rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 7: MENA SAI micro data openness sub score vs. ODIN 2020 openness sub score.**

<!-- Start of picture text --> Algeria 0.0 26.3<br>Bahrain 0.0 66.7<br>Djibouti 7.1 29.1<br>Egypt , Arab Rep. 21.4 33.8<br>Iran, Islamic Rep. 21.4 37.9<br>Iraq 14.3 52.1<br>Jordan 7.1 48.7<br>Kuwait 0.0 45.5<br>Lebanon 14.3 48.2<br>Libya 0.0 14<br>Malta 14.3 49.1<br>Morocco 28.6 71.8<br>Oman 7.1 93.2<br>Qatar 0.0 43.6<br>Saudi Arabia 0.0 44.1<br>Syrian Arab Republic 0.0 25.6<br>Tunisia 35.7 61.8<br>United Arab Emirates 0.0 85.9<br>West Bank and Gaza 42.9 75.4<br>Yemen 0.0 32.5<br>0.0 50.0 100.0<br>2020 ODIN openness score Microdata openness sub score<br><!-- End of picture text --> <mark>Source: Author’s compilation using own data and 2020 ODIN data from Open Data Watch -- Open Data Inventory</mark> <u><mark>http://www.opendatawatch.com</mark></u>

**<mark>Figure 8: ODINs poverty and income openness scores vs MENA MAI consumption/welfare data openness score</mark>**

<!-- Start of picture text --> 100 100100 100 100<br>100 80<br>7070<br>80 60 60 60 6060 60<br>50 50 50 5050 50 50 50 50 50<br>60 4040 40 40 40<br>40 20<br>20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0<br>0<br>2018 ODIN openness score (Poverty and Income category)<br>2020 ODIN openness score (Poverty and Income category)<br>MENA MAI (Consumption data openness score)<br><!-- End of picture text --> <mark>Source: Author’s compilation using own data, 2018/19 and 2020 ODIN data from Open Data Watch -- Open Data Inventory</mark> <u><mark>http://www.opendatawatch.com</mark></u> Note: Countries without poverty and income openness score for the 2018/19 and 2020 ODIN are not shown.

29
A more concerning equity consideration may also arise if students’ performance at baseline is correlated with their socio-economic status (SES). In that case, the heterogeneous effect could imply that the program benefits more students of high socio-economic status. However, unlike for baseline academic performance, we do not find significant interaction effects of gender or socioeconomic background with PSI-PMI program on student performance (Table 6). Table A5, in the appendix, provides the results if we interact the PSI-PMI program with socioeconomic background, in which case we find no statistically significant interaction effect.

**Table 6** : Effects of the PSI-PMI Program on Student Learning Outcomes: Interaction Effect

|||Outcome variable:<br>|||
|---|---|---|---|---|
|Variable|Outcome variable:<br>Standardized<br>mathematics score|WASSCE<br>mathematics<br>score|Outcome variable:<br>Standardized<br>mathematics score|Outcome variable:<br>WASSCE<br>mathematics score|
||Within-school c|ontrolgroup|Between-schoo|l controlgroup|
||(1)|(2)|(3)|(4)|
|PSI-PMI program|0.477**|0.477*|0.632**|0.279|
||(0.204)|(0.262)|(0.259)|(0.357)|
|Interaction effect|||||
|(Program*Gender: 1=Female)|-0.020|0.164|0.004|0.215|
||(0.147)|(0.131)|(0.164)|(0.169)|
|Student-level covariates|YES|YES|YES|YES|
|School-level covariates|YES|YES|YES|YES|
|Average mathematics score|||||
|for control|38.78|20.18|33.37|20.74|
|Standard deviation of|||||
|mathematics score for control|21.91|15.60|18.51|15.15|
|Observations|875|873|1,044|1,037|
|R-squared|0.265|0.334|0.226|0.235|

_Note:_ The results are based on equation (2). The outcome variables are the standardized mathematics score from the researcher designed exam and the standardized WASSCE mathematics exam score. Robust standard errors are in parentheses. PSI-PMI = Progressive Science Initiative and Progressive Mathematics Initiative; WASSCE = West African Secondary School Certificate Examination. *** p < 0.01, ** p < 0.05, * p < 0.1. _School covariates_ variables include: presence of electricity in school, good desks in school, school has a library, a computer lab, running water in the school, student enrollment, student-teacher ratio, percentage of qualified teachers, percentage of female teachers, experience of the school principal, age of school principal, administration experience of school principal, level of education of school principal, school principal is female, share of students with high performance in mathematics, English, and science on the WASSCE. _Student-level covariates_ variables include the following: percentage of students not repeating any grade, percentage of students earned credit and above on GABECE mathematics, percentage of students earned credit and above on GABECE English, percent of students earned credit and above on general science, all at baseline. And socioeconomic characteristics of students—percent of students in households that has a quality roof, a wall, television, car, access to grid electricity, <u>parents have some education.</u> 24
Both FIF and WIF groups had similar types of proposed activities. These activities included crop production, natural resource management, livestock and poultry rearing, apiculture, and off-farm activities.

# **4. Data and Baseline Discussion**

In order to study the effect of these two interventions on agricultural productivity and income, three rounds of data were collected to form a panel data series from 1,595 households, of which 1,214 are used for the analysis in this paper. Round 1, the baseline, was collected from August to September 2010 and covered 2,675 households. Randomization only occurred after the baseline had been collected. Only 1,806 households that were members of a FIF group with an eligible proposal were kept in the study and followed up through to the endline. One and a half years after FIF was implemented, and a year after WIF, midline data were collected (March to April 2012). Finally, in June 2013 endline data were collected. In this section, we discuss existing baseline differences in our main outcome variables.

From the original sample of 1,806 FIF eligible households, 1,595 households were interviewed in all three rounds and 1,214 of them are used for this study. In our analysis, we only used households that satisfied our selection criterion: married couples.<sup>9</sup> Our sample has a total of 634 FIF and 158 WIF households. From these two groups, there are a total of 76 households that receive both FIF and WIF treatments. In addition, we have a total of 498 pure control households. Figure A1 in the appendix lays out the study treatment arms.

Our study sample is primarily male-headed, as shown in Table A1 in the appendix, where 98 percent of our sample at baseline was male-headed. At baseline, the average age of the household head was 44.5, with an average household size of 6.4 members and 0.03 members enrolled in school.

One critical issue for the robustness of our results is the comparability of households at baseline, prior to intervention. In appendix Table A2 , we present the baseline balance checks between treatment and control households for the FIF, WIF, and the sample that received both FIF and

> 9 The 381 single-headed households were dropped from the study as they did not satisfy the selection criterion for our analysis—married couples—which targets either female or male spouses or both.

10
much lower levels in real terms (figures 1 and 2). Therefore, the PS hypothesis began to fit the data well.

Second, the rationale and effectiveness of the bias against primary commodity sectors was being questioned. Bates (1981) called for a reconsideration of policies on commodity markets in order to promote economic development in rural communities. Lal (1985) criticized pricing policies and marketing arrangements in commodity‐ dependent developing countries. The tide took a definite turn against industrialization policies with the publication of two influential reports: the 1986 World Development Report, which highlighted the problems associated with policy interventions in agricultural commodity markets (World Bank 1986) and a detailed assessment of distortions associated with primary commodity sectors (Krueger, Schiff, and Valdès 1992).

Third, research on the long‐term behavior of ToT was aided by two important papers. With regard to estimation, Engle and Granger (1987) introduced cointegration— a procedure that enabled researchers to separate meaningful long‐term relationships from spurious correlations. On the subject of data, Grilli and Yang (1988) constructed a price index consisting of 24 internationally‐traded primary commodities since 1900.

At least 45 studies have examined the PS hypothesis since the 1980s, half of which support the PS hypothesis (Appendix A). The mixed evidence is not surprising given that ToT followed different paths across commodity groups and time. Interestingly, the results are mixed even for the studies that used the Grilli‐Yang data, implying that conclusions regarding the PS hypothesis are sensitive to methodology as well (of the 40 studies that tested the PS hypothesis since 1988, 23 have utilized the Grilli‐Yang data).

# **3. Modeling Food Price Trends**

The testable implications of the ToT‐income relationship can be examined within a 2‐ sector, closed‐economy framework as a move from equilibrium �� to ��, in response to an exogenous income shock (figure 3). Let ���, ��� and ���, ���denote consumption and prices of the primary and manufacturing commodities in period _i_ , and �� denote income, _i_ = 1, 2. Income level in the first period, �� , is consistent with consumption bundle [���, ��� ] and a price ratio of ����⁄����. As income increases to �� in period _2_ , it leads to a consumption bundle of [���, ��� ] and a price ratio of ����⁄����. Now assume neutral technical change and non‐homothetic preferences such that the increase in the consumption of the manufacturing commodity is larger than its primary commodity counterpart, ��� ���� � ��� ���� . These assumptions imply that ����⁄���� < ����⁄����, hence the inverse ToT‐income relationship consistent with Kindleberger’s thesis and, by extension, Engel’s Law. Conversely, under homothetic preferences and biased technical change against primary commodities, a positive ToT‐income relationship will emerge, consistent with the Malthusian path discussed earlier. (Note that non‐homotheticity of _4_
<!-- Start of picture text --> SKILLFINDER“—<br><!-- End of picture text --> <!-- Start of picture text --> @&) harambee— WORKFOR WORK —<br><!-- End of picture text -->

# Welcome

canada You have access to a database of young entry-level candidates who have ae . been assessed by the Harambee Youth Employment Accelerator on a range of cognitive ("hard") and non-cognitive ("soft") skills.

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Email us at: All candidates provided in this database have undergone a two-day assessment harambeeproject@povertyactioniab.org = process at Harambee and hold a matric or equivalent certification.

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## Candidate Database

<!-- Start of picture text --> Numeracy: Concept Formation: Control:<br>@ TOP # MIDDLE # LOWER @ TOP # MIDDLE # LOWER @ TOP # MIDDLE # LOWER<br>Choose: a Communication: Flexibility: Grit:<br>. een @ TOP # MIDDLE # LOWER #@ TOP # MIDDLE # LOWER @ TOP # MIDDLE # LOWER<br>@ Alexandra<br>@ Atteridgeville<br>@ Auckland Park Search: en’...<br>ee eel ition Age Numeracy Communication ConceptFormation Flexibility Control Grit<br>1 C214 ‘Soweto (Other) 35 MIDDLE MIDDLE TOP TOP TOP TOP<br>2 C527 Ekhuruleni 35 LOWER TOP LOWER MIDDLE LOWER TOP<br>3 C473 Tembisa 35 LOWER LOWER LOWER LOWER MIDDLE MIDDLE<br>4 C445, Finetown 35 LOWER MIDDLE LOWER LOWER LOWER LOWER<br>5 C104 Alberton 3 TOP MIDDLE MIDDLE LOWER LOWER MIDDLE<br>6 C673 Hillbrow 34 MIDDLE MIDDLE LOWER MIDDLE TOP MIDDLE<br>7 cS519 Other 34 TOP MIDDLE LOWER TOP MIDDLE LOWER<br>8 C589 Kaalfontein 34 LOWER MIDDLE MIDDLE TOP LOWER LOWER<br>9 c771 Germiston (Other) 34 TOP MIDDLE LOWER LOWER LOWER LOWER<br>10 C947 Leratong Village 34 LOWER LOWER LOWER MIDDLE LOWER LOWER<br>‘Showing 1 to 10 of 3.249 entries Previous IJ 2 3 4 5 325 Next<br><!-- End of picture text -->
We used the estimated river flows, tidal effects, salinity of the river flows, and salinity of the tides to estimate location-specific river salinity for the southwest coastal region for December 2049 to March 2050. The model was estimated for 27 alternatives for the future: 40cm, 52cm, and 67cm RMSLR for nine scenarios of upstream water flows for the A2 scenario; 40cm, 52cm, and 67cm RMSLR for nine scenarios of upstream water flows for the A1b scenario; and 35cm, 47cm, and 62cm RMSLR for nine scenarios of upstream water flows for the B1 scenario. The BoB model and SWRM were run under the assumption that all 61 polders (of which 13 are sea-facing) in the coastal area are functioning well and will not be overtopped by 2050.

# **4. Results**

The effects of climate change on river salinity in the southwest coastal region of Bangladesh show up in two ways: intrusion of the salinity frontier inland (see the isohalines displayed in Figure 5) and higher location-specific salinity. As expected, the estimates indicate that the extent of the intrusion of the salinity frontier is primarily determined by RMSLR. For each emission scenario, salinity differs marginally across GCMs.<sup>33</sup> The B1 emission scenario with 35cm RMSLR is the best future scenario (least increase in salinity from the baseline) and the A2 emission scenario with 67cm RMSLR is the worst future scenario (largest increase in salinity from the baseline).

## _Overall Effects in the Southwest Coastal Zone_

Area estimates indicate the following changes from the baseline (March 2012) to the best and worst case future scenarios (March 2050):<sup>34</sup>

- “Slight saline” river area is likely to decrease from 22 percent at the baseline to 16 and 13 percent in the best and worst case scenarios, respectively.

- “Slight to moderately saline” river area is likely to decrease from 35 percent at the baseline to 30 and 21 percent in the best and worst case scenarios, respectively.

> 33 GCMs agree about the change in future temperature, but do not agree on the direction of change in future rainfall in Bangladesh, much less on the magnitude in either direction. Hence, it is important to consider alternative GCMs in any analysis of changing climate. However, the problem of river salinity in coastal Bangladesh is a dry season phenomenon and therefore differences in future rainfall projections associated with differences in GCMs turned out to be insignificant in this case.

> 34 Other monthly estimates are available from the authors upon request.

22
23

# **Conclusions**

The empirical results discussed in sections III and IV suggest a number of conclusions with respect to the economic recovery that began in Poland in 1992.

**_First,_** there is unmistakable evidence that 1990 reforms brought a significant process of structural change. We confirm Berg's (1994) claim and provide broad evidence on resource reallocation. Furthermore, with the use of a statistical model applied to labor productivity growth, we attempt to disentangle structural vs. cyclical determinants of the recovery, and suggest that the former have outweighed the latter.

**_Second,_** the detail of our two data sets has allowed us to formally investigate the performance of public versus private enterprises. We found that the productivity response of state enterprises has been significantly different from that of private firms (with the latter largely outperforming the former). Our statistical model has therefore confirmed the anecdotal evidence available to observers of the Polish economy. The successful case-studies of state enterprises reported by Pinto et al. (1993) and Pinto and van Wijnbergen (1995), therefore, seem to provide only a partial picture of the performance of the public sector -- as the same authors also suggested. However, we too found signs of adjustment among public enterprises.

**_Third,_** size matters, at least among private enterprises. Overall, there seem to be increasing returns to scale, with the exception of very large enterprises (possibly because relatively many of them were previously state-owned, and need further restructuring). On the other hand, the fact that size is not significant among public enterprises can be interpreted as evidence that several of them do not adopt yet optimal technologies and production processes.

**Our** results have been confirmed with the use of two different data sets.
# Demand for “Safe Space”: Avoiding Harassment and Complying with Norms<sup>_∗_</sup>

Florence Kondylis<sup>_†_</sup> Arianna Legovini<sup>_†_</sup> Kate Vyborny<sup>_‡_</sup> Astrid Zwager<sup>_†_</sup> Luiza Andrade<sup>_†_</sup> Originally published in the Policy Research Working Paper Series on _June 2020._ This version is updated on _November 2022._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords** : sexual harassment, gender, public transit, mobility, revealed preferences, Implicit Association Test, stigma

> _∗_ This draft benefited from comments from Girija Borker, Peter Christensen, Simeon Djankov, Erica Field, Emma Frankham, Seema Jayachandran, Gabriel Kreindler, John Loeser, Shomik Mendiratta, Sveta Milusheva, Pedro Olinto, Dev Patel, Martin Raiser, Dan Rogger, three generous anonymous reviewers, and participants in seminars at ASSA 2020, Duke University, Gottingen, INSEAD, ITU, NEUDC, and the World Bank. We thank the World Bank i2i fund, the Knowledge for Change Program, the Umbrella Facility for Gender Equality, and the World Bank Brazil Country Office for generous research funding. Tuanni Borba and Rafael Dantas superbly coordinated all aspects of data collection. Finally, we thank the staff at Premise and SuperVia for their dedication in implementing this research design. This trial was registered in the 3IE database under ID-5a125fecae423 and Duke IRB D0910/2017-0401 and AEA RCT Registry AEARCTR0006142. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Replication package and Online Appendix are available at `https://github.com/worldbank/rio-safe-space` .

> _†_ Development Economics, World Bank

> _‡_ Duke University
Even as problems remain, the firm-level studies have already improved on the macroliterature in a number of respects. Numerous firm-level papers have now shown that important within-country heterogeneity exists. Variation in local business climate does indeed matter for explaining differences in firm performance. Much the same point is made by single-country, regional business climate studies such as the one on China by Hallward-Driemeier et al. (2006). Moreover, the much larger sample sizes made possible by going to this disaggregated level allow for more robust results than in the macrostudies. The information obtained from the business climate surveys is also much more detailed and practical than aggregate indicators, allowing, for instance, insights about the variation of business climate effects across regions and different types of firms.

Building on this, a rich research program becomes possible. Below, we start by outlining the econometric issues and limitations of the current literature, and summarize the lessons derived from them. We then highlight what in our view are the most promising areas for future research. Finally, we open the debate on potential improvements in the design of existing survey questions.

_1. Econometric Lessons from the Current Literature_ The standard approach in the current literature based on enterprise survey data has been to use regression analysis to identify which – if any – business climate indicators determine firm performance and to what extent. Almost universally, the basic specification of these regressions has been: Firm Performance = β1 + β2(IC Indicators) + β3(Firm Characteristics) + β4(Additional Controls) + ε When interpreting results from these regressions, it is important to keep some basic characteristics and limitations of the approach in mind. Significant coefficients of the explanatory variables are only obtained if there is variation in these variables. Thus, the results presented above efficiently pinpoint existing bottlenecks that explain observed variations in firm performance, but they are less useful for identifying universal problems. For instance, Hallward-Driemeier, Wallsten and Xu (2006) find that access to banking services is not a significant determinant of firm performance in China. However, this does not mean that increasing the availability and efficiency of financial services is unimportant for improving Chinese productivity. As the authors point out, ‘it only means that the state-owned banking sector has not contributed significantly to regional firm growth’<sup>91</sup> . In other words, the fact that Chinese state-owned banking has not had a systematic impact on firm performance means that it does not show up as a determinant of actual _variation_ therein. But the common lack of efficient banking services may still be responsible for sub-optimal levels of firm performance throughout China. This methodological issue is particularly relevant for studies with small samples, because expanding the number of observations will tend to introduce more variation and thus allow more general statements.

> 91 Hallward-Driemeier, M., S. Wallsten and L.C. Xu. 2006: 645.

26
# **Financing Rural Development for a Harmonious Society in China:**

**Recent Reforms in Public Finance and Their Prospects**

**Achim Fock and Christine Wong**<sup>∗</sup>

This paper is drawn from the report _China: Improving Rural Public Finance for the Harmonious Society_ , Report No. 41579-CN, November 2007. We would like to thank Loren Brandt, Bert Hofman, Stuti Khemani, Stephen Mink, Scott Rozelle, Linxiu Zhang, Yanlong Zhang, Feizhou Zhou, as well as the many other collaborators and commentators for their contributions to the earlier report. We also acknowledge the Department for International Development (DFID), U.K. for financial support. Wei Xing provided able assistance in updating the data for this paper.

> ∗ The World Bank and University of Oxford, respectively.