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26
generally stabilizes over time. This is especially true for domestic value added of intermediates. As to the dynamics for trade in parts and components, the results confirm that there are one‐ year anticipation effects and that the impact of deep agreements persists after the entry into force of the agreements. The broader country‐coverage uncovers that this dynamic is driven by deep PTAs involving North and South countries.

Finally, a concern is that in a world where production is fragmented internationally, GVC trade between two countries is not only affected by their trade agreements but also by the trade agreements signed by any country along the value chain (Noguera, 2012). As deep agreements may have a stronger impact on bilateral GVC trade than shallow agreements, it is well possible that the level of depth of preferential trade agreements signed by third countries along the supply chain could indirectly affect GVC‐related trade between two countries. We build on the approach by Noguera (2012) to control for the indirect effect of deep trade agreements and find that the coefficients of the modified gravity regressions are larger than those of the standard gravity, confirming the existence of indirect effects of signing deep PTAs through third countries.

The rest of the paper is organized as follows. Section 2 discusses the data used in the paper. Section 3 presents the empirical analysis focusing on the impact of PTA depth on GVC integration, while Section 4 focuses on the impact that different sets of provisions in deep trade agreements have on countries with different levels of development. Section 5 presents robustness tests. Concluding remarks follow.

# 2. Data

In this section, we take a first look at the data on the content of trade agreements and present the measures of PTA depth and GVC trade used in the analysis.

## a. Deep trade agreements

In the literature, the effects of PTAs on trade are generally estimated by including a dummy equal to one when two countries are involved in an agreement (Limão, 2016). In our econometric analysis, we also estimate the coefficient of a dummy for PTAs, but we take a step forward by estimating the effects of deep trade agreements using three new measures of depth. The data on the content of deep agreements come from a new database at the World Bank that covers 260 6
# **1. Introduction**

Accurate measurement of food consumption is key to the assessment and monitoring of multiple welfare dimensions, including food security, nutrition, health, and poverty. These household welfare measures are, in turn, central to the design of many development projects and policies aimed at improving well-being. It is therefore critically important for effective policy design and targeting to ensure that food consumption is collected with minimal measurement error.

With economies developing, and consumption patterns rapidly changing across the developing world, some traditional survey modules are becoming less informative over time. One recent trend is the growth in the popularity of meals prepared, packaged, and consumed outside the home (in commercial establishments and locations such as schools, work, restaurants or on the street) in contrast to the more traditional meals prepared and consumed within the home. As a result, food consumed outside the house is taking up an ever-growing share of households’ food budget. For example, the percentage of households reporting consuming meals outside the home increased from 20 to 46 percent between 1981 and 1998 in the Arab Republic of Egypt, from 23 to 39 percent between 1994 and 2010 in India, and from 42.3 to72.4 percent in Colombia (Smith, 2015; Smith et al., 2014; DANE). In China, household per-capita expenditure in FAFH rose at an average annual rate 9.5 percent from 2002 to 2011, and the share of FAFH in total food expenditure rose from 18.2 to 21.5 percent (You, 2014).

However, food consumption survey modules that were designed with more traditional eating patterns in mind may be failing to appropriately measure this growing segment of food consumption, with important consequences for welfare measurement. In India, Smith (2015) find that the increase in undernourishment at the time of falling poverty rates can be partly explained by lack of measurement of calorie consumption from FAFH. Borlizzi and Cafiero (2017) show that failure to account for children’s school meal consumption in Brazil results in an overestimation of inequality in calorie consumption. Farfan, Genoni, and Vakis (2017) find that total poverty rates are 16 percent lower (or almost 6 percentage points) while extreme poverty rates are 18 percent higher (or 1 percentage point) when FAFH is taken into account. The impacts on the poverty gap and severity of poverty indicators are even larger. Furthermore, the direction of the change cannot be established ex-ante. Despite the importance of FAFH for welfare measurement, no study has rigorously tested the accuracy of different methods for collecting FAFH consumption information. This lack of evidence is highlighted in the methodological guidelines on food consumption recently endorsed by the UN Statistical Commission, which call for more research on the collection of FAFH (FAO and WB, 2018). Our study is the first to undertake this effort.

2
|Akita<br>Iwate|Akitakomachi<br>Akitakomachi|21,741<br>21,025|21,859<br>20,877|
|---|---|---|---|
|Yamagata|Hananomai|20,304|20,394|
|Fukui|Fukuhikari||20,501|
|Tottori|Yamahikari||20,558|
|Yamaguchi|Yamahikari||20,345|
|Shiga<br>Hyogo|Nihonbare<br>Nihonbare||20,109<br>20,125|
|Okayama<br>Gifu<br>Niigata<br>Ishikawa<br>Niigata<br>Aomori<br>Aomori<br>Niigata<br>Niigata<br>Yamaguchi<br>Okayaina<br>Hokkaido<br>Aomori<br>Hokkaido<br>Haokkaido<br>Standard|Asahi<br>_<br>Hatsushimo<br>Yukinosei<br>Notohikari<br>Echigowase<br>Tsugaruotome<br>Mutsukaori<br>Niigatawase<br>Todorokiwase<br>Yamahoushi<br>Akebono<br>Kirara-397<br>Mutsuhomare<br>Soraiku-125-Go<br>Yukihikari<br>Rice|<br>20,819<br>20,863<br>20,357<br>20,371<br>20,119<br>19,981<br>19,501<br>19,059<br>18,565<br>18,588<br>16,392|21,222<br>21,068<br>21,031<br>21,007<br>20,804<br>20,393<br>20,413<br>20,150<br>19,946<br>19,871<br>19,901<br>19,652<br>18,803<br>18,797<br>18,767<br>16,392|

Source: Japanese Ministry of Agriculture and Fisheries.

command the highest price (around 21000-25000 yen per 60kg). Further, _Koshihikari-brand,_ rice produced in certain areas command much higher prices than those produced in other areas. _Koshihikari_ rice produced in _Uonuma,_ a tiny city in Niigata Prefecture, is considered to be the best of the best. Table 5 shows the difference in such rices in 1996. As shown in the table, _Koshihikari_ rice produced in Niigata Prefecture is 55 percent more expensive than standard government rice, and the price of _Uonuma-Koshihikari_ rice is almost double of that of standard 12
O.A.M.D.G.

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# Figure 2.1: INDIA, evaluation of performance of the ICA method

I. Kernel<sup>1</sup> estimates of output and input densities in the complete case and in the sample after imputing missing values by the ICA method <!-- Start of picture text --> A. Sales (log) B. Materials (log)<br>0 5 10 15 20 25 5 10 15 20 25<br>log-Sales log-Materials<br>Complete case ICA method Complete case ICA method<br>C. Capital stock (log) D. Employment (log)<br>0 5 10 15 20 6 8 10 12 14 16<br>log-Capital log-Employment<br>Complete case ICA method Complete case ICA method<br>.25 .25<br>.2 .2<br>.15 .15<br>Density Density<br>.1 .1<br>.05 .05<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> II. Table of descriptive statistics and tests of equality of distributions of output and inputs in the complete case and in the sample with imputation by the ICA method

|||**# Obs.**<br>**(# imputed)**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**One-sample K-S**<br>**Test (p-value)**|
|---|---|---|---|---|---|---|---|
|Sales (log)|Complete case|5841|12.08|2.30|1.30|22.79|0.000|
||ICA meth.|5935 (94)|12.07|2.29|1.30|22.79|0.000|
|Materials (log)|Complete case|5597|11.44|2.30|2.94|22.20|0.000|
||ICA meth.|5933 (336)|11.40|2.28|2.94|22.20|0.000|
|Capital (log)|Complete case|4555|10.31|2.11|1.85|20.73|0.000|
||ICA meth.|5918 (1363)|10.28|2.10|1.85|20.73|0.000|
|Empl (log)|Complete case|6164|10.82|1.33|6.54|16.16|0.000|
||ICA meth.|6321 (157)|10.82|1.34|6.54|16.16|0.000|

Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values. The null hypothesis of the one-sample Kolmogorov-Smirnov Test is that the cumulative distribution differs from the hypothesized theoretical normal distribution.

Source: Authors’ estimations with ICSs data.

21
# **Introduction**

Construction is a $1.7 trillion industry worldwide, amounting to between 5 and 7 percent of GDP in most countries. It accounts for a significant part of global gross capital formation –a little under one-third.<sup>2</sup> The sector’s role in economic development is undeniable –housing, roads, utility networks, schools and clinics are all built assets.

Because of construction’s central role in development, corruption in the sector can be especially harmful. In particular, corruption that leads to poor quality construction or which supports an environment of poor project selection and insufficient maintenance can significantly reduce the economic return to investments, and carry high human costs in terms of injury and death. And while there is an undoubted role for the government in the sector both as customer and regulator, there is also a serious concern with the capacity of many governments to adequately fulfill these roles. This paper discusses what we know about the nature and extent of corruption in the construction industry and some approaches to governance and project design which may help to limit the development impact of that corruption.

# **The Global Construction Industry**

Construction involves large, complex, non-standard activities in which quality can be very hard to assess. Projects can involve a multitude of players –the client, consultant engineers and architects, financiers, insurers, a main contractor and scores of subcontractors (see Figure 1). Large construction projects frequently attract international contractors, although much of the work is subcontracted to local firms.

The international construction industry is dominated by comparatively few very large firms, with Bechtel, Skanska AB and Taisei Corporation, all active in developing markets, having combined annual revenues of over $50 billion, for example (Datamonitor, 2006). In turn, within most developing countries, the domestic construction industry is made up of a few larger firms, often state-owned, and a large number of small firms. In Vietnam, the 900 state-owned construction firms account for 60 percent of sector turnover, with nearly 7,000 private firms accounting for the rest of the market. About half of all construction enterprises employ fewer than fifty workers, with a considerable percentage of the labor force informal (Copplestone, 2006).

The sector is closely intertwined with government. A considerable portion of public investment goes to construction –not least, governments remain the dominant provider of infrastructure services worldwide, accounting for 78 percent of investment 1984-2003 (Estache, 2006). Government investment in road transport alone can account for between 2 to as high as 3.5 percent of GDP.<sup>3</sup> Governments frequently own construction firms –as in Vietnam-- or have construction departments. Governments also have a significant role

> 2 WDI report Gross Capital Formation globally at 21 percent of GDP.  Construction share in GDP is from UNSTATS.

> 3 http://www.worldbank.org/transport/whytsimp.htm.

1
25 prices of these sectors. The price changes,<sup>and the resulting changes in the</sup> sectoral value added, are then translated<sup>into changes in the wage incomes and</sup> food costs of the socioeconomic groups.<sup>Unfortunately this latter link is not</sup> well described in their paper. This method<sup>allows for the use of an econometric</sup> approach on the macro side. It also may<sup>permit the use of existing macromodels,</sup> as long as the important intermediate<sup>variables are endogenous.</sup> e. Microdata Analysis There are a number of methodologies that<sup>examine household microdata.</sup> Some of these can be used in combination<sup>with the previously described method,</sup> which links a macromodel with a micro analysis.<sup>Kanbur (1987a, 1987b) suggests</sup> the following approach. First, using a household survey, create<sup>a poverty</sup> profile that is disaggregated by socioeconomic<sup>groups that are relevant to the</sup> policy instruments (for example, groups involved<sup>in tradable vs. non-tradable</sup> production to study a devaluation). The poverty<sup>index should be decomposable</sup> by groups so that the poverty indices can be<sup>aggregated into total poverty, and</sup> the index should be sensitive to the "depth"<sup>of poverty within the poor.2 5</sup> Next, assuming that policies of expenditure reduction<sup>or expenditure switching feed</sup> into individual incomes either additively or<sup>multiplicatively, Kanbur calculates</sup> closed form solutions for the elasticities of sectoral<sup>poverty with respect to</sup> income (and relative price) changes. Using a macroeconomic<sup>projection of the</sup> impact of policies on sectoral incomes, these<sup>elasticities can then be used to</sup> estimate the effects on poverty. The analysis<sup>is static and only appropriate</sup> for small changes in income, and the feedback<sup>of poverty changes back to the</sup> economy are not included, making this a partial<sup>equilibrium analysis.</sup> This 2 5There is a growing concensus on the use of the Foster, Greer, Thorbecke (FGT) poverty measure. See Kanbur (1987a, 1987b).
<!-- Start of picture text --> 2<br>ae<br>=c sae te pe ie<br>Cc er oe —t a on<br>on a a ve ~<br>ae. / pa<br>I > al 7 4<br>5<br>& oe nas “ —*- rare— a<br>— ae<br>Nn<br>1998 2000 2002 2004 year 2006 2008 2010 2012<br>—+— Grade 7 —-— Grades<br>—+—. Grade 9 —-=— Grade 10<br>—+——. Grade 11 —-—+— Grade 12<br><!-- End of picture text -->
<!-- Start of picture text --> variables, based on their respective variances. A,, allows for contemporaneous<br>relations amongst these variables.<br>The matrix A, is allowed to vary from country to country. We use the<br>tax revenue elasticities shown in Table 1 to obtain heterogeneous values of<br>A,, (2,3). However, in order to maximize the estimation’s sample size, we do<br>not allow heterogeneity in the other values of the A matrix.<br>A reduced-form version of (2), where both sides of this equation are mul-<br>tiplied by A>', can be estimated via OLS. This provides us with estimates<br>of A; *C; and of the error terms €,4 = A;*Bun4.<br>The estimated variance-covariance matrix of €,, gives J (J — 1) equations<br>for the estimation of the unknown parameters in A, and B:<br>E [Ent@,4] = 4,BB’ (A;1)’.<br>There are J (J — 1) unknowns in A,, and an additional J in B,,. J restrictions<br>are required on the elements of A, to estimate the remaining elements. To<br>summarize the structural restrictions, let<br>1 arg yg<br>A, = FAgy Aty1 "|1 .<br>The parameters of greatest interest are ag, and ayy, as they determine the<br>effects of government expenditure and taxes on output. We leave them un-<br>restricted. We use our tax elasticity estimates to determine a,-. Following<br>Blanchard and Perotti (2002) we also note that government purchases (as<br>opposed to transfers) have no automatically stabilizing components and set<br>dyg = 0. (In the PVAR estimation of this section, we use data on government<br>consumption as g/'.)<br>16<br><!-- End of picture text -->
Policy Research Working Paper 9875

# **Abstract**

Although research has established the importance of state capacity in economic development, less is known about how to build that capacity and the role of external partners in the process. This paper estimates the impact of a typical development project designed to build state capacity in a low-income country. Specifically, it evaluates a multilateral development bank project in Tanzania, which incentivized investments in local state capacity by offering grants conditional on institutional performance scores. The paper uses a difference-in-differences methodology to estimate the project impact, comparing outcomes between 18 project and 22 non-project local governments over 2016–18. Outcomes were measured through two rounds of primary surveys of nearly 500 local government officials and nearly 3,000 households. Over the course of the project, measured state capacity improved in project areas, but due to comparable gains in non-project areas, the project’s value-added to change in state capacity is estimated to be zero across all the dozens of relevant variables in the surveys. The data suggest that state capacity is evolving in Tanzania through endogenous changes in trust and legitimacy in the country rather than from financial incentives offered by external partners.

This paper is a product of the Development Impact Evaluation 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 vdimaro@worldbank.org, devans@cgdev.org, skhemani@worldbank.org, and tscot@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