rafmacalaba/datause-encoder-data huggingface dataset:
reforms which is sub-sector specific and time-varying between 1997 and 2004. The services sub-sectors covered by the EBRD index are electric power, water distribution, road transport, telecommunications, banking reform and interest rate liberalization, and non-bank financial institutions. To combine the EBRD index with the WIIW database, we average the index to match the sub-sectors covered by that database.<sup>25</sup> To combine the EBRD index with the KLEMS database, there is no need for aggregation since the services sub-sectors covered by the EBRD index match almost perfectly the NACE 2- digit sub-sectors covered by that database.<sup>26</sup> Panel A of Figure 14 illustrates the correlation between the average EBRD index and average WIIW labor productivity levels in the corresponding sub-sector during 19972004. The EBRD index ranges from 0 to 4 with higher values of the index indicating more liberalization achieved. The figure provides some evidence of higher WIIW labor productivity in countries where services sub-sectors are more liberalized. While the evidence is weaker within the group of EU-5 countries, it is stronger when contrasting EU-5 countries and Croatia where more liberalization has been achieved with other South Eastern European countries and Ukraine where less liberalization has been achieved. The evidence of a positive association between liberalization and performance is clearer for financial intermediation, with Poland being an exception.

Panel B of Figure 14 illustrates the correlation between the average EBRD index and average KLEMS labor productivity levels in the corresponding NACE 2-digit subsector in 1997-2004. Focusing on these more disaggregate sub-sectors, there is no clear evidence of higher KLEMS labor productivity in EU-8 countries with more liberalized services sub-sectors. One possible rationale for the difference in findings across Panels A and B of Figure 14 is the fact that Panel A focuses on a more homogeneous group of countries for which faster and deeper liberalization was required for EU accession in 2004. Another rationale for the difference in findings between Panels A and B is the

> 25 Specifically, we average (1) the EBRD index for electric power and the EBRD index for water distribution to match the sub-sector electricity, gas, and water; (2) the EBRD index for road transport and the EBRD index for telecommunications to match the sub-sector transport, storage and telecommunication; and (3) the EBRD index for banking reform and interest rate liberalization and the EBRD index for nonbank financial institutions to match the sub-sector financial intermediation.

> 26 The only adjustment required is to average KLEMS labor productivity in insurance and pension funding (NACE 66) and in activities auxiliary to financial intermediation (NACE 67) to match the EBRD index for non-bank financial institutions.

30
index. I will also test whether the 1993 PPP was derived from actual price surveys—in which case we refer to the country as a 1993 “benchmark country” (following common practice).

The third variation entails testing a nested model encompassing the above specification and the inflation-adjustment method used by the World Bank’s _World Development Indicators_ to update PPPs over time between ICP rounds. By this method, the extrapolated PPP for GDP for date _t_ (>1993), using the 1993 ICP round (say) as the benchmark, is given by: where _DEFti_ is the GDP deflator (or CPI when updating the consumption PPP) for country _i_ at date _t_ (where _i=US_ denotes the US deflator). The encompassing test entails adding a term in ln( _DEF_ 05 _i_ /<sup>_DEF_</sup> 93 _i_ ) to equation (3). If one cannot reject the joint null that the coefficient on this extra variable is unity, while    0 , then the inflation-adjustment method is supported.

# **4. Results**

I will use the PPPs for GDP for 2005, 1993 and 1985. The 1993 and 2005 PPPs are those estimated by the World Bank; the 1985 PPPs are from PWT (as 1993 was the first ICP round for which the Bank estimated global PPPs).<sup>17</sup> Figure 2 gives the empirical density functions (using normal kernels) for 1993 and 2005. The price level index is below 100% for the bulk of the data in both years. Even so, the index rose for 74% of countries. And it rose by 10 percentage points or more in almost half the countries (44% to be precise). For 2005, the (unweighted) mean price index across the 133 countries that participated in the 1993 ICP was 59% (median 49%) as compared to 53% in 1993 (median 41%). The mean ln( _P_ 05 _i_ /<sup>_P_</sup> 93 _i_ ) is 0.162, with a standard error of 0.026 (n=133).

So an upward revision to the price level is generally indicated. However, the bulk of this was for countries with initially low price levels. Indeed, the cumulative distribution functions implied by the densities in Figure 2 are virtually indistinguishable for price levels above 60%.

Regressing ln( _Pri_ ) on ln( _Yri_ ) , the data confirm the static (cross-sectional) Penn effect within each round, as widely reported in the literature; the regression coefficient of ln( _P_ 05 _i_ ) on

> 17 The 1993 PPPs for OECD countries were based on price surveys done in 1996, backcast to 1993. I will test robustness to excluding the OECD.

10
Policy Research Working Paper 8451

# **Abstract**

“Shared prosperity” has become a common phrase in the development policy discourse. This short paper provides its most widely used operational definition—the growth rate in the average income of the poorest 40 percent of a country’s population—and describes its origins. The paper discusses how this notion relates to well-established concepts and social indicators, including social welfare, poverty, inequality, and mobility, and reviews some of its design shortcomings. The paper then looks at household survey data to assess recent progress in this indicator globally. The analysis finds that during 2008–13, mean incomes for the poorest 40 percent rose in 60 of 83 countries. In 49 of them, accounting for 65 percent of the sampled population, it rose faster than overall average incomes. Finally, the paper briefly reviews a (non-exhaustive) range of ‘pre-distribution’ and ‘redistribution’ policies with a sound empirical track record of raising productivity and well-being among the poor, thus contributing to shared prosperity.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/research. The authors may be contacted at fferreira@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
Policy Research Working Paper 9766

# **Abstract**

This research explores the relationship between laws that discriminate on the basis of gender and the probability that a female-owned business begins operating in the informal sector. This is achieved by tracing the origins of formal businesses surveyed in the World Bank Enterprise Surveys and merging this with information on the level of legal equality between genders as measured by the Women, Business and the Law database. In addition, the research explores whether starting a business informally has any differential effect on subsequent firm performance depending on the gender of the owner(s). The results show that gender discriminatory laws increase the likelihood that firms with female owners will begin operations in the informal sector; as expected, this does not hold for enterprises that are solely owned by men. Furthermore, the research provides evidence that firms that began operations informally have poorer performance years later—a relationship that exists both for firms with female owners and for firms fully owned by men. The results show notable variation by region.

This paper is a product of the Global Indicators Group, Development Economics and the Office of the Chief Economist, Middle East and North Africa 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://www.worldbank.org/prwp. The authors may be contacted at mhyland@worldbank.org and aislam@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
By manipulating this we obtain: Evidently, those measures only constitute a proxy for the costs of the reserve requirements and depend on a certain number of restrictive assumptions. In the empirical implementations, we decided to use an average of the tax equivalent variable for different maturities and we experimented with different measures for the expected rate of exchange rate depreciation (or appreciation)<sup>v</sup> .

The theoretical foundations of the regressions estimated in this section can be found in the models of capital flows to developing countries surveyed in Fitzgerald (2002) and Mody & Taylor (2002). We included variables identified as important in the literature on the determinants of capital flows focusing on push and pull factors (Agenor & Montiel, 1999), such as domestic and foreign interest rates. In addition, we tried to capture the important role of information asymmetries in determining capital flows by including the U.S. junk bond spread in the regressions following (Mody & Taylor, 2002). In fact, those authors argue that the high yield spread is important in explaining the supply of flows to emerging markets, partly because default risk in the U.S. could be correlated to default risk in emerging markets, but also because the empirical evidence shows that a rise in the high-yield spread is capable of predicting a slowdown in real economic activity, which is consistent with financial accelerator models (Gertler & Lown, 1999). This predicted slowdown in economic activity could lead to a reduction in net capital flows.

Moreover, and from the viewpoint of micro level portfolio choice, Fitzgerald (2002) argues that the high yield spread may capture endogenous cycles in foreign investor’s risk tendencies. We also introduce lags of capital flows as an explanatory variable with the objective of capturing herding, inertia or bandwagon effects that may arise in international capital markets in the face of asymmetric information and bounded rationality.

It is clear that endogeneity problems are likely to arise in the estimation of equations describing capital flows. In addition, right-hand-side variables are also measured with error. In this context, it is likely that the residuals of the regression will be correlated with 10
> <sup>- 6 -</sup>

contributions, irrespective of when they were made, but now investment returns are also taken into account.

**<u>Table 2  ATP Coverage, Contributions and Benefits</u>**

||**1993**|**1995**|**2000**|**2004**|
|---|---|---|---|---|
|ATP members (000s)|3,450|3,550|4,271|4,355|
|ATPpensioners|409|423|467|535|
|Allpensioners|710|709|706|746|
|ATP/Allpensioners (%)|58%|60%|66%|72%|
|Contributions (DKKbillion)|4.05|4.64|6.30|6.68|
|Totalcontributions/GDP(%)||0.46%|0.49%|0.45%|
|Totalcontributions/Wage bill(%)||0.78%|0.86%|0.78%|
|Benefits (DKKbillion)||2.60|3.23|4.84|
|ATPbenefits/GDP(%)||0.26%|0.25%|0.33%|

Source: ATP and Statistics Denmark Benefits take the form of annuities, calculated at an interest rate of 2 percent, but with the possibility of bonus payments from accumulated investment reserves. Bonus payments aim to maintain the real value of pensions. Lump sums are allowed when the amount of accumulated capital is small. Bonus payments reflecting the investment performance of ATP are made to both cash pensions and accumulated balances.

Coverage of ATP increased rapidly over the years (Table 2). The total number of member accounts exceeds the total labor force, a feature that is found in most other countries that do not suffer from evasion and under-reporting and reflects the temporary exit of economically active people from the labor force. The number of ATP pensioners has increased steadily relative to the number of recipients of the social pension. It reached 72 percent of all pensioners in 2004. It is projected to reach 98 percent when the extension of ATP membership to universal participation will reach maturity.

The limited role played by ATP is highlighted by the size of contributions and benefits relative to GDP. These respectively amounted to 0.45 and 0.33 percent of GDP in 2004. Total contributions are less than 0.8 percent of the wage bill.

# **B.   Occupational Pensions**

Because both the social pension scheme and ATP pay modest benefits, most workers are also covered by private occupational pension schemes that have been promoted by collective bargaining. Participation is not mandated by law, but is effectively imposed by collective labor agreements. Three statutory supplementary pension schemes, in addition to ATP, have also been created on ad hoc basis. Two of these also have universal coverage, while participation in the third is voluntary and narrow.
Table 3a: Shares of plants with financial loans by industry

||||Total c|ell count|||Average industry<br>organized sector<br>share in output,<br>||Total|share w|ith finan|cial loan|s|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1989|1994|2000|2005|2010|Average|1989-2010|1989|1994|2000|2005|2010|Average|
|15<br>Food products and beverages|25,170|32,502|39,544|39,544|20,425|31,437|0.777|0.16|0.12|0.10|0.13|0.09|0.12|
|16<br>Tobacco products|8,579|17,207|12,996|12,996|3,624|11,080|0.714|0.01|0.02|0.01|0.00|0.02|0.01|
|17<br>Textiles|23,289|45,945|35,806|35,806|11,246|30,418|0.796|0.10|0.10|0.07|0.07|0.07|0.08|
|18<br>Wearing apparel; dressing and dyeing of fur|1,760|2,475|42,254|42,254|19,855|21,720|0.646|0.14|0.19|0.04|0.07|0.04|0.10|
|19<br>Leather tanning; luggage, handbags, footwear|2,876|3,738|4,229|4,229|1,569|3,328|0.791|0.22|0.16|0.12|0.15|0.07|0.14|
|20<br>Wood and wood products; straw and plating articles|14,397|24,492|26,942|26,942|8,508|20,256|0.217|0.09|0.06|0.05|0.07|0.05|0.06|
|21<br>Paper and paper products|1,680|1,806|2,402|2,402|1,816|2,021|0.917|0.07|0.09|0.08|0.05|0.09|0.07|
|22<br>Publishing, printing and media reproduction|2,375|2,608|4,809|4,809|2,280|3,376|0.623|0.25|0.20|0.20|0.24|0.15|0.21|
|23<br>Coke, refined petroleum and nuclear fuel|344|476|353|353|373|380|0.975|0.34|0.29|0.16|0.28|0.46|0.31|
|24<br>Chemicals and chemical products|4,385|6,285|5,685|5,685|4,780|5,364|0.969|0.08|0.10|0.10|0.06|0.07|0.08|
|25<br>Rubber and plastic products|2,236|2,975|3,713|3,713|2,696|3,067|0.876|0.20|0.29|0.22|0.28|0.12|0.22|
|26<br>Other non-metallic mineral products|8,797|14,356|13,906|13,906|8,692|11,931|0.803|0.11|0.10|0.09|0.13|0.16|0.12|
|27<br>Basic metals|2,569|3,163|2,882|2,882|2,798|2,859|0.967|0.37|0.33|0.22|0.41|0.15|0.30|
|28<br>Fabricated metal products, except machinery|7,107|8,751|14,346|14,346|6,924|10,295|0.687|0.18|0.15|0.11|0.14|0.12|0.14|
|29<br>Machinery and equipment, n.e.c.|6,031|8,060|6,069|6,069|4,443|6,134|0.883|0.15|0.13|0.22|0.20|0.12|0.16|
|30<br>Office, accounting and computing machinery|134|131|79|79|103|105|0.921|0.69|0.35|0.44|0.35|0.23|0.41|
|31<br>Electrical machinery and apparatus, n.e.c.|1,647|2,151|2,501|2,501|2,725|2,305|0.851|0.23|0.26|0.25|0.19|0.21|0.23|
|32<br>Radio, television, and comm. equipment|615|760|508|508|497|578|0.939|0.27|0.20|0.41|0.31|0.04|0.25|
|33<br>Medical, precision and optical instruments, watches|508|563|671|671|606|604|0.907|0.45|0.49|0.28|0.41|0.31|0.39|
|34<br>Motor vehicles, trailers and semi-trailers|679|955|1,387|1,387|1,206|1,123|0.973|0.37|0.41|0.23|0.51|0.28|0.36|
|35<br>Other transport equipment|1,149|1,498|1,104|1,104|993|1,170|0.944|0.24|0.31|0.24|0.17|0.19|0.23|
|36<br>Furniture, manufacturing n.e.c.|11,472|17,142|22,153|22,153|10,016|16,587|0.339|0.09|0.07|0.06|0.12|0.05|0.08|
|Nationwide|127,799|198,039|244,339|244,339|116,175|186,138|0.820|0.10|0.09|0.07|0.08|0.06|0.08|

Notes: See Table 1a. Table provides industry-level estimates of the share of manufacturing plants with financial loans. Shares use sample weights for population-representative values.
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bias from missing recipient characteristics. Results on donor type variables and donor aid share in equation 4.1 are surprisingly similar to those in equation 1.1, which included no fixed effects. Equation 4.2 tests robustness of donor type results to controlling for differences in donors‟ priorities or mandates, as proxied by the sectoral composition of their aid. These variables, constructed from the DAC‟s Creditor Reporting System, vary by donor and year, but not over recipients. They potentially matter in either of two ways. First, sectoral priorities may be correlated with other aid delivery preferences. Second, aid in sectors such as infrastructure is mostly delivered in the form of projects, so less likely to use country systems than (for example) education aid, which often takes the form of sectoral budget support to help fund teacher salaries.<sup>18</sup> The five sectoral variables in equation 4.2 are all significant, relative to the base category of all other sectors (including multisectoral aid). Infrastructure, health and governance aid have negative signs. Donors providing larger shares of their aid for governance reform may be the ones that perceive greater governance-related risks, accounting for the negative relationship with use of country systems. Of the donor type variables, non-DAC bilaterals are no longer significantly different when the sector shares are added, but this is due in part to missing sectoral data for Turkey: the non-DAC bilateral dummy is in effect a Korea dummy in equation 4.2. The coefficient on vertical funds increases in magnitude and significance, controlling for the fact they mostly provide health aid, which is associated with lower use of country systems. Equation 4.3 adds three more proxies for donor preferences, but at the expense of dropping the multilaterals due to missing data. Technical assistance as a share of aid is not significant. Contributions to multilateral donors as a share of total aid predicts reliance on country systems: donor countries that are reluctant to delegate to multilaterals tend to be the

> 18 The PDMS does not collect information on sector, so we cannot directly compare use of country systems across sectoral categories.

27
In addition to a full food security module<sup>9</sup> , the survey also included a full displacement module between August 2015 and December 2018. The module collected information regarding a household’s district and whether the household was currently displaced. Furthermore, if the household was currently displaced, the module also inquired about the month and year of displacement, and the origin governorate. Because of the panel component of the survey, we are able to identify the origin and migration districts of 1,116 households that became displaced only after their first interview, and these are the households on which the analysis primarily focuses.<sup>10</sup> Importantly, previous work illustrates that there is little observable difference in food access following displacement of these 1,116 displaced households for whom we can observe more detailed displacement information and the 6,970 displaced households that were only first reached by the survey after they had become displaced (e.g., D’Souza et al. 2022).

We further merge the WFP survey with data on violence from the conflict. For the 1,116 displaced households for which we observe the origin and migration district, we merge the number of violent incidents and the number of fatalities occurring in both the origin and migration districts at and around the time of displacement; and for nondisplaced households we merge the same violence outcomes in the district in which the household lives at the time of the most recent survey. Prior to 2016, we use conflict data from the Uppsala Data Program; and after 2016, we used data from the Armed Conflict Location and Event Data Project (ACLED). From these data sources, we use number of fatalities caused by violent incidents and the number of violent incidents as our main indicators of violence. However, for 2016 and on, we are also able to disaggregate each indicator by major types of violence- ground battles, remote violence (e.g., airstrikes), and thus we are unable to identify the share of non-responses related to active phone numbers. 9The survey captures the number of times households in the week before the survey have consumed from important food groups, the number of times households have resorted to five common food coping strategies, and whether the household received food assistance in the past 30 days. These questions form the basis for computing the household Food Consumption Score (FCS) and reduced Food Coping Strategies Index (rCSI).

> 10For households that were surveyed only after displacement, we can identify the origin governorate but not the specific district. Governorates are very large and similar in size to a state in the United States, while districts are much smaller and more similar to a county in the United States.

9