rafmacalaba/data-use-mentions-extended huggingface dataset:
`7.962065)`|
|`3 314005602 `|`Kenema`|`(-11.18639 `|`7.885936)`|
|`4 314007819 `|`Koidu`|`(-10.97163 `|`8.642281)`|
|`5 320058940 `|`Kambia`|`(-12.91934 `|`9.125073)`|
|`6 320060481 `|`Kamakwie`|`(-12.24125 `|`9.496301)`|
|`7 320060535 `|`Pujehun`|`(-11.72124 `|`7.356632)`|
|`8 320060540 `|`Zimmi`|`(-11.31032 `|`7.312338)`|
|`9 370327499 `|`Goderich`|`(-13.28887 `|`8.432966)`|
|`10 370495828 `|`Murray Town `|`(-13.26534 `|`8.491613)`|

Fuzzy string matching calculates the string distance between each village or parish name in the VHF data and each placename in the gazetteer file that falls within the exact same district and chiefdom.<sup>A12</sup> An exact match returns a distance of zero; “FREE TOWN” and “FREETOWN,” for example, would return a distance of 1. We do not match any entries with a string distance that exceeds 2.

While the geo-coding process introduces measurement error, we expect this is uncorrelated with treatment and, thus, only going to attenuate our estimates. To bolster this assumption, we look at whether placenames in the gazetteer file tend to be more numerous or longer in treated versus control sections. We see no indication that treated sections have significantly more or shorter placenames; moreover, the placenames are not more likely to contain a space between words (see Table E.27).

> A10 Of
b) In real terms over time The comparison of minimum wages across countries refers to period 1985-93, but most of the minimum wage data actually correspond to the years 1985-88. The issue is whether a similar analysis using more recent data would'have yielded the same conclusions. After all, per capita output measured in real terms declined substantially in most CFA countries during the 1980s and early 1990s. In C6te d'lvoire, it decreased at an annual rate of 4.6 percent, and in Niger it did it at an annual rate of 4.1 percent. Although in a less dramatic way, per capita output also decreased in Cameroon (-2.2), Togo (- 2.1), Central African Republic (-1.6) and Gabon (-1.6 too).8 The current misalignment of minimum wages could therefore be much higher than suggested by the results in Table 6.

Minimum wages declined in real terms during this same period though, as shown in Figures 5 and 6. Their annual rate of variation per country, from the time when the terms-of-trade levels reached their peak until before the devaluation of the CFA Franc, is indicated in parentheses in the Figures, Although the specific dates chosen for the calculation of these rates (1977 and 1992 respectively) are somewhat arbitrary, the results would be similar if a few years were added or subtracted at both ends of the interval. What these rates show is that inflation allowed a substantial decline of real minimum wages in all countries where there are data, with the exception of Niger. In most cases, the minimum wage declined at a faster pace than per capita output. So, by the time the devaluation of the CFA Franc took place the misalignment of minimum wages was probably not larger than indicated by the international comparison in Table 6.

With the exception of Chad, which experienced a positive growth rate of 3.2 percent per year during this period, all of the other countries in the region had
9 nearest available population Census, which was used to generate the small-area welfare estimates, is also included.<sup>8</sup> Finally, the table also lists the poverty line used in each country, both in national currency (in survey year prices) and US dollars (in 2006 prices and at PPP exchange rates)<sup>9</sup> . As noted in the introduction, we have opted to use national poverty lines, which better capture the meaning of poverty in each specific country. This has the drawback that poverty measures are not defined with reference to comparable standards of living across countries. The alternative of imposing a constant poverty line across countries, however, would have an even greater disadvantage. Had we selected a low internationally comparable poverty line, such as $1-a-day, we would be comparing traces of poverty driven largely by measurement error and transitory shocks in the richer countries (such as Albania and Brazil) with real poverty in Kenya and Sri Lanka. Had we instead selected a higher line, like those used in Albania, Brazil or Morocco, we would be comparing “reasonable” poverty incidences in the richer countries, with the bulk of the population in the poorer countries. Since this paper is largely about the _relative_ extent of poverty in larger and smaller towns, the absolute level of the poverty line is of limited importance. We do, nevertheless, examine the sensitivity of our results to varying the poverty line in some of the countries, in Section 4.

A more serious caveat is that we have made no attempt at systematically correcting for differences in the cost-of-living across different urban categories. In some settings, these differences may be substantial, and future research should attempt to take them into account.<sup>10</sup> Similarly, with the exception of Kenya, we have used consumption expenditure (or income) per capita as the individual welfare indicator throughout. If there are substantive differences in family size or composition across different urban categories, one
or _tolas_ , and are very similar in caste composition. In Madhubani district, Brahmins and Bhumiars are a majority in both villages, and their tolas are located close to the main resources of the village—the temple, pond and school—while all the other tolas extend southwards in decreasing order of status in the

> 2 We used 2001 census data because the treatment villages were from Phase I, which was initiated in 2006 by selecting poorer villages on the basis of that census.

> 3 For the purpose of keeping their identities anonymous, we have re-named the villages and informants.

6
’s pensions were included in the survey, World Bank (2020) finds that approximately 95% of the individuals receiving pensions with values equivalent to about 2 minimum wages were receiving both a worker and a survivor’s pension. By focusing on thresholds equivalent to 1.5 and 2.5 minimum wages, we thus aim to maximize the likelihood of correctly identifying a rural pension beneficiary.

> 28 Information on daycare or preschool attendance for children 0-4 years old is not available in the PNADC. According to the Education Census 2019 (Brazil, 2020b) about 3.8 million children were enrolled in daycare facilities and about 81 percent enrolled in the public system or in ‘creches conveniadas’ (a public entity that receives a per student transfer from the municipal government) and about 5.2 million children ages 4 and 5 were enrolled in preschools (of which 74 percent were in public facilities). A back of the envelope calculation suggests that in 2019 about 5 million

16
Figure 5: LIC policy frameworks (A) Countries meeting DeMPA minimum requirements, select categories

- (B)  Countries meeting DeMPA minimum requirements, select categories

<!-- Start of picture text --> Number of countries Number of countries<br>12 Most recent DeMPA 12<br>Most recent DeMPA<br>10 1st DeMPA 10<br>1st DeMPA<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>(C) Central bank transparency index (D) Exchange rate regimes<br>Index Number of LICs<br>6 LICs Other EMDEs 35 Flexible Pegged<br>5 30<br>25<br>4<br>20<br>3<br>15<br>2<br>10<br>1<br>5<br>0 0<br>Legal Structure DMS Audit with FP with MP Domestic Borrowing External Borrowing LGLD CFF and CBM SD, SC and BCP Debt Records<br> framework Managerial DA and DS<br>Evaluation and Reporting Coordination Coordination<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015<br><!-- End of picture text --> Source: Bloomberg, Debt Management Performance Assessments (DeMPA), Dincer and Eichengreen (2014), International Monetary Fund, Shambaugh (2004), World Bank.

A.B. BCP=Business Continuity Planning; CBM=Cash Balance Management; CFF=Cash Flow Forecasting; DA=Debt Administration; DMS=Debt Management Strategy; DS=Data Security; FP=Fiscal Policy; GLD=Loan Guarantees, On lending Derivatives; MP=Monetary Policy; SD=Segregation of Duties; SC=Staff Capacity. Sample covers 17 low-income countries.

C. Unweighted averages. The range of the index is from 0-15, 0=least transparent and
# **References**

Amin, M. and Y. C. Soh (2020). Does Greater Regulatory Burden Lead to Higher Corruption: Evidence Using Firm-level Survey Data for Developing Countries. Forthcoming _World Bank Economic Review_ . <u><mark>https://doi.org/10.1093/wber/lhaa007</mark></u> Amin, M. and C. Okou (2020). Casting a Shadow: Productivity of Formal Firms and Informality. _Review of Development Economics_ , 24(4): 1610-1630.

<mark>Aterido, R., Hallward-Driemeier, M., & Pages, C. (2011). Big Constraints to Small Firms' Growth? Business Environment and Employment Growth Across Firms.</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>, 59 (3): 609 – 647.</mark> Ayyagari, Meghana, Asli Demirguc-Kunt, and Vojislav Maksimovic. (2011). Small vs. Young Firms Across the World. Policy Research Working Paper 5631, World Bank, Washington, DC. Bernard, A. B., & Jensen, J. B. (1999). Exceptional exporter performance: Cause, effect, or both? _Journal of International Economics, 47_ (1), 1-25.

Bloom, N., & Van Reenen, J. (2007), “Measuring and Explaining Management Practices Across Firms and Countries,” _Quarterly Journal of Economics_ 122 (4): 135 – 1408.

Coleman, S. (2000). Access to capital and terms of credit: A comparison of men-and womenowned small businesses. _Journal of Small Business Management, 38_ (3), 37-52.

De Mel, S., McKenzie, D., &Woodruff, C. (2011). What is the Cost of Formality? Experimentally Estimating the Demand for Formalization. Working Paper, University of Warwick, Coventry. De Rosa, D.
Table 1: **Calibration**

||Values|Target|
|---|---|---|
|Risk aversion|_σ_ = 2|Arellano (2008)|
|Output cost after default|_γ_ = 0_._96|Arellano (2008)|
|Risk free interest rate|1.7 %|Arellano (2008)|
|Core’s income process|_ρ_= 0_._96, _σε_ = 0_._003|Data|
|Periphery’s income process|_ρ_= 0_._92, _σε_ = 0_._004|Data|
|Output diference| ̄_yc/_ ̄_yp_ = 1_._2|Data|
|Core’s borrowing limit|66%|Treaty|
|Periphery’s borrowing limit|83%|Treaty|
|Calibrated parameters|||
|Core’s discount factor|_βc_ = 0_._89|0.6% spread|
|Periphery’s discount factor|_βp_ = 0_._88|2.4% spread|

Table 2: **Business Cycle Statistics: The Benchmark Model and the Data**

|||**Data**|**Ben**|**chmark**|
|---|---|---|---|---|
|_mean(%)_|Core|Periphery|Core|Periphery|
|Debt/Y|66|83|66|79|
|Spread|0.6|2.4|0.5|1.9|
|C/Y|77|80|98.8|98.6|
|_std(%)_|||||
|Debt/Y|8.10|19|0.16|0.36|
|Interest rate|0.9|2.16|0.019|0.042|
|C/Y|1.15|1.
or ii) are fair” we assign a value of one if a respondent states that people are fair and zero otherwise. For the variables that expresses a person’s concern with crime, we assign a value of one if the person is very concerned about crime, and zero if they are somewhat concerned or not concerned at all. The precise wording of the questions along with the answer scales and the definition of the binary indicators can be found in Appendix A.1.

In choosing how to code survey responses, researchers face a trade-off. Having binary outcomes facilitates the comparison of the effect sizes across different outcomes, and allows one to use simple methods such as linear probability models or probit or logit. The downside is that binary indicators may not capture subtle changes in a person’s perceptions. For example, if a person’s self-reported trust changes from “agree completely [that people can generally be trusted]” to “agree”, our binary indicator would not capture this change. The alternative would be to have several mutually exclusive indicators for the different answer options, and to use appropriate methods such as multinomial logit. In our judgment, it is more useful to have results that are comparable across outcomes and to use simpler yet more transparent methods, which is why we choose to construct binary indicators.

The SOEP includes geo-identifiers for a person’s county of residence. We link the survey data to the asylum seeker inflow data based on a person’s county of residence in 2014, the year before the

**SOEP – Descriptive Evidence** Figure 4 displays the means of the outcome variables in the survey waves before and after the inflow (2013 and 2018). Each panel reports the share of people whose outcome takes value one, split by people residing in counties with above- and below-median increases in the number of asylum seekers. For five out of seven questions, there is a difference in responses before the inflow. Before the inflow, people in low-inflow areas are more
” Policy Research Working Paper, World Bank.

Doss, Cheryl, Ruth Meinzen-Dick, and Allen Bomuhangi. 2014. "Who Owns the Land? Perspectives from Rural Ugandans and Implications for Large-Scale Land Acquisitions." _Feminist Economics_ 20(1): 76-100 <mark>Duflo, Esther. "Women empowerment and economic development."</mark> _<mark>Journal of Economic literature</mark>_ <mark>50, no. 4 (2012): 1051-79.</mark> Estudillo, Joanna P., Agnes Quismbing, and Keijiro Otsuka. 2001. “Gender differences in land inheritance and schooling investment in the rural Philippines.” _Land Economics_ 77 (1): 130-143.

Fiala, Nathan and Xi He. 2017. “Unitary or Noncooperative Intrahousehold Model? Evidence from Couples in Uganda. _The World Bank Economic Review_ 30 (Issue Supplement_1): S77-S85 Flores-Macias, Francisco and Chappell Lawson. 2008. “Effects of Interviewer Gender on Survey Responses: Findings from a Household Survey in Mexico.” _International Journal of Public Opinion Research_ 20(1): 100-110 Ghuman, Sharon J., Helen J. Lee, and Herbert L. Smith. 2006. “Measurement of women’s autonomy according to women and their husbands: Results from five Asian countries”. _Social Science Research_ 35(1): 1-28 Hanmer, Lucia and Jeni Klugman. 2016. “Exploring Women’s Agency and Empowerment in Developing Countries: Where Do We Stand?” _Feminist Economics_ 22(1): 237-263 Illo, Jeanne Frances and Dante Dalabajan. 2011. _Weathering the Crises, Feeding the Future: Philippine Food Justice Report._ Oxfam International, Oxford UK Huddy, Leonie, Joshua Billig, John Bracciodieta, Lois Hoeffler, Patrick J. Moynihan and Patricia Pugliani. 1997. “The Effect of Enumerator Gender on the Survey Response.” _Political Behavior_ 19: 197-220 28
In Mexico, the only two impact evaluations of childcare interventions, to our knowledge, find similar results. Seira et al. (2011) use surveys and data for seven Mexican states to show that mothers, especially those who were unemployed before signing up for governmentprovided childcare services, increased their labor participation and number of hours worked. Calderón (2014) looked at national data to find that the National Childcare Facilities for Working Mothers (NCFWM) program increases FLFP, but argues it only explains 23 percent of the total increase of FLFP in the period studied. We intend to fill that gap by presenting other determinants of FLFP.

Further, both papers evaluate the short-term impacts of governmental childcare services in 2007-2010, immediately after the NCFWM program’s implementation and the 2008-2009 economic crisis. This study explores the relevance and long-term effects of childcare services 10 years later.

# **III. Decomposition of Labor Market Indicators**

Mexico still has one of the lowest FLFP rates in Latin America at 43.6 percent in 2017 (Figure 1). Although FLFP in Mexico has increased significantly by about 10 percentage points since 1990, this number is still low compared to the average FLFP rate for Latin America, close to 53 percent.<sup>3</sup> Nevertheless, after the crisis, Mexican women swiftly entered the labor force, narrowing the labor participation gender gap. Figure 2 shows how the gap

> 3 In Peru and Chile, for example, the FLFP rates increased 26.3 and 17.4 percentage points, respectively, during 1990-2017. Hence, their labor force participation gender gaps also reached lower levels: Peru reduced the gender FLFP gap from 30.43 to 14.8 percentage points, while the FLFP gap in Chile decreased from 40.5 to 23.3 percentage points (World Bank, 2020).

10
the countries in Europe and Central Asia have data protection laws explicitly governing the use, collection, and processing of personal data. Estonia, Moldova, and the United Kingdom have also imposed a full range of cybersecurity requirements on data processors and controllers. But when it comes to the regulatory environment for enablers, the region is still lagging. For instance, the Kyrgyz Republic is still in the process of drafting an e-commerce law.

**Figure 3. Scores on enablers and safeguards across regions**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>North Europe & Latin Sub-Saharan East Asia & Middle East South Asia<br>America Central Asia America & Africa Pacific & North<br>Caribbean Africa<br>Enabler score Safeguard score<br><!-- End of picture text --> _Source:_ Author’s calculation based on results from the diagnostic.

# _4.3 Comparison across dimensions under the enabler and safeguard pillars_

In addition to the uneven development of regulatory environments for enablers and safeguards, country performance on the underlying dimensions also varies significantly (table 4). On average, countries in the higher-income group tend to perform better across all dimensions. However, no country income group demonstrates an advanced regulatory framework across all dimensions, indicating room for significant improvement. All income groups achieve their highest scores on the dimension of e-commerce, with more than 70 percent of the regulatory good practices adopted on average. Public intent data and cybersecurity and cybercrime are two other dimensions where most countries are performing relatively well, except for those in the low-income category. All income groups show a basic or evolving level of development for the regulatory framework related to cross-border data flows and private intent data. On average, only 17 percent of regulatory best practices are adopted under
*<sup>**_f_**</sup> _), an_ **_inversion_** _is a set of unobserved exogenous variables (_ **_T_** _,_ **_b_** _,_ **_N_**<sup>_e_</sup> _,_ **_F_** _) and unobserved endogenous variables (t,_ **_p_**<sup>_−_</sup> _,_ **_P_** _,_ **_R_** _,_ **_E_** _,_ **_X_** _,_ **_β_** _) such that the resulting set of endogenous variables is an equilibrium of the economy with the resulting set of exogenous variables and with no QRs._

Intuitively, the inversion process picks values for unobserved variables to rationalize the data. For example, consider equation (6), which expresses equilibrium agricultural production ( _Qik_ ) as a function of productivity ( _Tik_<sup>_A_), prices, and land mass. Suppose that, at a given moment, the observed</sup> _Qik_ is “too high” in the sense that the equation’s left-hand side is larger than its right-hand side. The inversion process will then increase productivity _Tik_<sup>_A_enough to balance the equation.</sup> Appendix E provides details on the inversion procedure. We use data from 2019, which we choose as our baseline year because it precedes the 2021 fertilizer ban and other unusual events, such as the Covid-19 pandemic, and also due to household data availability.<sup>67</sup>

# **VII Counterfactuals**

In this section, we use the estimated model to investigate the GE effects of Sri Lanka’s fertilizer import ban (and of other potential alternative policies). Through counterfactual exercises, we demonstrate how government policies like import restrictions limit fertilizer access and how fertilizer subsidies
# **Table A2 (Continued). Cote d’Ivoire 2013 Social Accounting Matrix – Make Table (Million Local Currency, FCFA)**

|**Sector**|C15|C16|C17|C18|
|---|---|---|---|---|
|S15–Formal|601,481||||
|S15-Informal|422,381||||
|S16–Formal||647,726|||
|S16-Informal||344,119|||
|S17–Formal|||4,382,138||
|S17-Informal|||1,759,503||
|S18–Formal||||2,237,855|
|S18-Informal||||68,095|
|Tax on products|7,200|29,646|89,088|60|
|Tax on exports|0|0|0|0|
|Import duty|0|0|0|0|
|Imports|37|132,131|287,293|82,784|
|Trademargin|13,198|0|0|0|

42
the 2017–22 period (Cisco 2018). How cross-border data flows are regulated may shape a country’s competitiveness and opportunities in international trade. For instance, data localization conditions may deter some firms from opening offices in some countries.

The enablers pillar begins with an examination of the regulatory environment for e-commerce/etransactions. According to the UNCTAD (2020), the global value of e-commerce sales was estimated at $25.6 trillion in 2018, accounting for about 30 percent of global GDP. Comprehensive e-commerce regulations support inclusive e-commerce by suppressing digital inequities (UNCTAD 2019). They also could increase trust in the use of data to facilitate e-transactions. In addition, public intent data dimension measures regulatory provisions related to data collected for public purposes. An enabling regulatory environment that promotes the use and reuse of public intent data not only allows governments to achieve better development outcomes through evidence-based policy but also to create opportunities for private sector growth and innovation, as well as contribute to the development of the society as a whole through improved transparency and accountability. Private intent data dimension covers provisions regulating data collected with the original intent of pursuing commercial purposes. The recent booming of data-driven businesses has shown the potential of private intent data in supporting innovation, efficiency, and inclusion.

# **4. Findings**

# _4.1 The global picture_

Diagnostic results show the different regulatory environments that countries have adopted, enabling data use for market players on the one hand while safeguarding the rights of data subjects on the other (map 1). According to their scores on the safeguards and enabler pillars, countries fall into four groups: advanced, moderate, evolving, and basic. A score of 75–100 confers an advanced level for enabling/safeguarding and is coded green; a score of 50–75 confers a moderate level, coded yellow; a score of 25–50 indicates an evolving regulatory level, coded orange; while a score below 25 is considered basic and coded red.

6
variation across indices. SEE has advanced the most on reforms in banking and infrastructure, followed by the Caucasus (FSU3), while European CIS countries (FSU2) have done the most in the non-bank financial area, followed by SEE. The Central Asian republics have made the least progress in all three areas, with one country—Turkmenistan—not advancing at all in any. What follows briefly discusses current policies for financial and infrastructure services.<sup>5</sup>

> 4 See Annex 1 for more detailed information on the construction of the EBRD reform indices.

> 5 What follows draws on a more detailed discussion in Eschenbach and Hoekman (2005).

7