rafmacalaba/data-use-mentions-extended huggingface dataset:
and average income have a correlation of 0.72.

I have panel data on several aspects of public pre-primary education quality:<sup>33</sup> pre-primary students per teacher, the fraction of pre-primary teachers with at least some post-secondary education, and the fraction of pre-primary schools with a designated school building, electricity, an

> 32I measure income polarization using individual-level data on all voting-age (16+) persons in the 2000 Brazilian Census, and following the method described in Duclos, Esteban, and Ray (2004). I set the parameter _α_ , capturing the importance of local clustering of income, to an intermediate value of 0.5.

> 33All measures of pre-primary education quality are not available for 1995, but are available for 1996-2008.

20
reduced by less and relative labor inputs have remained fairly constant between male- and female-headed households over the past decade.

However, our headline result of a large reduction in the agricultural productivity gender gap masks heterogeneity and lurking issues that will be key for policy makers in the coming years.<sup>5</sup> First, while our results focus on women’s productivity, their efficiency gains relative to men must be put in perspective with the relative land loss they experienced during the same period. Women have become more efficient, but they are given less to work with. The land loss suffered by female-headed households is also starting to impact food crop productivity, where the gender gap remains high: the lower number of fields managed by the household emerges as a big driver in 2016, perhaps due to higher exposure to risk. Strengthening women’s land rights, either through spousal co-titling, direct land transfer or safeguarding of inheritance rights, should be a key policy priority within agricultural programming in the country.

In addition to the remaining gender gap in food crop productivity, the RIF decomposition highlights that we still see large gender gaps among lower-productivity households in 2016. Since our productivity measure is closely related to income and asset-based poverty, as covered in Section 3, this means that targeting the poorest half of female-headed households, in particular as regards their food crop production, should be a key policy priority.

What does our analysis have to say about what the remaining drivers of the gender gap for these households are? In addition to the lower number of fields managed by the household, as discussed above, the other main driver is lower use of male household labor. Indeed, labor constraints, in particular lower use of _male_ labor, have emerged as a key cause of the gender gap across Sub-Saharan Africa (O’Sullivan 2014). This result has also been shown to hold for Côte d’Ivoire; agriculture programs in the country should consider implementing creative solutions such as labor vouchers and directly engaging husbands
for example, only 5 percent of the workforce worked in a different regency than their residence.<sup>11</sup> Second, regencies hold significant administrative powers following the 1999 decentralization reform in Indonesia. Those include also the minimum wage setting. We use the pre-decentralization reform division in 292 regencies to ensure the consistency of the analysis over time.

In our sample, 34% of plants change product market over the sample and 5% of plants change commuting zone. To ensure consistency over the years, we drop all such plants. However, our results are robust to just dropping observations after plants change product or location, or to not dropping them at all.

After cleaning the data and keeping observations with non-missing values for all the dependent variables considered, we end up with an unbalanced panel of 14,142 plants between 2009 and 2014—including 346 product markets (5-digit industries) and 274 commuting zones (regencies). We take the census year 2006 as our base year to maximize representativeness. Online Appendix Table A1 presents summary statistics of all variables used in the empirical analysis.

# **3.2 Measuring Product Market Regulation**

Bringing the insights of the model in Section 2 to the data requires identifying a suitable measure of entry costs and an exogenous source of variation. To that end, we collect granular data on investment restrictions across Indonesian product-markets and exploit two consecutive Indonesian Presidential Decrees— _Daftar Negatif Investasi_ , or Negative Investment List (NIL).

The concept of a NIL was introduced in Indonesia in 2000 through the Presidential Decree 96/2000, which aimed to create a single repository of the many existing sectoral restrictions on investments at the central government-level. In fact, it was not until 2008 that the NIL approach was properly enforced, as the Investment Law No.25/2007 replaced the old Investment Law (No. 1/1967), which used a _positive_ list approach to restrictions, i.e. whatever market is not included in the regulations is to be considered
Políticas para as Mulheres do Rio de Janeiro sob a Secretaria Estadual de Assistência Social e Desenvolvimento Humano, SEASDH-RJ). All data collection followed the recommendations on the ethical conduct of research on domestic violence issued by the World Health Organization (2001). Although the profile of the interviewees was heterogeneous, most of the women were white, with low levels of education and aged between 40-59 years. Most were not working at the time of their interview. Partners and ex-partners were the most common perpetrators of violence, but some women also reported violence at the hand of siblings, children and grandchildren, overwhelmingly male. Almost all cases contained elements of psychological violence, such as controlling behavior, insults and threats and most cases involved physical and sexual violence. Most women interviewed had suffered at the hands of more than one aggressor over the course of their lives (see Table 1). Table 2 summarizes the themes explored during interviews.

**<u>Table 1: Sample characteristics</u>**

|Under 40|4|31%|
|---|---|---|
|40-59|6|38%|
|**Age**<br>Over 60|5|31%|
|Missing data|1|6%|
|**Total**|**16**|**100%**|
|**Number of**<br>One perpetrator|6|38%|
|**perpetrators over the**<br>**course of lifetime**<br>More than one<br>perpetrator|10|63%|

_8_
<mark>Unlike the ODIN, the ODB assesses not just NSOs but overall government data, i.e., all government data regardless of whether the NSO contributes to making the data available. The most recent edition (the fifth edition) known as the leaders’ edition of the ODB covers far fewer countries than the ODIN. Although previous editions of the ODB covered over 100 governments, the</mark> _<mark>Leaders Edition</mark>_ <mark>covers only 30 governments that have publicly committed to adopt the International Open Data Charter Principles or the equivalent G20 Anti-Corruption Open Data Principles – only one of the 30 countries – Saudi Arabia- is in the MENA region. The ODB scores countries on three sub-indexes as follows: readiness; implementation and; impact. Each of the sub-indexes are weighted equally and further subdivided into various components weighted equally within each sub index. The readiness sub index measures the ability of governments to secure positive outcomes from open government data initiative. This is measured through three components focusing on: (1) Government; (2) Citizens and Civil Society; and (3) Entrepreneurs and Business. These components include measures relating to the existence of open data, and a range of interventions that support engagement with and re-use of open data. For the implementation sub index, the ODB asks researchers to complete a checklist of 10 questions</mark><sup>25</sup> <mark>assessing data openness for various data categories (see annex table A5 for the various data categories).</mark><sup>26</sup> <mark>Finally, for the impacts sub index, the ODB takes the approach of treating online, mainstream media and academic publications about open data impacts as a proxy for existence of impacts. Researchers were asked to score the extent of impact on a 0 – 10 scale. The scoring guidance directed assignment of the highest scores for peer-reviewed studies showing impact and emphasized the importance of sources
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1.2<br>0.8<br>0.4<br>0<br>-0.4<br>-0.8<br>-1.2<br>Macroeconomic Stability Index-India<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

<mark>Note: Data is from World Development Indicators. Years refer to respective Calendar Years. Index is constructed as an average of the standardized indexes of inflation (CPI inflation), current account deficit</mark> (percent of GDP), and fiscal deficit (percent of GDP).

<mark>These results highlight the importance of having in place a policy framework that limits vulnerabilities, and maximizes policy space for responding to shocks. Elements of such a framework include maintaining a sound fiscal balance, a sustainable current account deficit, an environment conducive to investment, managing capital flows so as to encourage relatively stable longer-term flows and discouraging volatile short-term flows, avoiding excessive appreciation of the exchange rate, holding a large stock of reserves, and preparing banks and corporates to handle greater exchange rate volatility.</mark>

# **3.4 Current Cyclical Dynamics**

Next, we analyze the dynamics of the Indian economy in the last few quarters and put them in context with the long-term experience discussed above. Most recent commentaries on the Indian economy focused on an ominously declining growth rate over a five-quarter period, from 9.3 percent in Q4, 2015–2016, to 5.6 percent in Q1, 2017–18. Below we analyze the growth rate of quarterly GDP and its decomposition for the period starting 2013–14 through 2017–18 (Figure 13 and Figure 14).

Two points are noteworthy. First, growth in the two quarters of Q1, 2016–17, and Q2, 2016
We offer a simple model to motivate our empirical strategy. The model demonstrates how some reforms could be classified as small or large depending on the relative magnitude of the costs and benefits of registration. Our results imply that in countries with high initial registration costs, the benefits of registration are significantly below the costs of registration, likely because of limited access to finance or rigid labor markets. This is consistent with Djankov et al. (2002) who show that high registration costs do not serve public interest, but only benefit politicians and bureaucrats.<sup>3</sup> The results in our paper suggest non-trivial economic magnitudes on the number of newly registered businesses. For example, we find that across OECD countries in our sample, on average, a reduction of 50 percent or more in the number of procedures, days or costs leads to an average increase in the number of new registrations of 14, 19 and 30 percent, respectively. These results are remarkably consistent with previous studies of registration reforms in two different OECD countries. A reform in Mexico that decreased the number of procedures by about 60 percent resulted in a 5 percent increase in the total number of firms, which translates to a 24 percent increase in new registrations in the municipality of Guadalajara (Bruhn, 2008). A reform in Portugal that reduced costs and days by 50 percent or more resulted in an increase in firm startups of about 17 percent, mostly among “marginal” firms that would have been most deterred by burdensome regulations, such as small firms in low-tech sectors (Branstetter, et al., 2010).

Our analysis is motivated by earlier studies that find that new firms are the ones most likely to grow (Lingelbach, et al., 2005; Johnson, et al., 2000) and to create new jobs (Audretsch, et al., 2006; McMillan and Woodruff. 2002). For example, studies using longitudinal data sets on

> 3 Our results are also consistent with de Mel, et al
year 2020 with respect to the overall trend in enrollment rates, net enrollment rates, or enrollment in private education, conditional on all the above-mentioned controls. Enrollment

> 3 Table A.1 in the Appendix shows basic information on the Latin American national household surveys used in this paper.

> 4 For simplicity, we use the terms enrollment and attendance interchangeably. The typical question included in household surveys asks whether the child is attending school, understood as whether the child is participating in the educational process (either in presence, during a normal year of schooling, or through remote learning during school closures in the year of the pandemic).

6
(World Bank, 2019).

For Timor-Leste, agriculture is likely to retain a major role in the livelihoods of the poor, and overall economic growth, in the years to come. The success of the agricultural transformation process depends on policy decisions that should be guided by an understanding of the potential drivers of growth of the agriculture sector. The findings of this study provide evidence that complements existing works on Timor-Leste’s agriculture sector that identifies increasing agricultural productivity as a policy necessary for sustaining economic growth of the country. Investment in the sector can be improved by crowding in private sector financing and by using the limited public funds more strategically. Yet, further study and new data are needed to conduct more detailed analyses on where and how specifically investments in the agriculture sector could be effectively allocated to reap the highest potential impact in increasing rural income, which can lead to poverty reduction. It is expected that the upcoming agriculture census will provide an opportunity to do so.

16
- K. Diaconu, J. Falconer, A. V. Verbel Facuseh, A. Fretheim, and S. Witter. Paying for performance to improve the delivery of health interventions in low- and middle-income countries. _Cochrane Database of Systematic Reviews_ , (12):CD007899, 2020.

- T. Doran, E. Kontopantelis, J. M. Valderas, S. Campbell, M. Roland, C. Salisbury, and D. Reeves. Effect of financial incentives on incentivised and non-incentivised clinical activities: Longitudinal analysis of data from the UK Quality and Outcomes Framework. _British Medical Journal_ , 342: d3590, 2011.

- E. Duflo, R. Hanna, and S. P. Ryan. Incentives work: Getting teachers to come to school. _American Economic Review_ , 102(4):1241–78, 2012.

- E. Fehr and L. Goette. Do workers work more if wages are high? evidence from a randomized field experiment. _American Economic Review_ , 97(1):298–317, 2007.

- D. Filmer and N. Schady. Does more cash in conditional cash transfer programs always lead to larger impacts on school attendance? _Journal of Development Economics_ , 96(1):150–157, 2011.

- F. Finan, B. A. Olken, and R. Pande. The personnel economics of the state. NBER Working Paper w21825, National Bureau of Economic Research, 2015.

- National Population Commission. Nigeria demographic and health survey 2018 - final report. Technical report, 2019.

- G. B. Fritsche, R. Soeters, and B. Meessen. _Performance-based financing toolkit_ . The World Bank, 2014.

- R. G. Fryer, S. D. Levitt, J. List, and S. Sadoff. Enhancing the efficacy of teacher incentives through loss aversion: A field experiment. Technical report, National Bureau of Economic Research, 2012.

- V. Gauri, J. C. Jamison,
|

Notes: Results from post-intervention health worker survey. Sample limited to clinics that received the basic provider training. Malaria prevalence refers to prevalence estimated by the health worker. A health worker is coded as feeling pressure to prescribe if s/he answers yes to the question: Do you ever feel pressure from patients to prescribe certain medicines when you think they are not necessary? Providers answering yes were then asked to specify which medications. Antimalarial also includes quinine.

A0
constructed. When we use changes in short-term borrowing (Balance Sheet data item #34) as a measure of credit, we find 45.63% of our sample to be Phoenix Miracles – these firms had a positive increase in sales without a positive increase in short-term borrowing from 2009 to 2010.<sup>22</sup> Even amongst these candidate Phoenix Miracles, we find that 90% (unreported in the table) of the firms had an adequate cash balance in their balance sheet to cover their liquidity crunch. That is, the ratio of the absolute value of changes in short-term borrowing from 2009 to 2010 to their balance sheet Cash and Short-term Investments position in 2010, was <1 for 90% of the firms. When we look at the firms that are not candidate miracles, 27.2% of the firms had a positive increase in sales and short-term borrowing, 9.29% had a decline in sales but an increase in short-term borrowing and 17.88% of firms had a decline in sales and short-term borrowing.

The percentage of Phoenix Miracles is reduced to 8.96% when we consider the changes in Long-term Debt (Balance Sheet data item #9), Net Capital Stock Issuance ( _Sale of Common and Preferred Stock,_ data item #108 – _Purchase of Common and Preferred Stock,_ data item #115), Cash from Dividends ( _Cash Dividends,_ data item #127), and Other Financing activities ( _Financing Activities-Other,_ data item #312). And of these firms, 59% (unreported in the table) had adequate cash balances to cover their liquidity crunch.

In Panel C we examine the different accounts in the Statement of Cash flows to identify alternate sources of liquidity for the firms during the recovery period. First, we re-define credit to not only include net financing from changes in short-term debt, long-term debt, equity issuances, and other financing activities but also cash from other sources such as that which could have been saved by
component or measurement error leads to substantial drops. Hence, the figure indicates that in both Russia and in Poland, the living standards are more stable than the measured data would indicate. Nevertheless, even after correcting for measurement error or transitory shocks, individuals in Poland and especially in Russia face considerable fluctuations in living standards.

Table A2 in the appendix shows the size of expenditure shocks as well as their breakdown in transitory and permanent components for different demographic groups in Poland. Table A3 shows the same information for Russia. The tables show that for most demographic groups, neither the size nor the composition of the shocks is significantly different from the overall mean. However, there are a few notable exceptions. Both in Russia and in Poland, individuals with access to land face larger shocks that tend to be less permanent, but these differences are only statistically significant in Poland. In Poland, the size of the shock (as a fraction of expenditure) increases with income, while it displays a U-shaped pattern in Russia. In both countries, households with household heads aged 51-64 with higher education face the smallest shocks, but this difference is only significant in Poland.

<!-- Start of picture text --> Figure 5: Median absolute shocks after correction for<br>transitory component and measurement error.<br>Russia Russia Poland Poland<br>Income Shocks Expenditure Shocks Income Shocks Expenditure Shocks<br>st 50 g 60 - 60 . 60<br>5~0 5 .5 50 .50<br>o a 0 0<br>40 . 40 ° 40 | 40<br>CD 303<br>S 30 30<br>0 0~~~~~~~~~~~~~~~~<br>. 20 l 20 . 20 E20 l<br>U 10 l l 10 lo 10 * 10<br>42 0 0 42<br>b £O C ca b , b '<br>
F(t). The mean value of Vi is  i and the standard variance is  i. The WTP model can be written as, where  i is a random term with a mean of zero. Individual i knows his valuation distribution. When given a price tij, the probability for the person to say ―yes‖ to the offered price, tij, will be, Pij = Prob(Vi>tij)=1-F(tij) (2) Once Pij, the probabilities for individual i to agree to the price tij, is known to a researcher, either by assigning numerical values to the verbal MBDC data or by directly asking individuals of their numerical likelihood information as did with the SPC approach (Wang and Whittington, 2005), equation (2) can be estimated for each individual. The estimation model can be constructed as follows: Pij = 1-F(tij) + λi (3) where λi is an error term with a mean of 0 and a standard variance of <sup>2</sup> .  can be constant for a respondent i, but are different for different respondents. Pij is a dependent variable, which is the likelihood answer given by respondent i at price j. Pij takes values between 0 and 1, and can be viewed as a continuous variable. tij is an independent variable, which corresponds to the bid price proposed in the questionnaire, and tij is also a continuous variable.

Assume a specific functional form for Fi(•), such as of a normal distribution, with a mean  ( _tij_  <u></u> _i_ )  i and a standard variance  i , i.e., F(tij)=  _i_ , then the model (3) becomes, The major purpose is to estimate and analyze  i, the mean value of Vi for each respondent, which is a function of personal information such as personal characteristics and uncertainties, etc. A two-stage approach
# **Annex F – Validation and Precision**

One concern often raised regarding poverty map results is that the poverty estimates may not be precise enough for the purposes to which they are put. One way to assess this is to measure whether there are clear and statistically significant differences between the resulting poverty rates at the district level that can be usefully integrated into the work of the World Bank, counterparts, and other partners. This can be demonstrated graphically by reporting the poverty rates with the associated 95% confidence interval.

Because there are some districts in which there are no survey observations, these must be imputed out of sample when using the FH approach. In this minority of cases, the root mean square errors are larger than average, and should be used with caution. These cases of relatively high imprecision are noted in the district level tables with red stars (*). However, it is also important to note that most cases in this application are in sample do not suffer from this limitation of the FH approach.

**Figure 32: Upper and Lower-bound Confidence Intervals, Including Out-of-Sample Predictions**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>fh_55 upper_kaz lower_kaz upper_kgz lower_kgz<br>upper_tjk lower_tjk upper_uzb lower_uzb<br><!-- End of picture text --> Another way to demonstrate performance is to benchmark against “gold standard” results using the Elbers, Lanjouw, and Lanjouw (ELL) method previously published by the World Bank. The following graph demonstrates the comparison visually for 17 previously published maps that used the ELL approach. The results from the Central Asia poverty map perform quite well in this comparison for those cases that are in-sample.