rafmacalaba/data-use-mentions-extended huggingface dataset:
supplement this with information on asset purchase programs. Policy interest rates are straightforward to observe, with data widely available across countries over long periods. This makes it possible to find comparable historical precedents and perform the empirical analysis decomposing the shocks driving these cycles.

Section II identifies the turning points of the rate cycles that inform the analysis in the remainder of the paper. Each rate cycle consists of an easing and tightening phase, comparable to the expansion and contraction phases that together constitute a business cycle. Using monthly data on nominal policy rates and new asset purchase programs, we modify the dating algorithm from Bry and Boschan (1971) and Harding and Pagan (2002) to identify 212 distinct rate phases (111 of tightening and 101 of easing) from January 1970 through May 2024. There are substantially less frequent turning points after 2010, with many economies in extended easing phases until after the pandemic, highlighting the importance of using a longer time series to understand today’s tightening phase. This rich database of the turning points in rate cycles should provide a useful reference for future work on a wide range of topics related to monetary policy.

Section III describes the key characteristics of the resulting rate cycles, including their relationships with macroeconomic variables and how these characteristics and relationships have

> 2 Related research examines the interactions between different types of cycles, particularly business, credit, and financial cycles, e.g., Stock and Watson (1999), Bernanke and Gertler (1989), Kiyotaki and Moore (1997), Claessens, Kose, and Terrones (2012), and Jordà, Schularick, and Taylor (2017).

> 3 Other research uses different approaches to identify easing and tightening phases. For example, de Soyres and Saijid (2024) defines an easing phase as when the policy rate decreases compared to the previous quarter and had not decreased already in the year prior. Caldara et.al. (2024) focuses on “global tightening windows” which last two years and begin when: (i
as firm-level fixed effects to control for all time-unvarying characteristics of a firm. Finally, we cluster the standard errors at the firm level.<sup>34</sup>

# **5. Results**

We now discuss our findings. We first summarize the effect that the FD Law had on aggregate state outcomes, including public fiscal balance, economic activity, and poverty rates. We then discuss our loan-level evidence on the unwinding of crowding out in the credit markets after the introduction of the FD Law. Next, we describe our firm-level results of the impact of the crowding-out unwinding on firms’ real outcomes. Finally, we discuss the extent to which the impact of the FD Law depends on the ex-ante composition of states’ fiscal spending.

# **5.1. Impact of the FD Law on Fiscal Balance, Economic Activity and Bank Lending of States**

In this section, we first discuss the impact that the debt ceilings introduced in the FD Law had on states’ fiscal balances. We then summarize how aggregate economic activity of states as well as their poverty rates and income inequality measures were affected by the Law.

# _5.1.1. Fiscal Balance of States_

Table 2A reports our estimates of equation 1 on the fiscal components of states. In Panel A, we show the results for the yearly fiscal expenditure indicators of states. The first indicator, _Total Expendituress,y_ , corresponds to the ratio of total public spending over a state’s GDP. Consistent with the fiscal austerity that the new debt ceilings impose, we find evidence that more ex-ante indebted state governments contract their fiscal expenditures after the introduction of the FD Law (columns 1 and 2). A one-standard-deviation increase in the ex-ante public indebtedness of a state resulted in a 4.4 percent contraction in public spending after the Law (with the state’s ratio of public expenditure to GDP dropping by -0.012*0.7=0.7 p.p.). The second
beginning  of  our  sample at  1990  (Export90).  The  export  status  variable  in  column  8  takes  the  value  1  if  the  firm reported  positive  exports  during  every  year  between  90  and  98.  Finally,  when  we  use  the magnitude  of  exports  for  the  sub-­‐sample  of  exporting  firms  to  represent  the  relevance  of exports  to  the  firm  instead,  we  obtain  yet  again  estimates  of !!" and !!" that  are insignificantly  different  from  zero.<sup>37</sup> Our  findings  using  worker  level  data  and  taking  endogenous  worker  mobility  into  account, suggests  an  insignificant  differential  effect  of  trade  policy  on  the  wages  of  workers

> 37   Other   robustness   checks   we   have   conducted   but   we   do   not   reported   to   conserve   space   include defining   a   firm   as   exporter   if   the   firm   exported   only   to   non-­‐Mercosur   countries   (and   omitting exporters   to   Mercosur   countries   from   the   sample)   and   using   a   more   detailed   industry   classification available   only   for   the   1994-­‐1998   period,   which   allows   us   to   identify   changes   in   industry   level   ERP   at a   more   disaggregated   level.   We   find   our   main   conclusions   to   be   robust.

25
used to fill in missing information but since many of the terms were not available online either, they had to be requested directly from the institutions. The other problem was that financial institutions were not always willing to disclose their product portfolio.<sup>24</sup> This resulted in a product dataset with 451 saving and 281 credit products of which 365 and 240 had complete information, respectively.<sup>25</sup> Data for products with complete information are used to generate Table 1 and to assess the total costs of the products offered to the auditors.

We use data from the questionnaire, and market products dataset to construct variables related to the amount of information provided by the institutions.<sup>26</sup> We first matched the product offered to the

> 24 This was particularly true in Ghana where the collection of the market products dataset was completed 10 months after the study. For Peru and Mexico the dataset was completed three and four weeks after the study, respectively.

> 25 If a transaction account has up to 2 missing cost terms is considered to have complete information and is used in the analysis. The rest of products (investment account and credit) are considered to have complete information if they have up to one missing cost term.

> 26 For savings products, the fees include (1) opening fee; (2) management / administration fee; (3) membership fee; (4) debit card fee; (5) the interest rate offered; (6) the total annual earnings (APY); (7) inactivity penalty; (8) account inquiry cost; (9) cash withdrawal cost; (10) debit card replacement fee; (11) penalty for not maintaining a minimum balance and (12) early withdrawal fee for investment accounts. For credit products, the fees include (1) interest cost; (2) insurance (e.g. life, default);

13
households where both they and their husbands report that decisions are made jointly also underreporting incidences of IPV.<sup>2</sup> We use the last available survey round for each of the 12 countries where the decision making question was administered to couples and the domestic violence module was administered to married women. Hence, our data set covers the years 2010-2016. Restricting to non-missing observations results in a sample of 31,243.<sup>3</sup>

# **3.2 Empirical Strategy**

The objective of this paper is to understand the importance of joint decision making and agreement between couples over decision making on the incidence of IPV. Thus, the empirical strategy developed throughout this paper consists of two main parts. In the first part, we use the wife’s response on who makes the decisions regarding major household purchases to see how it is correlated with incidence of violence. We estimate the following OLS specification: Where i is an indicator of whether wife _i_ faced violence (any physical, emotional or sexual) in the last 12 months in country yy _c_ ; D i is wife’s response on who makes decisions about major household WW

> 2 Data from Cullen (2020), which analyzes the magnitude and predictors of misreporting on intimate partner and sexual violence in Nigeria and Rwanda, finds no evidence for this pattern of underreporting.

> 3 The countries are Burkina Faso, Burundi, the Comoros, Côte d’Ivoire, Ethiopia, The Gambia, Kenya, Mali, Nigeria, Rwanda, Zambia, and Zimbabwe. It should be noted that when looking at the relationship between decision making and IPV, our sample changes due to data availability and conditionally applicable questions. We re-run our analysis for our smallest, most restrictive sample where all variables are available to ensure that our results are not driven by sample selection issues (results available upon request).

11
received multiple follow-up visits, similar to those in the second group. The differences between module arms are small but not zero, and they have no impact on the findings in this paper. We control for diary type whenever relevant.

The SHWALITA survey was conducted in 24 villages per district, in 7 districts. The resulting data set includes responses for 1,512 diary households. After dropping households that did not purchase any of the items that we study or that did not complete the end-line 10
problem with our estimation results. Also, due to the short span of the data, we do not exploit variations over time in the level of income. Hence, what our results seek to establish is the differential effect of the number of required documents on the time cost across countries that are at different levels of income to begin with. The same holds for our measure of country size which equals the log of the average level of total population of a country where the average is taken over 2001-2005 ( _Population_ ). The data source for _Population_ is World Development Indicators, World Bank.

Briefly, the mean value of _Income_ equals 8.0 and the standard deviation equals .94. For _Population_ , the corresponding figures equal 15.6 and 2.03, respectively. We check all our results for potential outliers, especially with respect to countries that are very small (island countries) and the very large countries.<sup>3</sup> Our estimation equation takes the following form

# 2.3 _Other explanatory variables_

The remaining explanatory variables in the regression results discussed below are motivated to guard against the potential omitted variable bias or spurious correlation problem. We would like to mention here that our main focus is on the interaction terms in the equation above. That is, how the relationship between the number of required documents and the time cost varies across

> 3 We do find that Afghanistan, China and Tanzania have unduly large effects on our results. Including China in the sample makes our results weaker while the opposite holds for Afghanistan. For Tanzania, the results vary depending on the specification. To ensure that our results are not unduly affected by individual countries, we exclude Afghanistan, China and Tanzania from our sample.

7
garbage- g 8 § g 8 2 9<br>0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100<br>© Poorest 20% 9 Some or complete college 9° Urban<br>© Richest 20% © Some or complete elementary; or no schooling © Rural<br><!-- End of picture text --> Source: Author's calculations using 2015 NNS and FIES data. Notes: Indicators in italics are not used as components of the aggregate indicators of overall adequacy in each of the five nutrition driver categories
In order to implement this simulation in a computationally efficient manner, while respecting both the PISA sample and test design we estimate Lorenz curves of the learning distribution.<sup>57</sup> This procedure relies on simple summary statistics of the country level PISA data (15 equally spaced bins with the average test score in reading), computed using sample weights, replication weights, and the assessment’s plausible values. These data are then used to estimate the Lorenz parameters.

The basic building blocks of this methodology are the following two functions: _Lorenz curve Lc_ = _L_ ( _Pc_ ; B _c_ ) _Proficiency measure_ : _Pc_ = _P_ (mu _c_ / _z_ , B _c_ ) where _L_ is the share of the bottom _p_ percent of the student according to learning scores for a specific country _c_ ; B is a vector of (estimable) parameters of the Lorenz curve for a specific country _c_ , _P_ is a proficiency measure written as a function of the ratio of the mean learning score _mu (_ for a specific country _c)_ to the proficiency threshold _z_ and the parameters of the Lorenz curve of country _c_ .

The Lorenz curve captures all the information on the pattern of _relative learning inequalities_ in the student population. It is independent of any considerations of the absolute learning level. The share of students below a proficiency level captures an absolute standard of the student population. To calculate the parameters of the Lorenz curve, we test two functional forms – the Beta Lorenz<sup>58</sup> curve and the General Quadratic (GQ)<sup>59</sup> Lorenz curve. For the purpose of this exercise the General Quadratic (GQ) Lorenz curve was preferred, as it provided belter results both in terms of internal and external validation.<sup>60</sup> In the context of this exercise, we compute the share of learners below the PISA minimum proficiency level (MPL), the average learning gap in respect to the MPL, and the average
WPS3784

**Table 1. Poverty Incidence and Poverty Gap in Rwanda**

||**Headcount Index (%)**<br>**P0**|**Poverty Gap**<br>**P1**|
|---|---|---|
|National|42.92|0.16|
|Urban|4.09|0.01|
|Rural|46.06|0.17|

Note:

1. The headcount index, P0, is the proportion of population with per capita expenditure below the poverty line

The poverty-gap, P1, estimates the average distance separating the poor from the poverty line, as a proportion of that line 2. Poverty line: 50,145.73 RwF = Income per capita of the last individual in the fourth decile of expenditure ranked by adult equivalent 3. All farm households in urban provinces are considered as "rural"

**-** **<u>Table 2. Determinants of the Probability of being non poor in Rwanda</u>**

|||**Standard**<br>|**Wald 95%**<br>|**Confidence**<br>|<br>|<br>|
|---|---|---|---|---|---|---|
|**Parameter**|**Estimate**|**Error**|**Lim**|**its**|**Chi-Square**|<br>**Pr> Chi-Sq**|
|Intercept|-0.3879|0.0178|-0.4229|-0.353|472.47|<.0001|
|Dummy Access to Road|0.1077|0.0059|0.0962|0.1192|336
consumption as a share of GDP; Freedom Houses measures of civil liberties and political rights; and World Bank Governance Indicators, government effectiveness and corruption. Not one of these indicators is significant at the p _<_ 0.05 level when I include the variables in the equation together as a group or when I include them separately. The coefficient estimates for the individual-level explanatory variables barely change when I include these country-level indicators.

The most likely explanation for why I am not finding any robust countrylevel effects is that there is more variation in attitudes within countries than between countries. The intra-class correlation coefficient for the country level is only 0.03. Thus, only 3 percent of the variation in deference to the tax department can be attributed to country-level factors. By contrast, 20
water pipes and flush toilets in their households.

To see how these assumptions translate into changes in mortality, we first predict mortality under the true (observed) distribution of water and sanitation access, which, by construction, yields current mortality rates. In a second step, we recode all water and sanitation variables to reflect the counterfactual assumptions, and predict child mortality under the assumption that all other child, household and survey characteristics remain unchanged. We apply the DHS country-specific survey weights for these mortality calculations.

Table 6 shows the results of these counterfactual computations. In column 1 of Table 6, we show current under-5 child mortality rates (deaths per 1000 live births) as measured in the

> odds of dying by 7.9%: (1-0.921)*100. Given the “small” probability of child death within a given year in general (0.021, Table 4), odds ratios are very similar to probability ratios in this case. 16 See Section 2 for a more detailed discussion on sample selection.

18
Our estimations yield a number of interesting results. First, we find a strong positive association of higher outreach with the traditional indicators of financial development (Table V).<sup>14</sup> Specifically, we find a positive and significant correlation of private credit to GDP, liquid liabilities to GDP and total deposits to GDP with all our indicators, with the notable exception of loan-income and deposit-income ratios. Also, it does not appear to be the case that greater outreach comes at the expense of higher overhead costs to total assets or higher interest margins.<sup>15</sup> Second, not surprisingly, we find outreach to be correlated with population density and economic size. In particular, more densely populated countries have higher geographic branch and ATM penetration, while there is no robust correlation with the indicators measuring demographic penetration of bank outlets and the indicators measuring the use of banking services. This is confirmed by the regressions in Tables VI-IX. At the same time, we find that larger economies have higher bank and ATM penetration and show higher use of loan and deposit services. This suggests economies of scale in banking service delivery.<sup>16</sup> Third, the positive association of institutional and financial development extends to the access to and use of banking services (Table VI).<sup>17</sup> Here we use as one of our measures of institutional quality the Kaufman, Kraay and Mastruzzi (2003) _Governance Index_ , which averages six sub-indices measuring rule of law, control of corruption, voice and accountability, political stability, government effectiveness and regulatory quality. Further, we use the Heritage Foundation _Index of Barriers to Economic Freedom_ - an average of ten sub-indices including

> 14 We do not include the financial sector indicators in the regressions, since unlike for the other variables, there is a strong case for bi-directional causality, which might bias the OLS coefficients and renders interpretation problematic.

> 15 This interpretation has to be taken with a grain of salt
Policy Research Working Paper 6355

# **Abstract**

This paper uses small area estimation techniques to update Vietnam’s province and district-level poverty map to 2009. It finds that poverty rates continue to be highest in the northern and central mountainous regions, where ethnic minorities make up a large fraction of the population. Poverty has fallen in most provinces and districts over this decade, but the pace of poverty reduction has been least pronounced in those localities with high initial poverty or inequality levels. As a result, poverty rates have become more spatially concentrated over time, which is consistent with widely observed growth processes linked to agglomeration. The authors hypothesize that this makes geographic targeting of the poor more relevant as a means to re-balance growing welfare disparities between geographic areas. Simulations indicate that in both 1999 and 2009, geographic targeting for poverty alleviation improves upon a uniform lump-sum transfer and this becomes more evident the more spatially disaggregated the target populations. The analysis further indicates that the gains from geographic targeting have become more pronounced over time in Vietnam. Although poverty reduction in Vietnam has been impressive, further progress may thus warrant increased attention to geographic targeting.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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 planjouw@worldbank.org, mmarra1@worldbank.org, and c_nguyenviet@yahoo.com.

_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
Sheahan, M., & Barrett, C. B. 2017. Ten striking facts about agricultural input use in Sub-Saharan Africa. _Food Policy_ , 67, 12-25. https://doi.org/10.1016/j.foodpol.2016.09.010

- Sheahan, M., & Barrett, C. B. 2014. _Understanding the agricultural input landscape in SubSaharan Africa. Recent plot, household, and community-level evidence_ . World Bank Policy Research Working Paper No. 7014. Washington, DC.

- Simoes, A. J. G., & Hidalgo, A. H. 2011. “The Economic Complexity Observatory: An Analytical Tool for Understanding the Dynamics of Economic Development.” In _Scalable Integration of Analytics and Visualization: Papers from the 2011 AAAI Workshop_ .

- Xu, Z., Guan, Z., Jayne, T. S., & Black, R. 2009. Factors influencing the profitability of fertilizer use on maize in Zambia. _Agricultural Economics_ , 40(4), 437-446.

- Zezza, A., McGee, K., Wollburg, P., Assefa, T. and Gourlay, S., 2023. From necessity to opportunity: lessons for integrating phone and in-person data collection. _European Review of Agricultural Economics_ , 50(4), pp.1364-1400.

21
|Share of HH with improved floor|0.18<br>|0.02<br>|0.16|
|---|---|---|---|
|Share of HH with cellphone|0.45<br>|0.01<br>|0.73|
|Share of HH with fridge|0.02<br>|0.00<br>|0.27|
|Share of HH with stove|0.02<br>|0.00<br>|0.32|
|Share of HH with computer|0.02<br>|0.00<br>|0.35|
|Share of HH with oxcart|0.03<br>|(0.01)<br>|0.24|
|Share of HH with bicycle|0.33<br>|(0.02)<br>|0.28|
|Share of HH with motorcycle|0.04<br>|(0.00)<br>|0.77|
|Share of HH with car|0.01<br>|(0.00)<br>|0.74|
|Share of HH with radio|0.28<br>|0.01<br>|0.12|
|Share of HH with television|0.06<br>|0.00<br>|0.50|

Notes: the difference in means are calculated using a regression of each variable against the indicator variable equal to 1 if the village was matched and zero otherwise. They are weighted by the number of households in each village and the standard errors are clustered at district level.

# 3. Methodology

We propose four different targeting methods using census and survey data combined with geospatial data to understand the most effective way to target the poor population: (