rafmacalaba/datause-encoder-data huggingface dataset:
|Job search assistance||||||
|---|---|---|---|---|---|
|HEI coordinates job interviews with firms|0.56|0.50|0.65|0.48|**0.04**|
|HEI trains students for job interviews|0.68|0.47|0.63|0.48|0.29|
|HEI provides job market information|0.75|0.43|0.81|0.39|**0.07**|
|HEI has an employment center|0.35|0.48|0.34|0.47|0.80|
|_Faculty_||||||
|Number of faculty|15.45|9.17|16.08|8.61|0.41|
|Percent of faculty||||||
|<40 years old faculty|38.44|27.25|34.86|26.25|0.14|
|that are women|40.95|25.21|38.83|23.31|0.32|
|with BA degree|85.89|25.91|84.41|26.62|0.52|
|with graduate degree|70.12|28.25|65.40|29.85|**0.07**|
|with 5yrs+ industry experience|63.32|27.65|62.17|28.59|0.65|
|working in the industry|46.39|29.79|41.84|27.82|**0.07**|
|Important for faculty hiring||||||
|Practical experience|0.87|0.34|0.88|0.33|0.75|
|Research skills|0.37|0.48|0.44|0.50|0.13|
|Important for faculty evaluation||||||
|Classroom observation|0.53|0.50|0.49|0.50|0.26|
|Class planning|0.49|0.50|0.44|0.50|0.26|
|Student evaluation|0.79|0.41|0.70|0.46|**0.03**|
|Students and peers' informal comments|0.24|0.43|0.24|0.43|0.86|
|Peer evaluation|0.26|0.44|0.24|0.43|0.52|
|Almost all or all faculty participated in professional training last year|0.65|0.48|0.67|0.47|0.68|
|Faculty are evaluated more than once per year|0.77|0.42|0.77|0.42|0.97|
|_Other Practices_||||||
|Update or review admin data more than once per year|0.62|0.49|0.55|0.50|0.13|
|Percent of governing body that belongs to:||||||
|Private sector|17.47|19.70|14.12|16.96|**0.03**|
|Government|5.47|11.29|7.78|13.92|**0.04**|

74
XVi INTRODUCTION Figure 4. World Bank Regions <!-- Start of picture text --> IBRD 24229<br><!-- End of picture text --> <!-- Start of picture text --> SUB-SAHARAN AFRICA WORLD BANK REGIONS<br>© LAC (Including North America)<br>© SOUTH ASIA<br>EASTASIAAND PACIFIC<br>®!)<br>EUROPE and CENTRAL ASIA<br>(~)<br>(i) NORTH AFRICA and MIDDLE EAST , ,m,<br><!-- End of picture text --> <!-- Start of picture text --> OC=8ER1992l<br><!-- End of picture text -->
- 10 -

discount (a negative entry) that the French associates pay over other groups Table 2 Comparative Unit Value Information for France's Exports of Iron and Steel Products

||||Premiu|m or Discount|Paid by Frenc|h Associated<br>Coun|tries 1/|
|---|---|---|---|---|---|---|---|
||French f.o|.b. exports|||Dev|eloping Countrie|s|
||to associa|ted<br>countries||All|All|||
||Value Un|it Value (S)|Total 2/|Developed|Non-French|Latin America|Middle-Eas,|
|Year|(SOOO)|||||||
|1962-63|<br>118,446|<br>167.0|37,9|40.5|36.9|26.8|50.6|
|1964-65|<br>98,593|<br>151.5|27.5|29.8|21.8|20.4|23.5|
|1966-67|<br>86,042|<br>143.8|24.6|26.8|21.0|21.6|18.9|
|1968-69|<br>101,180|<br>150.0|28.5|31.3|23.9|32.7|14.2|
|1970-71|<br>119,695|<br>199.30|29.6|32.6|16.7|13.3|13.0|
|1972-73|<br>187,362|<br>234.80|23.0|26.9|18.6|22.0|16.6|
|1974-75|<br>368,537|<br>386.70|18.1|26.4|8.1|16.7|17.2|
|1976-77|<br>341,378|<br>375.8G|13.1|20.4|-3.6|10.2|2.9|
|1978-79|<br>465.702|<br>496.60|19.5|19.8|26.1|24.1|12.1|
|1980-81|<br>489,195|<br>581.20|25.4|28.6|20.9|26.3|-11.2|
|1982-83|<br>350.566|<br>458.30|6.6|8.3|8.6|6.0|-13.8|
|1984-85|<br>318,623|<br>442.90|17.4|15.7|36.2|34.2|16.8|
|1986-87|<br>269,537|<br>668.00|40.1|37.0|66.5|54.7|10.9|

Net Revenue Gains or Losses (1000) Actual dollar amount 3/. ..............431,169.0 Present value of gains or losses 4/ ... 876,1P3.0

- 1/ The French associated country premium or discount (P/Df) has been computed from the following formula:

- (4) P/Df = [(Uf - Ug) UgI x 100.

where Uf is the unit value for the French associates and U is the unit value for the comparator group of countries.

- 2/ Excludes the French associated countries in Africa.

- 3/ The actual dollar amount of the gains and losses (Adf) has been computed from; (5) Adf = (Uf - UO) x qf

where qf Is the quantity of French associated country imports and UO is the average unit value paid by all other developing .countries. These values are then summed over the 1962-87 period.

- 4/ The present value in 1987 of all annual gains or losses computed from equation (5). The present value estimate is based on an assumed discount rate of 8 per cent.

Note: See Appendix Tables 3 through 13 for the four and five-digit SITC products included in these computations.
# Hit and Run? Income Shocks and School Dropouts in Latin America<sup>1</sup>

Paula Cerutti<sup>2</sup> , Elena Crivellaro<sup>3</sup> , Germán Reyes<sup>4</sup> , Liliana D. Sousa<sup>5</sup> JEL Classification: D1, I21, N36, O15 Keywords: Latin America, Education, Youth employment, Income shocks.

> 1 Views expressed are those of the authors and do not necessarily represent official views of the OECD or World Bank.

> 2 Organisation for Economic Co-operation and Development (OECD). E-mail: paula.cerutti@oecd.org

> 3Organisation for Economic Co-operation and Development (OECD). E-mail: elena.crivellaro@oecd.org

> 4 The World Bank/Cornell University. E-mail: gjr66@cornell.edu

> 5 The World Bank. E-mail: lsousa@worldbank.org
# **7 Conclusion**

This paper contributes to understanding the energy price-firm competitiveness nexus in an emerging economy context. Firms use four main coping mechanisms to navigate energy price increases: innovation and competitiveness, substitution across fuel types and other inputs, and absorption or pass-through. Our results indicate that while electricity price increases are associated with reduced production and firm size, fossil fuel price hikes result in increased capital investment, manifesting in improved productivity, thereby supporting the _strong_ version of the Porter Hypothesis. However, these productivity increases do not translate into higher profitability (at least not in the short run), indicating that such price hikes are at least partly absorbed by firms.

These broader results mask heterogeneity by firm attributes. The strong version of the Porter Hypothesis can only be observed in large firms. Small firms are more negatively affected by surges in energy prices. Likewise, exporters and foreignowned firms are also less affected compared to domestically oriented firms. Moreover, energy-intensive firms are also found to experience more extensive Porter-type innovations, and their business metrics and employment are typically more affected by energy price hikes than firms in non-energy-intensive sectors.

Given the firm heterogeneity in outcomes, policy reforms affecting energy prices and accompanying measures are best targeted based on solid micro-level analyses. The most vulnerable firms, such as the smaller and domestically oriented firms, may not have the means to adjust to energy price fluctuations and may need complementary support to undertake the necessary changes and investments. A better understanding of how existing inequalities interact with the risks posed by energy price policies, taking into account firm capabilities and management skills, will inform a more efficient policy design.<sup>23</sup>

> 23A word of caution is warranted here in over-interpreting. These results remain broadly illustrative as they build on the publicly available version of the ENIA data set, which includes firm-level panel data up to 2015. Additional work is required to merge post-2015 years to the panel dimension of the data set and include the introduction of the exogenous Carbon tax, which became effective in 2018, in the observation period.

30
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Both sources agree, however, that the sanitation MDG will not be achieved. In fact, sanitation has become a top priority of the government, whose efforts should only be reinforced.

It becomes clear when comparing the figures from the JMP and the government that one of the challenges facing the sector is the harmonization of the norms used to define ―improved‖ services to water and sanitation. The norms used by the government (National Directorate of Water Supply), ONEA, and the National Statistical Office, not only differ among themselves but also against those of the JMP. Differences include whether access to a service refers to availability or actual use, and whether a temporary malfunction of a facility rules it out as providing service to a community or not.<sup>3</sup> While progress in urban areas has been remarkable, access to improved water and sanitation in rural areas is less impressive; upcoming challenges include further extending the service to rural areas and improving the technology mix in urban areas, while controlling—if not reducing—costs. This is particularly challenging since gains in access in recent years have been achieved through low-end technologies. On average, increased access to improved water has come from adding wells/boreholes and stand posts while, as a percentage of total population, fewer people have gained access to piped water. Increased access to improved sanitation has come predominantly from more latrines (table 11).

**Table 11. Expansion of the safest water and sanitation technologies**

|a. Water|||b.Sanitation||
|---|---|---|---|---|
||Population gaini<br>(per year, %)|ng access||Population gaining access<br>(per year, %)|
||1999–2003|2003–07||1999–2003<br>2003–07|
|Piped water|0.7|(0.2)|Flush toilets|0.3<br>0.7|
|Stand posts|1.4|1.6|Improved latrines|4.4<br>1.7|
|Wells/boreholes|(0.8)|4.7|Traditional latrines|(2.2)<br>1.9|
|Surface water|2.3|(2.2)|Open defecation|1.0<br>(0.1)|

_Source:_ WHO Joint Monitoring Program 2010 from Demographic and Health Surveys for 1999 and 2003 and Questionnaire à Indicateur de Base du Bien-Etre for 2007.

Extending access to higher-end options, which in the case of water means piped water and stand posts, results in high historical costs. Despite this, ONEA has very successfully achieved cost recovery— but as a consequence, Burkina’s tariffs are among the highest in the continent (figure 16). For additional extensions of utility water, while maintaining cost-recovery policies, costs will need to be either controlled or reduced.

> 3 WSP (2010) discusses the differences among norms in detail.

26
10 education – are more problematic. In the literature, the cleanest strategies for identifying exogenous variables affecting migration and/or the receipt of remittances have focused on short-term economic shocks. For example, Yang (2006) uses panel data from the 1997 Asian currency crisis to analyze how short-term changes in currency rates affect the value of international remittances received by Filipino households. Since our Ghana data come from a single, cross-sectional survey, we are not aware of any identifiable exogenous shocks to exploit in our data set.

To address the problem of endogenous variables, we construct two instrumental variables using the following procedure. Past research has found that migration networks are important in migration decisions and the receipt of remittances (e.g. Woodruff and Zenteno, 2007, Munshi, 2003). Since ethnicity and religion represent two important forms of association in Ghana, we assume that households in Ghana will form migration networks on the basis of ethnicity and religion. On this basis, we partitioned the data from the 2005/06 Ghana GLSS 5 Survey (sub-sample) into 15 ethno-religious groups. We defined these ethno-religious groups by classifying households according to five religious and three ethnographic groups. The five religious groups are: (1) Catholic and Anglican; (2) Presbyterian and Methodist; (3) Pentecostal, Spiritualist and other Christian; (4) Muslim; and (5) all others.<sup>13</sup> The three ethnographic groups include: (1) households belonging to ethnic groups representing 17% or more of the population in 1998/99 (e.g., Asante and other Akan); (2) households belonging to ethnic groups representing between 10% and 15% of the population in 1998/99 (e.g., Fanti, Ga-Adangbe, Ewe); and (3) all other households.<sup>14</sup>
- 35 -

# **REPERENCES**

- Acharya, Meena. (1982). "Time Use Data and the Living Standards Measurement Study". LSMS Working Paper No. 18, The World Bank, Washington, D.C.

- Ainsworth, Martha, and Juan Munioz. (1986). "The C6te d'Ivoire Living Standards Survey: Design and Implementation". LSMS Working Paper No. 26, The World Bank, Washington, D.C.

- Alcimir, Oscar, and J. Sourrouille. (1980). "Measuring Levels of Living in Latin America: An Overview of Main Problems". LSMS Working Paper No. 3, The World Bank, Washington, D.C.

- Birdsall, Nancy. (1982). "Child Schooling and the Measurement of Living Standards". LSMS Working Paper No. 14, The World Bank, Washington, D.C.

- Booker, William, Parmeet Singh and L. Savane. (1980). "Household Survey Experience in Africa". LSMS Working Paper No. 6, The World Bank, Washington, D.C.

- Chander, Ramesh, Christiaan Grootaert and Graham Pyatt. (1980). "Living Standards Surveys in Developing Countries." LSMS Working Paper No. 1, The World Bank, Washington, D.C.

- Deaton, Angus. (1980). "The Measurement of Welfare: Theory and Practical Guidelines". LSMS Working Paper No. 7, The World Bank, Washington, D.C.

- Deiton, Angus and John Muellbauer. (1980). Economics and Consumer Behavior. Cambridge Universitv Press. Cambridge, U.K.

- Gertler, Paul, and Jacques van der Gaag. (1989). The Willingness to Pay for Medical Care: Evidence from Two Developing Countries. Forthcoming. The World Bank, Washington, D.C.

- Gertler, Paul, and Paul Glewwe. (1989). "The Willingness to Pay for Education in Developing Countries: Evidence from Rural Peru". LSMS Working Paper No. 29, The World Bank, Washington, D.C.

- Glewwe, Paul. (1987a). "The Distribution of Welfare in the Republic ot Cate d'Ivoire". LSMS Working Paper No. 29, The World Bank, Washington, D.C.

- Glewwe, Paul. (1987b). "The Distribution of Welfare in Peru in 1985-86". LSMS Working Paper No. 42, The World Bank, Washington, D.C.

- Glewwe, Paul, and Jacques van der Gaag. (1988). "Confronting Poverty in Developing Countries: Definitions, Information and Policies". LSMS Working Paper No. 48, The World Bank, Washington, D.C.

- Clewwe, Paul, and Dennis de Tray. (1988). "The Poor During Adjustment: A Case Study of the Cota d'Ivoire". LSMS Working Paper No. 47, The World Bank, Washington, D.C.
<u>β</u><sup>1</sup> of ODA inflows increase growth by 0.022 percentage point (note that =β<sup>1</sup> since γ<sup>1</sup> = 1−γ<sup>1</sup> β<sup>1</sup> −γ<sup>2</sup> β<sup>2</sup> −γ<sup>3</sup> β<sup>3</sup> γ<sup>2</sup> = γ<sup>3</sup> = 0) . FDI also seems to support growth, although the magnitude is much smaller and it is only significant at 5% level. Public debt, on the other hand, shows no impact on growth. Commodity index and growth of trade partners have positive signs, but are not significant.

|VARIABLES<br>Growth of real outputp|er capita|
|---|---|
|[1]<br>[2]|[3]|
|log ODA inflows<br>0.022***<br>0.025**||
|[0.007]<br>[0.010]||
|log FDI inflows<br>0.002**<br>0.002||
|[0.001]<br>[0.001]||
|log public debt inflows<br>-0.002<br>-0.004||
|[0.002]<br>[0.003]||
|log ODA inflows (t-1)<br>-0.011<br>[0.009]||
|log FDI inflows (t-1)<br>-0.001<br>[0.002]||
|log public debt inflows (t-1)<br>-0.004<br>[0.004]||
|log ODA inflows (t-2)<br>-0.008<br>[0.009]||
|log FDI inflows (t-2)<br>-0.000<br>[0.001]||
|log public debt inflows (t-2)<br>0.005<br>[0.003]||
|Growth of Trade partners<br>0.772<br>-0.558|1.452**|
|[1.142]<br>[0.909]|[0.627]|
|Commodity Index<br>0.125*<br>-0.027|-0.015|
|[0.071]<br>[0.099]|[0.103]|
|Constant<br>-11.843*<br>-141.735***|-14.233**|
|[5.939]<br>[7.351]|[6.970]|
|Observations<br>770<br>547|1,080|
|R-squared<br>0.287<br>0.361|0.229|
|Clustered-Robust standard errors in brackets||
|*** p<0.01, ** p<0.05, * p<0.1||

Table 3: Impacts of capital inflows on output growth The finding helps shed light in the debate on the effectiveness of aid. While existing literature still struggles with the flows-growth endogeneity issue, we strive to isolate an exogenous component of aid flows. We find that “exogenous” foreign aid helps output growth, a result consistent with the findings of 21
**<u>Table 1. Share of stunted (HAZ <</u> -2) and underweight (WAZ < -2) children**<sup>**28**</sup> **<u>by various characteristics</u>**

|||**Boy**<br>|**s**<br>|||**Girl**<br>|**s**<br>||
|---|---|---|---|---|---|---|---|---|
||**199**||**1998**||**199**||**199**|**8**|
|Characteristic|_Mean_|_Std. Err._|_Mean_|_Std. Err._|_Mean_|_Std. Err._|_Mean_|_Std. Err._|
|**Height-for age**|||||||||
|_mother’s education_|||||||||
|none|0.546|_0.007_|0.533|_0.006_|0.525|_0.007_|0.564|_0.007_|
|primary|0.446|_0.013_|0.438|_0.011_|0.445|_0.012_|0.454|_0.011_|
|secondary|0.314|_0.009_|0.293|_0.007_|0.328|_0.009_|0.316|_0.007_|
|_household’s wealth_|||||||||
|_tertile_|||||||||
|poorest|0.567|_0.009_|0.554|_0.009_|0.537|_0.010_|0.579|_0.010_|
|middle|0.504|_0.009_|0.469|_0.008_|0.489|_0.009_|0.501|_0.008_|
|richest|0.361|_0.007_|0.340|_0.006_|0.378|_0.008_|0.368|_0.006_|
|_mother’s caste_|||||||||
|scheduled caste|0.545|_0.013_|0.505|_0.010_|0.529|_0.014_|0.529|_0.011_|
|scheduled tribe|0.513|_0.015_|0.523|_0.012_|0.447|_0.015_|0.535|_0.012_|
|other|0.463|_0.006_|0.408|_0.005_|0.461|_0.006_|0.438|_0.006_|
|**_All_**|0.478|_0.005_|0.438|_0.004_|0.469|_0.005_|0.467|_0.005_|
|**Weight-for age**|||||||||
|_mother’s education_|||||||||
|none|0.592|_0.006_|0.536|_0.006_|0.587|_0.006_|0.578|_0.007_|
|primary|0.512|_0.011_|0.488|_0.011_|0.509|_0.010_|0.493|_0.011_|
|secondary|0.380|_0.008_|0.315|_0.007_|0.350|_0.008_|0.343|_0.007_|
|_household’s wealth_|||||||||
|_tertile_|||||||||
|poorest|0.624|_0.008_|0.570|_0.009_|0.593|_0.008_|0.598|_0.010_|
|<br>middle|0.563|_0.007_|0.491|_0.008_|0.558|_0.007_|0.532|_0.008_|
|richest|0.409|_0.007_|0.353|_0.006_|0.411|_0.007_|0.386|_0.007_|
|_mother’s caste_|||||||||
|scheduled caste|0.568|_0.012_|0.517|_0.010_|0.572|_0.012_|0.548|_0.010_|
|scheduled tribe|0.590|_0.012_|0.551|_0.012_|0.559|_0.012_|0.574|_0.012_|
|other|0.520|_0.005_|0.424|_0.005_|0.509|_0.005_|0.459|_0.006_|
|**_All_**|0.532|_0.004_|0.455|_0.004_|0.522|_0.004_|0.489|_0.005_|

_Source_ : NFHS I and II.

> 28 Children aged 0-4 in the 1992 survey, and 0-3 in the 1998 survey.

19
was collected with the 1996 test, ANEP constructed a socio-economic index, and excluded schools with belonged to quintiles 1 and 2 (the most advantaged ones) from participation in the full-time program. This rule was adhered to in all cases, which means that the 66 schools from quintiles 1 and 2 can be excluded from the sample.

# 4.1 Fixed E¤ects Model

In the ...xed e¤ects model, we control for time invariant observable and unobservable school characteristics using school-level ...xed e¤ects. We include also time dummies, to control for unobservable changes common to all the observations in a particular year, student characteristics, and teacher characteristics. The e¤ect of the full-time program is captured by the parameter of a variable that measures the number of years a school was exposed to the program before the 2002 follow-up achievement test. Since the sample excludes schools that were already full time at baseline in 1996, the highest number of years of exposure to the program is six. Since primary school has six years, and the achievement tests were taken in sixth grade, this is also the maximum number of grades that any child would have been exposed to the full-time model. The only potential bias left in this speci...cation comes from time-variant school characteristics. The ...xed e¤ects model speci...cation is as follows: where all variables from 1 are included, Djk is a categorical variable that takes value 1 if j = k, and 0 otherwise, Tt is a categorical variable that takes value 1 if t = 2002 and 0 otherwise, and "ijt is a stochastic error term. t can take values 0 (for 1996) or 1 (for 2002). The length of exposure is calculated as follows:

# 4.2 Propensity Score Matching Model

Finding a comparison group for participating schools in the absence of a clear targeting mechanism is a challenge. As mentioned above, schools that converted to the full time system were quite di¤erent from other schools at baseline. Even controlling for observable characteristics separately in the regression, the distribution of the two groups will be di¤erent, and the results from a full-sample ...xed e¤ects model may be biased if there are di¤erential trends between the full-time schools and the other schools which are not related to the full-time program. To address 10
<!-- Start of picture text --> st<br>|<br>|<br>|<br>& |<br>2|<br>3<br>a|<br>=<br>5<br>ooao es _ |<br>5 |<br>re |<br>a MEAN 95% MPE<br>Bo2|<br>ao2|<br>|<br>wo |<br>a 2000 4000<br>Total household income 2005<br><!-- End of picture text -->
Table 2 Shares of Earmarked Revenues in Government **Ravenuesa** (percentages)

||1970|1975|1980|1981|1982|1983|1984|1985|1986|1987|
|---|---|---|---|---|---|---|---|---|---|---|
|Share in National<br>Government Revenues|11.3|17.5|27.8|27.0|28.3|27.9|34.4|31.3|27.7|_27.7_|
|Share in Departmental<br>Government Revenues|93.9|82.5|88.6|85.8|86.6|85.5|78.3|76.6|76.5||
|Share in Municipal<br>Government Revenues|30.1|30.2|31.3|30.5|29.3|33.9|27.0|22.9|16 <sup>.</sup>3 <sup>b</sup>||
|Share in Government<br>Revenues All Levels|21.i|23.5|33.4|32.3|31.1|33.5|39.1|35.5|32.0||

# Source: Estimates based on Tables Al and A2 in Statistical Appendix.

a. Earmarked revenues are listed in Statistical ADpendix Table A2, excluding payroll taxes, the operating profits of nonfinancial public enterprises and, at the national level, "other" sources of earmarking. Revenues are total tax and non-tax revenues at each level of government; at the national level, revenues from the 4% ad valorem tax on coffee exports are added 1970-1983, because the:e monies did not pass through the budget in those years.

b. Likely to be an underestimate due to missing data.

municipal incomes have been generally trending downward over the period. Between 1970 and 1986 the share of the central government's RDEs ir. the total rose from 44% to 75%, while the shares of the departmental and municipal governments fell from /-. to 23% and 9% to 2% respectively.

7. By far the biggest contr:ibuting factor to the increase was the rise in revenue sharing between levels of government. "Situado fiscal" and "cesion a las ventis" contributed about 39% of the total increase in earmirking over the period. Other major contributors were: departmental alcohol/tobacco/gambling taxes (23%), the gasoline tax (14%), import duties to support PROEXPO, IFI and Caja Agraria (11%) and the coffee export tax