rafmacalaba/datause-encoder-data huggingface dataset:
where A represents percentage change. The first component ASJ tells how migration of a sub-group affects the changes in poverty incidence. The second component AK, tells the change of inequality of a sub-group. The third component, API,, tells how mean expenditure changes in a sub-group, j. This methodology will be applied to locality decomposition in Ghana, and socio-economic group decomposition within each locality, respectively, presented in sections 4.a) and 4.b) bellow.

# _4.a) Povertv decomposition by locality in Ghana_

Table 4a presents the decomposition of the change in poverty incidence between 1988 and 1992 into the changes of the three components presented in equation (3) in each of the three localities, both weighted and not weighted. Table 4a shows that there was a 15 percent decrease in poverty incidence between 1988 and 1992 for Ghana. Table 4b presents the same decomposition between 1989 and 1992, which shows that there was a 14 percent decrease in poverty incidence. Figure 13 presents the contribution of each component from each locality to the total poverty reduction in Ghana between 19881992. In this 15 percent decrease, 70 percentage comes from the increase in mean expenditure per capita, _P1,,,_ in Rural Areas (10.4, the weighted change, divided by 15).

Table 4a also shows that the improvement in inequality, KJ in rural areas contributed substantially, over 30 percent, to the overall poverty deduction in Ghana between 1988 and 1992. The comparison between 1989 and 1992, however, shows that the distribution in Rural Areas were worsened, contributing negatively to the poverty reduction (Table 4b). Mean expenditure (per capita) decreased by 17 percent in Accra between 1988 and 1989, and the inequality also worsened significantly, contributing negatively to the poverty reduction in Ghana. In Other Cities, there is a 9 percent increase in mean expenditure per capita between 1988 and 1992, contributing 14 percent to the poverty reduction in Ghana (2.1, the weighted change, divided by 15). The inequality is also improved in Other Cities, contributing another 9 percent to the overall poverty reduction in Ghana.

Table 4a Ghana: Decomposition of the percent change in poverty incidence by locality, GLSS1-GLSS3

|||_AS._|A|(K1)|_-A_|_P,j_|_APO_|
|---|---|---|---|---|---|---|---|
||Actual|Weighted|Actual|Weighted|Actual|Weighted||
|Accra|-1.4|_-0.03_|153|_2.9_|17|_0.3_||
|Other Cities|-3.7|_-0.9_|-4.4|-1.0|-9.0|_-2.1_||
|Rural Areas|1.6|_1.2_|-6.6|_-5.1_|-14|_-10.4_||
|Ghana|||||||_-15_|

13
# 6-. The_Quantity and Qualitv of Provider-Client Transactions

Even though there are serious difficulties in measuring project impact on fertility or even contraceptive acceptance, efforts can be made to examine the impact of programs in terms of intermediate effects such as the amount and quality of the contact between the project and its intended beneficiaries. The NAS report to which we referred in the introduction has stressed the importance of examining the extent to which programs are reaching clients as a means of both understanding program and client behavior better and of monitoring and evaluating program effects. While it can always be argued that fertility or mortality effects will take a substantial amount of time to take place and even longer to measure, the same cannot be so easily said of the number of visits that fieldworkers make to potential users of contraception or of the observed quality of services provided in clinic settings. Moreover, the quantity and quality of client transactions can be studied empirically in a number of ways (e.g., through surveys or participant observation). Unfortunately, until recently, there was little to indicate that a concern with these issues had been built into Bank (or, in fairness, other donor) projects. Recently some of the operations staff of PHN have been working with the Policy and Research Division to develop ways of building these concerns into projects. It may prove to be an important step towards defining measurable outcomes for PHN projects.

# 7. The Observed Behavior of Beneficiaries

# a. The Demand for Contraception

One of the objectives of population projects is to increase knowledge of contraception and hopefully, in the process, to increase the demand for contraception. The dynamics of demand for contraception are not well understood. The measurement of demand is itself controversial. Traditional measures such as "unmet need" or family size desires have often been used to measure demand, but they have recognized disadvantages in that they do not measure intensity nor deal with the family context or the cost constraints on behavior. These measures are normally developed from surveys that may be too broadly based to relate to the particulars of a specific market. New approaches to assessing demand, perhaps drawn from the experience of social marketing or private sector projects, may be helpful in this area.

Most observers believe that if population programs are to be effective in the long run, they will have to incorporate demand generation elements. This need has been recognized in many of the Bank population projects, but, unfortunately, demand side activities seem to have been among the weakest elements in these projects. In Kenya and India, for example, t-here have been 48
# **4.1 Results**

Table 3 presents the OLS estimates of the within-group transformation of the regression equation (3).

||log(trade)|
|---|---|
|RTAm|0_._215<sup>_∗∗_</sup><br>(2.91)|
|RTAin|_−_0_._329<sup>_∗∗∗_</sup><br>(-6.05)|
|RTAout|_−_0_._097<br>(-1.57)|
|share1|0_._898<sup>_∗∗∗_</sup><br>(33.92)|
|share2|_−_0_._196<br>(-1.35)|
|RTAm _×_ share1|0_._094<br>(1.55)|
|RTAm _×_ share2|-0.012<br>(-0.10)|
|RTAin _×_ share1|0_._093<sup>_∗∗_</sup><br>(3.00)|
|RTAin _×_ share2|_−_0_._083<sup>_∗_</sup><br>(-2.09)|
|RTAout _×_ share1|_−_0_._706<sup>_∗∗∗_</sup><br>(-22.91)|
|RTAout _×_ share2|1_._47<sup>_∗∗∗_</sup><br>(10.05)|
|Country pair FE|Yes|
|Country_×_time FE|Yes|
|Observations|796,107|
|R2|0.7982|
|t statistics in parentheses||
|* p<0.05, ** p<0.01, *** p<0.001||

Table 3: OLS estimates of regression equation (3) To understand the importance of Global Value Chains (GVC) for the effect of Regional Trade Agreements on trade flows, it is insightful to compare the results from model (1), which does not account for GVC, with the results from model (1), which does account for GVC. Comparing Table 1 to Table 3, one main message 14
**Table 1:** Summary Statistics

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
||P<br>|anel A: LA<br>|POP (2004-<br>|2018)<br>||
|Trust in President|8983|.454|.498|0|1|
|Trust in Municipality|12283|.558|.497|0|1|
|Trust National Legislature|12076|.393|.488|0|1|
|Confdence in Judicial System|12107|.382|.486|0|1|
|Trust in Media|10721|.558|.497|0|1|
|Trust in Church|12201|.605|.489|0|1|
|Male|12392|.481|.5|0|1|
|Age|12354|39.063|16.432|17|99|
|Age Squared|12354|1796|1493|289|9801|
|Years of Schooling|12227|8.287|4.977|0|18|
|Unemployed|12392|.07|.256|0|1|
|Living in Rural Area|12392|.379|.485|0|1|
|<br>|Panel<br>|B: Latinob<br>|ar ́ometro (2<br>|004-20<br>|18)<br>|
|Income Distribution Perceived as Unfair|12580|.515|.5|0|1|
|Generalized Trust|12128|.215|.411|0|1|
|Economy is Improving|11433|.158|.365|0|1|
|Democracy is Better|11542|.705|.456|0|1|
|Satisfed with Democracy|11817|.347|.476|0|1|
|Male|12580|.475|.499|0|1|
|Age|12580|39.011|16.02|18|94|
|Age Squared|12580|1778.486|1436.62|324|8836|
|Livingin Small City|12580|.184|.388|0|1|

when the president is described as the lowest trusted institution among all the institutions studied here.

In Panel B I plot attitudinal measures from Latinobar ́ometro. Several patterns also emerge from this figure. First, the 2008 crisis seemingly triggered a positive shock on people’s views about democracy. The fraction of respondents saying that democracy was the best kind government and were satisfied with democracy reached their highest value in 2009. Second, starting in 2010, satisfaction with democracy dramatically plummeted from 60% to less than 20% in 2018. People’s preference for democracy followed a similar downward trend. Third, while generalized trust remains very low over the whole period, it started to experience a steady decline since 2008.

Figure 2 reveals another interesting pattern: for all trust measures in LAPOP, respondents living in rural areas show higher levels of trust relative to those living in urban areas. The same patterns emerge in Figure 3 when distinguishing between individuals living in municipalities with weak and strong state capacity. Those living in municipalities with low levels of public goods provision (proxied by the share of households with access to electricity and water services) tend to trust relatively more. Interestingly, this pattern is less clear when one looks at attitudinal measures from Latinobar ́ometro. Indeed, it can be seen in Figure 4 that views of unfairness of income distribution are not systematically stronger in any particular type of municipality. Likewise generalized trust 7
# **Table 2.  Developed and Developing Country Use of Antidumping and DSU Challenges, 1995-2005**

|**Country**|**Number of New**<br>**Antidumping**<br>**Investigations,**<br>**1995-2005**|**Number of New**<br>**Antidumping**<br>**Measures Imposed,**<br>**1995-2005**|**Number of Challenges**<br>**to New Antidumping**<br>**Investigations Under**<br>**the DSU**|
|---|---|---|---|
|**Total developed economy**<br>||||
|**antidumping users**|1169|687|66|
|Australia|179|67|1|
|Canada|134|84|1|
|European Union|327|219|5|
|United States|366|234|58*|
|Other developed economies|163|83|1|
|**Total developing economy**||||
|**antidumping users**|1671|1117|69|
|Argentina<br>|204<br>|147<br>|4<br>|
|Brazil|122|66|1|
|China<br>|123<br>|68<br>|0<br>|
|Colombia|27|12|0|
|Egypt<br>|50<br>|30<br>|1|
|India|425|316|39**|
|Indonesia|60|27|0|
|Malaysia|35|25|0|
|Mexico|85|76|7|
|Pakistan|12<br>|8<br>|0<br>|
|Peru|60|40|2|
|Philippines<br>|17<br>|9<br>|1<br>|
|South Africa|197|113|5|
|Thailand<br>|34<br>|27<br>|1<br>|
|Turkey|101|86|1|
|Venezuela|31|25|1|
|Other developing economies|88|42|6|
|Total|2840|1804|135|

Note: Data for the initiations and measures used in this table compiled by the author from WTO (2006a,b). The data on WTO disputes is compiled by the author and is available as <u>http://people.brandeis.edu/~cbown/global_ad/data_files/DSU-WTO-v2.1.xls</u> in Bown (2006b). *The 58 US antidumping investigations were challenged under 26 different case groupings by 10 different countries. **The 39 Indian antidumping investigations were challenged under three different DSU case groupings by 3 different countries– the EU (DS304) challenging 31 investigations, Bangladesh (DS306) challenging 1 investigation and Taiwan (DS318) challenging 7 investigations.

28
Table 5. Preference for Equality and for Eliminating Inequality (Ordered Probit): 1999-2001 (Robust z-statistics in parentheses)

|||Restricte<br>|d Sample<br>|
|---|---|---|---|
||Eliminate<br>|Preference for<br>|Eliminate<br>|
||Inequality<br>(1)|Equality<br>(2)|Inequality<br>(3)|
|Age<sup>1</sup>|-15.775|-18.507|-10.401|
||(5.92)**|(5.75)**|(3.38)**|
|Age-squared<sup>1</sup>|125.141|134.868|94.824|
||(4.39)**|(3.89)**|(2.93)**|
|Female|-0.094<br>|-0.106<br>|-0.087<br>|
||(6.64)**|(6.18)**|(5.35)**|
|No. of Children|-0.011|-0.016|-0.002|
||(1.91)|(2.26)*|(0.35)|
|Married|0.048|0.026|0.041|
||(2.98)**|(1.31)|(2.20)*|
|_Education_||||
|Incomplete Technical/Vocation Education|0.047|-0.011|0.128|
||(1.86)|(0.37)|(4.40)**|
|Complete Technical/Vocation Education|0.067|0.095|0.188|
||(2.71)**|(3.26)**|(6.48)**|
|Incomplete Secondary Education|0.102|0.092|0.185|
||(4.22)**|(2.99)**|(6.15)**|
|Complete Secondary Education|0.144|0.186|0.274|
||(6.38)**<br>|(6.87)**<br>|(10.11)**<br>|
|Incomplete University Education|0.201|0.233|0.302|
||(7.44)**|(6.91)**|(9.83)**|
|Complete University Education|0.28|0.329|0.392|
||(10.64)**|(10.64)**|(13.46)**|
|_Income_||||
|Lower Income|-0.084|-0.066|-0.013|
||(4.80)**|(3.12)**|(0.62)|
|Higher Income|0.153|0.192|0.11|
||(9.26)**|(9.73)**|(5.83)**|
|_Labor Force Status_||||
|Part-Time Employed|-0.01|0.058|-0.019|
||(0.37)|(1.85)|(0.68)|
|Self-Employed|0.173|0.244|0.193|
||(5.55)**|(6.91)**|(5.82)**|
|Unemployed|-0.121|-0.088|-0.039|
||(4.03)**|(2.43)*|(1.09)|
|Retired|-0.057|-0.024|-0.077|
||(2.10)*|(0.71)|(2.48)*|
|Other|-0.012|0.02|0.01|
||(0.57)|(0.94)|(0.42)|
|_Country Group Dummies _<sup>_2_</sup>||||
|Central and Eastern Europe|-0.955|-0.938|-0.746|
||(20.40)**|(19.13)**|(16.29)**|
|Baltics|-0.349|-0.182|-0.175|
||(21.11)**|(9.15)**|(8.85)**|
|Other Former Soviet Union (FSU)|-0.371|-0.171|0.093|
||(13.50)**|(4.89)**|(2.72)**|
|Developing countries|0.001|0.046|0.38|
||(0.06)|(1.85)|(15.68)**|
|Observations|30,454|21,302|21,302|

* significant at 5 percent; ** significant at 1 percent Note: Dependent variable is a 10-point scale from 1 (Preference for equality) to 10 (Preference for inequality). and a 5-point scale from 1 (Eliminating inequality very important) to 5 (Not at all important).

1Age and per capita income divided by 1,000, to produce sufficently large coefficients for display.

2Benchmark group are the advanced economies.

18
# **GDP-Employment Elasticities across Developing Economies**<sup>**1**</sup>

Constantin Burgi<sup>2</sup> Shoghik Hovhannisyan and Camilo Mondragon-Velez<sup>3</sup> Keywords: Employment Elasticity of Growth, Jobs, Jobs and Development, Informal Sector, Jobs Diagnostics JEL: E24, J46, O17, O41

> 1 The views expressed in this paper are those of the authors and do not necessarily represent those of the International Finance Corporation, the World Bank Group, or their management. We would like to thank Aart Kray, Emelly Mutambatsere, Marcio Cruz, Pablo Fajnzylber, and Xiao Jiang for very useful comments and suggestions.

> 2 University College Dublin, corsbu@gmail.com

> 3 International Finance Corporation – World Bank Group, SHovhannisyan@ifc.org and CMondragonvelez@ifc.org.
Information management was improved so that schools were responsible for systematically organizing information on enrollment, learning achievements, finance, and so forth, and reporting it to the division office. Schools were required to set improvement plans every year and compare them with actual achievement. This dynamic process was monitored by the division-level education department. School finance was also decentralized to some extent to relax the school budget constraints because Philippine public schools are not allowed to charge school fees. TEEP schools were free to raise their own funds from communities, parents, and others, although resources were admittedly limited in many poor communities. These reforms in public schools were expected to improve education quality, which would then in turn increase returns to schooling in the labor market (see for example, Yamauchi [2005] on returns to schooling).

The selection of TEEP provinces was purposive because the program was intended to cover the most depressed provinces identified in the Social Reform Agenda. TEEP allocation was rather different in the Philippines’ three macro regions. In the northern macro region of Luzon, TEEP was concentrated in the Cordillera Administrative Region, a mountainous region in the center of northern Luzon. In the central macro region of Visayas, TEEP provinces were relatively evenly distributed. In the southern Mindanao macro region, TEEP divisions were clustered, though not to the same degree as in northern Luzon. The TEEP provinces are highlighted in the map in Figure 2.1.

Using school-level data, Yamauchi and Liu (2011) show that national achievement test scores increased by about 12–15 points if students were exposed to TEEP for six years. The researchers used cohort panel data from grade 4 in school year 2002/03 to grade 6 in 2004/05 to difference out cohort-school-specific unobservables, combined with propensity score matching. This study demonstrates a significant increase in student learning performance. This study also demonstrates a significant increase in student learning performance due to (1) new constructions of school buildings,<sup>15</sup> (2) instructional training of teachers and (3) distribution of textbooks especially at earlier grades.

# **3. Model**

In this section we introduce a model to characterize the effects of school quality on schooling

> 15 See, example, Case and Deaton (1999) and Hoxby (2000) for the effects of class size on educational outcomes.

6
Table A.12: Eects of contact farmers' adoption of SLM practices on maize revenue

||Total Maize Revenue|
|---|---|
|Adopted mulching on own plot|1458.547<br>[1032.677]|
|Adopted strip tillage on own plot|723.044<br>[572.914]|
|Adopted pit planting on own plot|436.835<br>[1502.123]|
|Adopted contour farming on own plot|1078.387<br>[3145.182]|
|Adopted crop rotation on own plot|957.972<br>[960.541]|
|Adopted row planting on own plot|-1206.388<br>[1472.065]|
|Adopted improved fallowing on own plot|-1871.335<br>[968.155]|
|N<br>|342|
|R<sup>2</sup>|0.187|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey, 2012, 2013. Notes: Regressions include the same explanatory variables as models in Table 4. Additional controls include dummies for usage of all inputs displayed in Table 8, as well as labor allocated to maize production as displayed in Table 9. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level.

55
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18b

# TABLE U1, COLOMBIA MACRO INDICATORS 1984<sup>TO 90</sup>

||1984|1985|1986|1987|1988|1989|1990|
|---|---|---|---|---|---|---|---|
|NATIONAL ACCOUNTS||||||||
|GDPGROWrH (%)|_3.35_|3.11|5.82|5.S3|4.44|3.24|3.70|
|RELATIVE PRICE||||||||
|REAl EFFECrIVE EXCH '85|89.06<br>|100.00<br>|125.74<br>|137.17<br>|138.84<br>|149.2S<br>|169.40<br>|
|BILATERAL REL EXRt/1|84.89|100.00|11' 01|122.93|123.04|131.06|140.43|
|NOMINAL VARIABLES (%)||||||||
|DEVALUATION (AVG EXI)|27.85|41.16|36.50|24.89|23.32|27.87|31.28|
|CPI INFLATION|16.14|24.04|18.88|23.30|28.11|25.84|29.12<br>|
|WPI INFLATION|18.30|24.90|22.00|24.90|28.30|28.20|26.52|
|Ml GROWI1. /2|24.11|10.74|35.80|37.69|25.74|29.10<br>|25.80<br>|
|M2 GROWIH /2|23.91|20.23|35.60|36.31|21.11|31.40|29.90<br>|
|MONErARY BASE GROWTH /2|23.S4|17.41|30.60<br>|34.40<br>|19.88<br>|30.40<br>|23.20<br>|
|SEIGNORAGE/3 (%)|2.03|1.44|2.18|2.46|1.44|2.03|_I.S3_|
|RATIOS||||||||
|MI/GDP|12.77|10.98|10.91|II.SS|10.93|10.88|10.35<br>|
|M2/GDP|21.18|19.78|19.62|20.57|18.74|18.98|18.65|
|RESERVE MONEY/GDP|10.68|9.74|9.30|9.62<br>|8.67<br>|8.72<br>|8.12<br>|
|TOTAL DEFICIT/GDP I4|6.30|3.50|-0.20|1.40|2.60|1.60|0.90|
|INTERNATIONAL<br>TRANSACTIONS (S)||||||||
|XPORTS FOB*|4273|36S0|5331|S661<br>|S343<br>|6029<br>|710S<br>|
|IMPG*I5 POB|4027|3673|3409|3793<br>|4516<br>|4S48<br>|5105<br>|
|TRADE BALANCE|246|-23|1922|1868|827|1481|2003<br>|
|CURBAL|-1401|-180|383|336|-200|42<br>|446<br>|
|CHANGE IN NErINT'L.RES.|-1261|234|1464|-22|360|163|S8S|
|INT RESERVEStIMPORTS/5|12.3|14.3|24.3|33.8|32.8|32.8|33.0|

It Agains US dollan

- 2/ Source: IFS and Banco do Is Republica, Colombia

3/ Defind **as** change in temve money over<sup>GDP.</sup>

- 4/ Source: Wodd Bank

_5_ /In week of imports, source: Yeady IFS, 1990<sup>IS Esimate.</sup>

* 1990 figues arn timates from Colombia CEM
companies serving the region’s larger cities. Melendez (2008) analyzes subsidies for electricity, water, gas, and phone services in Colombia.

The majority of these studies use household survey data on expenditures to back-calculate household water and electricity use (Foster 2004; Foster and Araujo 2004; Melendez 2008).<sup>4</sup> This estimation strategy is also common in subsidy incidence analyses that examine electricity and water subsidies only. Appendix A summarizes studies that examine the incidence of subsidies in the water supply sector (for a review of subsidy incidence analyses in the electricity sector, see Cardenas and Whittington 2019). However, there are two main problems with this back-calculation approach. First, when self-reporting their electricity and/or water bill, the respondent may simply guess the past month’s bill because its amount is either not remembered or unknown. Second, researchers in this vein typically use the average price paid based on the existing tariff structure. Yet if the price structure used by a utility company is based on an IBT, then the result is an error in the estimation of the levels of water and electricity actually used.

Another factor that merits consideration is that, in these studies, the estimates of costs of production and delivery are based on national or regional cost estimates—not on data from the utility companies themselves (Foster 2004; Foster and Araujo 2004; Melendez 2008). Foster and Yepes (2006) focus on cost recovery levels of water and electricity utility companies in Latin America, but they do not have access to information on the full average cost of service provision. Hence they rely on data from Global Water Intelligence for estimates of operation costs and of maintenance and capital costs for water and electricity utility companies. There are very few studies in the literature that obtain or estimate the average unit cost based on data from the utility companies actually serving

> 4 Foster (2004) uses data from a household survey that asked respondents to present their electricity/water bills, when available: 28% of the respondents were able to show their water bill and 35% were able to show their electricity bill. At the same time, 42% were unable either to present a water bill or to report their water expenditure; and 35% were unable to show their electricity bill or report their electricity expenditure.

6