rafmacalaba/datause-encoder-data huggingface dataset:
||2.5|Closure+|
|---|---|---|
|Non-Tradable|19.3|Additive of NTP|
|LFPR, Pay-Gap and Sectoral|11.9<br>9.6|Closure<br>Closure+|
||23.6|Closure|
|Complete Convergence|22.1|Closure+|
||45.5|Additive of NTP|

_<mark>Notes:</mark>_ <mark>Closure represents complete convergence of the respective gap. Closure+ represents</mark> appreciation/depreciation is applied to wages due to the displacement of workers between sectors. SSA and OECD stands for closure of the gap between Liberia and the averages found in SubSaharan Africa and OECD countries, respectively. Additive represents the additional value added by the non-tradable sector. The “additive of NTP” presented in the “Complete Convergence” section is the sum of the closure scenario estimate and the value of NTP.

# **5.1 The Status Quo: Women’s Economic Contribution in 2020**

Liberian women contributed an estimated USD 1.08 billion to the Liberian economy in 2020 (Table 2). The estimate is derived from demographic, educational, and economic parameters. According to mediumvariant UN projection, the population of Liberia was 5.06 million individuals. The working age (20-65) population was 45.4 percent of the entire population and had an average educational attainment of 4.6 years. Economically, GDP was USD 4.05 billion, GDP was USD 800, and per-worker GDP was USD 2,125 in 2020. The additive economic contribution of natural resources is estimated at USD 1.27 billion, or 31.4 percent of GDP. Women’s contribution represented 26.8 percent of GDP and 39 percent of labor’s contribution to GDP. These initial estimates are referred to as the “status-quo” that will serve as the benchmark for the study.

**Table 2. Macro-Economic Statistics for Baseline Scenario (2020)**

|**1. GDP**||
|---|---|
|1.1. GDP (USD)|$4,049,744,636|
|1.2. GDP per capita (USD)|$800|
|1.3. GDP per worker (USD)|$2,125|
|**2. Contribution of Women**||
|2.1. in USD|$1,084,390,549|
|2.2. as % of GDP|26.78%|
|2.3. as % of Labor Output|39.04%|
|**3. Value Added by Sector**||
|3.1. traditional, female (USD)|$647,951,018|
|as % of GDP|16.00%|
|3.2. modern, female (USD)|$436,439,531|
|as % of GDP|10.78%|
|3.3 traditional, male (USD)|$735,858,976|
|as % of GDP|18.17%|
|3.4. modern, male (USD)|$957,620,009|
|as % of GDP|23.65%|

14
22

# Table 3

|The RMSM Balance of Payments: Co|ndensed Form (Nominal USS)||
|---|---|---|
|Exogenous:|||
|Export Price Index|XPIt||
|Import Price Index|MPIt||
|Exchange Rate:|Et||
|Other Factor Services:|OTHFSY,||
|Net Private Transfers:|NETCTRt||
|Long-term and Other Capital:|LTCAPFt and OTHC'4Pt||
|Endogenous:|||
|Resource Balance:<br>,|RESBALt-<br>EXPt- IMPt|27)|
|. Exports, Goods and NFS:|EXPt- Xt*XPIt*Et|31)|
|IImports, Goods and NFS:|IMPt -M<br>*MPIt*Et|32)|
|Net Factor Services:|NETFSYt-<br>INTGAPt+OTHFSYt|28)|
|IInterest, New Borrowing:|INTGAPt-<br>DODGAPt1 *it|37)|
|. Debt Outstanding (stock)|DODGAPt-<br>DODGAPt,+GAPFILt- AHTGAPt|36)|
|Current Account Balance:|CURBAL,-<br>RESBALt+NETFSYt+NETCTR,|26)|
|Capital Account Balance:|NETCAPt-<br>NETGAPt+ LTCAPFt + OTHCAPt|29)|
|. Amortization, New Borrowing:|AMTGAPt-<br>GAPFILt-|35)|
|Change in Reserves:|CHGRES,-<br>(IMPt- IMPt1)*(1/6)|33)|
|Closure:|||
|Unidentified<br>New Borrowing:|GAPFILt-<br>-<br>CURBALt- AKTGAPt+...<br>-<br>LTCAPF- OTHCAPt- CHGRESt|34)|
Policy Research Working Paper 6884

# **Abstract**

Argentina’s Plan Nacer provides insurance for maternal and child health care to uninsured families. The program allocates funding to provinces based on enrollment of beneficiaries and adds performance incentives based on indicators of the use and quality of maternal and child health care services and health outcomes. The provinces use these resources to pay health facilities to provide maternal and child health care services to beneficiaries. This paper analyzes the impact of _Plan Nacer_ on birth outcomes. The analysis uses data from the universe of birth records in seven Argentine provinces for 2004 to 2008 and exploits the geographic phasing in of _Plan Nacer_ over time. The paper finds that the program increases the use and quality of prenatal care as measured by the number of visits and the probability of receiving a tetanus vaccine. Beneficiaries’ probability of low birth-weight is estimated to be reduced by 19 percent. Beneficiaries have a 74 percent lower chance of in-hospital neonatal mortality in larger facilities and approximately half this reduction comes from preventing low birth weight and half from better postnatal care. The analysis finds that the cost of saving a disability-adjusted life year through the program was $814, which is highly cost-effective compared with Argentina’s $6,075 gross domestic product per capita over this period. Although there are small negative spillover effects on prenatal care utilization of non-beneficiary populations in clinics covered by Plan Nacer, no spillover is found on their birth outcomes.

This paper is a product of the Health, Nutrition and Population Unit, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gertler@haas.berkeley.edu and pgiovagnoli@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
4

# **II. Growth and structural changes since World War II**

The comparatively poor growth performance of the Australian economy for most of the twentieth century contrasts with that since the late 1980s when it out-performed many other advanced economies in terms of GDP per capita growth (World Bank 2006). This was a period of especially rapid total factor productivity (TFP) growth (Parham et al. 1999, Dowrick 2001), in contrast to Britain for example where much of its catch-up was due to growth in employment and hours worked per worker (Card and Freeman 2002). Australia’s annual TFP growth rate accelerated a full percentage point during the 1990s (Parham 2004). Since that was not the experience of other OECD countries, Parham asserts that domestic factors must provide a major part of the explanation, one important factor being the greater openness of the Australian economy to trade and investment.<sup>1</sup> 1 Other areas of domestic reform that occurred simultaneously and boosted the gains from trade reform include deregulations (especially in the labour market), de-monopolization and privatization of government enterprises (especially in communications, utilities and transport infrastructure), and better management of the macroeconomy. There is now evidence that the rate of growth of productivity in Australia has since slipped behind that in several OECD countries (Dolman, Parham and Zheng 2007), perhaps due to the slowdown in Australia’s pace of reform in recent years (Productivity Commission 2007). A more recent econometric study by Diewert and Lawrence (2006) demonstrates that productivity growth has been the dominant contributor to the growth in real welfare in Australia since 1960, with the terms of trade playing only a very minor role (even though prices in international markets for primary products relative to manufactures have been on a slight downward trend over the past century – see Pfaffenzeller, Newbold and Rayner 2007).
# **Tables**

Table 1: Balance in observables across treatment arms

||(1)<br>Control|(2)<br>Cash|(3)<br>Work|(4)<br>(1) vs. (2)|(5)<br>(1) vs. (3)|(6)<br>(2) vs. (3)|
|---|---|---|---|---|---|---|
|Female|0.32|0.29|0.30|0.24|0.45|0.49|
|Married|0.82|0.81|0.76|0.34|0.04|0.31|
|Age|28.39|29.03|28.01|0.74|0.41|0.17|
|Household size|4.99|5.23|5.14|0.52|0.61|0.78|
|Formal education|0.48|0.44|0.51|0.70|0.14|0.07|
|Past Ag. Work|0.62|0.66|0.65|0.74|0.64|0.92|
|Math ability (index)|0.00|0.01|0.04|0.91|0.43|0.25|
|Digit Span Score (Total)|0.00|0.08|0.13|0.63|0.18|0.35|
|Wellbeing|0.00|0.07|0.07|0.22|0.04|0.71|
|Life Satisfaction|0.00|-0.04|0.04|0.62|0.05|0.22|
|Self-worth (relative)|-0.00|0.11|0.00|0.40|0.32|0.93|
|Worked in the last month|0.11|0.11|0.10|0.88|0.49|0.39|
|Worked in Myanmar|0.72|0.72|0.76|0.57|0.81|0.38|
|Hours Idle (avg) demean|2.97|3.31|3.01|0.99|0.39|0.46|
|Idle Feelings|1.66|1.73|1.67|0.31|0.06|0.66|
|Locus of Control|0.00|0.10|0.15|0.35|0.07|0.47|
|Power Perceptions|-0.00|-0.07|-0.00|0.19|0.85|0.07|
|Work Perceptions|0.00|-0.09|-0.01|0.61|0.63|0.31|
|Persistent Illness (_>_7)|0.30|0.33|0.28|0.89|0.26|0.24|
|Days Healthy|25.51|24.82|25.78|0.39|0.15|0.02|
|PHQ Scale|0.00|0.13|0.00|0.31|0.80|0.18|
|Depression (Base)|0.76|0.82|0.77|0.26|0.91|0.14|
|Stress (index)|-0.00|-0.13|-0.00|0.24|0.96|0.18|
|Number of conversations|0.00|0.01|0.02|0.85|0.68|0.46|
|Number of conversations +|9.25|8.96|9.94|0.34|0.69|0.07|
|Number of conversations -|3.45|4.04|3.84|0.45|0.40|0.88|
|Family Injuries (Burma)|1.79|1.70|1.68|0.58|0.26|0.72|
|Observations|165|165|415||||
|Joint F-Test||||0.30|0.40|0.27|

**Notes:** Columns (1), (2), and (3) show the average value of the variable in the respective treatment arm. Column (4) shows the p-value of the difference in means between the control and cash treatment groups, Column (5) shows the p-value between control and work, and Column (6) shows the p-value between cash and work.

34
Figure B5 Public-Private Differential, 2001 <!-- Start of picture text --> .6<br>.5<br>.4<br>.3<br>Public<br>.2<br>Private<br>.1<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Quantile<br>Coefficient<br><!-- End of picture text --> Figure B6 Public-Private Differential, 2007 <!-- Start of picture text --> .35<br>.30<br>.25<br>Public<br>.20<br>.15<br>.10<br>Private<br>.05<br>.00<br>-.05<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Quantile<br>Coefficient<br><!-- End of picture text --> 51
30 in the home or occasionally combined with home duties. Women might choose a better paying job in **the** formal **wage** labor market, If they **were** not also productively engaged in nontarket family activities. This argument does not imply that women are biologically pre-disposed to look after children or even that nursing of infants cannot, **at a** cost, **be** rearranged to fit work routines of women in the formal sector (Blumberg. 1984; Priedl, 1975; Nerlove, 1974). The conclusion of many observers is, nonetheless, that customary forms of **sex** segrogation in the labor market in developing countries lead to a sisallocation of woman's labor. The reduction of sex segregatioB in the labor market should raise current output and encourage investment by families in women (Rosenzweig and Schultz, 1982a). Devising mechanism that encourage poor families to invest more heavily in the market oriented skills of girls is a ehallenge to policy In certain parts of the world. Improving the operation of factor markets that discriminate bttwven men and women **as** productive agents can also involve social costs, for -interventions use economic and administrative resources that are very scarce in most low income countries.

Comparisons of ProductLivty In **the** Marke; and Hose Because of the difficulty of measuring and valuing nonmarket production, economists have focussed on the marketable conpon6nt of income of families and Lndividuals, includlng the market value of products or factors sold or exchanged, not of purchased inputs. Within thls limited context, household surveys have found it notoriously hard to **estimate** the net return to selfemployed entrepreneurs. Even surveys such **as the** Current Population Survey in the United States account for only about half of the National Income Account estimates of the value added by self-employed producers such as farmers. With far less sophisticated accounting procedures available in low income coun-
|||1996q2-<br>1999q4|2000q1-<br>2003q4|2004q1-<br>2007q4|2008q1-<br>2011q4|2012q1-<br>2014q4|
|---|---|---|---|---|---|---|
|te|Fed|3%|8%|9%|6%|3%|
|ra|Copper|22%|13%|16%|29%|15%|
|ge|M2|15%|24%|24%|20%|31%|
|han|Output Gap|9%|10%|9%|4%|6%|
|xc|Exchange Rate|30%|32%|33%|32%|40%|
|E|CPI|19%|13%|8%|8%|6%|
||Fed|2%|4%|5%|6%|1%|
||Copper|10%|6%|13%|23%|14%|
|PI|M2|7%|15%|18%|20%|24%|
|C|Output Gap|21%|21%|15%|10%|21%|
||Exchange Rate|5%|3%|8%|6%|6%|
||CPI|55%|51%|41%|35%|34%|

Notes: The first panel reports the contribution of each variable to the fluctuations of the exchange rate averaged across quarters within five sub-periods. The second panel reports the same information for fluctuations in CPI.

_Table 1: Historical variance decomposition of exchange rate and price fluctuations_ Figure 5 illustrates these results for the exchange rate and depicts the year by year variation. It is apparent that the factors contributing to exchange rate fluctuations have varied quite substantially over time, but also that the amplitude of the shocks – and therefore that of the exchange rate fluctuations – has declined since about 2009. The graph also offers insights about the nature of individual fluctuations. For instance, the early 2000s saw a number of stark movements in the exchange rate, with money appearing to be the main driver. The strong appreciation in 2005, corresponding to the debt relief under the HIPC initiative, enters mainly as a genuine exchange rate shock. While the proportional contribution of the price of copper appears rather moderate over much of the sample period, the abrupt depreciation in 2009 illustrates its potential importance, as it accounts for about two-thirds of the variation. The variety of the drivers of these shocks further underlines the importance of our research question, whether the ERPT will vary with the underlying economic shock that originally caused the exchange rate to move.

<!-- Start of picture text --> 20<br>10<br>0<br>-10<br>-20<br>Copper Fed Money Output gap Exchange rate CPI<br>-30<br>1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> _Figure 5: Historical decomposition of changes in the exchange rate (USD/ZWA)_ Figure 6 decomposes changes in consumer prices in the same way. As expected from the low amplitude of the IRFs presented in section 5.1, the contribution of the other variables in our 17
12 to report benefit incidence by type of care and by health program, than to report the benefit incidence from aggregate spending on health care. The former allows a quick assessment of the distributional impact of realocating resources within the health sector, whereas the latter only allows one to gauge the overaU impact of moving resources into or out of the health sector. **Compt2table** General Equilibrium Models Piggott and WhaUey (1987) have analyzed net fiscal incidence for the Australian economy within a computable general equilibrium (CGE) context. The model is quite complex even though Piggott and Whalley make many simplifying assumptions, such as efficient provision of public goods. The main point of their exercise is to compare the results from a general model that accounts for the consumer surplus from public goods and for the marginal welfare costs from taxation to the results from a more typical case in the literature where taxes and t nefits are simnply allocated among households throughout the income distribution.

Three major points emerge from their model. First, for marginal increases in public goods, consumer surplus effects from additional public goods are minimal, but the welfare effects of taxes used to finance the additional public goods are substantial. Second, if public goods are substantiaUy reduced, the consumer surplus effects can be substantial and the reduced consumer surpluses affect low-mcome and high-income households differentally. According to their simulations, smaller marginal reductions in public goods favor higher income households. A third finding is that the general equhilibnum welfare results can range from favoring high-income households to favoring low-income households depending on their assumptions about the elasticities among substitution of goods in the household utility functions.

The results of the general equilibrium analysis do not bode well for reaching definitive answers on benefit incidence or on net fiscal incidence. The sensitivity of the general equilibrium results to parameter specifications of the model means that definitive answers to benefit incidence depend on the accurate estination of the parameters of the model. Absent better empirical knowledge about functional form and elasticity parameters, general equilibrium models may dramatize the weaknesses in benefit incidence studies but the models may also not yield precise estimnates of fiscal incidence.
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 19. Financial flows to Mali’s infrastructure, average 2002 to 2007**

US$ millions per year

||O&M<br>Public sector|Public<br>sector|Ca<br>ODA|pital expenditure<br>Non-OECD<br>financiers|<br>PPI|Total<br>CAPEX|Total<br>spending|
|---|---|---|---|---|---|---|---|
|Information and communication technologies|101|41|1|1|28|72|173|
|Power|94|19|10|12|20|61|155|
|Transport|10|42|79|24|5|150|159|
|Water supply and sanitation|26|7|34|1|0|42|68|
|Total|230|109|124|38|53|325|555|

Source: Derived from Foster and Briceño-Garmendia 2009.

O&M = operations and maintenance; ODA = official development assistance (from OECD member states); PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development.

**Figure 25. Mali’s existing infrastructure spending is typical for a low-income country**

<!-- Start of picture text --> Sub-Saharan Africa<br>nonfragile low-income countries<br>middle-income countries<br>ECOWAS<br>fragile low-income countries<br>resourch-rich countries<br>Niger<br>Senegal<br>Mali<br>Ghana<br>Cote d'Ivoire<br>Nigeria<br>Burkina Faso<br>Cape Verde<br>0 2 4 6 8 10 12<br>% of GDP<br>capital expenditure operations and maintenance<br><!-- End of picture text --> Source: Derived from Foster and Briceño-Garmendia 2009.

43
Policy ReseaRch WoRking PaPeR 4780

# **Abstract**

Using for the first time household survey data from 26 post-Communist countries, covering the period 1990–2005, this paper examines correlates of unprecedented increases in inequality registered by most of the economies. The analysis shows, after controlling for country fixed effects and type of survey used, that economic reform is strongly negatively associated with the income share of the bottom decile, and positively with the income shares of the top two deciles. However, breaking economic reform into its component parts, the picture is more nuanced. Large-scale privatization and infrastructure reform (mostly consisting of privatization and higher fees) are responsible for the pro-inequality effect; small-scale privatization tends to raise the income shares of the bottom deciles. Acceleration in growth is also pro-rich. But democratization is strongly pro-poor, as is lower inflation. Somewhat surprisingly, the analysis finds no evidence that greater government spending as share of gross domestic income reduces inequality.

This paper—a joint product of the Poverty Team in Development Research Group, and Human Development Network in Europe and Central Asia (ECA) Region—is part of a larger effort in the department to study social effects of economic policies during the transition to market economy.. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at bmilanovic@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
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