rafmacalaba/data-use-mentions-extended huggingface dataset:
In states like.. Orissa.. there is no system of the annual revision of the record of rights. ..The records are usually brought up-to-date only during re-survey and settlement. As these operations are usually done at very long intervals the records remain out of date most of the times (Appu, 1997: 102-3).

Poor land records often lead to:

- **_Difficulty in protecting land rights:_** Land records specify the rights of individuals and the state with respect to a particular parcel of land. It is difficult to uphold rights if they are not recorded or updated regularly.

- **_Difficulty in targeting development initiatives:_** An inefficient land records system makes it difficult to retrieve timely information for purposes of policy formulation and targeting of state benefits.

- **_Increase in rent-seeking:_** Lack of records restricts the flow of information and gives rise to economic rents that may be captured by government officials.

- **_Increase in rural/agrarian violence:_** While poor land records do not directly lead to agrarian violence, they contribute by making it difficult to enforce rights and target benefits to the poor.

- **_Encroachment of government land:_** Poor or partial maintenance of land records has led to the steady encroachment of government land.

- **_Uncertainty in conveyancing._** An imperfectly maintained land records system increases the costs of all land transactions and prevents the development of a freely functioning land market.

- **_Inability of landowners to access credit:_** Poor land records make it difficult to use land as a collateral to raise credit in both institutional and informal markets.

Various plan documents have recognised these factors, and a conference of revenue ministers on land reforms in 1985 reached a consensus that 'computerisation of land and crop-based statistics should be taken up on pilot basis at tehsilVrevenue-circle level' (GOI, 1985:54). Computerisation of land records (COLR) is now being implemented to redress
majority of primary teachers have difficulties with high-school material.

The tests used in this research were developed by the Center for Educational Assessment of the Indonesian Ministry of Education and Culture ( _Pusat Penilaian Pendidikan_ or _Puspendik_ ). The tests consist of a 20 item math component, a 20 item science component, and a 20 item Indonesian language component. Another 40 items were used to assess pedagogical knowledge and social and personality traits. Teachers had 2 hours to complete the entire test. This meant that teachers had 1 minute and 12 seconds on average for each question. All questions were 5-option multiple choice questions. Approximately 1,700 primary school teachers in 240 public primary schools were tested.

The test intended to roughly reflect what the Center for Educational Assessment expects teachers to know. Scores of around 60 percent correct or higher, then, are considered “a pass”. But what does passing really mean? The Center for Educational Assessment may have been too ambitious for example, by setting targets too high. It is even more difficult to compare such raw scores internationally as standards for _appropriate levels of knowledge_ may differ between countries. Comprehensive studies specifically designed to evaluate teachers’ subject-matter knowledge across countries, such as PISA or TIMSS for students, do not exist. Hanushek, Piopiunik, and Wiederhold (2014) cleverly measure cross-country variations in teacher knowledge from OECD’s Programme for the International Assessment of Adult Competencies (PIAAC). PIAAC does not focus on teachers explicitly, but as the PIAAC samples are large enough, sufficiently many teachers are part of it. The first round of PIAAC however does not survey any of the developing economies.

As cross-country comparisons of teachers’ subject knowledge are lacking in the developing world, we rely on introspection of the reader of this paper to make an assessment of whether Indonesian teachers are up to standard. For the analysis we select two items from the 20-item math component of the subject-matter test of the Midline study (fielded in April-May 2011). Midline data of teachers is arguably the most reliable source
# **3 An Empirical Example**

We illustrate the uses of this model using data from pediatric care units for a baseline evaluation of hospitals in Liberia (Bawo et al., 2015). In this evaluation, performance is measured with actual patients (do) as well as on vignettes designed to measure both knowledge and capacity. In the vignettes, knowledge (know) is measured by asking health workers to demonstrate how they would treat a patient “assuming you have all the necessary equipment and supplies.” The capacity (can) of health workers is measured by evaluating whether the equipment and supplies the health worker proposed to use in the vignette were actually present. For example, if a health worker knows that protocol requires the use of a particular medicine, but that medicine is out of stock, capacity will be lower than knowledge. The instruments and data collection are discussed in more detail in Bawo et al. (2015) and in Appendix A.

Figure 6 shows the average relationship among the three measures of quality for each health worker in the sample.1 In addition, we plot the average performance, capacity, and knowledge as a green point in each graph and use them to show the average value of each gap. The different interpretations of the average gaps and the underlying relationships between the input measures are informative. The average know gap is about 55%, the average know-can gap is about 2.5%, and the average can-do gap is 8.9%. The imbalance in the gaps might suggest that increasing knowledge is the most important way to improve performance. In addition, the small can-do and know-can gaps might suggest that improvements in equipment and motivation are not important.

However, the relationships between knowledge and capacity and capacity and perfor-

> 1The relationship shown as dashed lines is derived from non-parametric representation of each health worker in the respective two-dimensional spaces. We used locally weighted kernel regression with a bandwidth of 10 percentage points and the Epanechnikov kernel.

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accountability and “healthier” forms of citizen engagement. An emerging literature points to the scope for bottomup (popular movements) or top-down (leadership) shifts that bring in a new approach to policy making.<sup>76</sup> It is not by any means guaranteed. Political institutions do not change easily. The persistent policy volatility and accompanying economic volatility of the last twenty-five years was not an accident. That said, a minimal degree of “normalcy” to policy making could have an important impact in terms of signaling the administration’s commitment—where one can define “normalcy” in terms of publishing reliable macroeconomic data, assuring that government payments (including debt service) are reliable and timely, establishing a coherent monetary and exchange rate regime, and limiting reversals in policies at the microeconomic level. In addition, simply reverting to a renewed cycle of rapid policy reversals could reinforce the reputation that these cycles are to be expected in Argentina. For example, pragmatic reforms to state-owned enterprises or the national pension system that improve fiscal and financial sustainability might enhance a return to credibility more effectively than “re-privatizing” these entities.

> 75 World Bank 2016, page 3.

> 76 See, for example, Bidner and François (2013) on the emergence of political accountability.  Also see Persson and Tabellini (2009) and Besley and Burgess (2002).  See Açemoglu and Robinson (2001) for a model of more radical regime shifts from democratic to nondemocratic states, motivated by the degree of inequality and redistribution. Keefer and Vlaicu (2008) present a model of why some new democracies may stay in low credibility states whereby clientelism and patronage politics persist.

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observations that are classified as either ‘authorized leave’ or ‘official duty’ is in this range for the five states with the lowest overall absence rates - even treating the stated reasons for absence as being fully true (tables available on request).

To estimate the cost of teacher absence, we use teacher salary data from our surveys and use administrative (DISE) data on the number of primary school teachers by state (Table 9 - columns 1 and 2).<sup>27</sup> We provide three estimates of the fiscal cost of teacher absence in columns 3 to 5 of Table 9 (based on assuming the rate of legitimate teacher absence to be 8, 9, and 10 percent respectively), and these calculations suggest that the annual fiscal cost of teacher absence is around Rs.81-93 billion (US$1.4-1.6 billion/year at 2010 exchange rates).

> 26This waste is especially costly in low-income countries because they typically have low tax/GDP ratios and hence face greater fiscal constraints on mobilizing resources for public investment.

> 27The salary figures in our surveys do not include the fiscal cost of the benefits provided to civil service teachers. Imputing the value of these benefits is difficult for the majority of teachers who are on a defined benefits pension program. However, newer cohorts of government employees are covered by a (less generous) defined contribution retirement program where the government contributes 10% of pay to a retirement account. We use this conservative estimate and add 10% to the average salary figures. No adjustment is made for medical

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comes from the 2012-13 District-level Household and Facility Survey (DLHS) of India. The columns present 2SLS estimates specified in equations **??** and **??** . We define close elections as those in which victory margin is less that 3 percent of votes. In Panel A, the dependent variables are indicators for the presence of each health facility. In Panel B, the dependent variables are indicators for whether a village is connected to each health facility with all-weather roads. In Panel C, the dependent variables are indicators for whether each public program has been implemented in the village. All regressions include district controls and state fixed effects. District-level controls include the share of female population, the share of urban population, and the share of SC/ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

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# **Table A4: Ghana 2013 Management Survey**

|**Management Practice**|**Topic**|**Ghana 2013 questions**|**Score 1 (2013)**|**Score 3 (2013)**|**Score 5 (2013)**|
|---|---|---|---|---|---|
|**CS-Autonomy**|**Roles**|Examines how well procedures<br>for routine work are structured|<br>Procedures for undertaking<br>routine operations are<br>undefined or are rarely<br>followed in practice. The<br>organization relies almost<br>entirely on top-down<br>assignment to get work done,<br>so execution depends on the<br>individuals involved. Main<br>rationale for current practice is<br>“that’s the way it’s always been<br>done”.|Procedures for routine<br>operations are defined and<br>generally followed, but may be<br>inefficient or arbitrary. Where<br>responsibility for work is<br>assigned to individuals, follow-<br>up and checks sometimes<br>occur but are inconsistent.<br>There is some consideration of<br>how best to undertake day-to-<br>day operations but this is<br>mainly on an_ad hoc_ basis.|Routine operations procedures<br>have been consciously<br>structured to improve quality<br>and efficiency, and staff<br>consistently implement these<br>procedures.|
||**Flexibility**|Examines processes for and<br>attitudes towards continuous<br>improvement<br>Examines whether practice<br>improvements are effectively<br>disseminated throughout the<br>organization|Problems are only
new source of cross-country narrative fiscal data and the other is based on structural shocks estimated from a proxy SVAR.

These results should be interpreted with caution for a few reasons: first, the sample includes only a selected number of OECD countries for a sample period between 1978 and 2014 as my choice of sample is constrained by available sources of narrative fiscal data. Hence the results may not generalize to Emerging Markets and Developing Economies(EMDEs). Second, my empirical results for consumption is robust to alternative definitions of high-debt regime but not to alternative definitions of austerity. In my empirical results for GDP growth, coefficient estimates for government spending is not significant. Finally, austerity programs are often accompanied by other structural reforms. which this paper does not examine. In sum, empirical results in this paper should not be readily interpreted as suggesting that fiscal austerity is always the appropriate course of action whenever debt level is high.

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</sup> of _θ_ , where _θ_<sup>ˆ</sup><sup>_I_</sup> satisfies the following assumption, which is a double-sample analogue of Assumption 4:

**Assumption 9** _The estimator θ_<sup>ˆ</sup><sup>_I_</sup> _of the model parameters θ satisfies the following properties:_

# _where Vθ_<sup>_Iis a symmetric positive-definite κ × κ asymptotic covariance matrix ofθ_ˆ</sup><sup>_I._</sup>

13 Since data on _x_ is already being collected for all households in all clusters of the sample, the marginal cost of collecting data on _y_ from all clusters is identical to the cost of collecting _y_ for the same number of households from half the number of clusters, say. Given that any two households from different clusters carry more information than a pair of households from the same cluster, due to spatial correlation, it is optimal to collect _y_ for a sub-sample of households from all clusters. The added advantage of collecting household expenditure data from all clusters is that it allows the user to construct unit value prices for each cluster which are needed to convert nominal household expenditure data into real terms. Alternatively, the survey could collect its price data by visiting local markets, i.e. by including a community price module. In this study we will abstract away from spatial (and temporal) price adjustments, and assume that this is taken care off. Note however that this is not a trivial matter, see e.g. van Veelen and van der Weide (2008).

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# The Risk of Polygamy and Wives’ Saving Behavior

_Marie Boltz and Isabelle Chort_ JEL codes: J12, D13, D14, O12 Marie Boltz is with the Department of Business Administration, Pontificia Universidad Javeriana, Bogotá, Colombia; the Centre for Research in the Economics of Development, University of Namur, Belgium; e-mail <u>marie_boltz@javeriana.edu.co. Isabelle Chort is with the Université Paris-Dauphine,</u> PSL Research University, IRD, LEDa, DIAL, 75016 Paris, France; e-mail: <u>isabelle.chort@dauphine.fr. We would like to thank the Editor Andrew Foster, three</u> anonymous referees, Luc Arrondel, Jean-Marie Baland, Xavier Gine, Laurent Gobillon, Marc Gurgand, Sergei Guriev, Kenneth Houngbedji, Elise Huillery, Alain de Janvry, Sylvie Lambert, Karine Marazyan, Joan Monras, Pauline Rossi, Elisabeth Sadoulet, and Denni Tommasi for precious comments and suggestions, and Philippe De Vreyer and Sylvie Lambert for providing us with the PSF data. We are also grateful to participants to the 2015 DIAL Development Conference at the Université Paris-Dauphine, the 2015 German Economic Association, the Research Group on Development Economics Conference in Kiel, the 2015 Journées de Microéconomie Appliquée in Montpellier, the 2015 Annual Conference of the Royal Economic Society in Manchester, the 2015 CSAE Conference in Oxford, the 2015 Annual Doctoral Conference of the Association for the Development of Research in Economics and Statistics at the Centre d’Economie de la Sorbonne in Paris, the 2014 Northeast Universities Development Consortium Conference at Boston University, the 2014 Jamboree conference of the European Doctoral Program in Quantitative Economics at the Paris School of Economics, the 2014 5th GRETHA International Conference on Economic Development in Bordeaux, and the audience to seminar presentations at the Paris School of Economics. This research is part of the NOPOOR project, which is funded by the European Union under the 7<sup>th</sup> Research Framework Programme (Theme SSH.2011.1) - Grant Agreement No.
defined at the four or six digit level of aggregation in the Harmonized System (HS-6 or HS-4) (e.g. technical requirements in the production of the textiles and apparel sector examined here).<sup>3</sup> A growing literature concludes that these requirements necessary to prevent trade deflection −i.e. importing via the low tariff partner and then re-exporting duty-free within the preferential area− really serve as protectionist devices that end up impeding market access for the intended beneficiaries.

This evidence is based on two ingredients: (i) utilization rates of preferences (at the tariff-line level)or the share of imports entering a market under preferential access , and; (ii) synthetic ordinal indexes, based on simple observation rules intended to capture in a single ordinal index the restrictiveness of multiple and complex product-specific RoO (for example having a change of tariff classification combined with a technical requirement is more restrictive than only having a change of tariff classification requirement).

Repeated analysis of disaggregated data shows a positive correlation between the extent of preferential access and the value of the constructed restrictiveness indexes (a higher value of the index indicating a more restrictive product-specific RoO). The data also show a tapering off or even a decline in utilization rates as preferential margins increase, presumably because it becomes more costly to satisfy rules becoming increasingly complex. These correlations have led researchers to conclude that RoO can be “made-to-measure” protectionist devices. With a large share of North-South trade taking place under preferential status, getting a better grasp of the effects of RoO is now a first-order priority in improving our understanding of the overall restrictiveness of trade policy.<sup>4</sup> The difficulty with the available evidence is the presumption that variation in utilization rates is a plausible indicator of the costs of a RoO regime. The data often show high utilization of preferences for tariff lines with zero Most Favored Nation (MFN) tariffs even though compliance costs are estimated at around 2-3% of the product price (see for instance
**Figure 15. Individuals with an account at a bank or other financial institution, %**

<!-- Start of picture text --> 30% 28%<br>26%<br>20% 20%<br>20%<br>17%<br>16%<br>15%<br>13% 13%<br>9% 11% 10% 10% 10%<br>10% 7% 7% 6% 7% 6% 8% 8% 7% 9% 8% 9% 9%<br>6%<br>4% 4% 4% 4% 4% 4%<br>3% 3%<br>2%<br>0%<br>All (15+) Rural Male Female In Labor Out of Young Older Primary secondary Poorest Richest<br>Force Labor Adults Adults Education education 40% 60%<br>Force (25+) or less or more<br>By Gender By LF Participation By Age By Education By Quintile<br>Account 2011 Account 2014 Borrowed to start, operate, or expand a farm or business 2014<br><!-- End of picture text --> _Source_ : Global Findex Data 2011, 2014.

Women rely to a larger extent than men on informal institutions for access to money and finance. For example, women have a harder time coming up with emergency funds according to Global Findex 2014 data. While almost half of the women surveyed (48 percent) reported not being able to come up with emergency funds, only 39 percent of the men reported the same. There is also a noticeable difference in the source of these emergency funds, with 64 percent of women relying on family and friends, compared to 42 percent of men. There are a number of informal financial institutions in Sudan, and Kevane and Stiansen highlight three: _sandug_ , _dayn_ , and _shayl._
deviations exceeding 1 standard deviation also reduce firms’ performances in heat-sensitive sectors in UMCs whereby firms in both types of sectors in HCs are not affected. The stronger impact of temperature deviations on firms in heat-sensitive sectors corroborates the robustness of our causal interpretation of the results.

Overall, the results highlight that—consistent with recent microeconomic studies—the effect of climate change is not limited to specific aggregate sectors, such as agriculture, construction, or specific manufacturing sectors such as food or textiles. Instead, the impact of temperature changes seems to primarily depend on task-specific exposure to heat across all sectors. Moreover, small, young, or non-exporting firms in developing countries often face stronger constraints to adaptation, making them more vulnerable to the impact of rising temperatures.

# **6.4 How a favorable business environment enhances firms’ resilience to climate change**

The stronger impact of rising temperatures on small and young firms points to binding constraints for those firms to adapt.<sup>6</sup> For example, limited access to affordable finance, high transaction costs, or long waiting times due to cumbersome procedures to get construction permits can limit firms’ investment in resilient buildings or cooling technologies. Deficiencies in labor, health, and safety regulations such as indoor temperature limits or in the implementation/inspections to enforce these can further undermine firms’ adoption of more resilient technology or practices.

The WBES is unique in that it provides information on the business environment, allowing us to directly estimate how it impacts the vulnerability of firms to climate change. We therefore construct four business environment indices measuring (i) access to finance, (ii) business regulations, (iii) the quality of electricity services, and (iv) security. The indices

> 6Burke et al. (2024), for example, suggests that the ability of firms to cope with climate shocks is strongly mediated by the business environment in which a firm operates.

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both formal organizational response (such as that by non‐governmental organizations who are named in national databases), as well as informal community _initiatives_ which might not have any organizational (or physical) structure and be significantly more fluid in nature.

A first useful distinction can therefore be made between _informal_ and informal community initiatives, as described in box 3.

# **Box 3: Distinguishing between formal and informal community‐based initiatives**

**_Informal community initiatives:_** usually have no organizational infrastructure, might not have an actual facility, often rely on voluntary effort, and operate without external support. However, they may have social support structures and systems.  For example, grassroots or indigenous community initiatives and self‐help groups.

**_Formal community initiatives:_** usually have some form of institutional infrastructure and status that might include a facility, legal registration, a bank account, a management committee, defined responsibilities, paid staff, strategic plans, and they may receive external support.  Others may have all the characteristics of institutional status but may be denied legal registration because of the stigma attached to their work and/or the people they represent. For example, community‐based organizations (CBOs), non‐governmental organizations (NGOs), faith‐based organizations (FBOs).

9