rafmacalaba/data-use-mentions-extended huggingface dataset:
We see that the participation rate declines rather slowly until one reaches about the 50<sup>th</sup> percentile of the rural distribution. In fact there is no perceptible decline in participation in a neighborhood of the national headcount index of poverty of 36%; households just below the official poverty line are no more likely to participate in MGNREGS than those just above the line. The marked decline in participation rates does not emerge until we get to the upper half of the rural consumption distribution. By the 90<sup>th</sup> percentile, the participation rate reaches about 10%. Although far fewer ―rich‖ rural households participate, there are still some. This could reflect recent shocks, or poor individuals within generally well-off households.

The rationing rate follows a U-shape, declining initially as consumption increases until one reaches about the median, but then rising. However, this clearly stems from the high rationing rates in poor states that we have demonstrated above. Thus we see that, when we add state fixed effects, a steady increase in the rationing rate emerges as consumption increases across the whole distribution (Figure 4(b)).

The fact that the rationing rate tends to be lower for the poor does not, however, imply that more rationing would improve targeting. What the numbers in Table 3 and Figure 4(b) reflect is the rationing process at a given level of participation. When the participation rate rises through a reduction in rationing the self-targeting mechanism will start to play a bigger role. We will see evidence of this when we compare targeting performance across states with very different participation rates.

Also notice that, amongst participants, the days of work received shows a slightly positive gradient with consumption per person. The pro-poor targeting is achieved through both demand for work and the rationing of work, not by the amount of work actually received.

It is of interest to compare targeting performance across states. There are many measures of ―targeting performance‖ in the literature that
32 regressions being based on an explicit theoretical foundation, and is thus relatively less ad hoc than a fully reduced-form growth regression. Another important reason for choosing the semi-reduced form specification is the documented robustness of the highly statistically significant positive effects of the growth rate of capital on economic growth. This is compared to that of the investment rate, which does not generate such robust results. Given that capital is one of the two inputs into aggregate output, it is important to properly account for its role in the production process before we are able to correctly examine the roles played by the knowledge economy in economic development.

If we are estimating equation (7), we see that the estimated coefficient of the growth rate of capital will simply be the elasticity of aggregate output with respect to capital. On the other hand, if we were estimating the per-worker specification as seen in equation (8) then the estimated coefficient of the growth of capital per worker would represent the elasticity of output per worker to the capital-labor ratio. A detailed account for the generation of the capital stock data is given in Appendix A.

# **Growth Rate of Labor**

The growth rate of labor only appears as a regressor when we are estimating equation (7). As in the case of capital, the estimated coefficient of the growth rate of labor is the elasticity of aggregate output with respect to labor.

Data for the total labor force used in this paper are comprised of people who meet the International Labour Organization definition of the economically active population: all people who supply labor for the production of goods and services during a specified period. It includes both the employed and the unemployed. While national practices vary in the treatment of such groups as the armed forces and seasonal or part-time workers, in general the labor force includes the armed forces, the unemployed, and first-time jobseekers, but excludes homemakers and other unpaid caregivers and workers in the informal sector.

# **Initial Income**

One of the key implications of the neoclassical
Policy Research Working Paper 10056

# **Abstract**

Public-private partnerships are used to procure public infrastructure. Despite involving private investors and concessionaires, they impact the public budget like traditional provision and create fiscal risks. This paper develops a conceptual framework to assess whether and how public-private partnerships shift risks to concessionaires and financiers. It uses this framework to describe and assess the Chilean public-private partnerships program. The paper identifies renegotiations as the major source of fiscal risk, which involved additional investments, increasing the cost by about one-third over the original project cost estimates. The 2010 law reform on public-private partnerships introduced changes to the renegotiations regime and began the routine use of variable term contracts. Using contractual data, the analysis finds evidence suggesting that renegotiations fell dramatically. The paper also calculates the realized internal rates of return for 50 highway and airport public-private partnerships, using cash flow data for the entire public-private partnerships program, which started in 1991. The average internal rate of return is 6.8 percent, with averages of 9.1 and 3.1 percent for fixed and variable term public-private partnerships, respectively. The returns show a large dispersion, which suggests that infrastructure projects are intrinsically risky and private participation entails significant risk shifting from the budget to concessionaires and financiers.

This paper is a product of the Infrastructure Chief Economist Office. 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 author may be contacted at alexander.galetovic@uai.cl.

_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
year, and 254 from the old sample not all of which are to be followed in subsequent years.

# **7.1. Capital**

To construct the establishment capital stock series, we follow a methodology close to Black and Lynch (1997). The 1988 Census reports information on the capital stock. We use machinery capital. We avoid overestimation of the amount of depreciation by calculating an average depreciation rate by industrial sector and year. The resulting depreciation rate is then used for all firms within each sector yearly. We further exclude the value of assets sold in our measure of capital, assuming assets have been totally depreciated at that point. Thus, the equation for estimating the capital stock for years later than 1988 is: with 18
WPI Core|0.93*|0.27|1||||
|CPI Headline|0.58*|0.91*|0.39*|1|||
|CPI Food|0.34*|0.89*|0.10|0.92*|1||
|CPI Core|077*|074*|067*|086*|061*|1|
|<br>Note:*denotes sig|.<br>nificance at 5%|.<br>level.|.|.|.||

**Table 4: Level and volatility of CPI inflation and its components (quarterly data from 1997q22019q4)**

||Mean|Standard deviation|Coefficient of<br>Variation|
|---|---|---|---|
|CPI|6.41|3.31|0.52|
|CPI, Food|6.29|5.01|0.80|
|CPI,Fuel|6.96|4.96|0.71|
|CPI, Core|6.47|2.76|0.43|

# **Persistence**

Contrary to popular presumption, food price inflation has not been higher than core and headline inflation (Table 4).<sup>17</sup> Neither is it more persistent. We estimate first order autocorrelation coefficients for rolling windows of 20, 30 and 40 quarters. The coefficients for core and headline inflation are higher than for food price inflation.<sup>18</sup> Nor has inflation persistence declined over time (in contrast to evidence for the United States – e.g. Fuhrer 2010).

> 17 See Appendix B for details.

> 18 Unit root tests conducted on all inflation measures provide mixed results. While we cannot reject the null of unit root for core inflation at 5% level
discrepancy in the measures of the stock of education relative to the enrollment rate. Whether or not the DHS are "better" is left for a different forum, but the fact that those from the DHS are transparently based on household surveys make these data particularly attractive.

_Wealth differences in enrollment_ The main advantage of using household surveys to carry out this analysis, however, is that various dimensions of inequality can be explored, and in particular wealth using the asset index approach. Gaps in educational enrollment and attainment across different wealth groups are large in almost all developing countries. Filmer and Pritchett (1999a), using a subset of the countries analyzed here, show that the difference in the median grade attained by 15 to 19 year olds from the richest and poorest households reaches as high as 10 years (India), and is commonly between 3 and 5 years in other countries.

Why would we expect to see wealth differences in education? A review of the elasticity between "income" and several educational outcomes can be found in Behrman and Knowles, (1997). As those author's discuss, a simplistic economic model where education is a pure investment, households are perfectly inter-generationally linked, credit markets are perfect and investment opportunities in education are equally distributed across households implies that investments in education will not be related to a family's present financial wealth. The assumptions of such models can break down on many fronts. Credit markets may not be perfect an*d the poor may have less access to it, there may be a large "consumption" component to education and wealthier households will therefore consume more of it. In addition, the opportunity costs of children's time spent in schooling, as well as the expected return to that schooling, may differ by household wealth leading to differential observed investment. 14

**14** For more discussion on these reasons for wealth difference **s** ee Filmer and Pritchett (1 999b). In particular, that paper argues that large cross-state
<!-- Start of picture text --> Capital Distortion vs Productivity Output Distortion vs Productivity<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Log TFPQ (Percentile) Log TFPQ (Percentile)<br>Cote d’Ivoire Ethiopia Cote d’Ivoire Ethiopia<br>Ghana Kenya Ghana Kenya<br>.5 2<br>1<br>0<br>0<br>Capital Distortion−.5 Output Distortion<br>−1<br>−1<br>−2<br><!-- End of picture text --> Figure 5: Distortions vs. Productivity

# **6.4 Productivity and Distortions Over the Life Cycle**

Our analysis so far has focused on measuring the static effect of resource misallocation, but distortions are likely to also have important dynamic implications through the effect that greater misallocation has on firms’ incentives to invest in technological upgrading. As already mentioned, the fact that more productive firms are “taxed” more could discourage firms from investing in productivity enhancing technologies, and as a result generate slower life cycle productivity growth, which in turn leads to slower employment growth.

Hsieh and Klenow (2014) document a notable difference in the post-entry dynamics of firm performance between developing and advanced economies. Using comprehensive manufacturing census data, they find that while firms in the US grow by a factor of eight by the age of 40, Mexican firms grow by a factor of two and such growth is much slower in India. The authors attempt to rationalize the flatter growth of productivity over firms’ life-cycle in developing countries through an age-dependent component in the distribution of distortions across firms. Indeed, they find that firms get progressively more taxed as they age, and show quantitatively through a model of innovation that this age-dependent component of distortions undermines productivity growth. Furthermore, they show that the dynamic response in the underlying distribution of physical productivity magnifies the losses from misallocation that result from a
**Specialization without Regret: Transfer Rights, Agricultural Productivity and Investment in an Industrializing Economy**

Among those mutable factors that constrain economic performnance in low income and transitional economies, property rights over land rank high by most calculations.' With a few important exceptions, the literature that has tried to quantify the economic effects of property rights has concentrated on the security with which individuals hold rights over land. The goal of this paper is to broaden the focus of these earlier efforts to include transfer rights, meaning an individual's liberty to rent or sell the use, income and other rights that he or she holds.

Theoretically this paper shows that transfer rights permit specialization in off-farm activities economy industrializes and its population begins to specialize in non-agricultural activities. Transfer rights should also facilitate (shadow) factor price equalization across households and resolve problems of allocative inefficiency that are likely to become more severe under the pressure of industrialization. This paper's econometric analysis of household level panel data from China largely corroborates these theoretical propositions concerning transfer rights. With its finding that transfer rights seem to matter more than tenure security, the empirical analysis also suggests some novel directions for future property rights reform in China.

The liberalization of transfer rights has emerged as a contested and yet poorly understood policy issue in a number of countries, both in those where land is or until recently has been held under customary tenure arrangements, and in those where earlier redistributive reforms reshaped land ownership structure. This paper considers transfer rights in the context I See for example the discussion in Alston, Libecap and Schneider (1996) and the many references contained therein.

1
have combined both sexes, neglecting this well-established fact that mortality rates of men are higher than those of women, and that this might vary by country income level.

This study draws on recent data which were not available for earlier studies to examine the extent to which the gender gap in mortality shifted in high- and middle-income countries during the COVID-19 pandemic. Unfortunately, underdeveloped civil registration systems for reporting on male and female deaths limits data availability in low-income countries, a persistent limitation in the study of global population-level mortality patterns.

# **Methods**

# _Data sources_

We use the “Global excess deaths associated with COVID-19 (modelled estimates)” dataset (May 19, 2023 update), produced by WHO’s Technical Advisory Group (TAG) on COVID-19 mortality assessment. This dataset contains estimates of expected all-cause deaths, all-cause deaths (actual or predicted if actual was not available), and excess deaths by age-group and sex for 194 countries for 2020 and 2021. Expected all-cause deaths were forecasted using historic country-level monthly mortality data prior to the pandemic and serve as reference point in the absence of COVID-19. Excess deaths were computed as "the mortality above what would be expected based on the non-crisis mortality rate in the population of interest” by differencing actual/predicted all-cause deaths from expected all-cause deaths (World 2
<!-- Start of picture text --> Afghan refugees in Iran under UNHCR<br>3500000<br>3000000<br>2500000<br>2000000<br>1500000<br>1000000<br>500000<br>0<br>1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019<br>Figure 1 - Number of Afghan refugees in Iran under United Nations High Commissioner for Refugees<br>(UNHCR). Source: UNHCR database available at https://www.unhcr.org/refugee-statistics-statisticsstatistics (accessed<br>21/06/2020).<br>GDP growth (%) in Iran GDP growth<br>non-oil GDP growth<br>16.0 14.2<br>11.0 8.7<br>7.9 7.0 6.2 7.3 7.8 6.6<br>5.5<br>4.8<br>6.0 3.2 3.5<br>2.5 2.3<br>0.8<br>1.0<br>-0.1<br>-4.0<br>-4.9<br>-6.1<br>-9.0<br>-8.5<br>US started war against Taliban.<br>The Taliban resurgence started. Period of this study<br>The Soviet Army intervened in Afghanistan<br>Internal confilict following Soviet exit<br>Taliban is founded Taliban established Islamic state.<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> _Figure 1 - Number of Afghan refugees in Iran under United Nations High Commissioner for Refugees (UNHCR). Source: UNHCR database available at_ _
as illustrated in Figure 10. The top panel displays on-time payment rates for the control group, distinguishing between women (in red) and men (in blue). Tax compliance increases with property valuation but is otherwise similar for men and women. The bottom panel reports the treatment effects for each quintile—i.e., the difference between treated and control units—alongside 95% confidence intervals. While women present homogeneous responses across quintiles, men exhibit a slightly negative gradient. Notably, in the lowest quintile, the personalized tax letter boosted timely payments of men by 8 percentage points—twice the increase observed for women.<sup>11</sup> Taken together, our findings indicate that men and women exhibit similar payment rates and responses to personalized tax letters. However, some of our evidence suggests that men might be slightly more sensitive to this type of intervention. For instance, men tend to respond more quickly to tax letters, though women eventually catch up. Additionally, men with lower-valuation properties tend to react more strongly to the intervention. Our findings thus somehow downplay the potential importance of gender-dependent strategies to enhance property tax compliance.

# **6 Conclusion**

The increased access to administrative data and tax authorities’ willingness to engage in randomized experiments offer a new opportunity to shed light on the differential effects of taxation by gender. In the context of property taxation in the municipality of Tres de Febrero, Argentina, we first document large gaps in property ownership by gender, particularly among high-value properties, consistent with other settings where these gaps were investigated. Nonetheless, administrative records show that compliance, measured by timely payment of property tax liabilities, is very similar across properties with male and female owners. Furthermore, we exploit a randomized trial aimed at improving compliance and show that reactions to an enforcement letter are similar across genders.

The overall absence of gender differentials in baseline compliance and response to the intervention is surprising. Surveys have shown that in contrast to men, women tend to think that
.48|0.10|
||1980|-|1.13|3.68|0.24|
||1990|-|1.2|3.23|0.36|
||1997|-|4.12|0.54|-|
||1998|-|3.21|8.25|1.05|
|Sri Lanka|1975|0.33|0.64|12.02|-|
||1980|0.12|1.24|2.63|-|
||1990|1.18|1.64|2.81|-|
||1995|0.39|1.98|-|-|
||1998|0.97|2.65|4.12|-|
|Nepal|1998||1.28||1.75|
|South Asia||1.3|MERCOSUR||28.6|
|European Com|munity|53.4|Canada-USA|FTA|64.3|
|NAFTA||56.3|LAFTA||22.3|
|Andean Pact||7.4||||

Notes: Data are presented column-wise (i.e. Figures show trade complementarily of country listed in column 1 with each country listed in row 1). Sources: Calculated from the WITS Database, World Bank. Each country index is derived by computing the complete schedule of exports at SITC 4-digit level . The complementarity Index for other regions EU, NAFTA, Mercosur, LAFTA, and Andean Pact is obtained from Yeats (1998).

The measures for the Andean Pact were an exception among these groups. They were low enough to suggest that imports and exports between the member countries were largely similar and may have contributed to its failure (Yeats, 1998). The weighted average of the complementarity index for South Asia does not compare well even
<!-- Start of picture text --> Coefficients<br>Coef 95% confidence intervals<br>OLS<br>Raw gap<br>Characteristics<br>Predicted gap<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantile<br>.2<br>0<br>Gap<br>-.2<br>-.4<br><!-- End of picture text --> _Note: These figures are based on the STATA command mmsel. The conditional gap is based on earnings equations including controls for years of education, potential work experience, search method (friends and relatives), unlimited contract, having had an apprenticeship contract, working in a small-sized firm (5 or less employees), union membership, living in a rural area. With a larger number of variables, the command does not work, due to the lack of sufficient information. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 41
# **Introduction**

Economic integration of the African continent rests on two pillars: large investments in transportation infrastructure and the ratification of an ambitious pan-African trade agreement. Both aim at reducing barriers to trade, but in different ways. Better transport infrastructure reduce transport costs between African countries and between them and the rest of the world, and trade facilitation programs reduce queuing times at borders. Preferential trade agreements (PTA) reduce frictions directly between trading partners by reducing tariffs and harmonizing policies on e-commerce, investment, and intellectual property. Infrastructure improvements and trade reforms are therefore potentially complementary: reducing trade frictions alone may not be sufficient if the contracting parties are poorly connected; similarly, improving infrastructure connectivity alone may not be sufficient in the presence of trade barriers (tariff and non-tariff). The contribution of this paper is to address the question of how the effects of both pillars of integration operate in combination using granular data on transport infrastructure, information on the depth of regional trade agreements and general equilibrium trade modeling.

To quantify the joint impact of trade reforms and infrastructure investment in Africa, we first build a new citylevel database of travel times using a geographic information system (GIS). We map the current infrastructure and border delays for the entire continent (both within Africa and between Africa and the rest of the world). We then add the infrastructure projects as listed in the Program for Infrastructure Development in Africa (PIDA) and assess their impacts on reducing transportation times. For trade policy, we rely on data from the World Bank’s Deep Trade Agreements database (Mattoo, Rocha & Ruta 2020); which contains detailed information on the content of trade agreements on a range of topics, including competition policy, investment, capital movements, and intellectual property rights protection. We follow the cluster analysis on the distribution of provisions done by Fontagn ́e, Rocha, Ruta & Santoni (2023) to sort the agreements according to their ambition into Shallow, Medium, and Deep treaties. We use both datasets to