rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9779

# **Abstract**

Estimates of average per capita consumption and income from national accounts differ substantially from corresponding measures of consumption and income from household surveys. Using a new compilation of more than 2,000 household surveys matched to national accounts data, this study finds that the gaps between the data sources are larger and more robust than previously established. Means of household consumption estimated from surveys are, on average, 20 percent lower than corresponding means from national accounts. The gap with gross domestic product per capita is nearly 50 percent. The gaps have increased in recent decades and are largest in middle-income countries, where annualized growth rates for consumption surveys are systematically lower than national accounts growth rates. The paper shows that the gaps in measures across these two sources have implications for assessments of economic growth, poverty, and inequality. The study finds that typical survey measures of consumption and income may exaggerate poverty reduction and underestimate inequality.

This paper is a product of the Development Data 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/prwp. The authors may be contacted at userajuddin@worldbank.org, djolliffe@worldbank.org, or espen.prydz@gmail.com.

_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
- Net market income subtracts direct taxes, Personal Income Tax (PIT) and employee contributions to the Unemployment Insurance fund (UIF) and Skills Development Fund from market income.

- Disposable income is constructed by adding direct cash transfers to net market income. (This measure is closest to household consumption on which the Gini coefficient in South Africa is usually constructed.) In South Africa direct cash transfers include, for example, the old age, child, disability and foster grants.

- Post fiscal income adds the impact of indirect taxes and subsidies to disposable income. In South Africa, indirect taxes included in this analysis includes the VAT, excises on alcohol and tobacco, and the fuel level.

- Final income adds in-kind benefits such as health and education to post fiscal income.

**2. Progressivity and Redistributive Effect: Basic Concepts and Definitions**

One common way to measure the progressivity of a tax (transfer)<sup>18</sup> is by comparing the cumulative distribution (also known as cumulative concentration shares) of their burden (benefit) with the cumulative distribution of market income. This is known as the tax (transfer) redistribution approach.<sup>19</sup> In the case of spending, it is also useful to compare the cumulative distribution of benefits with the cumulative shares of total population. To illustrate, Figure 4 presents a Lorenz curve where the population is ranked along the horizontal axis using market (sometimes called original or reference) income, and the cumulative shares of taxes paid or transfers received is plotted along the vertical axis. The latter are concentration curves.

Figure 4. Diagrammatic representation of the progressivity of taxes and transfers <!-- Start of picture text --> 1<br>transfer: progressive in absolute terms (pro-poor)<br>45 degree line: per capita tax/<br>transfer equal for everyone (poll<br>tax/transfer)<br>tax: regressive<br>transfer: progressive<br>tax: progressive<br>transfer: regressive<br>pre-tax/pre-transfer income Lorenz curve<br>transfer or tax: neutral (flat)<br>0 Cumulative proportion of population (ordered by pre-tax/pre-transfers income) 1<br>Source: Lustig and Higgins (2013).<br> Cumulative proportion of income, taxes and<br><!-- End of picture text --> The classification of taxes (transfers) in the remainder of the paper uses the following definitions when referring to whether taxes or government spending are progressive or not:<sup>20</sup>

> the auto-consumption component. Market income does include the imputed value of owner’s occupied housing, though. See Table 9 for an illustration of the average level of income for each of these concepts across the distribution.

> 18 Note that the words “tax” and “transfer” apply to any form of government revenue and spending, respectively.  For example, government spending on education and health are “transfers.”

> 19 See, for example, Duclos and Araar (2006), p. 136.

> 20 Based on Duclos and Araar (2006) and Lustig (2014).

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# **1 Introduction**

Given the economic importance of services trade, surprisingly little is known about trade costs in services. Transparent border measures like tariffs are less important in services than relatively opaque regulations that affect trade, for example, in professional and financial services. In addition, transport costs do not take the form of well-defined freight rates but hard-to-measure forms such as the costs of electronically delivering business services. The absence of explicit measures of either protection or transport costs suggests that an analytical model of trade flows can help reveal information about barriers to services trade.

We use the structural gravity model and a newly constructed data set on production and trade in services to provide much new information about inferred services trade barriers. The paper makes three principal contributions. First, we estimate a rich pattern of border barriers at an unprecedented level of detail, varying by country, sector and over time. Such elaborate estimates of border barriers are predicated on the availability of services output data at a correspondingly disaggregated level. Second, since such services output data are not typically available outside the developed country realm, we develop a projection method that can, in principle, generate the required information, thereby facilitating the estimation of trade costs in services when key underlying data are missing or suspect. Third, the structural gravity theory employed for this procedure also helps us decompose border barriers. The empirical results are of interest in their own right as they shed light on the role of domestic institutions, geography, size, and digital infrastructure as determinants of border barriers and international trade of services.

We start by estimating a gravity model, described in Section 2, for 12 service sectors and 28 countries. For that purpose, we construct of a database combining information on services trade and production, respectively, covering the period 2000 to 2007. The broad sectoral and geographical coverage as well as the inclusion of intra-national trade flows sets this data set apart from previous gravity estimations.<sup>1</sup> The data are described in Section 3. The

> 1The gravity model has previously been used to explain the pattern of services trade. For instance, to

1
- Hodge, G. A. and C. Greve (2007), _Public Private Partnerships: An International Performance Review”,_ **Public Administration Review** , May-June, 545-558

- Irwin, T.C. (2007) _Government Guarantees: Allocating and Valuing Risk in Privately Financed Infrastructure Projects, Directions in Development_ , The World Bank

- Irwin, T. (2003), _Public Money for Private Infrastructure: Deciding when to Offer Guarantees, Output Based Subsidies, and Other fiscal Support_ , World bank Working Paper No 10

- Koopmans, J.F.M. (2005), “ _The formation of public-privatge partnerships: Lessons from nine transport infrastructure projects in the Netherlands_ ”, **Public Administration,** Vol 83, 1, 135-137

- Næss; P., B. Flyvbjerg and S. Buhl (2006), _Do Road Planners Produce More 'Honest Numbers' than Rail Planners? An Analysis of Accuracy in Road-traffic Forecasts in Cities versus Peripheral Regions."_ **Transport Reviews** , vol. 26, no. 5, September 2006, pp. 537-555.

- Trujillo, L. E. Quinet and A. Estache (2002) _“Dealing with Demand Forecasting Games in Transport Privatization”,_ **Transport Policy** , Vol. 9, No4, October, 325-33

- World Bank (2007), _Global Financial Markets_

- Yescombe, E.R. (2007), _Public-private partnerships: Principles of Policy and Finance,_ New York, Elsevier,

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# V. SUAKhRY AND CONCLUSION

This school survey and multi-level analysis were undertaken as part of a larger joint Ministry of Education and Culture-World Bank study of the education sector in Zimbabwe. The study had two purposes. First, it was designed to contribute to a policy dialogue on how to improve student achievement in secondary schools by identifying factors that explain variation in Form II exam scores. Second, it was designed to identify a sample of schools that were outliers in terms of student achievement on national exams, i.e. they performed better or worse than what would be expected given students prior academic achievement and certain basic school resource inputs such as textbooks and teachersV This guided the selection of a sample of schools for **site visits** to g&ther additional information on high and low performing schools (such as school management and expenditures) that may explain the differences Ln achievement.

The development and implementation of the survey instruments and the analysis of the data were completed within a very short period of time (only three months) which provided information rapidly for the sector study. However, the time constraint limLted the range of data collected; multicollLnearLty among the measured varLables further reduced the number of varLables Lncluded Ln the statistical models to assess thair lnfluence on student achievement. These limitations notwithstandLng, the study produced several noteworthy findings and id.ntLfied areas for further research.

The results of the sample survey showed, first, that students Ln high-fee paylng (trust) schools, former group A (elite government) schools, and mLsLon schools have higher levels of English and Maths achLevement than students Ln government groups B (less well-endowed government) schools and those in district council schools. Second, a substantial share of the variation in student achievement among students was attributable to the school school ls considered an outlier if the standardized residual is two or **sA** more standard devLatLons from the mean of 0.

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|**Tool name:**|**Augmented CGE Model with the Representative Household Approach**|
|---|---|
|**What is it?**|This technique is based on a CGE model with representative households that are linked to a household<br>module.|
|**What can it be used for?**|RH models can be used to analyze the impact of macroeconomic policy and external shocks on income<br>distribution, employment, and poverty.|
|**What does it tell you?**|RH models allow for a forecast of welfare measures and poverty outcomes consistent with a set of<br>macroeconomic policies in the context of a general equilibrium model.|
|**Complementary tools:**|**–**|
|**Key elements:**|The key features of the RH approach are, first, a CGE model that incorporates markets for factors and<br>commodities and their links to the rest of the economy and that generates equilibrium values for<br>employment, wages, and commodity prices, as well as “extended” functional distributions (that is,<br>labor differentiated by skill, education, gender, region, and sector of employment) and, second, a<br>mapping from the extended functional distribution into the “size” distribution (the distribution of<br>income across different households). In this approach, the representative households that appear in the<br>CGE (corresponding to aggregates or averages of groups of households) play a crucial role: the “size”<br>distribution is generated by feeding data on the simulated outcomes for the representative households<br>into a separate module that contains additional information about each household.|
|**Data/information:**<br>**s**|RH models require a SAM and distributional data describing the representative household groups or,<br>more specifically, a household survey.|
|**Time:**<br>**ement**|Only a few days to generate a base solution if data and skills are available. Between six months and a<br>year to collect data and work with the simulations.|
|**Skills:**<br>**quir**|Experienced modelers with substantial prior exposure to CGE models are required.|
|**Supporting**<br>**software:**<br>**Re**|Excel, EViews, Gauss.|
|**Financial cost:**|US$25,000–75,000 depending on the data that exist.|
|**Limitations:**|In the absence of a CGE model to feed into the RH module, the model is data intensive and difficult to<br>master.|
|**References and**<br>**applications:**|�<br>For an overview, see Löfgren, Robinson, and El-Said (2003).<br>�<br>Robilliard, Bourguignon, and Robinson (2001) on Indonesia.<br>�<br>Coady and Harris (2001) on Mexico.<br>�<br>Löfgren,Harris,and Robinson(2002).|

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|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings<br>|
|---|---|---|---|---|---|
|Becerra-Tomás et<br>al. (2021)|SRM of 8 studies (5<br>prospective, 3 cross-<br>sectional), 1 in China, rest<br>in HICs|(cross-sectional<br>n=72,559; cohort<br>7559 cases|Nut intake|T2DM|Nonsignifcant association between<br>total nut consumption and T2DM|
|Quan et al. (2022)|SRM of 9 cohort studies (7<br>articles), 1 from the Islamic<br>Republic of Iran, rest from<br>HICs|Adults 25-75<br>years<br>(n=383 211)|Potato intake|T2DM|Association found between potato<br>intake and risk of T2DM (RR 1.13, 95%<br>CI 1.02, 1.26)|
|Yang et al. (2020)|Meta analysis of 28 cohort<br>studies, 11 from the United<br>States, 10 from Europe, 7<br>from Asia|Total meat<br>consumption<br>(n=386,496)<br>Red meat<br>(n=663,144)|Meat and fsh intake,<br>highest vs lowest intake<br>categories|T2DM|A linear dose-response relationship<br>between total meat (RR 1.33; 95% CI<br>1.16, 1.52), red meat (RR 1.22; 95% CI<br>1.16, 1.28) and processed meat (RR<br>1.25; 95% CI 1.13-1.37) intakes and<br>T2DM risk. In addition, a non-linear<br>relationship of intake of processed<br>meat with risk of T2DM was detected.|
|Zhou, Tian, and Jia<br>(2012)|Meta-analysis of 13 cohort<br>studies, 1 from China, rest<br>from HICs|Adults<br>(n=367 757)|Fish consumption(9<br>studies, n=367,757), high<br>vs low intake|T2DM|Fish intake is weakly associated with<br>T2DM (RR 1.15; 95% CI 1.05, 1.27),<br>based on 7 cohorts without<br>heterogeneity|
|Micha et al. (2010)|SRM of 20 studies (17<br>prospective cohort, 3 case-<br>control), 1 from China, rest<br>from HICs|Healthy adults<br>(n= 1218 380)|Red meat(5 studies) and<br>processed meat(7<br>studies) consumption|T2DM|No association between red meat<br>intake and T2DM (RR 1.16; 95% CI<br>0.92, 1.46). Processed meat intake<br>associated with higher risk of T2DM<br>(RR 1.19;1.11,1.27)|
|Morze et al. (2020)|SRM of 113 reports (47 for<br>this update), 1 in the Islamic<br>Republic of Iran, 1 in Israel,<br>5 in China|n=3,277,684|Diet qualityassessed by<br>the HEI, AHEI, DASH<br>score|T2DM (16 studies)|Diets of the highest quality were<br>inversely associated with risk of T2DM<br>(RR 0.81, 95% CI 0.78, 0.85)|
|Uloko et al. (2018)|SRM of 23 studies (12 cross<br>sectional, 7 cross sectional<br>perspective, 4 prospective),<br>Nigeria|n=14,650|Unhealthy dietary habits<br>(not stated how this was<br>defned)|T2DM|Unhealthy dietary habits are a risk<br>factor for T2DM (OR 8.0; 95% CI 5.4,<br>10.5)|
|Maghsoudi et al.<br>(2016)|SRM of 10 cohort studies, 1<br>from China, rest from HICs|n= 404 528|Healthy and unhealthy<br>dietary habits|T2DM|Adherence to healthy dietary patterns<br>associated with lower risk of T2DM (RR<br>0.86; 95 % CI 0.82, 0.90); unhealthy<br>dietary patterns adversely afected<br>T2DM risk (RR 1.30; 95 % CI 1.18,<br>1.43)|

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While in West Bengal and Odisha, STs are better than CTs (though in West Bengal, this could be due to the fact that STs tend to be larger towns), in Jharkhand, CTs do better than STs. Bihar is again an outlier, where there is no clear pattern. Indeed, among the three sites, the ST, Jhanjharpur NP has levels of household electricity access much lower than in Satghara CT.

# **_Education and Health_**

As seen in the discussion on new economic activities, the state of health and education service delivery has led to the growth of a vibrant private provision economy even in these smaller settlements. This is aided by the rise of transport providers and better roads. However, there are public hospitals at two of our sites, viz. Jhanjharpur NP and Bishugarh-Cherra. This aspect was not investigated in detail during our fieldwork.

# **VII. Key Findings**

# **_Economic Transformation_**

This study is based on fieldwork at multiple locations in four states. These are Satghara, a census town, Simri, a large village, Jhanjharpur, a statutory town and block headquarters, all in the Madhubani district of Bihar; Erein, a census town near Bhadrak, a small class I district headquarters in Odisha; Bishnugarh and Cherra, a paired set of co-located census towns in Hazaribagh district of Jharkhand and Gopalpur, a census town in, Dakshin Dinajpur district of West Bengal. This spread allows us to ask how location-specific are the processes associated with urbanization and what commonalities, if any, one can notice in these sites.

A number of new activities are indeed common to many of these sites. These include activities connected to construction, transport – both passenger and freight, education, health and cellphones. In addition, better transport infrastructure, for instance, makes it possible for traditional activities like curd-making, which earlier were limited to the local market to now serve a much larger regional market, making it possible for more people to be employed in such activities. Large economic activities, like medium manufacturing appear not to thrive in these areas, in major part, due to the lack of reliable electricity supply, which would increase the cost considerably, by necessitating self-provision.

However, much of the economic activity of these census towns remains rather “ordinary” consisting mainly of non-tradable services and commerce. They are the sites that provide a wide variety of services to a growing rural market which is growing more able to afford them and access them, both physically in terms of infrastructure and socially, in terms of cultural mores. This banality of urbanization, its commonplace ubiquity and non-remarkable nature of transformation masks a very important character of this transformation, which is its resilience and permanence. Since it is not based on any major directed external agency (except perhaps Erein in our sites), the process is organic and can be expected to continue, with different degrees of vitality at different sites.

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- Spaull, Nicholas, and Stephen Taylor. 2015. “Access to what? Creating a composite measure of educational quantity and educational quality for 11 African countries.” _Comparative Education Review_ 59(1): 133-165.

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UNESCO. 2000. _<u>The Dakar Framework for Action</u>_ .

United Nations. 2000. _<u>United Nations Millennium Declaration</u>_

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Watkins, Kevin. 2000. _<u>The Oxfam Education Report</u>_ . Oxford: Oxfam.

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- Zenebe Gebre, Tihtina. 2019. “Free primary education, timing of fertility, and total fertility.” _The World Bank Economic Review_ 34(3):730-748.

- Zuilkowski <mark>, Stephanie Simmons, Benjamin Piper, Salome Ong’ele, and Onesmus Kiminza. 2018. “Parents, quality, and school choice: why parents in Nairobi choose low-cost private schools over public schools in Kenya’s free primary education era.”</mark> _<mark>Oxford Review of Education</mark>_ <mark>44(2): 258-274.</mark>

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