rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 6390

# **Abstract**

This paper addresses policies aimed at closing the ruralurban gap for one of the Millennium Development Goals (MDGs), the under-five mortality rate (U5MR). The paper relies on the Maquette for MDG Simulations (MAMS), a computable general equilibrium model, applied to the database of an archetypical low-income country. The scenarios, which focus on the period 2013–2030, include a “business-as-usual” base scenario and policy scenarios that analyze efforts to raise the rural population up to the urban level in terms of health services or the under-five mortality rate. The policy scenarios are implemented with alternative sources of fiscal space. The results indicate that, if current trends continue, considerable progress for MDGs should be expected by 2030. If the government raises rural health services, then the decline in the rural U5MR would accelerate. If most additional resources come from foreign grants or government efficiency gains, then the repercussions for other development indicators, including poverty reduction, would be positive. However, if most additional resources are from domestic taxes or borrowing, then progress for the rural U5MR would come at the expense of less progress for other indicators. Sensitivity analysis shows that these qualitative findings are robust to different values for two parameters related to initial rural-urban cost and service gaps. However, quantitatively, the results depend on the values of these two parameters, implying that individual country characteristics strongly influence the fiscal-space requirements for and consequences of equalizing ruralurban MDG services and outcomes.

This paper is a product of the e Development Prospects 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://econ.worldbank.org. The author may be contacted at hlofgren@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
Table A.17: Number of births: Hindus vs Muslims

||Hindus|Muslims|
|---|---|---|
||(1)|(2)|
|_Firstborn girl_|0.131***|0.161***|
||(0.016)|(0.056)|
|_Firstborn girl_ * _Post1_|-0.077***|-0.054|
||(0.019)|(0.058)|
|_Firstborn girl_ * _Post2_|-0.093***<br>(0.026)|-0.046<br>(0.065)|
|_Ideal no. of children_|0.347***|0.260***|
||(0.017)|(0.032)|
|_Ideal fraction of sons_|0.039**|0.011|
||(0.018)|(0.026)|
|N|67,433|9,038|
|Baseline mean|2.997|3.200|

NOTES: Estimates of specification (3) estimated using OLS. The dependent variable is the number of births at the time of interview. Baseline means are average of the outcome variable in each column for mothers who had both their first and last birth within 1973-1984. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

64
Given the identification of a number of distinct governance structures (or classes), LCA produces estimates of the posterior probability that each firm belongs to each class.

Thus, a second contribution of this paper is the discovery and estimation of governance structures in the six-country data set using LCA and an interpretation of the estimates.<sup>5</sup> As we describe in Sections III and IV, we find that pure bilateralism (i.e., a strategy combining only firstparty personal trust and second-party mutual interest without invoking any third-party agent) is the most common governance structure and that all governance structures embody bilateral enforcement mechanisms. Hence, none of the estimated governance structures corresponds to purely arm's length transactions, where firms rely only on impersonal mechanisms and formal institutions to support their transactions. A corollary is that bilateralism and formal institutions are rarely substitutes, and for many firms, they are complements. Additionally, while much attention has been devoted in the literature to various unpaid, third-party, mechanisms of supporting agreements, such as social clubs and culturally defined groups, our results suggest they are not very important in the enforcement of transactions.

A third contribution is to provide illustrative examples of the use of the data derived from LCA. We do so in Sections V and VI. In Section V, we present descriptive statistics on the variation in the importance of different governance structures across countries and regions. These are descriptive in the sense that they do not isolate ceteris paribus causal effects of single variables. We do, however, use techniques previously developed for LCA that produce consistent estimates

> <sup>5</sup> No existing studies obtain a cross-country, economy-wide picture on how these mechanisms are combined to form governance structures. To be sure, there are high quality studies that analyze particular combinations.  See, for example, Cao and Lumineau (2015) on contractual and relational governance, which is the focus of many studies in the business economics literature. Hendley et al. (2000),
World Bank database, we summarize them in Table 1. The database identifies 130 detailed IPRs provisions, broken into 16 broad categories. These provisions are simply listed as either 0 (absent in the PTA) or 1 (present). In Panel A we list eight categories that seem relevant for studying potential impacts on both trade and innovation.<sup>4</sup> For example, there are 15 international treaties or conventions covering intellectual property that members of IPAs might

> 4 The unlisted categories focus on procedural questions (such as transparency and cooperation mechanisms), do not strengthen existing IPRs (such as incorporation of existing IPRs treaties or recognition of the importance of biodiversity and traditional knowledge), or focus on non-trade issues.

6
|0|1|1|4|
|No. of countries|12|7|6|12|16|53|
|**Panel B: Survey information**|||||||
|Can get land information|0.197|0.208|0.093|0.210|0.189|0.189|
|Rich people can defraud|0.754|0.656|0.538|0.603|0.598|0.613|
|Poor people can defraud|0.158|0.120|0.167|0.194|0.161|0.167|
|Support women’s rights|0.778|0.731|0.470|0.795|0.714|0.723|
|No. ofcountries|3|3|3|10|15|34|

_Note:_ In panel A, row 1 reports cost of registering a property transaction (without a survey) as percentage of property value while rows 2-6 report the number of countries with a digital registry, a digital cadaster (not just scanned survey plans), or a digital registry and cadaster that are linked, most of the properties in the main city in the register or the cadaster, and the ability to obtain a first instance court decision for a typical land dispute in less than a year. Registration cost is as a share of property value. All information is based on 2020 World Bank ‘Registering Property’ Data.

For panel B, ‘ _Can get land information’_ is the share of those responding ‘very likely’ to the question ‘If you went to the local lands office to find out who owns a piece of land in your community, how likely is it that you could get this information?’ _Fraudulent registration (ordinary and rich)_ tabulates the share of respondents who think it is very likely for an ordinary or a
on a list of phone numbers generated by random digit dialing (RDD) or obtained from a mobile network provider is an alternative (or the only feasible option) especially when no previous contact information is available. These strategies also eliminate the need for sample clustering, which is generally required for cost-efficiency in in-person survey operations, and do not constrain ex ante the size of the sample that can potentially be contacted. However, both strategies have drawbacks in terms of sample representativeness. The lack of _a priori_ information on the potential respondents associated with each phone number impedes the analysis of non-responders and limits post-survey reweighting to counteract selection biases (Himelein et al., 2020). Further, a list obtained from a mobile network operator is unlikely to represent even the phone-owning population if it is from one specific operator in a country where there are multiple operators.

A mixture of these methods could also be implemented to arrive at a representative sample. For example, in a situation where a recent previous survey is available, but the sample of phone numbers is small, survey designers can supplement the list with additional phone numbers generated through RDD. This was the approach taken in the Kenya high-frequency phone survey on the socio-economic impacts of COVID-19 in which the World Bank is collaborating with the Kenyan National Bureau of Statistics (Pape et al., 2020).

_Increasing coverage, reducing non-response, and improving representativeness_ Given the above issues, survey designers can draw on several approaches to attempt to reduce the level and impact of these sources of bias in phone survey samples. In-person survey operations should invest in the first place in the collection of phone contact information of surveyed households and individuals 10
(2003) and is often referred to as the “ELL” poverty mapping method. The assumptions and data employed for ELL maps are further elaborated upon in Bedi, Coudouel, and Simler (2007). However, a pre-requisite for using the ELL approach is access to micro-level census data. In Central Asia, census data either do not exist (for instance, in Uzbekistan), are relatively dated (for instance, in the Kyrgyz Republic and Tajikistan), or are not made available for these purposes (for instance, in Kazakhstan). In such cases, the most common alternative approach is the Fay-Herriot (FH) method, which is adopted to generate the results described in this report.

The FH method allows estimation of indicators and rates using a combination of survey data and district-level indicators from available sources that are less subject to imprecision, such as administrative data or remote sensing. In this report, most of the publicly available sources used are derived from satellite imagery. The FH approach proceeds by matching accurate area-based information with indicators that are aggregated to the level of interest in the survey (the district, in this case). Starting from the relatively imprecise estimates from the survey, a statistical model is developed, which attempts to explain the variation of the welfare indicator at the district level (in this case, either the poverty rate, average consumption per capita, or the share of the middle class at the district level). Once the model is estimated, the direct survey estimates also enter into the final area-level results: the final estimated area-level poverty rate is a weighted average of the observed and model-based estimates for cases in which both estimates are present. For areas that do not appear in the survey data, the results rely entirely on estimates derived from the statistical model.

The report is structured as follows. The remainder of this section reviews the definitions of the key indicators to be estimated. Section II reviews the data used in the study (relying on both survey microdata, and complementary satellite
, s and s , V is an ifcreaFng function of C Under constant returns to scale A4j) - 2 Am(5) and V - C /Af(p).

- (10] For example, Walker and Gish (1977) found mobile services to be substantially less cost-effective than fixed services at the delivery of curative care.

- (111 See note 9.

- (121 The assumption of complementatity in the Strategy M production process woould likewise render that strategy more competitive.

- (131 The Ghaci Health Assessment Team presents estimates of total healthy-life-days-lost due to each disease.(1981, Tables 1, 2) Whether measured by ageand disease-specific mortality rates or by healthy-lifedays-lost in the populacton, the total burdea of a disease on society cannot be used directly to prioritize disease interventions. Instead it is necessary to estimate the marginal number of life-days-saved by an intervention and its marginal cost and then allocate resources so that the number of life-days-saved per unit cost is equalized across all interventions. (ibid., pp. 76, 77; Creese, 1979, pp. 24, 25). Tables 1 and 3 of this paper provide examples of possible approaches to translating the healthy-days-of-life-lost estimates from Ghana into impact measures of this sort.

- [141 The programming model of rural orimary health care in developing countries by Barnum et al (1980) has the advantage of modeling disease interdependence.
upturns and downturns in the rate of informal employment across all surveys. In other words, it is changes in the probability to be an informal worker, given certain attributes, what explains changes in informality rates. Different surveys give different results about the changes in these “beta” effects, but there is a common trend across all surveys: the probability of being informal has increased among those with only basic education.<sup>18</sup> Interestingly, one of the few demographic characteristics that have changed rapidly over the period is the proportion of workers with tertiary education, this effect has partially compensated the growing probability of informal employment among those without tertiary education. In other words, had the share in tertiary education not increased, the rate of informality would have grown even more.

We conclude that the evolution of informality rates over the past decade cannot be attributed to changes in the sectoral or demographic composition of the labor force. Rather, it appears that incidence of informality has been going up across all economic sectors and particularly among those without at least some tertiary education (the majority of the labor force). This leads us to conclude that informal employment is a quite pervasive phenomenon in Russian labor markets and it cannot be associated to economic sector or population specific influences.

# **_Is migration affecting informal employment in Russia?_**

There has been recent concern about immigrants in Russia and their labor market impact. The flow of these migrants, mainly stemming from Central Asian countries, increased until the economic crisis hit the region [see (Ryanzantsev, 2016)]. Given the attention to the link between rising informality and immigrants, we use the RLMS to investigate their personal and job-related characteristics and whether migrant workers -defined in this survey as non-Russians- are more likely to be informal.<sup>19</sup> 18 For instance, according to RLMS data -on average and after controlling for other personal characteristics- people with higher education are 15.7 percentage points less likely
of program schools represents a significant share of all private schools in these seven districts: using the 2005 National Education Census (NEC), a census of schools in Pakistan, we estimate that the program covers 21% of private schools in these districts (see Table 4).<sup>10</sup> _Program location_ : The program was initially designed to be targeted at districts ranked lowest in terms of adult literacy rates based on 2003/04 Multiple Indicators Cluster Survey data (Government of Punjab 2004). However, in phases 1 and 2, this targeting decision was not applied as PEF was limited in its institutional and logistical capacity at that time to effectively administer the program in poorly-ranked districts that had physical environmental challenges and limited transportation and accommodation options (Malik 2007). In contrast, in phases 3 and 4, the targeting decision was effectively applied. Consequently, as Table 3 shows, 51% and 89% of program schools are located in districts ranked among the bottom-quarter and bottom-half in terms of adult literacy rates, respectively.

_Program school characteristics_ : Table 5 presents the distribution of program schools by selected characteristics measured in September 2008. Aggregating the phases together (Column 5), the mean school size is 351 students. The majority of schools are middle level (59%), coeducational (83%), registered with local government authorities (87%), and rural (55%). Disaggregating by phase (Columns 1–4), the distribution of program schools by these characteristics is roughly comparable except for the level of the school: in contrast to the pattern in the aggregate sample, program schools that entered in phases 1 and 2 were mainly secondary schools (65–73%). School size also appears to be monotonically decreasing with phase; the mean size of program schools which entered in phase 1 is 561 students, whereas that of program schools which entered

> 10 NEC’s coverage of private schools might be incomplete. According to the survey documentation, private
|
|Dependency ratio in household|0.0209<sup>_∗∗∗_</sup><br>(0.00523)|0.0204<sup>_∗∗∗_</sup><br>(0.00523)||0.0224<sup>_∗∗∗_</sup><br>(0.00505)|||
|Female head of household|0.0331<sup>_∗∗∗_</sup><br>(0.00904)|0.0379<sup>_∗∗∗_</sup><br>(0.00975)||0.0405<sup>_∗∗∗_</sup><br>(0.00908)|||
|Asset index|0.0141<sup>_∗∗∗_</sup>|0.0172<sup>_∗∗∗_</sup>||0.0158<sup>_∗∗∗_</sup>|||
||(0.00505)|(0.00542)||(0.00506)|||
|Foodinsecurityindex(HFIAP)|0.00267|-0.00858||-0.00997|||
||(0.00947)|(0.0107)||(0.00979)|||
|Efective F-Statistic||||336.8|340.1|368.0|
|Hansen J statistic (p-value)||||0.613|0.109|0.741|
|N|7451|7453|7453|7450|7450|7450|
|R-squared<br>|0.553<br>|0.568<br>|0.827<br>|0.554<br>|0.556<br>
rates. Our objective in this paper is to provide a framework for evaluating the trade offs between approaches such as the Swiss formula that are sharply tops-down, and approaches such as the proportional cuts approach that are less targeted to reducing peak tariffs.

The potential benefits from use of a formula approach are large. If a suitable topsdown formula can be identified and implemented, we can be relatively sure that it will lead to a global welfare gain, since the social costs of tariffs generally rise more rapidly than the rates themselves<sup>1</sup> By contrast, approaches that focus on reducing relatively low, “nuisance” tariffs face the risk of reducing economic welfare and tariff revenues by diverting imports away from higher-tariff items (see Martin 1997).

An important message from analysis of actual tariff data appears to be that the critical feature of a formula approach for welfare in the importing countries is not so much the flexibility of the formula (i.e. its willingness to go soft on peak tariffs) but rather the specification of a targeted reduction in the average rate. In other words, as long as a reduction in the average is still met, introducing some added flexibility by moving closer to proportional tariff cuts may not greatly reduce the importing country’s gains from a tops-down approach. However, moving away from a strict tops-down approach in the industrial countries does seem to reduce the market access gains for low-income countries in industrial country markets.

This paper is organized as follows. In Section 2, we first provide some detail on the current market access landscape. This highlights important issues related to tariff peaks, unbound tariffs, and gaps between bound and applied rates that will be important determinants of success for any formula approach. In Section 3, we then consider some formula-based approaches. In Section 4, we set out the concept of “flexible formula” approaches. In Section 5, we consider some practical implementation issues. For illustration, and to assess the quantitative implications of alternative formulas, we examine
Notes 1 Tapscott (1995). 2 Statista.com. 3 Rosotto et al. (2018). 4 Coase (1937). 5 Freund and Weinhold (2004); Meltzer (2015). 6 Blinder and Krueger (2013); Dossani and Kenney (2007). 7 Wang and Mason (2005). 8 Alstyne, Parker, and Choudary (2016). 9 Freischlad (2018). 10 Haskel and Westlake (2018). 11 Brynjolfsson et al. (2008); Lee (2018). 12 Boyd (2018). 13 Parker and Van Alstyne (2005); Rochet and Tirole (2003); Weyl (2010). 14 Argenton and Prüfer (2012). 15 Argenton and Prüfer (2012); Eisenmann, Parker, and Van Alstyne (2006); Varadajan, Yadav, and Venkatesh (2008). 16 World Bank, World Development Indicators (database), http://databank.worldbank.org/source/world-development-indicators. 17 Corporaal and Lehdonvirta (2017); Dossani and Kenney (2007). 18 Corporaal and Lehdonvirta (2017). 19 Hallward-Driemeier and Nayyar (2017). 20 Brynjolfsson and Kahin (2002). 21Tencent Technology (2018). 22 Russell (2018). 23 Ling (2017). 24 Khan (2016). 25 Bork (1978). 26 Furman et al. (2019). 27 Brannon (2019). 28 Khan (2016). 29 Makovšek and Veryard (2016). 30 Jagasia (2017). 31 Farrell and Simcoe (2012). 32 Simcoe (2012). 33 Farrell (2012). 34 World Bank (2018a). 35 European Commission (2019). 36 Miller (2016); Parker (2016). 37 Lee (2019). 38 World Bank (2018b). 39 OECD (2013a). 40 OECD (2018b) 41 Meijer (2019). 42 HM Treasury (2018). 43 IMF (2019). 44
# 3. **Aggregate Governance** Inidicators

In this section, we organize a subset of the govemance indicators into six clusters corresponding to six basic aspects of governance, and describe how these indicators can be combined into aggregate governance indicators. In Appendix 2, we present six tables which identify the specific components of each governance cluster with reference to the tables describing sources in Appendix 1. The aggregate governance indicators described here are based on data for 1997 and 1998.8 We stress at the outset that the classification of indicators into clusters is not intended to be definitive. Rather, it reflects our own views of what constitutes a useful and interesting organization of the data that is consistent with prevailing notions of governance.<sup>9</sup>

# **Governance Clusters**

The first two governance clusters are intended to capture the first part of our definition of governance: the process by which those in authority and selected and replaced. We refer to the first of these as "Voice and Accountability", and include in it a number of indicators measuring various aspects of the political process, civil liberties and political rights. These indicators measure the extent to which citizens of a country are able to participate in the selection of governments. We also include in this category three indicators measuring the independence of the media, which serves an important role in holding monitoring those in authority and holding them accountable for their actions.

The second governance cluster is labelled "Political Instability and Violence". In this index we combine several indicators which measure perceptions of the likelihood that the government in power will be destabilized or overthrown by possibly unconstitutional and/or violent means. This index captures the idea that the quality of governance in a country is ccmpromised by the likelihood of wrenching changes in 8 As we expand the govemance database, we hope to construct comparable aggregates for other time periods, enabling comparisons over time as well as across countries.

9Users of the govemance database may question both the classification of individual indicators into these six