rafmacalaba/data-use-mentions-extended huggingface dataset:
higher in Niger than in Mali, meaning that health and education spending as a share of market income decreases more rapidly with increasing income deciles for Niger. In addition, education spending, especially for higher education, benefits mostly the richest individuals because they are more likely to attend university in developing economies in general.

**Figure 6. Distributional Effects of In-Kind Transfers in Mali and Niger, 2014**

<!-- Start of picture text --> 6.a In-kind transfers in health as a share (%) of market income per decile<br>5<br>4.5<br>4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Mali Niger<br>6.b In-kind transfers in education as a share (%) of market income per decile<br>25<br>20<br>15<br>10<br>5<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Mali Niger<br><!-- End of picture text --> _<u>Source</u>_ <u>: Authors</u> _Incidence of Indirect Subsidies: Energy Subsidies and Agricultural Subsidies_ Indirect subsidies included in this analysis are energy subsidies (only in Mali) and agricultural subsidies. For the former, the survey data are used to estimate annual household consumption of gas and electricity. Then, the subsidy per kilogram of gas and per kilowatt-hour of electricity is estimated by dividing the total public subsidy in 2014 by total annual consumption of gas and electricity. The average price of gas and the electricity rate brackets are also used to determine 11
this matter. To address this question surveys should ask about the firm's lines of business (defined as precisely as possible), whether the firm is a conglomerate (i.e., engaged in unrelated activities), the extent of vertical integration (i.e., produces its main inputs or has its own distribution channels), and whether the firm has any subsidiaries. With these measures, researchers can link productivity with the scope of the firm to see how firm scope changes under different institutional environments.

**The extent of markets.** Theory suggests that the extent of markets may fundamentally constrain firn development. Some believe that a small market, for instance, may help explain the so-called "missing middle" phenomenon in developing countries-the existence of some large firms and many small firms, but very few middle-sized firms, which are common in industrialized countries. Small markets have also been suggested as a barrier to technological change. Existing Bank surveys tend to neglect this issue. Many surveys can become more useful by asking certain questions about the extent of market: Where firms sell their products, the distance to a major city, and the firm's access to all types of transportation.

Two Bank surveys have made impressive efforts to explore market structure. One, the _RPED,_ asks two especially useful questions: (1) What characterizes a firm's competitors? (None, domestic firms, foreign competitor, or imports). (2) How does the firm set its prices? (Marketprice-taker, markup over costs, in line with imports, price discriminate in different markets, price increases with quality, follow largest competitors, government-set, negotiate-with-the-buyer, or set by a business association). Although the category of "domestic firms" in the first question is rather broad, the second question is especially rich.

The second, _Russia.- Economic and Civil Society,_ asks detailed information about market structure:

- How many Russian enterprises the respondent considers to be direct competitors.
42

**Table A.2. Revenues collected by Ho Chi Minh City from each district in 2010 and the resulting estimate of economic value in each district.**<sup>**27**</sup> **f**

||2010 Revenues<br>(billion VND)|Percent of Total<br>Ho Chi Minh City<br>Revenue|Economic Value<br>(billion USD)|
|---|---|---|---|
|_Ho Chi Minh_<br>_City Total_|_11,225_||_24.5_|
|Binh Thanh|470|4.5%|1.10|
|District 1|187|6.9%|1.70|
|District 10|232|2.9%|0.70|
|District 3|189|2.7%|0.65|
|Go Vap|548|5.4%|1.33|
|Phu Nhua|175|2.3%|0.57|
|Tan Binh|430|5.9%|1.44|

# **_Modeling Vulnerability_**

The vulnerability of people or economic assets can depend on a number of factors related to flooding – the flood depth, duration, rate, etc. Many vulnerability functions found in the literature are depth-damage relationships, mapping a certain level of depth to a certain percent of impact or loss. We use depth-damage curves in this study as well, given that SWMM provides us with information about inundation depth but not other flood characteristics.

Depth-damage curves for the population and economic assets in Vietnam or for comparable countries are not well developed, though recent efforts have begun to gather data that could support the development of such functions (Phi _et al._ 2012). Given the lack of good data on vulnerability, we have developed candidate depth-damage curves, but we treat key parameters of this relationship as
<!-- Start of picture text --> S1: Highly Suitable<br>S2: Moderately Suitable<br>Maize Soil Property Y<br>S3: Marginally Suitable<br>N: Not Suitable<br><!-- End of picture text --> _Figure 1._ Underlying crop-soil property suitability class structure, following FAO (1976). The maize suitability analysis completed here includes pH, cation exchange capacity (CEC), organic carbon, salinity (soil electrical conductivity), and plot slope (percent).

The following two-step empirical approach is employed: (1) estimate crop suitability measures at the plot-level; and (2) estimate stochastic frontier models to estimate production frontiers for each class of maize suitability. The contribution of this paper comes from the ability to execute each of these steps on the same sample and from being able to do so with objectively-measured soil properties and crop production. In addition to executing the aforementioned analytical steps using the MAPS plot-level soil data, the steps are replicated using the geospatially-derived soil property data from AFSIS.

# **4.1.Assigning Maize-Specific Soil Suitability Measures**

Estimating aggregate crop suitability measures requires comparing a vector of optimal soil properties against the levels of said properties observed on each plot. Crop suitability cannot be reduced to a single soil property, as several properties affect plant growth simultaneously, and soil property requirements vary by crop. The crop suitability framework set forth by FAO (1976), and illustrated in Figure 1, will serve as the foundation for the suitability classifications at the cropsoil property level. The maize suitability analysis completed here includes pH, cation exchange capacity (CEC), organic carbon, salinity (soil electrical conductivity), and plot slope (percent).<sup>11</sup> After identifying the suitability class of each soil property individually, based on the propertyspecific critical values borrowed from Naidu (2006) and further reviewed and modified with input from the World Agroforestry Centre, we utilize
countries. Third, income growth is also correlated with higher saving rates (Loayza et a. 2000). Large increases in GDP per capita (USD371.7 in 1990 vs. USD1,284.7 in 2019 in Pakistan) could have also broadened the segments of the population that can afford to delay some consumption into the future.

To what extent these demographic and economic characteristics have impacted household asset accumulation over the life cycle remains an empirical question. Households may have more incentives to save but lack the saving technology (financial inclusion) or ability (commitment, need for liquidity) to safely put money aside for decades. Using Findex survey data, Demigurc-Kunt et al. (2016) report that in South Asia only 10-15 percent of individuals save for old age in their 50s. However, a larger fraction (32 percent in Pakistan) report saving more generally, and those savings may ultimately finance old age consumption, even if that is not the most urgent intended use. In addition, some components of household net worth such as housing may not be identified as savings by respondents, despite being indirectly consumed in old age in the form of saved rent payments and, while relatively illiquid, could be sold as a last resort.

This paper estimates patterns in household wealth accumulation over the life cycle in a lower-middle-income, high-informality context, using data from Pakistan, where 88.5 percent of employment was informal in 2017 (Bossavie et al, forthcoming). To identify the effect of age on household wealth, we apply the age-year-cohort decomposition techniques described in Deaton (1997) to the different components of household wealth, as well as household consumption and income, distinguishing households in rural versus urban areas and in different parts of the household consumption distribution.

2
<!-- Start of picture text --> Polac,Pnning, and Research |<br>Education and Employment|<br><!-- End of picture text -->

Israel is a fitting placf to compare the outcomes of academic and vocational schooling. More than half the Israeli secondary school pupils attend vocational schools or vocational streams in comprehensive schools.

A study based on 1983 Israeli census data shows vocational schooling to be more costeffective than general academic education. In particular, vocational school completers who work in occupations related to their course of study earn about 10 percent more a year than their counterparts who either attended secondary schools or who attended vocational schools but were employed in occupations unrelated to their course of study.

Studies that show vocational schooling to be cost-ineffective compared with academic schools tend to concentrate on earnings, without taking into account such variables as the relevance of the type of occupation to the vocational studies pursued. Future evaluation studies should pay more attention not only to that variable but to issues of curriculum (including the type and scope of vocational studies).

This paper is a product of the Education and Employment Division, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Cynthia Cristobal, room S6035, extension 33640.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these fmdings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
under the nested error model, MSE(ˆ _τc_<sup>_EB_</sup> ) = _E_ (ˆ _τc_<sup>_EB_</sup> _− τc_ )<sup>2</sup> . Under the model-based framework, the target indicators _τc_ are regarded as random quantities (they are generated from the model) and hence incorporate uncertainty as well. Even if an estimator (or predictor) _τ_ ˆ _c_ is unbiased with respect to the model in the sense that the expectation of its prediction error is zero _E_ (ˆ _τc − τc_ ) = 0, it would still leave the variance of the prediction error var(ˆ _τc −τc_ ), which is not exactly equal to the variance of the predictor var(ˆ _τc_ ) because of the randomness of the target indicator _τc_ .

The MSE of the EB estimator is estimated using a parametric bootstrap procedure for finite populations introduced by González-Manteiga et al. (2008), which is computationally more intensive than the ELL bootstrap approach. This is because, in every bootstrap replicate, all the steps for obtaining the EB estimator are reproduced, including model fitting and the MC procedure for approximation of the conditional expectation defining the EB estimator. This means that the number of simulated census vectors is the product of the MC simulations and the number of bootstrap replicates, except when the target indicator has an explicit EB estimator (such as the poverty rate). In this latter case, the MC simulation procedure can be replaced by an explicit formula calculated without simulation, and then the number of simulated censuses will be only the number of bootstrap replicates.

The steps for the parametric bootstrap estimation of the MSE are as follows (MR, 2015):

1. Using the survey data, fit model (1) using a method providing consistent estimators. This yields the set of parameter estimates from the observed sample:

In the implemented sae R package, the REML method is used.

2. Use the estimates in _θ_<sup>ˆ
relies on the plausible assumption that within-country variation in rainfall over time is orthogonal to other determinants of import tari¤s.

# 4 Data

Our empirical analysis makes use of the following sets of data:

1. Import tari¤s We use annual data on bilateral applied tari¤s by country and product (SITC 2-dig., Rev. 4) from the TRAINS database of the United Nations Conference on Trade and Development. These data are generally available from 1988 onwards, but the extent of time coverage di¤ers considerably across countries. We construct both simple and import-weighted averages of bilateral applied tari¤s by countryproduct-year. For a given country-product pair, the former measures are de...ned as the simple average of applied tari¤s on imports from the various source countries in year t. The latter measures are de...ned as the weighted average of such bilateral import tari¤s, where the weights are the share of each source country in total countryproduct imports in a base period.<sup>13</sup> Data on bilateral imports used to construct these weights come from the United Nations Commodity Trade Statistics Database (COMTRADE). To account for tari¤ binding, we will further make use of analogous information on bound tari¤ rates.<sup>14</sup> These data also come from TRAINS, but are only available from 1995.

2. Rainfall and irrigation We use the data set on population-weighted rainfall levels by country-year constructed and made available online by Dell et al. (2012). The original source of the historical rainfall data is the Terrestrial Air Temperature and Precipitation: 1900-2006 Gridded Monthly Time Series (Version 1.01), compiled by Kenji Matsuura and Cort Willmott (2007) in conjunction with NASA. This database provides worldwide (terrestrial) monthly precipitation information at 0:5�0:5 degree resolution. Dell et al. (2012) use geospatial software to aggregate these rainfall data to the country-year level, weighting by the
while transactions of males only show a 17% reduction (to USD$207).

The balance data provide additional support for our conjecture that gender-based trust could be an important underlying mechanism. Since agents know the customer’s balance when a transaction is made, there is a risk that financial information is disclosed to others without the client’s consent. Visiting a female agent can be a strategy for women to mitigate this risk since female agents may be less likely to disclose information to other clients, compared to male agents.

# **7. Conclusion**

The research findings have practical implications for financial service providers. While the preference for same gender agents appears to be similar in strength between males and females, the implications vary along gender lines. Since females prefer female agents but only 23% of agents are female, female customers will have to travel farther on average to transact with an agent of their gender. The underrepresentation of female agents may thus represent a barrier to greater financial inclusion, 15
P a g e <u>|</u> **12**

regulations are consistent with purchasing power and urban land costs in different areas of the city. The cost of reserving land for wide streets and parking lots even in areas where most residents don’t own cars and walk in their immediate neighborhood is much higher in a mega-city like Delhi than a secondary town in a backward area. Yet rarely do the regulations, typically established with strong guidance from the State, reflect the costs imposed by rigid adherence to norms across cities or neighborhoods. (See Annex 1 for some illustrative examples from the Ahmedabad by-laws that make it hard to provide market responsive formal housing for low-income groups.) Figures3a and b below illustrate the major impact of these regulations on fastgrowing economically vibrant cities. In Mumbai, regulations have driven down permitted maximum FARs over the years, with the final reduction in 1991 (ironically coinciding with the dismantling of the license raj in other parts of the economy) to a 1.33 Maximum Allowable FAR for the entire greater Mumbai. Nearly all of the population increase over time has been absorbed in slums even as income has increased rapidly. Now 54 percent of the population lives in slums. Mumbai has a much higher share of slum population in spite of being better off. In Ahmedabad, over the period where highly restrictive planning and regulatory policies were pursued, the percentage of the population living in slums grew. (see Box 2). New census information will be very useful to assess the impact of the policy changes that were introduced in Ahmedabad in the 2000 plan.

Figures 4a and b illustrate, showing the household income class distribution for Mumbai and Ahmedabad.

12
||the public distribution system<br>and 0 otherwise.|||||
|---|---|---|---|---|---|
|Household males share|Share of working-age males<br>who are working(aged 15 and<br>over) in the household.|<br>0.177|0.113|0|1|
|Household females share|Share of working-age females<br>who are working(aged 15 and<br>over) in the household.|<br>0.029|0.078|0|1|
|Consumption per capita|Household’spatially deflated<br>monthly consumption per capita<br>indinars|<br>176,981|112,253|0|3,237,427|

# 3.2 Descriptive statistics

In the present analysis, we use the data gathered in the second round of the IHSES-II which is one of the most comprehensive surveys that provides information on household characteristics and living conditions in Iraq. The fieldwork was conducted between January 2012 and February 2013. The final data cover 25,146 households across 18 governorates of Iraq organized into 2,832 clusters for a total of 176,042 individuals. The respondent in each household is usually the head of the household or an authorized family member, although there are also questions on household members’ characteristics. One important feature of these data is the availability of information on household members’ migration history. It is this information that enables us to identify IDPs. These are people who have lived in another governorate – or their current governorate of residence but a different environment (rural or urban) – for an uninterrupted period of six months, and who have moved from that previous place for one of the following reasons:

- Forced displacement: Individuals who have been deported or forced out of their usual place of residence.

- Conventional armed conflict:
, middle (classes 1–8), and secondary schools (classes 1–10, 6–10).<sup>5</sup> Looking at the aggregate sample (pooled across phases), the mean program school size is 455 students; in schools with both girls and boys enrollment, the mean ratio is 1 (gender parity). Program schools have on average 17 teachers and 16 classrooms. The mean student-teacher and student classroom ratios are 27:1 and 29:1, respectively. The majority of program schools is middle level (64%), coeducational (97%), rural (59%), and registered (100%). The phase-wise statistics indicate that mean enrollment is positively associated with length of program participation, this relationship also holds for mean boys and girls enrollment. The number of teachers and classrooms are also positively associated

> 4 The organization is continuing to expand its set of instruments: for example, it is now considering supporting private organizations/individuals that adopt and manage non-functional public schools handed over by the government for this initiative.

> 5 The three higher secondary schools in the program are excluded.

8
# **1. Introduction**

The macroeconomic literature has long been interested in the contribution of infrastructure capital to aggregate productivity and output. Numerous theoretical papers have approached it using an aggregate production function including public capital as an additional input, first in the context of Ramsey-type exogenous growth models (e.g., Arrow and Kurz 1970) and later in endogenous growth models (Barro 1990, Futagami, Morita and Shibata 1993). This analytical literature has grown enormously in the last fifteen years, exploring a multitude of variants of the basic models, such as alternative financing schemes, simultaneous consideration of public capital and productive current spending flows, utility-yielding public capital, or public infrastructure congestion.<sup>1</sup> Quantitative assessments of the contribution of infrastructure are critical for many policy questions – such as the output effects of fiscal policy shocks instrumented through public investment changes (e.g., Leeper, Walker and Yang 2010; Ilzetzki, Mendoza and Végh 2010), or the extent to which public infrastructure investment can be self-financing (Perotti 2004). The empirical literature offering such quantitative assessments took off with the seminal work of Aschauer (1989) on the effects of public infrastructure capital on U.S. total factor productivity. The literature has boomed over the last two decades, with dozens of papers using a large variety of data and empirical methodologies, and with widely contrasting empirical results.<sup>2</sup> For example, Bom and Ligthart (2008) report that in a large set of empirical studies using industrialcountry data in a production function setting, estimates of the output elasticity of public capital range from -0.175 to +0.917.

However, much of the empirical literature on the contribution of infrastructure to aggregate output is subject to major caveats. Studies based on time-series have often ignored the non-stationarity of aggregate output and infrastructure capital, which typically display stochastic trends. This has sometimes led to implausibly high estimates of the productivity of infrastructure, owing to
|**0,0**|**0,0**|
|**Extra-neutral alcohol**|**1**|**1**|**8,3**|**33,3**|**100,0**|
|**Polypropylene bags**|**2**|**1**|**16,7**|**33,3**|**50,0**|
|**Triplex and particle boards**|**1**|**0**|**8,3**|**0,0**|**0,0**|
|**Iron & Steel Products**|**1**|**1**|**8,3**|**33,3**|**100,0**|
|**TOTAL**|**12**|**3**|**100,0**|**100,0**|**25,0**|
|**AEC 24 ALADI**||||||
|**Agricultural products**|**1**|**0**||||
|**TOTAL**|**34**|**9**|||**26,5**|

**Source: Calculations by the authors based on data from the Ministry of Trade. Note: agricultural products appearing in the Andean safeguard are rice and refined vegetable oils.**

# **_F. TWO CASE STUDIES_**

An analysis of the previous sections suggests that it is interesting to look into two sectors more deeply. The first one is the agricultural sector, from which rice was selected for