rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Ethiopia Somalia South Sudan Sudan<br>Years of SchoolingSchool attendanceFood SecuirtyPregnancy carePhysical safetyEarly marriageWaste DisposalDrinking waterElectricityCooking fuel HousingSanitationUnemployment Legal idBank account<br>.03<br>.02<br>.01<br>0<br>Absolute contribution<br>-.01<br>-.02<br><!-- End of picture text --> Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Notes: A positive number means that the gender gap in that indicator is in favor of male-headed household (female-headed households are worse off), and a negative number means that the gap is in favor of female-headed households. The sum of all indicator gaps gives the overall gender gap.

Similar patterns are observed in Sudan, except that lack of legal ID does not contribute to the household level gender gap. In contrast, in Somalia, the gender gap that favors female-headed households is driven by the differential in education and living standard measures. In general, in countries where female-headed households are worse off, the gap in lack of legal ID, lack of bank account, early marriage, and physical safety are larger than the gap in other indicators. In Somalia where the gap is in favor of female-headed households, the gap in education is a much larger contributor to the gender gap, followed by gap in lack of legal ID.

As noted above, female-headed households are generally poorer than their male-headed counterparts in Ethiopia, South Sudan and Sudan. Existing literature shows heterogeneity among households headed by married women living with a husband or a nonresident _(de facto)_ and those headed by divorced, separated, and single women _(de jure)_ (Klasen et al., 2015; Milazzo and Walle, 2015). Thus, to explore heterogeneity of multidimensional poverty among households headed by female, we further disaggregated by _de facto_ and _de jure_ female heads (see Table 7).

_Table 7: Multidimensional Poverty outcomes by De facto and De jure female-headed households and displacement status_

|||Refugees/ID|Ps||Hosts/Non-ID|Ps|
|---|---|---|---|---|---|---|
||Femalehous|eholdhead||Femalehous|eholdhead||
||Defacto|De jure|Difference|Defacto|De jure|Difference|
||**MPI**||||||
|Ethiopia|0.392|0.238|0.154*******|0.085|0.064|0.021|

23
Deaton and Dupriez (2009) implement an iterative solution to derive internally consistent P4s.<sup>45</sup> They do this for three price index methods, namely the Country Product Dummy (CPD) method and both Fisher and Törnqvist versions of the EKS method used by the ICP.

The Deaton-Dupriez P4s cannot be calculated for all countries and they cannot cover the same consumption space as the P3s from the ICP. The limitation on country coverage stems from the fact that P4s require suitable household surveys, namely micro data from consumption expenditure surveys that can be mapped into the ICP “basic heading” categories for prices; the DD P4s are available for 60 countries, which is about half of our sample. The 60-country sample is clearly not representative of the developing world as a whole and in some specific regions, notably EECA where the population share covered by surveys in the 60-country sample is only 8%, while overall coverage rate is 79%. As we will see, the 60-country sample is poorer, in terms of the aggregate (population-weighted) poverty count. Also, some of the 110 basic headings for consumption in the ICP were dropped by DD in calculating their P4s. These included expenditures made on behalf of households by governments and non-governmental organizations (such as on education and health care). Given that such expenditures are not included in household surveys they cannot be included in DD’s P4s. DD also preferred to exclude housing rentals from their calculations on the grounds that this was hard to measure and that different practices for imputing rental for owner-occupied housing had been used by the official ICP in different countries. There are other (seemingly more minor) differences in how DD calculated their P4s and the methods used by the ICP.

Using the P4s at country level kindly provided by Deaton and Dupriez, we have recalculated our global poverty measures. In all cases we recalculate the international poverty line under the new PPPs, as well as (of course) the poverty measures. Table 10 gives the results by region for 2005, while Figure 5 plots the estimates by year. In both cases we give our benchmark estimates for the official ICP PPP for consumption using all 110 basic headings for consumption and results for the 102 basic headings comprising those that can be matched to surveys less the extra few categories that DD chose not to include. Since the 60 countries used by DD did not include one of the 15 countries in our reference group, the poverty line is recalculated for 14

> 45 In general there is no guarantee that there is a unique solution for this method, although DD provide a seemingly plausible restriction on the Engel curves that assure uniqueness. They also use an exact, one-step solution, for the Törnqvist index under a specific parametric Engel curve.

29
substantial rise in national savings will be needed to deliver 8 percent growth. This will be made easier by increasing government savings and creating conditions for a big increase in productivity growth. The private sector will need to play a key role in the latter and this brings us to section V on the microeconomic foundations of growth. Section VI concludes with a summary of policy priorities.

The results in this paper are derived from a combination of the Government of Sri Lanka‘s (GoSL) macro-fiscal vision, cross-country experience from fast-growing economies and economic theory. GoSL‘s key macro-fiscal targets are as follows:<sup>4</sup>

- Double per capita income from US $2000 in 2009 to US $4000 by 2016

- Reduce government debt-to-GDP from 81 percent in 2010 to 60 percent in 2015

- Raise government savings from minus 2-3 percent of GDP to plus 1.5 percent

- Aim for inflation based on GDP deflator of 6 percent per year

- Target growth at 8 percent per year over the medium run.

# **II. CAPITALIZING ON THE PEACE DIVIDEND**

In principle, the peace dividend for Sri Lanka would flow through the following channels:

- <u>Reintegration channel: The reintegration of the war-torn North and East into the economy</u> would fuel a natural expansion of output.

- <u>Macro-fiscal channel:</u> This would exert positive benefits through two sub-channels.  First,

real interest rates on government borrowing would tend to decline along with sovereign risk premia (see Figure 2) while the currency would tend to appreciate as capital flowed in to take advantage of ̳undervalued‘ Sri Lankan assets, financial and real. As a result, the

> 4 _Source:_ Roadmap, Central Bank of Sri Lanka

4
States of over $1 billion a year even if only one in ten American patients travel abroad. The lower costs of health services abroad offer the opportunity to extend medical benefits to people who currently are not insured. This is a mode of trade that has tended to be ignored in CGE analyses.

These examples illustrate that CGE modeling needs to consider the interactions and complementarities between modes of supplying and obtaining services. Each service sector will have distinct features that affect supply and demand, including regulatory requirements. Thus analysis must take into account the sector-specific nature of policies that affect trade by impacting on the incentives (ability) to utilize the various modes of supply that are relevant for a particular sector. To date the CGE literature has not focused on such issues. This is one area where future research could have a high payoff, because in the absence of an accurate characterization of the relationships between modes of supply simulations, the effects of policy reforms may be far off the mark.

# **7 An Example**

The literature reviewed above covers a broad range of issues. In this section, we focus on an illustrative example. This builds on our earlier discussion of value added linkages between the services sectors and the rest of the economy. We focus on the case of Italy. Table 4 below provides a breakdown of the value added structure of the Italian economy. In Table 4, column A corresponds to values in Table 2, while column C and D values correspond to row and column totals in Table 3.<sup>30</sup> Clearly Italy’s economy is highly service intensive. On a value added basis, 72 percent of activities are in the service sector (column A). This is also reflected in the structure of exports. On a gross value basis, 82 percent of exports in terms of gross value are in manufacturing (column B). However, when we focus on the activity content of exports on a sector basis (column C in Table 4), this drops to 53 percent for manufacturing, while the services share of total value added in exports rises from 17 percent to 43 percdent. This reflects forward linkages, by which we mean that while most Italian exports are in goods, the production of those goods requires a great deal of service inputs. As such, in terms of the activities where value added activities take place when producing for export, much of this is in services. Finally, the last column (column D) provides a breakdown of which export sectors actually contain domestic value added. Here, we are looking at the total value added content of exports, broken down by sector, e.g., the share of Italian value added contained in total exports that is embodied in exports of automobiles. Like the gross value basis in column B, manufacturing is the platform for most exports on a value added basis (column D). Basically,

> 30An Excel based version of the calculations for Tables 2, 3, and 4 is available on request, as well as GEMPACK code for replicating value added decompositions for countries in the GTAP database, and a RunGTAP version of the application reported in this section.

28
# **References**

<mark>Ahluwalia, M., N. Carter, and H. Chenery (1979). “Growth and Poverty in Developing Countries,”</mark> _<mark>Journal of Development Economics</mark>_ <mark>, Vol. 6, No. 3, pp. 299-341.</mark> <mark>Aguiar, M., and G. Gopinath (2007). “Emerging Market Business Cycles: The Cycle is the Trend,”</mark> _<mark>Journal of Political Economy</mark>_ <mark>, Vol. 115, No. 1, pp. 69-102.</mark> <mark>Alvaredo, F., and L. Gasparini (2015). “Recent Trends in Inequality and Poverty in Developing Countries,” in A. Atkinson and F. Bourguignon, eds.,</mark> _<mark>Handbook of Income Distribution</mark>_ <mark>, pp. 697-805.</mark> Atamanov, A., J.P. Azevedo, R.A. Castañeda, S. Chen, P.A. Corral Rodas, R. Dewina, C. Diaz-Bonilla, D.M. Jolliffe, C. Lakner, K. Lee, D.G. Mahler, J. Montes, R. Mungai, M.C. Nguyen, E.B. Prydz, P. Sangraula, K. Scott, A.E. Wambile, J. Yang, and Q. Zhao (2018). _April 2018 PovcalNet Update: What’s New_ (Washington, DC: World Bank).

<mark>Bhalla, S. (2002).</mark> _<mark>Imagine There’s No Country: Poverty, Inequality and Growth in the Era of Globalization</mark>_ <mark>(Washington, DC: Institute for International Economics).</mark> <mark>Bourguignon, F., and C. Morrison (2002). “Inequality among World Citizens: 1820-1992,”</mark> _<mark>American Economic Review,</mark>_ <mark>Vol. 92, No. 4, pp. 727-744.</mark> Bourguignon, F. (2003). “The Growth Elasticity of Poverty Reduction: Explaining Heterogeneity across Countries and Time Periods,” in T. Eicher and S. Turnovski, eds., _Growth and Inequality_ (Cambridge, MA: MIT Press), pp. 3-26.

<mark>Cecchini, S., and A. Madariaga (2011).</mark> _<mark>Conditional Cash Transfer Programmes: The Recent Experience in Latin America and the Caribbean</mark>_ <mark>(Santiago, Chile: ECLAC).</mark> Datt, G., and M. Ravallion (1992). “Growth and Redistribution Components of Changes in Poverty Measures,” _Journal of Development Economics_ , Vol. 38, No. 2, pp. 275-295.

ECLAC (2021), _Social Panorama of Latin America 2020_ (Santiago, Chile: ECLAC).

Ferreira, F. (2012). “Distributions in Motion: Economic Growth, Inequality, and Poverty Dynamics,” in P. Jefferson, ed., _The Oxford Handbook of the Economics of Poverty_ (New York: Oxford University Press), pp. 427-462.

Ferreira, F., J. Messina, J. Rigolini, L. López-Calva, M.A. Lugo, and R. Vakis (2013). _Economic Mobility and the Rise of the Latin American Middle Class_ (Washington, DC: World Bank).

Frankel, J., C. Vegh, and G. Vuletin (2013). “On Graduation from Procyclicality,” _Journal of Development Economics_ , Vol. 100, pp. 32-47.

Galeano, L., A. Izquierdo, J. Puig, C. Vegh, and G. Vuletin (2021). “Can Automatic Government Spending Be Procyclical? NBER Working Paper No. 28521.

Gasparini, L., F. Gutiérrez, and L. Tornarolli (2007). “Growth And Income Poverty In Latin America And The Caribbean: Evidence from Household Surveys,” _Review of Income and Wealth_ , Vol. 53, No 2, pp. 209-245.

15
# **B Additional Results: Tables and Figures**

Table B.1: Effect of Years of Schooling on Number of Children Born – Replication of Table 2: Anderson-Rubin Weak IV Robust Confidence Sets

||Census<br>Census<br>+ DHS<br>Only|DHS<br>Only|
|---|---|---|
||(1)<br>(2)|(3)|
||Coefcient on<br>�<br>Years of Sch|oolingizy|
|Weak IV Robust 95%<br>Confdence Set|-0.676, -0.275<br>-0.576, -0.285<br>[0.000]<br>[0.000]|-1.567, -0.265<br>[0.003]|
|First Stage F-Statistic|14.80<br>37.62|5.93|
|Number of Clusters|32<br>32|30|
|N|83,005<br>69,083|13,922|

_Source_ : Author’s analysis based on data from the Ethiopian census of 2007 and the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016.

_Note_ : IV = instrumental variables. The dependent variable is number of children born. � Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure, _Izy_<sup>FPE</sup> . All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age when multiple survey waves are included. Column 1 combines data from the Ethiopian census (2007) and the DHS (2005, 2011, and 2016); each data source is used separately in columns 2 and 3. Standard errors are clustered at the zone level, and Anderson and Rubin (1949) confidence sets of the 2SLS estimate are shown along with the _p_ -value from the associated chi-squared test, given in square brackets.

A.7
**Table C.7. Log income Estimates based on Specification 1** **_Colombia 2008, 2010, 2012, 2014 and 2016_**

|**Dependent Variable:**|**2008**|**2010**|**2012**|**2014**|**2016**|
|---|---|---|---|---|---|
|**Log Per Capita Income**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Male|0.328***|0.306***|0.213***|0.212***|0.255***|
||(0.0557)|(0.0421)|(0.0409)|(0.0418)|(0.0422)|
|**Household head education**||||||
|Primary education|0.496***<br>(0.0494)|0.477***<br>(0.0328)|0.440***<br>(0.0352)|0.422***<br>(0.0359)|0.399***<br>(0.0372)|
|Middle school|0.789***|0.701***|0.694***|0.727***|0.651***|
||(0.0519)|(0.0381)|(0.0377)|(0.0383)|(0.0392)|
|Secondary education|1.059***|1.026***|0.919***|0.890***|0.858***|
||(0.0532)|(0.0349)|(0.0359)|(0.0383)|(0.0379)|
|Tertiary education|2.048***|1.918***|1.770***|1.708***|1.595***|
||(0.0529)|(0.0386)|(0.0378)|(0.0416)|(0.0411)|
|Male x Primary education|-0.150*|-0.136**|-0.0674|-0.0508|-0.0953*|
||(0.0594)|(0.0447)|(0.0434)|(0.0446)|(0.0451)|
|Male x Middle school|-0.129*|-0.107*|-0.0465|-0.117*|-0.140**|
||(0.0618)|(0.0506)|(0.0462)|(0.0490)|(0.0484)|
|Male x Secondary education|-0.131*|-0.169***|-0.0191|-0.0294|-0.109*|
||(0.0627)|(0.0465)|(0.0443)|(0.0475)|(0.0455)|
|Male x Tertiary education|-0.273***<br>(0.0620)|-0.190***<br>(0.0484)|-0.0680<br>(0.0457)|-0.0301<br>(0.0495)|-0.132**<br>(0.0480)|
|Constant|11.83***|12.03***|12.13***|12.30***|12.29***|
||(0.0665)|(0.0423)|(0.0511)|(0.0427)|(0.0465)|
|Observations|162555|167596|166006|162405|159879|
|R-squared|0.178|0.209|0.204|0.186|0.161|
|Adjusted R-squared<br>Clustered errors in parenthesis. *|0.177<br>p<0.1 **p<0.05|0.209<br>***p<0.01|0.203|0.186|0.161|

**<mark>Note:</mark>** <mark>Birth year fixed effects included in all regressions. Results are constrained to the sample of</mark> households whose heads were born between 1948 and 1973

**Source:** Own estimations based on GEIH from 2008 to 2016.

38
# PRE Workina Paner Series

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|WPS403 Innovative Agricultural<sup>Extension</sup><br>for Women: A Case Study of<br>Cameroon|S. Tjip Walker|||
|WPS404 Chile's Labor Markets in<sup>an Era of</sup><br>Adjustment|Luis A. Riveros|April 1990|R. Luz<br>34303|
|WPS405 Investments in Solid<sup>Waste</sup><br>Management: Opportunities for<br>Environmental Improvement|Carl Bartone<br>Janis Bernstein<br>Frederick Wright|April 1990|S. Cumine<br>33735|
|WPS406 Township, Village, and<sup>Private</sup><br>Industry in China's Economic<br>Reform|William Byrd<br>Alan Gelb|April 1990|K. Chen<br>38966|
|WPS407 Public Enterprise Reform:<br>AChallenge for the World Bank|Ahmed Galal|April 1990|G. Orraca-Tetteh<br>37646|
|WPS408 Methodological Issues<sup>in Evaluating</sup><br>Debt-Reducing Deals|<sup>Stiln Claessens</sup><br>Ishac Diwan|May 1990|S. King-Watson<br>33730|
|WPS409 Financial Policy and Corporate<br>Investment in Imperfect Capital<br>Markets: The Case of Korea|Mansoor Dailami|April 1990|M. Raggambi<br>37657|
|WPS410 The Cost of Capital and<sup>Investment</sup><br>in Developing Countries|<sup>Alan Auerbach</sup>|April 1990|A. Bhalla<br>37699|
|WPS411 Institutional Dimensions<sup>of Poverty</sup><br>Reduction|Lawrence F. Salmen|May 1990|E. Madrona<br>37496|
|WPS412 Exchange Rate Policy in<sup>Developing</sup><br>Countries|<sup>W. Max Corden</sup>|May 1990|M. Corden<br>39175|
|WPS413 Supporting Safe Motherhood:<br>A Review of Financial Trends<br>Full Report|L. M. Howard|May 1990|S. Ainsworth<br>31091|
|WPS414 Supporting Safe Motherhood:<br>A Review of Financial Trends<br>Summary|L. M. Howard|May 1990|S Ainsworth<br>31091|
|WPS415 How Good (or Bad)<sup>are Country</sup><br>Projections?|Norman Hicks<br>Michel Vaugeois|April 1990|M. Berg<br>31058|
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# **_Results: Changes in School Characteristics_**

To estimate the effects of the program on participating schools, we compare characteristics of treatment and control schools using the 2011-12 school census. Under the assumption that randomization was successful, the causal impact of the program on school characteristics can be estimated from a simple OLS regression of the following form: Ys = α+ βT+ Xs + εs Where Ys is the outcome of school s as measured in the 2011-12 census, T indicates assignment to the treatment group, and Xs indicates the commune in which school s is located, in order to account for the fact that the probability of selection into the program differed across communes (Duflo, Glennerster, and Kremer 2006). Results are presented in Table 3.<sup>9</sup> The TWP permits a fairly wide range of potential uses of the subsidies, so directors of treatment schools could have invested in improving existing infrastructure, expansion, staffing, furnishings, learning materials, and so forth. Our results are limited, however, to the data available in the school census on the five areas of school characteristics measured in the census: scope (levels taught and number of shifts); infrastructure (building materials, water, latrines, etc.); physical materials (desks, chairs, blackboards, etc.); staffing; and students (number, gender, and repetition rates).

# **<u>Table 3: Estimated impacts of treatment</u>**

Impacts of TWP observed in 2011-12 school year

> 9 Results run on the 652 schools matched only to the 2011-12 school census (rather than to both 2002-3 and 2011-12) are very similar to those presented in Table 3.

10