rafmacalaba/data-use-mentions-extended huggingface dataset:
# 2.2.2 Costs

The most obvious cost associated with formal definition of property rights in land is the expenditure needed to physically demarcate and delineate plots, to establish and maintain accurate records of land ownership, and to enforce these rights and resolve whatever disputes might arise.1<sup>6</sup> These costs are borne by individual land owners in situations, for example frontier settlement, where public enforcement of property rights is absent and individuals make defensive investments such as guards, fences, and other demarcation devices to demonstrate the legitimacy of their claims to property and to defend such rights against possible intruders (Muller, 1997). It has been shown that the privately "optimal" amount of spending by individuals on means of protection will be inefficient from a social point of view (Feder and Feeny; de Meza and Gould 1992). Furthermore, the defensive activities undertaken often have little social value and may generate negative externalities, an issue that has been emphasized with respect to the Brazilian Amazon where the need to demonstrate "productive" land use to establish ownership claims has been linked to increased deforestation (Binswanger 1988, Southgate et al. 1991). Even where they are not associated with externalities, defensive activities that are often undertaken in speculative attempts to secure "ownership" of large tracts of land, can lead to complete dissipation of the rents to be had (Allen 1991).17 Given the undesirable impacts of private rights enforcement, public provision -in the form of land records, police, and a judiciary- would therefore be preferable in all situations except ones characterized by very low levels of population density (Malik and Schwab, 1992). This is indeed observed throughout human history. The specific form in which land records are established will still depend on the relative costs and benefits from such an activity - something that depends partially on the technology and infrastructure available for record keeping. 1<sup>8</sup> At initial stages of human development assignments of property rights
-Tesdwcrs-teedwers Pay Dlffer oer 9-

d'Ivoire, and significant coefficients for sex in earnings functions results tend to disappear once tte latter are corrected for selection bias. Studies by Komenan (1987 a and b) using establishment-based surveys (which are less heterogenous and exclude the civil service) consistently found no sex differences.

Wi-h respect to the central question of this paper, the estimations reveal quite clearly that when one considers earnings before tax (Equations 1 and 2), teachers do enjoy a substantial salary premium of 23 percent. In the government sector this premium is larger (31%), which suggests that the overall premium is largely due to the government sector. Had a separate estimation for th- private sector been possible (it is not possible because the sample contains too few teachers in the private sector), the estimated premium for teachers would have been much lower there. However, when one considers the base salary augmented with the benefits received (Equations 3 and 4), the premium enjoyed by teachers vanishes completely. This is of course because teachers receive substantially less in-kind benefits and bonuses and commissions than their non-teaching counterparts (see table 1). This also suggests that teacher salaries may have been designed to compete with total salaries paid in the private sector, which necessitates a higher base salary for teachers. In conclusion, our results, so far, do not support the presence of an economic rent component in the total salaries of teachers in Cote d'Ivoire.

The use of an earnings function to establish this result implies two assumptions. First, it assumes that there are no differences in ability between teachers and non-teachers other than those captured by the variables in the equation. This follows from the standard assumption underlying all earnings function analysis, namely that there are no major "unmeasured" elements of human capital. Second, it is assumed that there
# **3. Data**

To better understand what characteristics help explain the effectiveness of IPAs, this paper brings together new data sources on sectoral FDI and IPA characteristics. This section describes these various data sets as well as the study sample used.

## **3.1 Data on sectoral FDI**

We create a new sectoral FDI database by combining data on FDI positions from EUROSTAT’s BPM6 database with the US Bureau of Economic Analysis (BEA). EUROSTAT’s database reports information on outward- and inward FDI from 39 OECD countries for the years 2013 to 2018. However, the amount of information reported differs significantly across countries due to different country-level reporting standards. As a result, some countries only report aggregate FDI stock (e.g. Switzerland) or report on only a small number of partner countries or sectors (e.g. Japan and the United States). We therefore only include data from 29 reporting countries. To create the widest available data on a country’s FDI, we use their information on outward FDI positions (as our interest is in both OECD and non-OECD countries). The sectoral data is available for a range of NACE<sup>4</sup> sectors at the 1-digit to 3-digit-level (country depending).

This data is then combined with the sectoral FDI database from the US BEA, which provides information on US FDI positions across the world since the 1980s. However, this relies on a distinct set of sectors that do not align directly to any NACE code. We therefore create our own concordance table to harmonize this with the EUROSTAT data. We also only keep the years 2013-2018 from this database, for which US BEA has information on over 200 countries. All FDI values are then transformed into constant 2018 USD terms. The summary statistics on FDI reporting countries is given in table 2, showing each country’s count, number of unique sectors and their unique country-pairs (i.e. the number of countries for
and adaptation, and inclusion.

This paper calls for a revitalized learning agenda on CDD that focuses on the _conditions under which_ CDD can work and the _design and implementation choices_ that will make CDD most effective within a given context. CDD approaches have a strong record of improving basic services and in some cases also improving economic welfare, participation, and conflict-mediation outcomes. Even as they explicitly harness existing (or establish new forms of) social institutions, however, it is less clear that CDD projects discernably expand social networks and decisively shift pro-social norms – and indeed whether it is desirable that they should singularly expect to be able to do so in such relatively short timeframes. More importantly, many key questions remain unanswered, especially on how variation in project design, implementation modalities, and interactions with contextual factors affect CDD outcomes. Although practitioners often hold tacit knowledge on these factors and incorporate that knowledge into the design and management of their projects, it is important to validate, expand on, and document this knowledge.

We identify gaps in the evidence on CDD and outline a research agenda to build knowledge on CDD’s impacts across four outcome categories, based on three sources of variation. We focus on four outcomes tied to social sustainability – cohesion, inclusion, resilience, and process legitimacy – because social

> 48 Such in-depth analysis is rare. One good example was the Local Level Institutions study in Indonesia that helped shape the design of the _Kecamatan Development Project_ and enabled long-term assessments to be made of social change in everyday village life. See Dharmawan, Jellema, and Wetterberg (2013). A key finding from this study was that conclusions reached after the first round of research were not always supported by research in the second round, raising the obvious specter of whether strident claims and counterclaims based on one-time assessments of interventions in any development sector might be upended if a subsequent round of research were to be conducted. 49 As of July 2023, based on CLD portfolio review.

33
consumption frequency for these rare items, which include, for example, Macaroni and Spaghetti or Pork. In contrast, the more frequently consumed items are associated with a much lower incidence error (albeit still positive). Finally, for the diaries, the pattern of error is reversed. The less frequently consumed items are reported at even less frequency in the household diaries, most likely reflecting the influence of personal leave out error for these items, while the more commonly consumed items are captured to a relative degree of accuracy by the household respondent.

Another striking feature underscored in figure 1 relates to the spread of the reporting error. In all five figure panels, there is a clear tendency for the incidence error to be much farther away from zero (in either a positive or a negative direction) for less commonly consumed items and for the spread to narrow substantially at higher consumption incidence. This patterns fits well with the findings of some of the social and cognitive psychology literature, discussed in section II, which highlights salience and regularity as key dimensions in frequency reporting, in particular if respondents do not count and recall but instead use ratebased estimation techniques.

Now that we have established that incidence reports are more accurate for high-incidence items (in terms of less spread in the reporting error), we ask a parallel question concerning the value reported. Here, we may hypothesize that the salience of high-value items in recall surveys leads to lower errors in the value reporting. Figure 2 plots value error as a function of benchmark consumption value. The first thing to note is that, in general, there is no reduction in variance for higher value items, contrary to the case of the incidence reporting of commonly consumed items. Recall that table 5 reveals a mix of over- or underreporting for module 1 (14-day long list), depending on the food group. The relevant scatterplot in figure 2 suggests that the net overreporting error is greater for consumption items of relatively low value. As the benchmark value increases, the net error approaches
# **3. Data**

## Program Expenditure Data

Data on program expenditure is collected from the Ministry of Rural Development (MoRD). Although the information on NREGA is publicly available, data on SGRY was specially sourced from MoRD and Datanet (India).<sup>8</sup> MoRD reports district-wise annual physical and financial statements for both the programs. Physical statements provide information on the number of public works completed and employment generated, while financial statements give statistics on the availability of funds and actual expenditure. I use data on “actual expenditure” when referring to program expenditure in the empirical models below. Data on SGRY is from its implementation in 2001 to 2007, its last operational year. Data on NREGA is from 2006 to 2010. In total, I use ten years (20012010) of district-level data on employment expenditure. The data used in this article is the most disaggregated and detailed data on program expenditure used in any comparable analysis on public workfares.

## Agricultural Wage Data

I use data on agricultural wages from the Agricultural Wages in India (AWI) series published by the Ministry of Agriculture. The AWI data has been extensively used for time series analysis on agricultural wages in India (see for example, Ravallion et al. 1993, Ozler<sup> ̈</sup> et al. 1996, and Berg et al. 2018). The AWI district-level wage data provides monthly information on daily wage rates. I construct a measure of the agricultural wage rate from 2001 to 2010 by taking the average daily wage rates for men and women for agricultural activities like plowing, sowing, reaping, and weeding.

_Matching AWI wage data with expenditure data:_ The AWI data, unlike the program expenditure data, is not reported for all districts. Data for nearly 40 percent of the districts are available for less than six out of ten years. To improve the signal-to-noise ratio of agricultural wages, I convert the monthly AWI data to annual frequency by taking 12-month averages in
of food aid|OLS regression<br>coeffcient|HH Survey|
||Proportion of respondents with unresolved<br>land or livestock disputes|0-1|HH Survey|
|Legitimacy of VH|Index measuring evaluations of VH<br>(trustworthiness, quality of relationship)<br>and compliance with VH (perceptions of<br>disposition to obey within village,<br>compliance with VH’s court)|Standardized<br>index (mean=0,<br>s.d.=1)|HH Survey|

22
determined for each stratum by dividing the proportion of population in that stratum (from the reference-survey) by the proportion of the sample in that stratum.</mark> <mark>The most prevalent method for weighting is iterative proportional fitting, more commonly referred to as raking. With raking, a researcher chooses a set of variables where the population distribution is known, and the procedure iteratively adjusts the weight for each case in the internet sample until the sample distribution aligns with the population for those variables. Raking is popular because it is relatively simple to implement, and it only requires knowing the marginal proportions for each variable used in weighting.</mark> Propensity score adjustment aims to correct differences caused by the varying inclinations of individuals to participate in web surveys (Duffy et al. 2005). That is, it adjusts for selection-bias due to observed covariates. More technically, a propensity score ( _psi_ ) is the conditional probability that a person will be in one condition rather than in another (e.g., ‘being in the web or the reference survey’) given a set of observed covariates used to predict the person’s condition. The web surveys are then weighted by the inverse of the propensity score, that is 1/ _psi_ (see Lee 2006, Schonlau et al. 2004, 2007). Since the propensity score refers to both the web and reference survey respondents, the propensity score weight for the reference survey is the inverse of propensity of being in the reference survey (1/(1- _psi_ )).

> 9 This general criticism is not only applicable to web surveys. In phone surveys, too, for example, coverage may be incomplete due to non-response. However, it is possible to randomly sample respondents from the potential covered population because telephone users list is usually known. This contrasts with a Web survey where there is currently no way to identify all Internet users (Valliant and Dever, 2011).

6
compare it with savings afterwards. However, 2006 REDS only reports cross-sectional information on savings. Note, however, that REDS is a panel survey, and the same households were also surveyed in 1999. Unfortunately, the survey questions were not exactly the same in both rounds, making it difficult to use the 1999 round for a panel-specification without significant measurement error.

To investigate whether FG parents save more than FB parents due to expected future dowry payments, we estimate the following specification for mother _i_ from caste _c_ in state _s_ and whose first child was born in year _t_ : where _Savingicst_<sup>2008denotes various measures of household saving last year reported in 2008;</sup><sup>_FirstGirli_</sup> indicates that the firstborn child is female; _Dowrycst_ is expected dowry defined as the average dowry paid by brides from caste _c_ in state _s_<sup>30</sup> who were married during the year of the child’s birth or the prior four years (i.e., during _t, t −_ 1 _, t −_ 2 _, t −_ 3 _, t −_ 4);<sup>31</sup> **X** _i_ is a vector of covariates comprising parents’ years of schooling and ages at the time of survey, and indicators for religion and month of survey. We report unweighted regressions in the main set of tables. However, our results remain the same when we use weights.<sup>32</sup> Standard errors are clustered at the state level. We also compute standard errors that are wild-cluster bootstrapped by state as a robustness check.

The coefficient _β_ 2 captures how savings in FB families respond to expected dowry receipts. The coefficient of primary interest is _β_ 1 which captures the differential response of FG families to expected dowry, relative to the response of FB families. To exclude other confounding factors related to the caste, state, gender, and year of birth of the
2015) and Abdi (2017). Contractual and relational transactional strategies can be used together or separately. Argyres et al. (2020) argues that a contractual approach might be embedded in a relational approach to produce more powerful inter-firm cooperation. For the roles of economics and sociology in conceptualizing these strategies, see, e.g., Poppo and Zenger (2002), Greif (2005).

> 12 For example, Poppo and Zenger (2002) measure relational governance by combining responses to three questions on the level of collaborative relationship, on the extent of shared goals, and the reliability of the partner to keep promises. Zhou and Xu (2012) measure it by aggregating information on whether the exchange is frequent and whether the parties keep each other informed about changes. Homburg et al. (2009) include multiple questions on trust, along with multiple questions on the extent to which the relationship is governed by written contracts.

> 13 See e.g., Homburg et al. (2009) who collect data on contracts in the US and Germany in the chemical mechanical and electrical industries. To examine the effect of national culture on the comparative use of contractual and relational governance, Handley (2015) collects data from firms that are large, US-based, and engaged in outsourcing of business processes. To analyze the effect of culture on the relationship between governance and of opportunism, Handley and Angst (2015) survey service providers that are located across countries but all having customers that are based in the US.

7
|---|---|---|---|---|
||Mean|Mean|Mean|Mean|Year|Source|
|Rural|79.6|78.8|82.0|82.6|2016|UNHS|
|Urban|67.5|67.4|72.7|69.0|2016|UNHS|

Having concluded the analysis of statistics at the national level, we now turn to the study of poverty by area of residence (Table 6.4), which does not show concrete differences among the estimators. The incidence of poverty is much higher in the rural areas than urban areas.

- <u>Table 6.4. Poverty head count ratio by area of residence</u>

|Unweighted|UNPS|Calibrated|Official Statistics|
|---|---|---|---|
|||GWSM base||

28
|(1,526,779)|(164,040)|(1,785,593)|(510,836)|
|Mean|0.884|0.810|0.954|0.935|0.829|
||(0.007)|(0.015)|(0.009)|(0.011)|(0.012)|
|Observations|8,850|3,058|1,646|1,949|2,197|

_Note_ : Each column reports the estimated total number of households using a coping strategy in response to COVID-19-relaated income loss and average household-level incidence of using a coping strategy. Adoption of a strategy is condition on experiencing a COVID-19 related loss of income per Table S8. Data come from the most round of phone survey data available. This is round 3 in Ethiopia and Nigeria, round 2 in Malawi, and round 1 in Uganda.

44
sensitive because it is not socially acceptable to all to support violence. The source of sensitivity for each sensitive question is listed in Table 2.<sup>13</sup>

> associated with the capital city in some countries. In order to get accurate information on the region, it is important to validate this region with self-reported regions in the survey.

> 13There are also potential variations in the level of sensitivity in sensitive questions. For example, one question asks whether the respondent disagrees with a humanitarian targeting plan, where the sensitivity could come from a belief that humanitarian agencies are wrong in their assessments, which is not necessarily sensitive to all individuals, or from the opinion that assistance should not be targeted at those most in need if the respondent does not receive assistance in those scenarios, which is a response that is potentially socially unacceptable to some. These potential ambiguities, or the lack of any ambiguity,

11
Policy Research Working Paper 9205

# **Abstract**

To comprehend how development really happens, it is necessary to understand the evolution of its drivers and their relationship with individuals’ income. This paper analyzes the expansion of access to education and basic services in Latin America and its association with the evolution of incomes in the region. The paper focuses on the importance of access to opportunities as one of the drivers of development and highlights the role of policy making. The findings suggest that access to education and basic public services early in life are positively correlated with incomes in adulthood. The analysis also suggests that countries follow a dissimilar path to increase access to education and basic services. The paper undertakes a comprehensive analysis of historic census records to add granularity to the assessment of the development of countries, matched with detailed individual-level information from household surveys of several countries in the region. The paper widens an ongoing area of research on the long-run relationship between access to opportunities during childhood and incomes in adulthood.

This paper is a product of the Poverty and Equity Global Practice. 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 gcaruso@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.