rafmacalaba/datause-encoder-data huggingface dataset:
generating the effects shown by NQ where the OLS is modestly negative and the IV effect is positive, yet food aid has no causal effect on conflict in the true DGP.

Figure A19 demonstrates how different relationships between ENSO and conflict affect the key parameter estimates. In the linear model, if the ENSO variable is not included as a control, the estimated coefficient of aid on conflict is positive, even though we have included year and country fixed effects and have assumed that aid is related to conflict only because more aid is sent to places that are at higher risk for conflict. Including the ENSO control does lead to a distribution of the coefficient that is centered around zero (the true causal effect), but only in the linear model. Country and year fixed effects would also have solved this problem, because across countries within a year, the influence of ENSO is constant. In the heterogeneous model, adding the ENSO control removes some of the upward bias, but not all of it, because the time trends in conflict differ exactly on the dimension in which aid is endogenously allocated to countries by conflict experience in a way that is not captured by constant linear control for ENSO.

These simulations highlight two important facts:

- i. Unless every pathway between wheat production and conflict is included as a control, the relationship between aid and conflict is likely to be biased even if the pathway only varies at the year or country level.

- ii. Even if a confounding pathway can be measured and included as a control, including it as a linear control may not eliminate the bias, because heterogeneous time trends and country responses to those trends imply that differencing out the average effects will still leave nonlinear trends in residuals of the first stage that are potentially correlated with the error term of the reduced form.

Given the substantial differences in conflict experience over time and across countries (particularly across countries that do or do not regularly receive aid), it is hard to assure that all sources of the heterogeneity have been controlled for appropriately, particularly given that the sources of this heterogeneity are unknown.

We illustrated this problem using ENSO data. But other examples exist of potential threats to the exclusion restriction NQ assume. For example, policies that affect relative food prices may influence both incentives to plant wheat in the US and conflict incidence 68
WPS3817

# **Unpackaging Demand for Water Service Quality: Evidence from Conjoint Surveys in Sri Lanka**

**Jui-Chen Yang, Subhrendu K. Pattanayak, F. Reed Jonson, Carol Mansfield,**

**Caroline van den Berg, and Kelly Jones**<sup>♠</sup>

### Abstract

_In the early 2000s, the Government of Sri Lanka considered engaging private sector operators to manage water and sewerage services in two separate service areas: one in the town of Negombo (north of Colombo), and one stretching along the coastal strip (south from Colombo) from the towns of Kalutara to Galle. Since then, the government has abandoned the idea of setting up a public-private partnership in these two areas. This paper is part of a series of investigations to determine how these pilot private sector transactions (forming part of the overall water sector reform strategy) could be designed in such a manner that they would benefit the poor._ _This paper describes the results of a conjoint survey evaluating the factors that drive customer demand for alternative water supply and sanitation services in Sri Lanka. We demonstrate how conjoint surveys can be used to un-package household demand for attributes of urban services and improve the design of infrastructure policies. We present conjoint surveys as a tool for field experiments and source of valuable empirical data. In our study of three coastal towns in southwestern Sri Lanka the conjoint survey allows us to compare household preferences for four water supply attributes – price, quantity, safety, and reliability. Sub-populations of poor and non-poor households are examined to determine if demand is heterogeneous. Our case study suggests that households care about service quality (not just price). In general, we find that households have diverse preferences in terms of quantity, safety, and service options, but not with regard to hours of supply. In particular we find that the poor have lower ability to tradeoff income for services, a finding that has significant equity implications in terms of allocating scarce public services and achieving universal water access._

## World Bank Policy Research Working Paper 3817, January 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ♠ Subhrendu K. Pattanayak is a Fellow, RTI International (subrendu@rti.org). Jui-Chen Yang and Kelly Jones are Associate Economists at RTI International. Reed Johnson is a Senior Fellow at RTI International. Caroline van den Berg is a Senior Economist in the Energy and Water Department of the World Bank. Carol Mansfield is a Senior Economist at RTI International.
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**_65_**
# **Box 2 explains the robustness checks conducted using EU-SILC and matched administrative tax data for measuring impacts.**

|Box 2. Robustness Checks – Using EU-SILC and Matched Administrative tax data and EU-SILC to measure<br>Impacts.|
|---|
|**In a related study, Robayo-Abril, Balaban, and Wronski (2024) utilize statistical data matching techniques to**|
|**align income data from the 2020 administrative tax dataset with the 2021 EU-SILC, referencing the income**<br>**year 2020.**The aim is twofold: to assess tax regulation adherence and to examine possible overestimations in the<br>proportion of individuals earning the minimum wage, as reported in tax records. Their findings highlight a notable|
|disparity in the prevalence of minimum wage earners when comparing data from EU-SILC and tax records.|
|**We explore using linked survey administrative tax data, which could provide valuable insights for several**<br>**reasons**. On the positive side, this approach presents comprehensive coverage of unemployment, capturing the entire|
|labor force and enabling the estimation of unemployment rates, albeit differing from the LFS approach, as the EUSILC|
|is not designed to measure labor market status. Additionally, it considers both employed and informal sector workers,<br>providing a more holistic perspective. The inclusion of income poverty data further enriches the scope of findings.|
|Moreover, this method accounts for a diverse range of worker characteristics, including observable factors like|
|education levels and occupations, allowing for a more refined characterization of the potentially affected group.|
|However, the share of minimum wage workers earning between the current and the proposed minimum wage in the<br>EUSILC is tiny, significantly limiting the sample size for this analysis. The EU-SILC measures income annually.|
|Respondents report their income in a previous calendar year. EU-SILC 2021 provides information on annual income<br>in 2020. The sample includes 5,519 full-time employees who worked for 12 months last year. If we simulate the<br>inflation-linked – adjustment based on the EU-SILC data, the increase in minimum wage affects only 1.31% of full-|
|time employees<sup>33</sup>. The number of affected employees is 72., which makes any investigation of the profile of the impact|
|impossible. Given the potential for more significant standard errors in the estimates, this study does not report these<br>estimates.|

> 33 The share of affected employees is lower firstly because of lower inflation in 2021 than in 2022 (5.1% vs. 13.8%). Moreover, the share of minimum wage earners in the EU-SILC is lower than in the administrative tax data (Robayo-Abril, Balaban and Wronski., 2024).

44
this context, understanding what role urban agglomeration plays in firms’ undertaking product and process innovations as well as investing in R&D is very relevant.

The remainder of this paper is organized as follows. The next section introduces the data. Section 3 describes the spatial distribution of innovation activities across developing Asian countries. Section 4 describes the empirical approach taken and presents the main results regarding agglomeration effects and firms’ innovation-related activities, followed by robustness tests and various extensions. Section 5 investigates the colocation of top university and firms as a channel through which agglomeration effects may be working. The final section concludes.

# **2. Data**

This paper’s analysis is based on a unique dataset that maps over 20,000 firms to nearly 500 cities across 25 developing economies in Asia. The firm data comes from the WBES conducted from 2012 to 2016.<sup>7</sup> The city data was developed using nighttime lights (NTL) satellite imagery, LandScan grid population data and other data sources.

# **2.1. Data on Firm Innovation**

WBES are firm-level surveys conducted in developing economies. Each survey consists of a cross-sectional representative sample of private firms mainly from an economy’s formal sector. Those in the agriculture sector and all state-owned enterprises are excluded. Surveyed firms are selected through stratified random sampling, based on the sector of activity and firm size. Since 2002, the data have been collected from face-to-face interviews with high-level managers or company owners with measures taken to ensure high quality of surveys.<sup>8</sup> The surveys cover a wide range of topics, with some of them country specific. A harmonized version of the WBES data that extracts and standardizes common information from each country is used. Apart from basic information on sample firms —such as number of employees, age, sector, and foreign direct investment (FDI) share—the surveys collect information about firms’ innovation-related activities through the following questions: (i) During the last 3 years, has this establishment introduced a new or improved product (i.e., a good or service)?

(ii) During the last 3 years, has [the surveyed] establishment introduced any new or improved process? These include methods of manufacturing products or offering services; logistics, delivery, or distribution methods for inputs, products, or services; or supporting activities for processes.

> 7  The terms firm and establishment are used interchangeably while recognizing that the unit of analysis of the WBES are establishments.

> 8 For instance, the firm identifiers are kept confidential and the survey is conducted by private organizations independent from government. To make sure questions elicit valid answers, various measures including translation and localization checks, different interviewees for the same firm, and post-survey consistency checks are taken before, during and after the survey. See Ayyagari, Demirgüç-Kunt, and Maksimovic (2011) for more discussion.

5
# **ADB Projects**

Data on recurrent expenditures were obtained from two types of ADB published documents: (i) Report and Recommendations of the President to ADB Board of Directors (RRPs) that contained project cost estimates, and, in some cases, details of economic and financial analyses; and (ii) Project Performance Audit Reports (PPARs) that contained actual project costs, and, in some cases, detailed post-evaluation economic and financial analyses. The source documents were downloaded from ADB's website. However, in the case of PPARs, hardcopies of the documents were requested from ADB's Secretary's Office since the appendices that contained the required information had been omitted in the web version. Over 70 project documents in the form of ADOBE Acrobat PDF files were downloaded, of which 44 projects (of the 70 downloaded) were included in the computation of the R-coefficients as only these projects contained the needed information.

# **Details of the Calculation of the R-coefficients**

The R-coefficients were estimated using one of the following methodologies, the choice of which was made based on the availability of the required information:

- (i) Ratio of the average annual incremental operating and maintenance expenditures contained in either the financial or economic IRR computations to the total project cost amount _(the total project cost differed slightly from the total investment cost since the latter included interest expense during construction and contingencies);_

- (ii) Ratio of the calculated average incremental recurrent expenditures that was part of the project cost estimate to the total investment cost; and

- (iii) Ratio of the observed/assumed annual incremental recurrent expenditures to either total project cost or total investment cost.

In addition to the arithmetic average of R-coefficients that was computed for each sector, the median was also obtained as an alternative measure of central location. The median was computed for both ADB- and World Bank-assisted projects.

12
ethnic tensions, democracy and accountability which do not fall in this group). The ICRG data are available on an annual basis for 113 countries.<sup>6</sup> In the annual data we selected five indicators from the ICRG data set, (see details in the data appendix table). The variables we considered are bureaucratic quality, corruption, law and order, expropriation risk, or repudiation risk.<sup>7</sup> The other data we use are five yearly data from the Fraser Institute available for the period 1970-2000. Two indicators are of interest: freedom from government regulation and legal structure/security of property rights. The first variable ranges from 0 (low freedom=high regulation) to 10 (high freedom=low regulation). This indicator is an aggregate of 15 sub-indices and is called “Regulation of Credit, Labor, and Business. This index reflects a mix between what is commonly accepted as desirable institutional outcomes and simply, less regulation. For example, under most conditions it is desirable that administrative procedures are not an important obstacle to setting up a business, that the percentage of deposits held in private banks is high (as opposed to deposits in public banks), that businesses are generally free to set their own prices and that interest rate controls and regulations do not lead to negative rates. But the indicator also includes a measure of whether hiring and firing practices are determined by private contract (as opposed to some government regulations). It is not clear how much regulation would be considered “good” in all contexts. For those who would contend that the indicator picks up mostly “less regulation”, it would help test the hypothesis that donors successfully promoted the Washington Consensus model of “liberalization, privatization and competition”. Alternatively, it would be a test of whether donors supported better institutions.

The second indicator measures aspects of: a) judicial independence, b) impartial courts, c) protection of intellectual property rights, d) military interference in rule of law and the political process, and e) the integrity of the legal system.

> 6 Note that other authors, e.g. Knack (2000and 2004) also use the ICRG data.

> 7 In the empirical section we do not present all the regressions due to space constraints but these are all available upon request.

11
# **3.  Data and the Estimation Method**

In order to maximize the available data coverage while considering the data quality, we use quarterly data for Macedonia from 1997Q1 to 2007Q3, and for the Slovak Republic from 1995Q1 to 2007Q3. All data for the Slovak Republic were obtained from the IMF’s International Financial Statistic except the nominal interest rate which was obtained from Datastream. For both countries, the output gap was constructed as a deviation of quarterly real GDP in logs from its potential levels estimated using the Hodrick-Prescott filter. The GDP series for Macedonia was obtained from the National Statistical Office. Inflation was calculated as an annualized percentage change in quarterly CPI, which for Macedonia was obtained from the National Statistical Office. The interest rate used for the Slovak Republic is the three-month interbank rate (middle rate) from Datastream. This rate tracks well the current monetary policy rate (the repo rate), on which data are available only since 2000. The interest rate used for Macedonia is the Central Bank Bill rate obtained from the National Bank of Macedonia. We have not used the interbank rate for Macedonia as the interbank money market was quite inactive over the analyzed period. The observable exchange rate employed is the real effective exchange rate that for Macedonia was obtained from the National Bank of Macedonia. The series of government consumption for Macedonia is readily available on the quarterly basis only from 1999 onwards, and we have extrapolated the series back to 1997Q1 using a constructed series of government spending on wages and salaries, and goods and services. The correlation coefficients between the levels and differences of the constructed series and the actual government consumption series over 1999Q1-2007Q4 is 0.95 and 0.98, respectively. Both government consumption series and the spending of government on wages and salaries, and goods and services were obtained from the Macedonian National Statistical Office. In addition, when calculating the ratio of government consumption to GDP we have used the CPI index to convert the real GDP series into current prices because nominal GDP is not available for Macedonia on a quarterly basis. All data are demeaned prior to the estimation. Giordani (2004) has recently pointed out that working with demeaned data avoids dealing with parameter instability and structural breaks which, he finds, largely affect the unconditional mean of the modeled series.

8
# **1 Introduction**

This survey reviews the empirical literature on economic costs of land-based freight transport. In addition to summarizing the measures and estimates of overland transport costs from the literature, it provides an overview of different methods and their data requirements, along with a critical assessment of the strengths and weaknesses of these methodologies.

The importance of road freight transportation is self-evident. While people and firms in developed countries take good roads and well-functioning transportation markets as a given, the situation is very different in low- and middle-income countries. Developed or developing, majority of trade is domestic in all countries, for which overland transport is the dominant form. In international trade, maritime shipping dominates in terms of weight and air shipping has an important share by value. Yet, land-based routes are also of prime importance for regional trade between neighboring countries. Therefore, road transportation plays a key developmental role by facilitating trade in short and intermediate distances.

To put the review in context, first note that the overarching question for economists and policy makers is to understand the magnitude and nature of _trade costs_ that affect economic interactions between and within countries. All frictions that firms and consumers face in accessing markets and other economic agents can be considered within the broad definition of trade costs. These include not just the cost of shipping physical goods, but also the cost of finding suppliers and buyers, the cost of contracting, and in the international context, tariffs and non-tariff barriers. Focusing on shipping, all expenses incurred in moving freight from the factory gate to the ultimate retail or consumption point constitute the _cost of logistics_ . Such itineraries may entail intermodal switching between modes of transportation, warehousing between different segments, multiple carriers between various nodes and the cost of coordinating all these interlocking activities. This survey focuses on a subset of this chain: _the cost of overland transportation_ in a single segment via a single mode, i.e., using either a railroad or a motorized road vehicle.

An overland shipment may entail the crossing of an international border between adjacent countries. When there are border checks and controls, delays and inspections can add to the total cost of shipping. Similarly, to the extent that overland freight transportation regulations differ across administrative units within a country, internal borders could contribute to shipping costs. Although trade-reducing effect of domestic borders are well-documented—see Coughlin and Novy (2021) for a recent estimation using US data and a summary of the related literature—actual evidence that ties these estimates to transport related frictions is scarce. In a rare example, Carballo et al.

2