rafmacalaba/datause-encoder-data huggingface dataset:
- 21

# Cost-Effectiveness of PROBECAT

What do these findings imply about the cost-effectiveness of the PROBECAT program? To answer this question we focus on two of the more significant outcomes identified in the previous analyses: first, the impact of program participation on the speed with which trainees tLnd jobs; second, the impact of program participation on monthly earnings. These two impacts, and their implications for the benefit streams associated with program participation, are combined with cost estimates to arrive at some tentative conclusions about the cost-effectiveness of the PROBECAT program.

The costs and benefits of the program are discussed below. We include both the direct training costs as well as the indirect costs associated with participation in the program. Direct costs (costs for instructors, training materials, and program administration) are readily available from STPS: in 1991, the average operating cost per course completed was about 350.4 thousand pesos. To derive estimates of indirect costs we use the following approach. We assume that at time to each unemployed worker faces two possible strategies: (i) to immediately initiate job search (control group); or (ii) to enter a training course and thus delay job search by the length of the course (trainees). We know, from our previous analysis, that the expected duration of search for trainees conditional on having attended the course (ie. once the course is over) is shorter than the expected duration of search for controls (by about 2.5 months). However, we also have to take into account the deferral of job search by trainees to participate in the program. We thus calculate the time to job for trainees by adding to search time the cost of delaying search by attending the training course (on average equal to 2.9 months). The cost of the training period is thus valued in terms of search time foregone. The benefit measures are calculated from the previous estimates of training effects on monthly earnings. Monthly earnings are predicted for trainees and controls using sample means of all regressors.

Table 14 summarizes the calculation of these cost and benefit measures for different groups of male trainees and controls. The first two columns show the mean duration of search for trainees (4 months) and controls (6.5 months), separately for those with and without prior work experience, and for younger (age 25 or less) and older (over 25 years) individuals. The difference between (2) and (1) measures the gain in search time (employment effect) attributable to the program. Column (3) adds the average duration of training (2.9 months) to the search time for trainees. Column (4) reports the difference between columns (3) and (2).
# **Greening Global Value Chains: Innovation and the International Diffusion of Technologies and Knowledge**

**Matthieu Glachant**<sup>**1**</sup> **, Damien Dussaux**<sup>**2**</sup> **, Yann Ménière**<sup>**2**</sup> **, Antoine Dechezleprêtre**<sup>**3**</sup>

_Keywords:_ green innovation, climate-mitigation technologies, international technology transfer, innovation policy, intellectual property rights, trade, economic globalization.

_JEL codes_ : F18, F64, O3, Q55

> 1 MINES ParisTech, CERNA, World Bank consultant.

> 2 MINES ParisTech, CERNA

> 3 London School of Economics and Political Science
**<u>Figure E2: Unconditional quantile regression of height-for-age z-scores on food security measures, expanded controls (Bangladesh)</u>**

<!-- Start of picture text --> HFIAS Score FCS<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>quintile quintile<br>95% confidence interval OLS 95% confidence interval OLS<br>RIF-OLS RIF-OLS<br>Child DDS Mother's DDS<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>quintile quintile<br>95% confidence interval OLS 95% confidence interval OLS<br>RIF-OLS RIF-OLS<br>.05 .15<br>0 .1<br>-.05 .05<br>-.1 0<br>-.15 -.05<br>.15 .15<br>.1<br>.1<br>.05<br>.05<br>0<br>-.05 0<br>-.1 -.05<br><!-- End of picture text --> 53
the younger and older age groups. For HAZ, this makes no difference. (KS has p-value < .000 in all cases). For WAZ, results are a little weaker for the older age group but still signi fi cant (the KS test for this group has a p-value <.04 for both boys and girls).

Table 4 provides another perspective on differences in child growth measures between migrant and non-migrant households. Children are classi fi ed by the severity of malnutrition into four groups, from severely malnourished (<-3 SD) to normal (>-1). Focusing fi rst on the very young (columns 2-4), we fi nd that children in migrant households are signi fi cantly less likely to be severely underweight and signi fi cantly more likely to be in the normal or mildly underweight group (p-value <.01). They are also signi fi cantly less likely to be severely stunted (p-value<.05) and this effect is more pronounced for girls. These effects are sustained in the older age group, with signi fi cantly fewer children from migrant households being severely malnourished (HAZ p-values<.01 and WAZ p-values <.05) and signi fi cantly more children being in the mild to normal group. We do not observe any gender differences in these outcomes for this age group.

These descriptive statistics, though based on simple mean comparisons, suggest that children in migrant households do better than their counterparts in non-migrant households overall and set the stage for our main question: do girls in migrants households do relatively better than their counterparts in non-migrant households as compared to boys. We turn to this issue next.

# 4 Main Results

Tables 5-6 present the estimation results for HAZ and WAZ respectively. The fi rst speci fi cation in all cases (column 1) presents the OLS estimates of the migration coefficient and its interaction with child gender under the assumption that the migration decision is uncorrelated with unobserved village and household attributes.

In speci fi cation 2 we relax the assumption that the migration decision is uncorrelated with unobserved household characteristics which could in fl uence child growth. Since the endogeneity bias in the migration coefficient could work in either direction, we have no priors on the direction of the bias. The instrument in this speci fi cation is the village migration network and its interaction with child gender. The explanatory power of the instruments, conditional on the included household and child characteristics, is extremely high in all cases. The coefficient estimates for the full set of controls for this speci fi cation are presented in Appendix Table A1.

12
plan to market but rather restructuring brought about by globalization and other forces.

In Table 1 we provide time series statistics on unemployment, inflow, outflow and vacancies in the six economies. In the left panel of the table, we give the inflow rate, outflow rate and _U/V_ ratio (a measure of labor market tightness), while in the right panel we express unemployment and vacancies as a share of the labor force in each country. As may be seen from the table, the six economies differ markedly in terms of their unemployment, flows and vacancy levels and rates.<sup>4</sup> West Germany is in the intermediate range, displaying between 1991 and 2005 an unemployment rate that increases from 5 to 10 percent, inflow rate that rises from 0.9 to 1.6 percent, outflow rate that declined only slightly, and a vacancy rate (as a share of the labor force) that fluctuated between 0.7 and 1.4 percent. The changes in these variables occur mostly in two waves, reflecting the business cycles and also a notable shift toward a service economy with higher natural labor turnover that translates into steadily rising inflows into unemployment (a rise by two-thirds over the 1991-2005 period). East Germany, in contrast, registers an open unemployment rate rising from near zero to 18.6 percent, inflow rate almost doubling from an already high level of 1.7 to 3.0 percent, outflow rate rising to rather high 13-15 percent by the mid 1990s and fluctuating around this level ever since, and vacancies as a share of labor force rising from 0.4 percent in 1991 to about 1 percent in the late 1990s and remaining at that level in the 2000s. For most of the 19912005 period, the East German part of the German economy hence displays a much higher unemployment rate driven primarily by extraordinarily high inflow rate (labor turnover in firms resulting in registered unemployment). Note however, that East Germany also has higher outflow rates relative to the number of unemployed and a similar vacancy rate as the Western part of Germany. In particular, the East German economy operates with a higher unemployment rate in the presence of very sizable active labor market

> Hungary, which experienced a steady growth during last decade, and Slovakia, which experienced a noisy but discernible decline during the same period.

> 4 Numbers presented are country aggregates based on our working district level database. Because some districts for some countries are excluded from our analysis due to changes of district borders, data in Table 1 could slightly differ from

− 5 −
-102-

- Conference of European<sup>Statisticians (1972). "Comparisons</sup> of Labour Productivity in Industry<sup>in Austria, Czechoslovakia,</sup> France and Hungary", Conf.Eur.Stats./WG 2i, Geneva, also in Statistical<sup>Standards</sup> and Studies, No. 24,<sup>UN, New York.</sup>

- S. Davies and R.E. Caves<sup>(1987), Britain s Productivity</sup> Gap, NIESR, Cambridge.

- E.F. Denison (1967),<sup>Why Growt-i</sup> :-.'es Differ, Brookings

- B.H. Dholakia (1974),<sup>The Sources of Economic Growth in India, Good</sup> Companions, Baroda.

- L. Drechsler, and J.<sup>Kux(1972),</sup> Mezinarodni Srovnari<sup>Produktivity Prace,</sup> Central Statistical<sup>Office, Prague.</sup>

- J.F. Early (1978),<sup>"Improving the Measurement of Producer Price Change",</sup> Monthly Labor Review,<sup>April.</sup>

- Economic Commission<sup>for Europe (1980), "Comparative</sup> GDP Levels", Economic Bulletin for Europe.

- ECLA (1963), A Measurement<sup>of Price Levels and the Purchasing Power of</sup> Currencies, Santiago, processed.

- E. Ehrlich (1967), "International Comparisons by Indicators<sup>Expressed in</sup> Physical Units", Acta Oeconomica,<sup>Vol. 2, No. 1-2, Budapest.</sup>

- Eurostat (1983), Comparison<sup>in Real Values of the Aggregates of ESA: 1980,</sup> Luxembourg.

- S. Fabricant (1940),<sup>The Output of Manufacturing</sup> Industries, 1899-1937, NBER, New York.

- A.W. Flux (1933), "Industrial Productivity in Britain and the United States", Quarterly Journal<sup>of Economics, November.</sup>

- M. Frankel (1957),<sup>British and American Manufacturing</sup> Productivity, University of Illinois,<sup>Urbana.</sup>

- Fundacao Getulio Vargas<sup>(1984), Contas Nacionais do Brasil: Metodologia e</sup> Tabelas Estatisticas, Rio.

- W. Galenson (1955),<sup>Labour Productivity in Soviet and American Industry,</sup> Columbia University<sup>Press, New York.</sup>

- A. Ghosh (1984),<sup>"Some Problems Relating to the Selection of Representative</sup> Commodity Baskets,<sup>the Problem of Quality Differences and the Treatment</sup> of Comparison Resistant<sup>Services in the ICP", Bellagio, processed,</sup> September.

- A. Ghosh (1985), "Comparison of Structurally Divergent Economies,<sup>Problems</sup> and Limitations", Economic and Politicial<sup>Weekly, January l9th.</sup>
|POST_Labor|POST interacted with a dummy variable that takes the value of 1 for labor abundant<br>countries. Labor abundant countries are identified as those with a ratio above one of its<br>national share of worldpopulation to its national share of world GDP.|World Bank WDI.|
|---|---|---|
|**Control variables:**|||
|Time(Time)|Equal to -1 theyearprior to reform,0 theyear of reform,1 theyear after reform,etc.||
|Time squared(Timesq)|The square of time||
|Real GDP (rGDP)|In thousands of constant 2005 international dollars, as PPP converted GDP per capita<br>(chain series)constant 2005prices multiplied bythepopulation.|Penn World Tables v. 7.0|
|Working age population<br>(WorkingAgePop)|Number of people who could potentially be economically active as the total population<br>between the ages 15 to 64.|World Bank WDI|
|**Instrumental variables:**|||
|External debt-to-GDP ratio (Debt)|Total external debt (debt owed to nonresidents repayable in foreign currency, goods, or<br>services)as a share of GDP.|World Bank WDI|
|External debt-to-GDP ratio<br>interacted with the change in world<br>interest rates(DebtTBill)|The external debt-to-GDP ratio interacted with the one-year change in the market yield<br>on U.S. Treasury securities at 10-year constant maturity.|World Bank WDI<br>United States Federal Reserve|
|Terms of trade (TofT)|Net barter terms of trade index, calculated as the percentage ratio of the export unit<br>value indexes to the import unit value indexes,measures relative to the baseyear 2000.|World Bank WDI|
|Five-year change in democracy<br>score(Dem5)|five-year change in PolityIV’s governing authority index, where index scores range<br>from -10(most autocratic)to 10(most democratic).|PolityIV|
|Lagged POST|A dummy variable that takes the value of 1every year two years after a country’s<br>reformyear.|Wacziarg and Welch (2008)|

Note: IFS = International Financial Statistics. WDI = World Development Indicators. KILM = Key Indicators of the Labor Market. TED = Total Economy Database. LFS = Labor Force Statistics.

40
<!-- Start of picture text --> 8.0 0.0<br>MPO-Mar WEO-Apr MPO-May<br>7.0 68 -0.5<br>6.0 -0.8 _<br>oZ -10 &2<br>35 °<br>2s 5.0 158<br>as N<br>or<br>4” 2<br>= 5 40 4.1 -2.0 8<br>gg =2.3 2<br>EE 3.0 25<br>Zc =<br>= 20 A -3.0 8<br>(C)<br>1.0 -3.4 -3.5<br>0.0 -4.0<br>—— Growth Projections for 2020 (p.p.) ——Student Drop-Out Estimates by Growth Projection Release<br><!-- End of picture text -->
TABLE 56: EFFECT OF HIGH WINNING REBATES ON CONTRACT EXTENTIONS

|Dependent Variable||Contract|Extension||
|---|---|---|---|---|
|Model|Model (1a):<br>CEM within<br>PEs across|Model (1b):<br>CEM within<br>PEs across|Model (1c):<br>CEM within<br>PEs across|Model (1d):<br>CEM within<br>PEs across|
||years|years|years|years|
|Regression Type||Lo|git||
|Control: Manual|||||
|Treatment: e-procurement|-1.044***<br>(0.17)|-0.944***<br>(0.18)|-1.190***<br>(0.17)|-1.101***<br>(0.18)|
|Winning Rebate (Continuous)|-0.006*<br>(0.00)|-0.005+<br>(0.00)|||
|Control: Manual # Winning Rebate|||||
|(Continuous)|||||
|Treatment: e-procurement # Winning||-0.019|||
|Rebate (Continuous)||(0.01)|||
|Control: Winning Rebate – Normal<br>(<17%)|||||
|Winning Rebate – High (17-27%)|||0.211<br>(0.17)<br>|0.317<br>(0.19)<br>|
|Winning Rebate – Very High (>27%)|||0.699**<br>(0.24)|1.124***<br>(0.32)|
|Control: e-procurement # Winning|||||
|<br>Rebate – Normal (<17%)|||||
|e-procurement # Winning Rebate – High||||-0.541|
|(17-27%)||||(0.44)|
|e-procurement # Winning Rebate – Very<br>High (>27%)<br>Ln of Contract Value|0.706***<br>(0.02)|0.706***<br>(0.02)|0.719***<br>(0.02)|-1.098*<br>(0.55)<br>0.720***<br>(0.02)|
|Procurement Method: OTM (LTM –|0.152**|0.159***|0.175***|0.174***|
|base)|(0.05)|(0.05)|(0.05)|(0.05)|
|PE Fixed Effects|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|Yes|
|constant|Yes|Yes|Yes|Yes|
|Pseudo R<br>2|0.171|0.172|0.172|0.172|
|N|22270|22270|22270|22270|

Note 1: + p<0.10 * p<0.05 ** p<0.01 *** p<0.001 Note 2: All Binary Logit Models in the table display logit coefficients We observe that e-procurement in general had lower contract extensions as compared to manual which corroborates the previous finding that e-procurement leads to better competition. We find weak mixed evidence suggesting that firms bidding at very competitive rates were recuperating their low prices in the contract implementation phase through contract extensions. Please note that the main channel of recuperating losses happens through cost overruns rather than time extensions with our models tracking only the latter. We find that across manual and electronic systems, winning rebates either have a significant 87
|Crops with purchased seeds (number of crops)|1.50<br>[1.55]|2,806|-.383***<br>(.090)|-.269**<br>(.121)|-.221*<br>(.112)|
|---|---|---|---|---|---|
|Fertilizer or pesticides (=1)|.865|2,783|-.037|-.002|.007|
||[.342]||(.024)|(.017)|(.032)|
|Number of crops grown by the household|4.45<br>[1.95]|2,806|-.951***<br>(.133)|-.053<br>(.237)|-.013<br>(.183)|
|Marketable crop farming (=1)|.225|2,806|-.143***|.097**|.186***|
||[.418]||(.027)|(.039)|(.038)|
|Marketable crop selling (=1)|.131|2,795|-.057**|.106***|.203***|
||[.338]||(.028)|(.036)|(.039)|
|Tree farming (=1)|.171<br>[.377]|2,801|-.113***<br>(.026)|.015<br>(.037)|.044*<br>(.024)|
|**Panel C: Acce**|**ss to extension s**|**ervices**||||
||Round 1<br>|||Impact estimat<br>|es<br>|
||Control mean|Obs.|Round 2|Round 1 x RCBP|Round 2 x RCBP|
|Contact with extension officer for advice or information (=1, head)|.821<br>[.384]|2,435|.184***<br>(.027)|.014<br>(.032)|.002<br>(.014)|
|Contact with extension officer was regular (=1, head)|.458|2,237|.504***|.076*|-.014|
||[.499]||(.030)|(.042)|(.017)|
|Rated DA advice very useful (=1, head)|.568|1,819|.085*|.153***|-.084*|
||[.496]||(.045)|(.047)|(.044)|
|Received advice from peer (=1, head)|.656<br>[.475]|2,522|-.134**<br>(.056)|-.003<br>(.039)|.058<br>(.045)|
|Received advice from contact farmers (=1, head)|.339<br>[.474]|2,422|.081*<br>(.045)|.111**<br>(.047)|.021<br>(.047)|
|Received advice from extension officers (=1, head)|.837|2,586|.123***|.003|-.016|
||[.370]||(.025)|(.031)|(.023)|
|Received advice from an NGO (=1, head)|.068|2,519|-.032**|-.025|.038*|
||[.252]||(.013)|(.016)|(.019)|

Notes: Standard errors in parentheses, clustered at the kebele level. * indicates significance at 10% level, ** at 5% level, *** at 1% level. Each row shows the coefficients of the regression of the outcomes in the left-hand side column on a time indicator (Round 2), and the interaction of the time indicators and the treatment variable (Round 1 x RCBP, Round 2 x RCBP). All regressions control for region and the following contemporaneous household variables: sex of household head, presence of a female spouse of the head, household size, number of men in the household, number of elderly in the household, number of adults, number of adolescents, number of children, dependency ratio, number of literate household members, number of students in household, number of rooms in the dwelling, roof material is iron sheets. Outcomes shown in Panel C and "Tried new agricultural activities in the past year" are individual respondent level variables, with only household heads included in the sample.

18
# Estimating the Number of Firms in Africa<sup>*</sup>

Marcio Cruz<sup>†</sup> Florian Moelders<sup>‡</sup> Edgar Salgado<sup>§</sup> Ariane Volk<sup>¶</sup>

## **JEL Classification** : D22, L25, L26

**Keywords** : Firms, Entrepreneurship, Africa, Private Sector Development, Market Analysis and Segmentation

*The authors thank Paolo Mauro, Denis Medvedev, Roberto Fattal, Xavier Cirera, Mark Dutz, Mariana Pereira, Justice Mensah, and participants of the authors’ workshop ”Digital Opportunities in African Businesses,” for valuable comments. Lucio Castro actively participated in earlier drafts of this paper and we are indebted to him. Lucien Ahouangbe, Kaleb Abreha, and Ghita Chraibi provided superb research assistance. This paper is a product of the IFC Economic Research Unit. Financial support from the Government of Japan is gratefully acknowledged.

- †International Finance Corporation, email: marciocruz@ifc.org (Corresponding author)

- ‡International Finance Corporation, email: fmoelders@ifc.org

- §International Finance Corporation, email: esalgadochavez@ifc.org

- ¶International Finance Corporation, email: avolk@ifc.org
population distribution favors grid expansion.<sup>9</sup> Inclusion would not change the evaluation of later investment stages, which are the focus of our study.

# **_Cost Estimation for the Grid Expansion Model_**

At each step—after a new BSP and its associated demand points have been identified—we compute total system cost as the sum of costs for power generation, transmission and distribution (Table 3). Generation, transmission and distribution unit cost estimates are largely drawn from a recent power sector study for Kenya’s Ministry of Energy (KMOE, 2008) and also from a World Bank technical study of small-scale technologies (ESMAP, 2007).

Generation costs at large power plants are assumed to be fixed and proportional to the current generation fuel mix (Table 4).<sup>10</sup> By assuming a constant generation mix we avoid the more complex issue of when to bring online new generators and focus more on the transmission and distribution aspects of investment decisions. We calculate capital, O&M and fuel costs per kW for each of the currently operating generation technologies and convert the total into a per kW unit cost.<sup>11</sup> After conversion to levelized costs we add these to levelized transmission and distribution costs.

Generators and BSPs are connected by HV transmission lines with length estimated as the shortest, most direct distance between them.12 For the MV network estimate, we inflate straight-line distances between settlements by thirty percent. These lines often follow roads, which tend on average to deviate from the shortest route between two points by that amount, and they are often routed around obstacles such as lakes or protected areas. This adjustment also partly compensates for the fact that realworld MV transmission systems include non-optimal configurations and redundant links.

> 9 For instance, in Kenya, as of late 2007, approximately one million of eight million households were connected to the national grid, largely in the areas of the largest cities: Nairobi, Mombasa, Kisumu and Eldoret (Parshall et al. 2009).

> 10 Note that only large (> 8MW) operational units are included in computing the current generation mix. Plants that are in the planning phases, deferred without construction starts or deactivated are not included in the calculation.

> 11 In computing levelized costs we follow convention and apply a discount rate of 10 percent. 12 High voltage includes 220 and 132 kV lines.

11
<!-- Start of picture text --> Regression with the<br><!-- End of picture text --> Figure 7: Intercept Coefficients and their Significance in the Fixed-effect Regression with the semi-parametric Method and Updated Data <!-- Start of picture text --> Intercept Estimates For Gini - UNIDO 2-digit Employment Data<br>°|<br>-<br>©<br>=, ‘<br>x ,ih<br>; i<br>es<br>Income midpoint<br><!-- End of picture text -->

Source: Authors’ calculations Similarly, Figure 7 plots the intercept of the semi-parametric fixed-effects regression of each subsample against its midpoint GDP per capita. Consistent with our earlier evidence that between-effects account for much the U-shaped relationship between diversification and GDP per capita, the estimated intercept curve is U-shaped and the intercept is estimated with consistent precision.

<!-- Start of picture text --> and the intercept is estimated<br><!-- End of picture text --> <!-- Start of picture text --> with consistent precision.<br><!-- End of picture text --> <!-- Start of picture text --> Diversification for<br><!-- End of picture text --> <!-- Start of picture text --> and Highly<br><!-- End of picture text --> 4.5 Patterns of Diversification for Resource-Poor, Resource-Rich, and Highly Resource-Rich Countries.

<!-- Start of picture text --> Countries.<br><!-- End of picture text --> <!-- Start of picture text --> and<br><!-- End of picture text --> Figure 8 displays the lowess curve for the Gini index of employment, value-added and exports for resource-rich and resource-poor countries separately.

<!-- Start of picture text --> exports for<br><!-- End of picture text --> <!-- Start of picture text --> and<br><!-- End of picture text --> <!-- Start of picture text --> countries separately.<br><!-- End of picture text --> 21