rafmacalaba/datause-encoder-data huggingface dataset:
variables in 1985–94. The table examines all countries and then compares resource-rich and non-resource-rich economies.<sup>35</sup>

|**Table 12: Differences of M**|**eans of Key **|**Econom**|**ic Va**|**riables B**|**efore an**|**d Aft**|**er 1995**|||
|---|---|---|---|---|---|---|---|---|---|
||**All**|**countries**||**Non-r**|**esource ric**|**h**|**Res**|**ource rich**||
|**Variable**|**1995-**<br>**2005**|**1985-**<br>**1994**|**t-**<br>**test**|**1995-**<br>**2005**|**1985-**<br>**1994**|**t-**<br>**test**|**1995-**<br>**2005**|**1985-**<br>**1994**|**t-**<br>**test**|
|**I. During All Episodes**||||||||||
|Savings (% GDP)|12.05|10.47|*|10.88|10.8||14.85|9.68|*|
|Investments (% GDP)|20.26|19.4||18.93|18.78||23.4|20.92|**|
|Private sector investment (% GDP)|12.51|11.46|**|11.23|10.6||15.43|13.47||
|Foreign direct investments net flow (% GDP)|4.95|1.50|*|3.63|1.41|*|8.23|1.75|*|
|Consumption (% GDP)|91.12|92.09||95.85|95.24||79.9|84.15|*|
|<br>Trade (% GDP)|76.58|68.43|*|72.73|66.11|*|85.77|74.1|*|
|Exports (% GDP)|32.27|28.06|*|28.86|25.67|*|40.32|33.92|*|
|Imports (% GDP)|44.27|40.36|*|43.86|40.44|*|45.25|40.18|*|
|Real effective exchange rate (2000=100)|103.52|182.78|*|100.06|186.64|*|109.18|174.71|*|
|Terms of trade (2000=100)|102.4|113.7|*|101.63|110.01|*|104.53|123.17|*|
|Current account (% GDP)|-5.58|-6.17||-6.43|-6.5||-3.71|-5.53||
|Consumer price index (%)|33.98|71.8||16.98|81.52||77.81|41.91||
|GDP deflator (%)|45.85|65.48||30.46|76.85||71.77|37.28||
|Public debt (present value, % GNI)|128.41|85|*|114.53|77.38|*|163.61|104.23|*|
|Government consumption (% GDP)|15.48|17.24|*|15.12|16.55|*|16.44|18.91|*|
|**II. During Growth Acceleration**||||||||||
|Savings (% GDP)|12.9|20.94|*|12.13|20.27|*|14.1|22.83|*|
|Investments (% GDP)|21.55|25.47|*|19.39|25.12|*|24.9|26.39||
|Private sector investment (% GDP)|13.51|14.15||11.6|13.46|*|16.45|16.23||
|Foreign direct investments net flow (% GDP)|5.02|2.4|*|2.35|2.08||9.44|3.24||
|Consumption (% GDP)|88.78|88.23||91.89|94.93||83.97|70.92|*|
|Trade (% GDP)|70.27|86.1|*|60.76|86.21|*|85.22|85.81||
|Exports (% GDP)|29.47|35.8|*|24.48|32.49|*|37.21|44.36||
|Imports (% GDP)|40.71|50.3|*|36.27|53.71|*|47.69|41.58||
|Real effective exchange rate (2000=100)|108.96|128.77|*|109.54|125.87|*|108.35|136.18|**|
|Terms of trade (2000=100)|104.38|99.06|**|105.51|100.72||102.08|94.14||
|Current account (% GDP)|-6.5|-4.46||-6.61|-6.7||-6.33|1.16|**|
|Consumer price index (%)|14.16|17.06||9.14|16.76|*|23.28|18.45||
|GDP deflator (%)|15.54|18.73||9.21|19.12|*|25.1|17.71||
|<br>Public debt (present value, % GNI)|120.91|94.52|**|89.75|94.98||172.42|93.14|*|
|Government consumption(%GDP)|15|17.7|*|13.3|16.74|*|17.85|20.16||
|Notes:||||||||||
|(*) t-test that means are not equal significant at the 5|% level.|||||||||
|(**) t-tes that means are not equal significant at the 1|0% level.|||||||||
|Source: Arbache and Page(2007a).||||||||||

> 35 So that the subsets’ sample sizes are consistent, oil-exporting countries are not accessed separately.

33
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household-specific factors will cause the actual expenditure to differ from predicted expenditure. These equations are described in more detail in Hentschel _et al._ (2000) and Elbers _et al._ (2001).

As noted above, equation _5_ is valid only if the full Census data are available for the second stage of the mapping procedure. When we are using a sample survey or a sample of the Census data in the second stage, this expression must be modified as follows: where V, represents the variance associated with the sampling error in the Census, taking into account the design of the sample. In this study, we rely on the software package Stata to calculate the variance associated with the sampling error, taking into account the design of the survey4.

In order to compare poverty headcounts in different regions or provinces, it is convenient to calculate the variance of the difference between two estimates of poverty. Hentschel et al (2000, footnote 17) provide an expression for the case when full Census data are used. Here we extend the expression to include the variance associated with sampling error: <!-- Start of picture text --> ap-p ~ ~ +ap_<br>var(P -P2= a1 2 var(t) a )+( _) __(<br>a18 a16 n-k-1 ~~~~~~~~(9)<br>+Vi(PI)+Vi(P2)+V3 (PI)+V 3 (P 2 )-2cov,(PI,P 2 )<br><!-- End of picture text --> where Vi(Pr) is the idiosyncratic variance of the poverty estimate for region r (the third term in equation 5), Vs(Pr) is the sampling variance of the poverty estimate for region r, and cov5(PI,P 2) is the covariance in the poverty estimates for regions 1 and 2 associated with sampling error.

> 4 This is accomplished with the "svymean" comrnand. Stata calculates a linear approximation (a _first-order_ Taylor expansion) of the sampling error variance based on information on the strata, the primary sampling unit, and the weighting factors. See Stata Corporation, 200 lb for more information.

10
circumstances, they have the potential to become the most important financial institutions for households, surpassing banks and insurance companies. But in several countries, mutual funds are also heavily used by corporations and institutional investors. This is often the case with money market mutual funds and short-term bond funds, which meet the liquidity needs of small corporations, while equity funds tend to be used by pension funds operated by small companies.

Brazil is a country where the non-household sector accounts for a large share of mutual fund shares. This is partly attributed to the tax on financial transactions that pension funds avoid by investing in mutual funds that are exempt from it. Other.countries with a large presence of non-households in mutual fund ownership include France and the United States. Presence of non-households among mutual fund investors complicates the analysis of the determinants of mutual fund growth since non-household investors are likely to be influenced by different factors in their investment decisions than household investors.

The use of mutual funds by nonresident investors creates another complication. Luxembourg is an extreme example of this phenomenon, but Hong Kong, Ireland, Singapore and Switzerland also have a strong nonresident presence. The holdings of nonresident investors are also probably large in absolute terms in the United States, although their relative share is unlikely to be important. In the case of Hong Kong and Singapore, reported statistics cover the whole of the asset management industry, including assets of foreign investors that are entrusted to local managers but are not invested in collective investment schemes. It is difficult to disentangle such investments from holdings of mutual fund shares.

A third complication arises from the institutional coverage of published statistics. Indeed, the annual mutual fund report of ICI publishes a table with aggregate data on mutual funds around the world but also includes a strong warning that because of differences in definitions and coverage, the, published data lack comparability. Differences relate, _inter alia,_ to the inclusion, or not, of closed end funds, unit-linked funds operated by life insurance companies, and retirement funds that operate on mutual fund principles (such as the AFP system of Chile or the defined-contribution pension 5
Changes in collective farm income led to a reduction of 1.7 points in total inequality. Of this reduction, 0.8 points was due to a change in the share of coilective farm income and the remaining reduction of .9 points was caused by the redistnbution of this income component in favor of poorer families.

Table 12 Expaining Changes in Inequality in Terms of Income Components

|hncome Componet|_ Due to Changes h<br>Shares|Due to Changes hI<br>Distribution|Total Change<br>Components|
|---|---|---|---|
|C|hange in Inequality bet|ween 1989 to 1991||
|S_lryIncom<br>|1<br>-1.61|<br>-2-3|-39|
|Collstive Farm Income|-0.8|-0.9|-1.7|
|Govt Cash Benefits|0|0|0|
|Smaliholding Income|0.4|0.4|0.8|
|Other sourcs|0.2|-0.1|0.1|
|Total Income|-1.8|-2.9|4.7|
|C|hange in Inequality bet|ween 1991 to 1992||
|Salary Income||<br>-1.61|<br>1.4|-0.2|
|Colctive Farm Income|I|-0.6|0.4|
|Govt Cash Benefits|-0.6|2.2|1.6|
|Smaliholding Income|1.9|-1.2|0.7|
|Other sources|-0.1|-0.7|0.8|
|aTotlIncome|0.6|1.1|1.|

The share of salary income of blue and white collar workers continued to decine dunng the 1991-92 period resulting in reduction of inequality by 1.6 points. In this period the wage and salay income was iutd in favor of the richer households resulting in increase in income inequality by 1.4 points. Tbus the net effect of the salary income of blue and white collar workes was to decrease inequality by only 0.2 points.

Govenment transfers had no effect on the inequality in the 1989-91 period. But in the subsewquent period, govarnmen **t** ansfers caused an increase in total inequaIy by 1.6 points. This increase came about mainly from the redistribution of government benefits in fivor of richer families. These results strongly suggest that the exsting social sector poicies must be changed. Govenment benefits must be targeted on the poor.

28
system. Following on from the horizontal equity analysis that does find a role for block grants in decreasing inequality, however, there is at least a suggestion of causality.

# **2. Conclusions**

We explore the institutional contradiction of implementing aid-financed programs to combat inequality in federal countries by analyzing results from the largest donor-financed program in the world: Ethiopia’s Promotion of Basic Services Program. Aid relies on the principle of local officials’ upward accountability to higher levels of government, and eventually to donors. But federalism shifts the accountability of subnational officials downwards to regional and local voters. What happens when aid agencies fund anti-inequality programs in federal countries? Does federalism undermine aid? Does aid undermine federalism? Or can the political and fiscal relations that define a federal system resolve this contradiction internally?

We present evidence showing that Ethiopia’s woreda block grants system promotes geographic and vertical equity in health and education outcomes. Block grants are positively associated with the poverty level: more resources are transferred to woredas with higher poverty rates. Poverty is negatively associated with basic service sector outcomes: a higher poverty rate implies a lower level of outcomes. And spending at the woreda level increases outcomes for most basic service sector indicators. These results imply that Ethiopia’s block grant system is helping narrow the gap between lagging and better-off woredas, and also between poorer and wealthier households. Ethiopia’s health and education indicators have shown impressive improvements across the board since the 1990s. But poorer woredas and poorer households have improved faster. These results dominate other cultural and socioeconomic factors that push in the opposite direction.

Our results should be read with two important caveats. First, our evidence concerns service delivery outcomes, not ultimate outcomes, such as increases in students’ knowledge or improvements in citizens’ health, which cannot necessarily be assumed to follow. Second, decreasing inequality should not obscure the fact that the quality of public services still varies greatly across woredas. The administrative data we use do not permit the measurement of service quality.

What drives these results? Convergence in different health and education indicators, sustained over time and across Ethiopia’s remarkably diverse regions and ethnic groups, implies 21
**Table 5: Regression results: Benchmark specification** Dependent <u>variable: Log of spread-to-benchmark at launch</u>

||(1)|(2)|
|---|---|---|
||Pre-crisis|Post-crisis|
|IG dummy|-0.330***|-0.078|
||(-3.71)|(-0.56)|
|S&P Rating|0.134***|0.085***|
||(6.03)|(3.32)|
|Log(size)|0.058|0.035|
||(1.20)|(0.69)|
|Years to Maturity|0.012***|-0.004|
||(3.87)|(-1.23)|
|U.K. Law|0.030|0.230***|
||(0.32)|(3.19)|
|VIX|0.013**|0.024***|
||(2.21)|(4.79)|
|GDP growth (_t-1_)|-0.009|-0.013|
||(-1.26)|(-1.07)|
|EAP|0.175*|-0.072|
||(1.73)|(-0.41)|
|ECA|0.183*|0.252|
||(1.67)|(1.21)|
|LAC|0.347***|0.004|
||(4.43)|(0.03)|
|SAS|0.368**|0.390***|
||(2.52)|(2.85)|
|SSA|0.493***|0.347**|
||(4.03)|(2.12)|
|Observations|328|162|
|R-squared|0.654|0.663|

Robust t-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Year dummies and a constant term were included in all specifications, but not reported. The default region is Middle East & North Africa (MNA); the regional dummies represent East Asia & Pacific (EAP), Europe & Central Asia (ECA), Latin America & Caribbean (LAC), South Asia (SAS) and Sub-Saharan Africa (SSA).

If bonds under U.K. law are indeed mispriced at launch, then it is natural to ask whether the spread difference disappears eventually. In this context, one would expect the coefficient associated with governing law to become insignificant if a similar cross-sectional regression were successively run at 60, 90, 120 and 180 days after launch. The results are shown in Table 6. Using secondary market data from Bloomberg,<sup>10</sup> we tracked each bond spread during the first six months. While some control variables remain constant throughout this period (bond maturity, tranche size, governing law, GDP growth before launch, and year and regional dummies), some change either periodically (S&P ratings) or even daily (VIX). We used the sovereign ratings instead of individual bond ratings on the right-hand side. We can see that the governing law coefficient is not only significant, but also relatively steady. So, the evidence indicates that the initial spread difference is quite persistent.

> 10 Bloomberg provides its own calculated mid yield spread to the benchmark bond.

13
well and samples migrants through their work locations rather than residences (which may favor younger migrants).

The average labor tax wedge faced by each worker and his (or her) employer if they decide to participate is 55 and 60 percent for local and migrant workers in 2005 and fell to 53 and 55 percent by 2010 (Table 5B). These differences are similar for migrant and local workers, and across years. Also, it is of note that the estimated average labor tax wedge is higher using survey data than administrative sources (Figure 5), perhaps reflecting the fact that workers who face a high labor tax wedge may be less likely to participate in social insurance.

# **4.3 Correlates of Participation in Employer-Based Social Insurance**

Results from the base model estimating the partial correlates of participation in the employer-based pension program are reported in Table 6. Column (1) reports marginal effects from a probit model with the base set of covariates. Increasing one year of schooling (at the average years of schooling) is associated with a 3 percent increase in the probability of participating in a pension, and any postsecondary education is associated with an additional 4.6 percent increase. As one might expect, older workers are more likely to be covered, with each year of age corresponding to a 3 percent increase in the probability of coverage. However, as one approaches age 60 the effect of age on coverage from one’s current employer starts to decrease: at this point more people who are working are already receiving pensions from early retirement, or alternatively, may still be in the workforce at an older age because they lack pension support altogether.

Additional covariates are then added to control successively for industry of employment, employer ownership type, and whether or not the worker has signed a labor contract. While it is important to keep in mind that unobserved characteristics (e.g., unobserved dimensions of ability) may be correlated with selection into an industry or work unit type, including such indicators allows us to get a sense of whether migrant status or gender, per se, are associated with lack of pension coverage, or whether lack of coverage is associated with higher concentrations of migrants or women in specific industrial sectors or firm ownership types. Figure 3 suggests that there are considerable differences across ownership sectors in participation, but that participation has risen for both migrants and local residents in foreign invested and private firms. If coefficients on gender or migrant status are driven to zero by including these additional controls for industry or ownership, then the source of differences may be driven (in part) by job sorting.

**_Gender Differences in Pension Coverage_ .** When examining factors influencing participation in employer-based pensions, results from the base model shown in column (1) of Table 6 suggest that a

20
# **1. Introduction**

Gender inequality has been at the core of the policy debate concerning development for the past few decades. This policy concern has been matched by an equal level of scholarly interest, which has produced a large body of research intended to show that reducing gender inequality leads to development for individual women and for women in general. This evidence has been used to provide support for inequality-reducing policies as a valid and effective tool to directly and indirectly promote development.

In this paper, we review the existing evidence from cross-country studies of inequalityreducing policies to assess whether and how this evidence can be used to inform policy. Our analysis shows that, although it is helpful to identify and understand aggregate patterns, evidence from cross-country studies is of limited use for policy design.

Cross-country research designs have three features that limit their value and use for policy implications. First, the direction of causality has not been identified. For example, the fact that the gender gap in education is lower in richer countries is consistent with both equality fostering development and development fostering equality. Distinguishing between these alternative interpretations is central to effective policy because policies that promote gender equality do not foster development unless causality runs from equality to development rather than vice versa.

Second, cross-country studies are typically silent on the mechanisms that drive or affect the relationship between gender inequality and development, and identifying these mechanisms is critical for the design of more effective policies. For instance, whether a policy that promotes girls’ schooling (e.g., with conditional cash transfers) directly fosters development through an increase in women’s labor force participation depends on the reason girls’ education was at a low level in the first place. If this situation is driven by social norms that limit women’s participation in the labor market and hence also limit or reduce girls’ return to schooling, exogenously increasing girls’ return to schooling will not increase labor market participation and development or economic growth. An understanding of the values, social norms, and other mechanisms involved in the relationship between gender 2
# Women in the Pipeline: A Dynamic Decomposition of Firm Pay Gaps

Jishnu Das<sup>_∗_</sup> Clément Joubert<sup>_†_</sup>

## June 23, 2020

**JEL Classification:** J16, J31, J7, M5

**Keywords:** Gender Pay Gap, Dynamic Decomposition, Pipeline effects

> _∗_ Georgetown University and Center for Policy Research, New Delhi. E-mail: jishnu.das@georgetown.edu.

> _†_ World Bank. E-mail: cjoubert@worldbank.org (corresponding author).
fell even more on inputs. The textile group (except garments) is a case in point, where tariffs fell from 36% to 30%, between 1999 and 2009, but raw materials decline significantly, some to 5% by 2009. ERP on textiles rose from 84% to 229% Consumer benefited, certainly, but returns to domestic value added in the industry increases by 145 percentage points.

Turning to the 23 aggregated sectors making up the Egyptian economy, we find that the average tariff across lines is approximately 8 percent and the dispersion of nominal protection on tradables is not very high, the levels of tariffs ranging between zero on fertilizers to 25.8 percent on food. The sectors with the relatively highest tariffs are food and tobacco (not production agriculture), textiles, aluminum products, other industries and non-metal industries, in that order. But there is an unexpectedly high dispersion of ERPs relative to what one would expect looking at only applied tariffs. Despite the relatively low dispersion of nominal tariffs, the dispersion of effective rates of protection is wide, from high positive to high negatives. Even excluding NTBs, the standard deviation of total ERPs is 373 percent in comparison to an average level of 190 percent. The bulk of the average ERPs and their dispersion derives not from energy subsidies but from the tariffs and output subsidies. The average ERP due to energy subsidies over these 20 sectors is 53 percent with a standard deviation of 82 percent. The estimates of the tariff equivalents of NTBs are also highly dispersed. And so the dispersion of ERPs is even greater if one adjusts for NTBs, as shown by comparing in Tables 5a and 5b.

A notable result of the ERP estimates by the sectors available from the 2006/2007 I-O matrix is that energy subsidies are very important. For example, in the absence of subsidies on natural gas electricity prices would have to rise. Transport and communications (non-tradables), while taxed indirectly through imported input use, enjoys a positive total ERP due to fuel subsidies. Sectors that are taxed on the trade policy side (especially due to tradable input use by non-tradable sectors) are compensated by energy subsidies, suggesting that energy pricing is part of a strategy to subsidize and promote certain industries and in effect offset the _dis-_ protection or taxation that results from tariffs on intermediate inputs. As mentioned in the text, the case of the cement sector is notable because energy subsidies appear to almost exactly offset the negative impacts of tariffs and indirect taxes. The fertilizer sector is also notable because, using Table 5a, it has zero nominal tariffs on the product side (a benefit to agriculture) but ends up with a very high a positive total ERP due to energy subsidies.

21