rafmacalaba/data-use-mentions-extended huggingface dataset:
. This information can potentially be taken from different sources (Decancq and Lugo, 2013). Subjective well-being data may be one source of information on u, as suggested by Decancq et al. (2015) or by in Decancq et al. (2019). Ideally, the survey that collects all relevant outcomes also collects information on life satisfaction or happiness. Preference elicitation surveys may provide another sources of information on u, as done in Decancq and Nys (2021). These surveys ask subjects to rank pairs of achievement vectors in one

> 60In spite of its paternalistic weights, it could be that the World Bank’s MPM has a larger signal-tonoise ratio than the fraction of extremely monetary poor individuals. That is, the additional information coming from its non-monetary variables may more than compensate for the bias coming from using equal-weights wˆ<sup>eq</sup> , which are bound to be different than weights w informed from individual preferences.

> 61There are other limitations associated with the “equal-weights” procedure. For instance, this procedure provides incentives to ignore relevant information. The reason is that the only way through which the practitioner can influence the weight attributed to a given variable is by changing the number of dimensions and/or changing the number of dichotomous variables considered within the same dimension. Under the equal-weights procedure, there is an incentive not to include one variable if another variable belonging to the same dimension is already included and has a clearly different importance for well-being. Indeed, the aggregation with equal weights would not look plausible if both variables were simultaneously included.

32
In general, the LSMS-ISA longitudinal samples coincide totally or partially (i.e., as a subsample) with an existing agricultural or household sample survey. For instance, the Ethiopia Socioeconomic Survey (ESS) interviewed a subset of agricultural households from the existing Agricultural Sample Survey (AgSS), complementing its exclusively rural sample with a sample of urban EAs. The Tanzania National Panel Survey (NPS) and the Uganda National Panel Survey (UNPS) are composed of a subset of EAs drawn from household budget surveys, namely and respectively the Tanzania Household Budget Survey (THBS) and the Uganda National Household Survey (UNHS). In Malawi, the Integrated Household Panel Survey (IHPS) tracked and reinterviewed a subsample of households from the Third Integrated Household Survey (IHS3). In Nigeria, the General Household Survey-Panel (GHS-Panel) is a subsample of the GHS core cross-sectional survey. Finally, in Niger, the longitudinal study followed the entire sample of the National Survey on Household Living Conditions and Agriculture (ECVM/A).

The LSMS-ISA surveys consist of two-stage probability samples which use the general population census for their sampling frame. In most samples of the LSMS-ISA, enumeration areas (EAs) are selected as primary sampling units with probability proportional to size. A sample of households is then randomly chosen from the complete listing of households in the selected EAs. Thus, the LSMS-ISA sample constitutes a random sample of EAs, households, and individuals.

The LSMS-ISA samples are meant to be nationally representative of households and of individuals. Ideally, longitudinal studies preserve representativeness over time, indicating that the sample should represent both the current population at each survey occasion and the dynamics over time of the initial population. To maintain both types of representativeness, longitudinal surveys follow up with people interviewed in previous survey rounds and add new individuals to ensure that new members of the population such as migrants and newborns are included (Glewwe and Jacoby 2000). To this end, panel surveys establish rules to define interview targets in follow-up rounds and create specific
> While the answer to the second question is not available in the published version of the survey, we were able to obtain bank names on a confidential basis. We then used the answer to this question to connect firms in WBES with bank-level data from Fitch Connect.

The full WBES covers 180,000 firms in more than 150 countries. However, we only have information on banking relationships for 41 countries (27 in Europe, 8 in Asia, and 6 in the Middle East and North Africa), including 63,000 records and 108 surveys. (Table 1). Of these initial records, about one-fifth belong to surveys that did not include the bank identity question.<sup>6</sup> Of the remaining records, which are all from surveys that include the bank name question, about one-third report the name of the bank. Lack of reporting could be due to the absence of a formal banking relationship, but there are many firms that report having a banking relationship but did not provide the name of a specific bank. This could be because the firm uses multiple banks or because it prefers not to divulge such information. Overall, 25 percent of surveyed firms (15,718 records) provided the name of their bank.

To merge bank-level data with firm-level data form WBES, we first dropped records (about 3% of the total) with inconclusive bank names such as “Commercial Bank”, “Cooperative bank”, “Doesn’t know” or

> 4 The survey focuses on low- and middle-income economies, but it also includes 5 high-income countries (Cyprus, Greece, Italy, Malta, and Portugal).

> 5 The question is “Which Bank Provided the Most Recent Line of Credit or Loan?”

> 6 Surveys conducted in 2008-2009 do not feature the bank name question.

6
. http://www.fsb.org/wp‐content/uploads/P160118.pdf.

Gennaioli, N., A. Martin, and S. Rossi. 2014. “Banks, Government Bonds, and Default: What do the Data Say?” IMF Working Paper WP/14/120. International Monetary Fund. Washington DC.

IIF (Institute of International Finance). 2018. “EM Government Debt‐Pitfalls Ahead.” Global Debt Monitor Snapshot. Institute of International Finance. Washington DC. June.

IMF (International Monetary Fund). 2012. _A Survey of Experiences with Emerging Market Sovereign Debt Restructurings_ . Monetary and Capital Markets Department. International Monetary Fund. Washington DC.

IMF (International Monetary Fund) 2014. _From Banking to Sovereign Stress: Implications for Public Debt_ . International Monetary Fund. Washington DC.

Laeven, L. and F. Valencia. 2018. “Systemic Banking Crises Revisited”. IMF Working Paper WP/18/206. International Monetary Fund. Washington DC.

Lanotte, M., G. Manzelli, A.M. Rinaldi, M. Taboga, and P. Tommasino. 2016. “Easier said than done? Reforming the prudential treatment of banks’ sovereign exposures”. Questioni di Economia e Finanza Ocassional Papers No.326. Banca D’Italia. Rome, Italy.

41
0.05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

53
Table F: The consistency between the household enterprise and the labor module in Malawi

||Inconsistent responses|Observations|
|---|---|---|
||(Share of household members that<br>worked on a plot, but did not report having<br>engaged in HH farming in the last 12<br>months)||
|Average|24.3%|5,010|
|Average (excluding owners and managers)|21.3%|4,497|
|Responsibility within the enterprise|||
|Manager|84.7%|59|
|Owner|82.5%|120|
|Worker|33.5%|334|
|Manager & owner|37.0%|871|
|Manager & worker|19.8%|116|
|Owner & worker|21.4%|112|
|Manager, owner & worker|17.3%|3,398|
|Hours worked (hours/year)<sup>1</sup>|||
|0‐200|34.0%|1,108|
|200‐630|22.7%|1,139|
|630‐1584|17.7%|1,123|
|>1584|10.9%|1,126|
|Gender|||
|Men|23.9%|2,367|
|Women|24.7%|2,643|
|Age|||
|5‐14|47.1%|280|
|15‐25|28.5%|942|
|25‐65|21.4%|3,580|
|>65|26.0%|208|

Source: Based on 2016/17 Malawi LSMS‐ISA data.

Note:<sup>1</sup> Calculated as the product of the number of hours/
noncognitive skills (Adhvaryu, Namrata, and Anant (2018); Fox and Kaul (2018)).

Second, and relatedly, to fully understand the poverty-reduction potential of noncognitive skills, looking at equilibrium effects through impacts on other individuals is crucial. Indeed, our results show that what matters for predicting income-earning is relative levels of noncognitive skills. Moreover, inequality in the household can be amplified through increases in the noncognitive skills of only one member. In the presence of non-unitary households, the composition of household economic participation, and who earns what matters for development outcomes beyond overall household income. For example, Duflo and Udry (2004)) show using data from Côte d’Ivoire that increases in rainfall that benefits traditionally female crops, shifts household expenditure towards food consumption and so improve nutrition of children. Thus, programs or events that unilaterally increase the noncognitive skills of one member (say, the husband) could have potential negative effects on other household members and the future generation if they result in the capture of intra-household resource allocation.

Lastly, our results imply that positive impacts of noncognitive skills, or socio-emotional skills programs, could be unlocked by an accompanying alleviation of inputs scarcity. Within agriculture, this highlights the potentially high multiplier effect of programs focused on property rights and stimulating markets for fixed inputs like land, which could attenuate the "battle of the sexes" and capture of household resources we observe. Such programs may also benefit
0.40|0.40|
|Owns radio|0.20||0.01||0.04||0.09||
|Owns sewing machine|0.20||||0.46||||
|Owns stove|0.65||0.71||0.46||0.47||
|Owns television|0.82||||0.27||0.63||
|Owns washing<br>machine|0.38||||||0.79||
|Owns fan|0.81||0.82||||0.47||
|Owns refrigerator|0.66||||0.67|0.66|0.80||
|Owns boat||||||-0.16|0.03||
|Owns landline||0.78||0.76|0.24||0.61|0.81|
|Owns motorcycle|||||0.02|0.47|0.10||
|Has flushed toilet|||||||0.48||
|Owns land||||||0.18|||
|Has good roofing<br>material||||||0.31|0.49||
|Owns electric water<br>pump<br>Source:Authors’ calculatio|ns from the|Global M|onitorin|g Database (|GMD)||0.54||

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> <u>Notes: This table shows the first factor loadings from principal components analysis (PCA) done with different assets</u> and household infrastructure and amenities. These factor loadings are the weights used in
# **6. Conclusions and Policy Implications**

The 2015 Tanzania Water Point Mapping data enables us to identify the failure rates of water points and schemes across their lifetimes as well as the factors contributing to these failures.<sup>22</sup> 29 percent of all water points are non-functional, and many failed within the first year. The results indicate that nearly 20 percent of water points are likely to fail in the first year of construction, while nearly 40 percent are likely to fail in the long run (within 20 years).

The empirical results from the logistic regressions confirm the evidence provided in the Tanzania context as well as those found in the literature. Water points managed by village committees had a much higher likelihood of failure than those managed by private operators or water authority. This result is similar to the ones found by Cronk and Bartram (2017) . Empowerment of village committees to manage and maintain the water points under their supervision needs to be emphasized as recommended by WaterAid (2009). Alternatively, a movement for more water points to be managed by private operators is also viable although there is a risk of profiteering which would be undesirable especially when the aim is to increase water access among rural populations (WaterAid, 2009). Additionally, following similar results from Foster (2013), it was found that water points technology also affects their functionality. Motorized pumps are more likely to fail than gravity flow pumps. In fact, as shown in the Shapley decomposition, the technology of water points accounts for about 58 percent of the total water point failures. Hence, there is a need to use appropriate technologies that is compatible with the hydrological conditions while also emphasizing the management and maintenance capability of community led user associations. Evidence also supports the recommendations of Holms et al., (2017) that villages should be given more autonomy regarding decision to install and maintain water pumps.

The variables described above such as the manner of how water points are being managed as well as their
misleading insights regarding the profiles of landowners and gender differences in land rights in a given country.<sup>5</sup> In fact, Slavchevska et al. (2021) show how different land rights, such as rights to sell, rent, or bequeath, do not necessarily overlap in Sub-Saharan Africa, and should not be used interchangeably. Relatedly, Kilic et al. (2021b) document that having rights to bequeath and sell land - but not rights to rent out or use as collateral - is significantly linked with investment and cash crop adoption in Malawi, with especially important effects for women. Individuals who are joint owners of the same parcel may also have different decision-making roles as it relates to crop choice, timing of cultivation and input use (Acosta et al., 2020; Twyman et al., 2015). Thus, land ownership should be understood as multidimensional and as a bundle consisting of rights and decision-making in customary tenure systems. In fact, the idea that property ownership contains a bundle of rights has been widely acknowledged in economics (Demsetz, 1967; Alchian and Demsetz, 1973; Schaleger and Ostrom, 1992; and Sikor et al., 2017).

The second reason for the knowledge gaps regarding the profiles of landowners in low-income countries is the fact that the available evidence has been informed by data collected in small-scale studies and the representative insights on land ownership and rights in customary tenure systems have been out of reach due to the gaps in the availability and quality of individual disaggregated survey data. Even if landowners and right holders are individuals, nationally representative survey data on landownership has traditionally been collected at the household-level. And even when the surveys attempted to identify sole or joint landowners were identified within households (and often for each parcel owned by any household member), there have been two major deficiencies in largescale survey data collection: (a) surveys have not systematically sought to identify exclusive and joint holders of specific rights, alongside reported and documented owners, and (b
-14.00|0.11|
|DensityPrimarySchool Numbers – Census 2011|0.86|0.90|-1.40|0.75|
|DensitySecondarySchool Numbers – Census 2011|0.06|0.07|-1.30|0.96|
|DensityMiddle School Numbers – Census 2011|0.22|0.24|-3.70|0.75|
|Proportion of Total Population Female – Census 2011|0.50|0.50|12.10|0.29|
|Proportion of Literate Population Person – Census 2011|0.49|0.50|-13.70|0.37|
|Proportion of Non-WorkingPopulation Person – Census 2011|0.48|0.50|-33.40|0.01|
|Observations|||772||

27
the second period. On the contrary, the use of a threshold model based on the value of the capital stock per capita for instance, makes it possible to identify the in‡uence of the infrastructures on the total factor productivity in the ...rst period.

The second major problem raised in the literature is the non stationarity and the non cointegration of the data used in the augmented production function (Tatom, 1991; Sturm and Haan, 1995; Crowder and Himarios, 1997). In time series, it is generally recognized that unit root tests based on linear speci...cations (ADF, KPSS etc.) are likely to conclude to the non stationarity of series when the true data generating model is a threshold model (SETAR, STAR etc.). Even if similar studies have not been done with panel unit root tests, it is obvious that the ...rst generation tests, as the Im, Pesaran and Shin’s test (2003) based on an average of individual ADF statistics, are likely to yield the same kind of results.

The last major problem, which is speci...c to the panel estimates, is the potential heterogeneity of the parameters of the production function and more particularly the heterogeneity of the elasticities of output with respect to infrastructure (or public capital) stocks. It is common, both in cross section and in panel data studies, to assume that the parameters are common across countries. However, this assumption may raise important problems when the countries of the panel are very di¤erent, as it is the case in our sample of developing countries. Consequently, the studies based on a production approach are generally based on speci...cations with ...xed or random individual e¤ects (Evans and Karras, 1994; Holtz-Eakin; 1994). But, to the best of our knowledge, few studies in the literature allow the other parameter of the augmented production function 4
and agricultural production. A likely reason, which we leave unexplored, is that weather in a given location does not vary that much over time. Thus, including household FEs, assuming a household does not move, is essentially a location FE. Once location is controlled for, weather varies very little year-to-year and small deviations from the time average in a location has little to no impact on agricultural production in the year. This raises questions about the use of weather as an instrument (as it is correlated with time-invariant household heterogeneity, specifically location) as well as the ability of economists to use weather to predict the effects of climate change (by definition, a slow process) on economic indicators.

A second regularity is that the sign on the weather coefficients can vary substantially. This is related to, but distinct from the ordinality issue. Within a single econometric specifications, rainfall from one EO product will be positively correlated with agricultural production while in rainfall from another product will be negatively correlated. Shifts in significance across specification can be the result of changes in relative size (cardinality). But what should raise concerns among applied economists is that in every country a researcher could produce whatever relationship they wanted between weather and the dependent variable by judiciously choosing the EO product, the weather metric, and the econometric specification. Essentially, a researcher using EO weather data has 19
# I. Introduction

Government procurement is a major market, accounting for 12% of world GDP in 2019 (Bosio et al. 2022). The COVID-19 pandemic and the ongoing Ukraine-Russian Federation crisis have further emphasized the role of governments in large sectors of the economy, including health, medical equipment and defense.<sup>1</sup> As a result, the use and allocation of government procurement funds have come under increased scrutiny due to their sheer size and importance.

Governments often use public procurement to pursue socioeconomic objectives, such as supporting the development of small and medium-sized local firms or promoting environmentally friendly practices. However, these objectives can come at a cost to economic efficiency, and can result in protectionist policies that limit the participation of foreign suppliers in procurement auctions. These policies can take various forms, including inflating the price bid by foreign firms to provide a preference margin to domestic bidders, requiring products and services to have a certain level of local content, mandating the use of specific technologies, or imposing strict licensing requirements.

The implementation of discriminatory regulations against foreign suppliers can result in significant barriers to trade and can increase the prices at which governments source goods and services. This raises important questions about the extent to which governments around the world implement domestic regulations that discriminate against foreign suppliers and the effects of these regulations on trade. To what extent do governments around the world implement domestic regulations that discriminate against foreign suppliers? And what are the effects of these regulations on trade openness and cross-border government procurement flows?

This paper aims to explore these questions by examining rules that could be characterized as protectionist in government procurement and their impact on cross-border trade. By shedding light on these issues, we contribute to the debate on the role of protectionist policies in government procurement and the broader implications for international trade.

To begin our analysis, we introduce a novel dataset about domestic laws that discriminate against foreign suppliers of goods and services in government procurement. For 140

> 1 According to reports from the U.S
Relative to other household surveys, unusual effort went into obtaining accurate data on consumption and income. While the community, individual and project activity surveys used conventional one-time interviews each year, the household surveys were quite different. The surveys were closely modeled on NBS’s Rural Household Survey (RHS) (which is described in detail in Chen and Ravallion, 1996). This is a good quality budget and income survey, notable in the care that goes into reducing both sampling and non-sampling errors. Similarly to the RHS, sampled households maintain a daily record on all transactions plus log books on production. Local interviewing assistants visited each household at two-three weekly intervals to monitor compliance and check questionable data entries or inconsistencies found at the local (countylevel) NBS office. Other trained interviewers also visited at regular intervals to collect additional data. This intensive interviewing method is in marked contrast to most surveys in which the respondent is visited only once or twice.

The consumption aggregate was built up from very detailed data on cash spending on all commodities and imputed values of consumption from own household production, valued at local selling prices. Living expenditures exclude spending on production inputs (which are accounted for in net income from own-production activities).<sup>12</sup> The income aggregate includes cash income from all sources and imputed values for in-kind income. Income is measured net of all production costs, including interest on debt (including loans from the SWP). The outmigrating workers were not tracked, although the income aggregate includes remittances received from family members who migrated, including those supported by the SWP. Remittances are expected to be the main means by which the out-migration component reduced poverty in the short run.

For the 2004/05 follow-up survey, exactly the same survey instrument was used as for the prior surveys. However, toward the end of the period, a rapid-appraisal module was designed by the author and refined based on field testing. The Chinese and local language versions of the module were refined on the basis of field