rafmacalaba/data-use-mentions-extended huggingface dataset:
should be known. In any event, for both these types of regulations it was assumed that the missing estimate (costs or benefits) were accurate for the purposes of calculating a change in benefit-cost ratio. Pesticides represent a slightly different case since the rules in question were bans on pesticide uses for certain crops in particular regions.

OMB scoring criteria are slightly different from HMN, especially for assessing the effectiveness of rules. Whereas HMN stopped at examining the “effects” of regulation in physical terms, OMB used a hierarchy. The most desirable benefit criterion is the total monetized effects of the regulation, if available in both the _ex ante_ and _ex post_ study. If this metric is not available, the benefits are the quantitative effects of the regulation, just as in HMN. OMB’s preferred metric for assessing accuracy is the change in benefit-cost ratio. If it is overestimated—that is, higher in the _ex ante_ versus the _ex post_ estimate, that means the performance of the regulation is not as good as was predicted in the _ex ante_

> 4 The Gianessi study of EPA pesticide cancellations (for particular crops in designated areas) was sponsored by RFF and was ongoing at the time the HMN manuscript was in preparation. We took results from the six finished case studies. In all, 31 case studies were completed, but after excluding pesticide rules for which farmers applied for and received emergency or permanent exemptions, 26 remain..

13
Policy Research Working Paper 9358

# **Abstract**

Pay levels for public sector workers—and especially teachers—are a constant source of controversy. In many countries in Sub-Saharan Africa, protests and strikes suggest that pay is low, while simple comparisons to average national income per capita suggest that it is high. This study presents data on teacher earnings from 15 African countries. The results suggest that in several (seven) countries, teachers’ monthly earnings are lower than other formal sector workers with comparable levels of education and experience. However, in all of those countries, teachers report working significantly fewer hours than other workers, so that their hourly earnings are higher. Teachers who report fewer hours are no more likely to report holding a second job, although teachers overall are nearly two times more likely to hold a second job than other workers. With higher national incomes, the absolute value of teacher earnings rises, but they fall as a percentage of income per capita. The study explores variation across types of teacher contracts, the association between teacher earnings and student performance, and the association between teacher earnings premia and other aspects of economies.

This paper is a product of the Development Research Group, Development Economics Vice Presidency and the Center for Global Development. 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 devans@cgdev.org, fyuan@g.harvard.edu, and dfilmer@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,
2,130|1,314|900|93|15|99|207|81%|
|Dominican<br>Republic|209|209|80|13|0|0|116|86%|
|Ecuador|543|543|294|66|0|124|59|61%|
|Peru|387|387|228|83|0|67|9|60%|
|Total|5,657|3,656|2,103|417|15|464|657|70%|

# _Source_ : Own calculations using WBSCPS.

_Notes_ : This table shows the universe, sample size, and effectiveness measures for the WBSCPS. For Dominican Republic and Ecuador, the universe of programs was included in the survey. For Peru, the universe of licensed programs was included. For Brazil and Colombia, representative samples were drawn from the States of Sao Paulo and Ceara and at the national level, respectively. Columns (1) and (2) show the number of programs in the universe and sample sizes for each country in the study, respectively. Columns (3) through (6) indicate the number of surveys by status for each country: column (3) indicates completed surveys; columns (4) and (5) indicate the number of surveys that SCP directors declined to answer or that did not answer because they were preparing for distance learning, respectively; and column (6) indicates surveys not answered for other reasons, such as lack of provost authorization. Column (7) shows the number of programs that were closed at the time of the survey but that were reported as open in the sampling frames. Columns (3) through (7) add up to total sample size (column 2). Adjusted response rate is the share of effective surveys (column 3) relative to all active programs in our sample (i.e., Sample size (2) – Closed programs (7)
"fractional," that is, it may take all possible values in the unit interval. Hence, a linear regression is not appropriate to analyze the effects of and PP3 on as it does not ensure that the predicted values of lie in the unit interval (Papke and PP2 PP1 Wooldridge, 1996). Instead, we should estimate a fractional outcome model (Papke and PP3 PP3 Wooldridge, 1996, 2008; Wooldridge, 2019). Given the panel nature of our datasets, we run the fractional Probit regression proposed by Papke and Wooldridge (2008).<sup>36</sup> This model guarantees that the predicted values of lie in the unit interval through a link function, that is, a cumulative distribution function (CDF). PP3 Let denotes a vector of explanatory variables. The fractional Probit model is as follows: xx 1 × KK i i i ) ii ii (4) EE(PP3 |xx , mm ) = ΦΦ(xx ββ+ mm where i ≤1, and refer respectively to the country and the year, is the standard normal CDF, is a vector of parameters to estimate. denotes the unobserved effects. 0 ≤PP3 ii ff ΦΦ(. ) As stressed by Papke and Wooldridge (2008), the use of a Probit link function, i.e., a standard ii ββ KK× 1 mm normal CDF instead of a logistic one, does not cause any "conceptual or theoretical" problem. Instead, in the presence of endogenous covariates, using the Probit link function is

> 36 For cross-sectional data, interested readers may refer to Papke and Wooldridge (1996). Wooldridge (2019) discusses recent theoretical developments on the fractional Probit within the framework of the correlated random effects modeling with unbalanced panels.

22
Policy Research Working Paper 6319

# **Abstract**

Participation in social programs, such as clubs and other social organizations, results from a process in which an agent learns about the requirements, benefits, and likelihood of acceptance related to a program, applies to be a participant, and, finally, is accepted or rejected. The authors propose a model of this participation process and provide an application of the model using data from a social program in Mexico. Their empirical analysis illustrates that decisions at each stage of the process are responsive to expectations about the decisions and outcomes at the subsequent stages and that knowledge about the program can have a significant impact on participation outcomes.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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 authors may be contacted at dcoady@imf.org, martinel@itam.mx, and susan.parker@cide.edu.

_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._ Produced by the Research Support Team
- Castillo, H., Price, A., Moobela, C., & Mathur, V. 2007. Assessing urban social sustainability: current capabilities and opportunities for future research. The _International Journal of Environmental, Cultural, Economic and Social Sustainability_ , 3(3), 39-48.

- Colantonio, A. 2010. Urban social sustainability themes and assessment methods. Proceedings of the Institution of Civil Engineers: _Urban Design and Planning_ , 163(2), 79-88.

- Cuesta, J, Niño-Zarazúa, M. & B. Lopez-Noval. 2021 Global estimates of socially excluded populations. Mimeo, World Bank, SSI Global Practice. Washington DC: World Bank.

- Cuthill, M. 2010. Strengthening the ‘social’ in sustainable development: Developing a conceptual framework for social sustainability in a rapid urban growth region in Australia. _Sustainable Development_ , 18(6), 362-373.

- Dempsey, N., G. Bramley, S. Power and C. Brown. 2011. The Social Dimension of Sustainable Development: Defining Urban Social Sustainability. _Sustainable Development_ 19, 289–300.

- Ghahramanpouri, A., H. Lamit and S. Sedaghatnia. 2013. Urban Social Sustainability Trends in Research Literature. _Asian Social Science_ ; Vol. 9, No. 4; 2013

- Giddings B, Hopwood B, O’Brien G. 2002. Environment, economy and society: fitting them together into sustainable development. _Sustainable Development_ 10: 187–196.

- Glasson, J., and G. Wood. 2009. Urban Regeneration and impact assessment for social sustainability. _Impact assessment and project appraisal_ , 27 (4), 283–290.

- Henson, S., Kambhampati, U., Mogues, T. et al. 2020. The Development Impacts of COVID-19 at Home and Abroad: Politics and Implications of Government Action. _European Journal of Development Research_ 32, 1339–1352

- International Co-operative Alliance, ICA, 2016. Sustainability Reporting for Co-operatives: A Guidebook. ICA
year raises the social status by only 0.02) pales relative to that for indicators to access to power (i.e., 0.82 for Congress or Consultative Conference memberships), indicating a higher return (in terms of raising political and social statuses) to political capital (and potentially rent seeking) than that to human capital investment, and that there is likely persistent discrimination against non-elite firms. Interestingly, the current implicit encouragement to attain political office for elites reflects the millennium-long tradition of the paramount importance of power in traditional Chinese societies.

_Evolution of firm characteristics_ **.** Based on the private enterprise surveys, the annual surveys of industrial firms, and the CSMAR database on listed firms, we obtain three findings. First, as shown in panels A (for private firms) and B (for listed firms) of Table 6, Chinese private firms have expanded rapidly in size whether as measured by firm assets or by the number of employees. The average number of employees from the private enterprise surveys rises from 61 in 1993 to 163 in 2012, an increase of 167 percentage points. Moreover, the capital intensity of private firms has quadrupled on average, indicating rapid technological upgrading.

Second, whether measured by input (R&D input) or output (number of patents, and sales of new products), Chinese private firms have become increasingly innovative.<sup>13</sup> They invest more in R&D and training, and have more patents and self-designed products over time (panel B in Table 6). Moreover, the intensity of firm innovation displays substantial variations across regions and firm size, with larger firms and coastal firms being significantly more innovative (panel B in Table 6 and Figure 4). Furthermore, private firms invest much larger proportions of their revenue in R&D than state-owned enterprises: the relative R&D intensity (i.e., R&D expenditure over sales) of private firms is seven-fold that of SOEs (
costs) in the service sectors may be quite large compared to remaining import protection of goods sectors. Ignoring policies affecting trade and investment in services, and more generally regulatory policies

> 38See the example in Section 7, as well as Balisteri, Rutherford, and Tarr (2009). In the latter, the authors work with survey data to benchmark barriers to foreign service firms as well as regulatory barriers that affect both foreign and domestic business service providers in Kenya. They identify large potential gains from reform (up to 50% of baseline consumption in the long-run). However, this is not from removing prevailing market access restrictions per se, but rather from regulatory barriers that are applied on a non-discriminatory basis. This message is consistent with our own assessment of Italy in this paper. Domestic regulation is likely as important, if not more so, as market access conditions.

39One of the authors of this paper recalls a presentation by Bob Stern of the Brown, Deardorff, Fox, Stern (1996) results. Stern characterized the modeling process at the time as follows. ”With modeling, we are lifting policy rocks, looking underneath for big effects. We don’t know much about services trade yet, but given the size of the sector, the rocks are big, and so the effects underneath are likely to be as well.” 37
.3 is to double the agricultural productivity and incomes of small-scale food producers by 2030. The target is monitored through indicator 2.3.1, volume of production per labor unit – that is, one of the productivity measures we found were subject to measurement error related to the

> 13 The Malawi IHPS 2016/17 data are omitted because the two-visit structure of that survey makes time between interview and beginning of fieldwork difficult to interpret.

17
|he dependent variable<br>publicly available data<br>olumn 3); in individual<br>uarterly disbursements<br> reported) and include<br>ables are winsorized at|(11)<br>All haven|2.4143*<br>(1.3702)<br>2.2195<br>(2.0202)<br>2,345<br>0.1021<br>YES<br>YES|
|---|---|---|
|t data. T<br>ens with <br>e data (C<br>erest is q<br>DP (not <br>d aid vari|(10)<br>Jersey etc<br>**and period**|-2.0679<br>(2.0260)<br>1,304<br>0.0808<br>YES<br>YES|
|le deposi<br> non-hav<br>y availabl<br>ble of int<br>ange in G<br>posit an|(9)<br>Belgium<br>**ty by haven**|-1.0347<br>(0.7264)<br>2,075<br>0.0828<br>YES<br>YES|
|icly availab<br>n 1); in all <br>ith publicl<br>atory varia<br>entage ch<br>els. The de|(8)<br>Lux<br>**Heterogenei**|2.7165**<br>(1.1969)<br>1,796<br>0.0873<br>YES<br>YES|
|with publ<br>ta (Colum<br>n-havens w<br> the explan<br>rterly
.532***|
||(0.536)|(0.268)|(0.363)|(0.187)|(0.127)|
|Observations|673|1,428|1,382|1,373|1,431|
|Group 1,2 and 3 vs Group 0|3.093***|2.953***|-4.642***|1.066***|0.624***|
||(0.465)|(0.279)|(0.325)|(0.165)|(0.161)|
|Observations|1,882|3,999|3,850|3,866|4,004|

Notes: The estimations are contrasts between the matching differences of the indicator between the group of exposure and the comparison group. Variables for matching: ln of the provider’s height, eligibility score of PROSPERA in 1997 (linear and square), classification as poor household in 1997, youth’s gender, provider’s gender, provider’s age (linear and square), provider’s education, in 1997, two-months period when the transfers of PROSPERA were first received in the household of origin, number of persons in the household in 1997, and labor income of the household in 1997. Standard errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations by propensity score matching based on data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the panel of ENCEL 1997-2000.

Absolute mobility in schooling is translated into 2.5 additional years on average for the group with lower exposure to PROSPERA and 3.7 additional years on average for youth who started receiving the program during early childhood
by the fact that many types of shipments are only rarely observed, which can mean that developing country inspection agencies that lack appropriate IT may not be able to recall the outcome of a previous inspection, or even to know whether or not an unusual shipment type has been observed/inspected in the past.<sup>2</sup> We argue that a key contribution of modern IT in this setting is to reduce ambiguity; the information storage and analytical tools that modern systems provide allow the inspecting agency to better use information from the import declaration to predict the conditional probability that a given shipment is non-compliant. Improved capacity to predict non-compliance leads to a better allocation of inspection resources, allowing key enforcement goals to be reached, even as the overall inspection burden is reduced. These are the primary goals of most risk management reforms.

Our focus on ambiguity is motivated by discussions with operational experts in trade logistics, but we also motivate this focus with stylized facts about conditional inspection rates in data from the Serbian customs agency. The data do not allow us to measure ambiguity directly, but we are able to show that shipment types that were more frequently observed in the prior year are less likely to be inspected in the current year. The negative relationship between lagged shipment frequency and the inspection rate remains, even after we control for heterogeneous risks. Although greater ambiguity about the compliance of infrequent shipments is not the only explanation for this empirical result, we maintain that - in the context of international trade - ambiguity is likely to be a quantitatively important explanation for the fact pattern we observe. While our theory is applicable to both developed and developing countries, the quantitative importance of ambiguity is likely to be much larger in developing country inspection agencies that lack sophisticated IT.

We develop a theoretical model that links the equilibrium allocation of inspection resources to the underlying probabilities of non-compliance and to ambiguity surrounding those compliance with import regulations.

> 2During field work in a middle-income country, one of the authors visited a border station in which an
# 5.  Results

## 5.1.   Food insecurity

We estimate the relationship between conflict and food insecurity using the round-based panel because the CSI data have been collected in each wave and visit to measure seasonal changes in food insecurity. Table 2 shows the results from estimating the effect of victimization on the standardized CSI and the (standardized) components used in constructing the index. An additional conflict event is associated with the increase in the CSI by around 0.044 to 0.052 standard deviations (table 2, columns 1–3). Results are robust to adding controls (geographical and household specific, as listed in table C.1) and regionsurvey round fixed effects that capture region-specific time trends (table 2, column 3).

After a conflict event, a household might receive assistance from informal safety nets to overcome the event’s consequences. In order to understand how receiving assistance might affect our findings, in column 4 of table 2 we removed households that reported having received any assistance after the most recent event (from the telephone survey) as well as households that reported having received either cash or in-kind transfers in the GHS panel. A small subset (only 6 percent) of the households reported any assistance. Removing these households from the sample brings down the coefficient estimate to 0.038 standard deviations, but the result remains significant at the 5 percent level. This suggests that households that received any form of assistance were perhaps slightly more affected by the adverse events than households that did not receive assistance.

Columns 5–14 in table 2 show that the household coping strategies used after a conflict event are most often the kind that reduce food consumption: limiting portion sizes, restricting adult consumption so children can eat, and reducing the number of meals. Interestingly, households do not increase their borrowing of food or their reliance on help, results that are in line with the low level of assistance reported after a conflict event.

In table 3 we analyze whether the effects of victimization on food
fewer than 500 households, the full village was listed. This was the case for 27 out of the 33 villages. In the remaining six cases, the villages were divided into segments of 100-120 households each, and three segments were randomly chosen for the listing. If key informants at the village level indicated that the Rohingya households lived in particular segments, then one such segment was chosen as one of the three segments where a full listing was to be implemented. Additional segments indicated by the informant might also be included for full listing through random selection.

Table 3 summarizes Rohingya prevalence by upazila. In addition to a low average prevalence of 1.3 percent in the validation sample, a significant proportion of villages reported zero prevalence. Figure 2 and Table 3 let us compare the refugee prevalence based on the IPA population count and the household listing in the validation sample. The full household listing reported different numbers compared to the key-informantbased IPA population count, both for the number of households and refugee prevalence. However, the full listing confirmed the low prevalence of Rohingya displaced outside camps.

**<u>Table 3: Prevalence by upazila, validation household listing</u>**

8
# **3 Data, Calibration, and Shocks**

## **3.1 Data Sources**

Our data construction process follows steps similar to those in RUV but uses 2019 as the base year (whereas in RUV the base year is 2000). A summary of the data-construction B. process is provided here, with additional details available in Appendix _Labor, input, and consumption shares:_ Data from the BEA (for U.S. states) and from ICIO (OECD’s Inter-Country Input-Output Database) is used to compute the value-added share in gross output (which is used as the labor share in the model) and the input-output coefficients for each region. Consumption shares can be inferred based on trade flows, labor shares, and intermediate input shares.

_Bilateral flows:_ The model requires bilateral trade flows in all sectors between any pair B.2. of regions in the sample. These are constructed in 4 steps outlined in Appendix _Labor flows across sectors and regions:_ Data for intersectoral mobility for each U.S. state is obtained from the Current Population Survey (CPS), while frictionless mobility is assumed for other countries. In the extension allowing for migration within the U.S., data for interstate mobility is obtained from the American Community Survey (ACS).

_Labor supply:_ Data on employment by sector (including home production) comes from the WIOD Socio Economic Accounts, the International Labor Organization, the U.S. Census, and the ACS. As in RUV, only observations for people aged between 25 and 65 are kept. Labor force participation is measured as the share of individuals in that age group who are either employed or unemployed.

## **3.2 Parameter Calibration**

Table 1 describes the parameters used in the baseline specification. Notice that _γ_ (the nominal growth rate of world GDP in U.S. dollars) and _δ_ (the downward nominal wage rigidity parameter) are not separately identified. For a