions. 12/ The antidumping code earns lower marks. The number of cases by code signatories -- 1288 over the seven years 1980-86, more than half of which led to antidumping duties or a restrictive consent arrangement -- is alarming. Even more alarming is the considerable progress the code has made to promote the acceptance by one country of another's trade restricting actions -- as if the objective were to bring order and agreement to the procebs of closing down the international trading system rather than to keep it open.

It is not hard to explain why the codes have not retarded the spread of trade restrictions. First, the codes provide a more elaborate '<lQ- r. an.d **.-- O.-** (10n87..
|||**T**<br>|**able 5 – Robu**<br>|**stness Che**<br>|**cks**<br>||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>selection|(2)<br>intensity|(3)<br>selection|(4)<br>intensity|(5)<br>selection|(6)<br>intensity|(7)<br>selection|(8)<br>intensity|
|Restrictivenesst-1|-0.013<br>[0.004]***|-0.957<br>[1.078]|||||||
|Restrictivenesst-2|||-0.015<br>[0.004]***|-2.058<br>[1.241]*|||||
|Index_other dest|||||0.007<br>[0.003]***|-1.670<br>[1.576]|||
|Restrictiveness -<br>index_other dest|||||||-0.011|-0.156|
||||||||[0.001]***|[0.338]|
|Common language<sup>d</sup>|0.002||0.002||0.002||0.002||
||[0.000]***||[0.000]***||[0.000]***||[0.000]***||
|Ln(1+tariff)|-0.001|-0.400|-0.001|-0.432|-0.001|-0.384|-0.001|-0.382|
||[0.000]***|[0.068]***|[0.000]***|[0.072]***|[0.000]***|[0.029]***|[0.000]***|[0.029]***|
|Zeta||3.442||2.681||3.899||3.695|
|||[1.607]**||[1.698]||[0.754]***||[0.753]***|
|zeta_sq||-0.294||0.010||-0.450||-0.353|
|||[0.852]||[0.884]||[0.410]||[0.408]|
|zeta_cb||-0.060||-0.116||-0.033||-0.048|
|||[0.155]||[0.160]||[0.075]||[0.074]|
|Mills||1.604||1.764||1.489||1.455|
|||[0.496]***||[0.543]***||[0.236]***||[0.236]***|
|Observations|1,724,491|80,121|1,336,085|61,404|2,109,457|98,065|2,109,457|98,065|
|R-suared||0319||0322||0321||0320|
|q<br>Robust standard errors in<br>positive trade between co<br>trade. Gravity controls fo<br>omitted from the table du<br>terms clustered byimpor|brackets *** p<br>untries or zero<br>r contiguity, co<br>e to space con<br>ter-product-yea|.<br><0.01, ** p<0.<br>otherwise. Th<br>lonial relation<br>straints. Import<br>rgroups. Supe|05, * p<0.1. De<br>e dependent vari<br>ship, and distan<br>er-time, exporte<br>rscript<sup>d</sup>is for d|. <br>pendent varia<br>able in the int<br>ce have their e<br>r-time, and pr<br>ummyvariable|ble in the select<br>ensity equation<br>xpected sign an<br>oduct-time effe<br>s.|. <br>ion equation e<br>is the natural l<br>d statistically<br>cts also includ|quals one if th<br>ogarithm of th<br>significant but<br>ed in all regres|.<br>ere is<br>e value of<br>were<br>sions. Error|

26
larger and/or more productive agricultural sector can supply cheaper inputs for the food processing sector. This leads to an increased derived demand for primary agricultural products and, in turn, to increased agricultural sector demand for machinery and equipment produced by the industrial sector. Similarly, an expansion of the industrial sector requires increased input from agriculture.

The service sector plays an important role in sectoral growth, due both to its presence in the co-integrating relationship and to it being weakly exogenous to the system in all three cases. The expansion of the service sector has positive effects on industry and agriculture due to increased demand for agricultural and industrial sector input. This is true even though the service sector does not appear to utilize agricultural produce to the same extent as the food processing industry.

The intersectoral relationships can also be quantified by means of impulse response analysis, which traces the accumulated dynamic response of a hypothetical oneunit increase of each variable (see Hamilton 1994). The findings of the impulse response analysis support our earlier findings of a positive industry-agriculture link in sectoral economic growth (see figures 2, 3, and 4). For all three countries, there exists a positive impact on agricultural growth from an increase in the industrial sector (in Ghana: the manufacturing sector) over a ten-year period. For CMte d'Ivoire, the initial one-unit increase in industry leads to an increase of three units in the agricultural sector after 10 years. For Zimbabwe, the accumulated effect is almost two units, while for Ghana the increase is only one unit.

When investigating the agriculture-industry link in the other direction, that is, from agriculture to industry, there is at least an initial positive dynamic response in all three countries. In the case of C8te d'Ivoire, this response holds throughout the period; for Zimbabwe and Ghana, an adverse response follows after five to seven years. The service sector overall has a positive impact on the other sectors. For the remaining Ghanaian subsectors, the evidence is mixed.

Altogether, the results from the impulse response analysis support our earlier findings of a positive agriculture-industry link with service as an important growthpromoting factor. We now take the analysis a step further by modeling short-run sectoral growth.

16