rafmacalaba/datause-encoder-data huggingface dataset:
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- <u>Panel C: Regulatory and supervisory capacity for microfinance (0 4)</u>

|||Dep var: % of W|omen borrowers<br>||
|---|---|---|---|---|
||Version 1 (|<br>Ful samples)|Version 2 (Dro<br>sam|<br>pping Rural bank<br>ples)|
||OLS|Robust Reg|OLS|Robust Reg|
||(1)|(2)|(3)|(4)|
|Regulatory and supervisory capacity for microfinance (0-4)|-0.0678***|-0.0560***|-0.0712***|-0.0303***|
||[0.000]|[0.000]|[0.000]|[0.006]|
|NBFI dummy * Regulatory and supervisory capacity for microfinance (0-4)|-0.0362|-0.0481***|-0.025|-0.0207|
||[0.209]|[0.010]|[0.382]|[0.202]|
|Combined group dummy * Regulatory and supervisory capacity for microfinance (0-4)|-0.0305|-0.0392*|-0.0125|-0.0146|
||[0.435]|[0.058]|[0.657]|[0.507]|
|NBFI dummy|-0.0146|0.0233|-0.0222|-0.0065|
||[0.827]|[0.598]|[0.744]|[0.866]|
|Combined group dummy|-0.1389*|-0.1231**|-0.0960*|-0.0752|
||[0.065]|[0.010]|[0.097]|[0.132]|
|Observations|611|611|557|557|
|Number of countries|39|39|39|39|
|R-squared|0.479|0.5|0.494|0.561|
|Adjusted R-squared|0.462|0.484|0.476|0.545|
|_Microscope index+ interaction term (P-value in brackets)_|||||
|NBFI dummy|-0.104***|-0.104***|-0.0962***|-0.0509***|
||[0.002]|[0.000]|[0.005]|[0.000]|
|Combined group dummy|-0.0983***|-0.0952***|-0.0837***|-0.0449**|
||[0.010]|[0.000]|[0.010]|[0.029]|

35
Table 5.3: Clearance time for express shipments, Taiwan(China) <u>before and after reform</u>

|Year|Average Clearance Time - hours|
|---|---|
|1996|48|
|2005|2|
|Source: WTO Negotiating G<br>Separate Customs T<br>document /TF/W/44,|roup on Trade Facilitation,Communication From The<br>erritory Of Taiwan(China), Penghu, Kinmen And Matsu<br>,<br>8 June 2005.|

# <u>Table 5.4: Growth of volume of express and regular shipments</u>

|Category|Growth 2001-2003<br>annual percentage rate|
|---|---|
|Express import and export shipments|58|
|Regular import and export shipments|11|

Source: WTO Negotiating Group on Trade Facilitation, Communication From The Separate Customs Territory Of Taiwan(China), Penghu, Kinmen And Matsu, document /TF/W/44, 8 June 2005.

The new system required 20 new processing lines, each with an X-ray scanning machine. The Express Division offers and has a staff of 117 officers, mostly relocated from other divisions as the work load shifted.

25
To further examine the relationship between central bank independence (CBI) and the strength of the interest rate pass-through, we replace our baseline measure of independence by Garriga (2016)— which enables us to maximize the estimation sample—with that of Dincer and Eichengreen (D&E; 2014) that can be complemented with a commensurate metric of central bank transparency. However, using the variables from D&E reduces our sample size by about 22 percent, eliminating all estimates with the median year before 1998 (175 estimations). Countries such as Chile, Ghana, Nigeria, and the Slovak Republic drop out of the sample as a result. The estimation results for this subsample using the alternative measure of CBI and the additional variable measuring central bank transparency (CBT) are reported in Table A5 in the Appendix (column 1). Due to increased collinearity among variables, only the GETS results are reported—that is, results after the general to specific variable selection procedure has been applied.

The results show a higher estimated precision of the pass-through compared with the full sample. One explanation might be lower credibility and transparency of monetary policy before 1998 (Crowe and Meade 2008; Blattner et al., 2008), which might have weakened the transmission of interest rates. Hence, the meta-analysis without study estimates from before 1998 shows a higher precision, implying a complete interest rate pass-through. This supplementary meta-regression supports our results of the baseline meta-regression. Namely, we confirm the statistical significance of the credit risk dummy, average lending rates, rates on loans for other purposes, and market capitalization. In addition, other factors turn out to be statistically significant. From the study-specific factors, we find that studies using panel data estimate a lower pass-through than studies using single dimensional data. This result highlights the importance of data structures for estimating the pass-through. From the macro-financial factors, we also find a statistically significant effect of trade openness—similar to our baseline model, in which the estimated negative coefficient did not reach the common significance levels. The removal of about 200 observations with the median year before 1998 leaves us with a sample that includes the pass-through estimates with median year ranging from 1998 to 2011. During those years, global trade, trade openness, and development of global value chains were on the rise (World Bank, 2017). The latter 21
<u>Table 2: Nonmarket and External Benefits of Education</u>

|Benefit type|Findings|
|---|---|
|Child education|Parental education affects child’s educational level & achievement|
|Child health|Child’s health positively related to parental education|
|Fertility|Mother’s education lowers daughter's births|
|Own health|More education increases life expectancy|
|Spouse’s health|More schooling improves spouse’s health & lowers mortality|
|Job search efficiency|More schooling reduces cost of search, increases mobility|
|Desired family size|More schooling improves contraceptive efficiency|
|Technological change|Schooling helps R&D, diffusion|
|Social cohesion|Schooling increases voting & reduces alienation|
|Crime|Education reduces criminalactivity|

Source: Based on and adapted from Wolfe and Zuvekas (1997) Externalities may be generated at all levels of education, but their magnitude may differ, implying different levels of subsidies. Many analysts would agree that lower levels of education produce more externalities than higher levels. Literacy and other knowledge imparted at the lower levels of education are generally seen as an effective means for achieving national identity and cohesiveness. Unfortunately, there is very little empirical evidence on the magnitude of externalities across education levels, though there are some intriguing new results. A one-year rise in the average primary schooling of neighboring farmers is associated with a 4.3 percent rise in output, compared to a 2.8 percent effect of one's own primary education in Uganda (Appleton 2000). Another study found that neighboring farmers' education raises productivity by 56 percent, while one's own education raises productivity by only 2 percent in Ethiopia (Appleton 2000); however, the 56 percent figure seems rather high. Overall, the results are inconclusive. In India, researchers have argued that, because other members of a household benefit from having even just one literate member who can read and write for them, the ‘true’ literacy rate is underestimated (Basu and Foster 1998).

16
<!-- Start of picture text --> One Hundered Percent Liberalized. . FDI<br>&<br>3<br>2g<br>&33so<br>58<br>a<br>°°<br>: t<br>°<br>5 4 BG) aD Sl fo} 4 2 3 4 5<br>Quarters Relative to Policy<br><!-- End of picture text --> <!-- Start of picture text --> One Hundred Percent Liberalized Sector<br>f=} .<br>=258 ° t t t t ft<br>s | t t se<br>fo}<br>tt ¢<br>lo}9<br>-14 -12 -10 8 6 4 2 0 2 4 6 8 10 12 14<br>Months Relative to Policy<br><!-- End of picture text -->
development (GDP per capita in 1980); the log of the size of the labor force in 1980; and net exports of minerals and agricultural products per worker.<sup>22</sup> We use the levels of GDP per capita and the size of the labor force in 1980 to minimize the risks of reverse causality between the right hand side variables and volatility. We use the log of the number of latent export lines in 2012 to proxy latent export diversification. Similarly, we use the log of the average number of products exported per year in the 1962-1980 period to proxy for the size of the initial cross-sectional export basket. We choose the initial value of our measure of cross-sectional diversification (as opposed to the average in the period that goes until 2012) to minimize the risk of colinearity between our two measures of diversification. Finally, as discussed above, the sample we use includes 95 countries for which we have trade, economic, and demographic data.

The estimates in columns 1 and 2 of Table 3, Panel A, confirm the negative correlation between latent diversification and ToT volatility found in Figure 7. Latent and cross-sectional diversification are negatively correlated with ToT growth volatility, and the estimated coefficients are statistically significant. Importantly, column 2 of Panel A shows that the negative correlation between ToT volatility and latent export diversification survives the inclusion of cross-sectional diversification, although the magnitudes of the coefficients are smaller.

It is important to note that the negative correlation between ToT volatility and cross-sectional and latent diversification can be the result of omitting other country characteristics linked to volatility and correlated with diversification. For instance, as highlighted above, volatility has been found to be negatively correlated to size and income, which, as shown in Table 1, are positively correlated with latent diversification. Yet, columns (3) and (4) of Table 3, Panel A show that the effect of latent diversification on ToT volatility is unaffected by the inclusion of size and income as control variables. Also, the results in these columns show that size and income have small point estimates, which are not statistically significant, and that the effect of initial cross-sectional diversification loses its significance.

We explore the sensitivity of the results to the inclusion of other trade characteristics in columns (5)-(7). In particular, we control for the role of natural resources, measured by net exports of energy and mining and agricultural exports per worker. First, some argue that commodity prices are more volatile than those of other products, which would imply that exporters of primary goods might face high ToT volatility.<sup>23</sup> In

> 22 Alternative specifications which included additional controls (trade openness and financial depth) did not yield much change in the coefficients of trade diversification.

> 23 To be sure, there is some evidence that commodity prices are not necessarily more volatile than the prices of manufactured-products (Arezki, Hongyan and Lederman (2014)).

24
In addition to IKP, households benefited from various government programs providing child care and nutrition, mid-day meals in schools, housing and sanitation, employment and other benefits (Table 4). Employment programs stand out, as their coverage grew tremendously with the introduction of the National Rural Employment Guarantee Scheme (NREGS) in 2006.

|**Table 4: Percentage of sample households bene**|**fiting from targe**<br>|**ted governm**<br>|**ent programs**<br>|
|---|---|---|---|
||_2000_|_2003_|_2008_|
|Self-Help Groups|20|44|71|
|Integrated Child Development Services|9|14|9|
|Midday meal and free hostels|8|29|27|
|Housing|14|6|19|
|Employmentprograms|0|9|41|

Note: Data refer to May 2000 (based on recall), December 2003, and June 2008 as per the wording of the questionnaire. Self-Help Groups refers to DPIP in 2000-2003 and IKP afterwards. Midday meal and free hostels refers to hostels only in 2000, as the midday meal was introduced in Jan. 2003. Percentages for ICDS and midday meals/hostels refer to all households; for households with children of relevant ages, they are: ICDS: 22, 47, and 39 percent; midday meal/hostels: 13, 49, and 49 percent respectively. Employment refers to Food for Work in 20002003 and NREGS in 2008.

Households with members in the IKP Self-Help Groups (SHGs) in 2008 were better off in terms of initial household expenditures and education levels (Table 5), underscoring the need to carefully match participants and non-participants when assessing impacts.

**-** **<u>Table 5: Sample initial indicators for IKP participants and non participants</u>**

||IKP<br>participants|Non-<br>participants|
|---|---|---|
|Household size 2004|4.4|3.8|
|Percent female-headed HH|7.6|11.3|
|Highest year of schooling in the family|7.3|5.6|
|No. of members that can write in the HH|2|1.4|
|Total monthly HH expenditure (2004 Rs.)|2,373|2,095|
|Percent owning land in 2004|59|54|
|Percent owning livestock in 2004|41|30|
|Percent owning farm assets in 2004|87|82|
|Percent SC/ST|35|27|
|Percent children ever attended school|90|88|
|Percent children currently in school|77|81|
|Percent participating in village meetings in 2000|14|6|
|Percent benefiting from employment programs|10|6|
|Percent benefiting from midday meals|54|45|
|Percent benefitingfrom housingschemes|6|6|

# **_4. Empirical analysis_**

Impact evaluations aim to assess the impact of a program on a set of outcomes. We are interested in how much outcomes changed for households participating in the program compared to households that did not participate. If Δ is the change in an outcome indicator for household i, we are interested in the difference between the change in the presence and the change in the absence of the program: _Program impact = E(Δi / P=1) - E(Δi / P=0)_ _(1)_ 6
|**TABLE OF CONTENTS**||
|---|---|
|**Abstract**|i|
|**Analytical Overview**<br>|ii – viii|
|**MAIN TEXT**||
|I:**Introduction and Approach**|1|
|II:**The Varieties of Globalized Regulation, and their Effectiveness**|5|
|_A: Government-centric Approaches to Globalized Regulation_|7|
|Treaties: The Montreal Protocol|8|
|Anti-Corruption regulation|9|
|Globalized regulation of banking through government networks|11|
|Government-centric approaches: patterns, challenges, opportunities|14|
|_B: Civil Regulation_|15|
|The ISEAL approach to collaborative regulation|16|
|Regulating labor standards|17|
|Civil approaches: patterns, challenges, opportunities|20|
|_C: Hybrid Approaches to Globalized Regulation_|21|
|Rulemaking|22|
|Monitoring and enforcement|23|
|Hybrid approaches: patterns, challenges, opportunities|24|
|III:**Enhancing the Effectiveness of Globalized Regulation**|25|
|_Bibliography_|29|
Table A2: Balance check: Follow-up 1

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Con|trol|Offe|red||
||Mean|S.D.|Mean|S.D.|p-value|
|**Panel A. Socio-Demographic**||||||
|Female|0.50|0.50|0.51|0.50|0.32|
|Married|0.11|0.32|0.14|0.35|0.24|
|Has Children|0.12|0.33|0.12|0.32|0.89|
|Age|27.53|2.84|27.57|3.09|0.65|
|Province: East|0.07|0.26|0.09|0.28|0.39|
|Province: Kigali|0.49|0.50|0.44|0.50|0.09|
|Province: North|0.14|0.35|0.17|0.37|0.30|
|Province South|0.16|0.37|0.20|0.40|0.07|
|Province West|0.13|0.34|0.10|0.30|0.18|
|Mother is employee|0.04|0.21|0.06|0.23|0.46|
|Father is employee|0.06|0.25|0.07|0.25|0.86|
|Wealth Index|-0.07|1.44|0.04|1.44|0.25|
|**Panel B. Education**||||||
|Years since graduation|1.94|0.91|1.87|0.95|0.32|
|High Education Diploma|0.21|0.41|0.20|0.40|0.86|
|Bachelor Degree|0.77|0.42|0.78|0.42|0.87|
|Masters Graduate|0.02|0.14|0.02|0.14|0.98|
|Public School|0.74|0.44|0.76|0.43|0.43|
|Good English Language|0.97|0.18|0.97|0.16|0.72|
|Good French Language|0.43|0.50|0.43|0.50|0.92|
|Good Swahili Language|0.14|0.35|0.12|0.33|0.53|
|**Panel C. Employment**||||||
|Has ever worked|0.84|0.37|0.86|0.35|0.45|
|Not paid job last month|0.59|0.49|0.60|0.49|0.82|
|Works as employee|0.10|0.30|0.08|0.28|0.33|
|<br>Works as self-employed|0.05|0.21|0.06|0.24|0.28|
|<br>Works as family employee|0.08|0.28|0.08|0.28|0.95|
|Works as casual employee|0.18|0.38|0.17|0.38|0.80|
|Has formal job|0.03|0.18|0.02|0.13|0.18|
|Job accordance to degree|0.32|0.47|0.27|0.45|0.13|
|Labor earnings (monthly USD)|42.99|81.69|34.69|76.01|0.12|
|<br>**Panel D. Training**||||||
|<br>Willingness to pay for course (USD)|173.18|989.14|135.13|318.61|0.46|
|Training: get job faster|0.59|0.49|0.56|0.50|0.45|
|Training: get higher pay job|0.30|0.46|0.28|0.45|0.55|
|Training: get better job|0.35|0.48|0.39|0.49|0.20|
|<br>Prefers to attend Kigali|0.67|0.47|0.67|0.47|0.48|
|Prefers to attend North|0.19|0.39|0.18|0.38|0.16|
|Prefers to attend South|0.15|0.35|0.16|0.36|0.73|
|**Panel D. Joint test for panels A-D**<br>||||||
|p-value (joint F-test)|||||0.40|
|Observation|433||436||869|

Notes: the p-value reported in Column 5 is obtained from a regression of each variable on an assigned to treatment dummy with standard errors robust to heteroskedasticity, controlling for lottery dummies. We do not test for differences in means for females since the lottery was randomized for each gender separately. **p-value (joint F-test)** : corresponds to the orthogonality test in a regression of assigned to treatment dummy on covariates, the regression also controls for lottery dummies.

27
suitable jobseekers with firms they had business relations (with or without a specific request from the firm). Specific matching involved pre-selecting candidates and matching them with employers in response to specific needs of a firm. As part of the matching services, the private employment service provider also recorded interactions with employers including whether the employer requested CVs of certain jobseekers and whether they followed-up with the jobseeker based on submission of a CV. Table 1 provides an overview of the services provided under the program.

Payments to the private employment service provider were based on performance. The firm received a payment for every IAP developed based on the profiling interview. An additional payment was made for each beneficiary who secured a formal job, whether through their own search or through a match secured by the provider. To independently verify employment of the beneficiary, administrative data from the Federal Employment Institute (FEI) was used. For the purposes of payment, a jobseeker is considered formally employed if s/he appeared in the FEI employment database for at least three consecutive months (for details see section 4 describing the data sources below).

# **2.3 Impact Evaluation Design**

The impact evaluation focused on studying the relative effectiveness of the different services offered by the private employment service using a randomized design. A secondary objective was to open the “black box” to examine decisions of job counselors **.** While most of the existing literature focuses on comparing private providers to the PES, the present study evaluates the relative efficacy of different services provided by the same (private) provider, and to better understand the (typically unobservable) priorities and decisions made by the private provider. To do this, the research team first stratified program beneficiaries by gender and the length of unemployment.<sup>4</sup> Each stratum was then randomly divided into two arms. The private provider was given the names of jobseekers to allocate to a treatment arm which received more intensive services that included both job counseling (CV and interview preparation, work readiness training and information about potential employers) as well as job matching services. It is important to note that the firm was free to design the specific forms of matching services and to whom they were provided (and indeed, as will be seen only one-fifth of jobseekers in treatment ended up receiving the more intensive specific-matching). Further, matching services were not required to be

> 4 There are four strata based on gender and whether an individual is long-term unemployed or not. Jobseekers who had been unemployed for more than 12 months were defined as long-term unemployed.

8
- **Raccuia, Livio** , “Single-Target Implicit Association Tests (ST-IAT) Predict Voting Behavior of Decided and Undecided Voters in Swiss Referendums,” _PLOS ONE_ , 10 2016, _11_ (10), 1–19.

- **Reuben, Ernesto, Paola Sapienza, and Luigi Zingales** , “How stereotypes impair women’s careers in science,” _Proceedings of the National Academy of Sciences_ , 2014, _111_ (12), 4403–4408.

- **Rooth, Dan-Olof** , “Automatic associations and discrimination in hiring: Real world evi–

- dence,” _Labour Economics_ , 2010, _17_ (3), 523 534.
members had not heard about the plans before. Thus, posters and displays at the plans' clinics about the insurance system might be an effective and inexpensive way of increasing enrollment.

Table C.6 Kinshasa Survey: Population, Attitudes **and** Opinion

|.....<br>.<br>.<br>.<br>.<br>.<br>.<br>..<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>Insu<br>Characteristic<br>(11|.<br>.<br>.<br>.<br>red Uni<br>74)**WM**|.<br>.<br>.<br>.<br>nsured<br>**UI3I)**<br>R|.<br>.<br>.<br>Valid<br> <br>esponses|
|---|---|---|---|
|.......<br>.....................................................<br>||||
|Wiould you be able topay10,000 Zaires<br>|||205|
|for an illness requiring minor surgery<br>||||
|inahospical?<br>||||
|Couldpaytoday<br>'|35X<br>|34X<br>||
|Could pay some other day<br>|58X<br>|552<br>||
|Could never pay or no response<br>|72<br>|112<br>||
|*Afterhowmany days? (Average)|11|12||
|Would you be able topay50,000 Zaires<br>|||205|
|for an Illness requiring minor surgery<br>||||
|in ahospital?<br>||||
|Could pay today<br>|132<br>|112<br>||
|Could pay some other day'<br>|752<br>|70X<br>||
|Couldc never pay or no response<br>|132<br>|20X<br>||
|*After howmanydays? (Average)|30|40||
|Where would you get the 50,000 Zaires<br>(see question above)?<br>|||205|
|Cash, from household<br>|38X<br>|222<br>||
|Saving clubl<br>or association<br>|11X|6X||
|By selling household items or Livestock<br>|52|102||
|Takingaloanfromsomebody<br>||||
|outside the household.<br>|232|232||
|Donation from somebody outside<br>||||
|the household<br>|02<br>|1X<br>||
|Contribution of famiLy mrbers<br>|92<br>|192<br>||
|Other<br>|3X<br>|42<br>||
|No response|112|152||
|Would you suggest this insurance<br>||||
|plan to other family mnbers?|852|85X|135*|
|What do you think of the organization<br>|||205|
|of health insurance schemes?<br>||||
|Favourabte<br>|**962**<br>|**922**<br>||
|Not favourable<br>|1X<br>|3X<br>||
|Do not know or no response<br>..<br>.................<br>......................................|3X|5X|...|

* Applicable only to St. Alphonse. In CASOP, filies had to join.

Table C.7 Kirahass Survey: Questions Asked Separately of Insured and Uninsured **Respondents**

|.....................................................<br>**QUESTIONS**<br>What are**the**inconweniences or problem<br>of your<br>bership?|**2**|
|---|---|
|Too expensive|6.92|
|Benefits not worth the feesIpay|1.42|
|Itisnecessary to cowforfollow<br>kVvisits.|**26.4X**|
|Other|**15.32**|
|lNothing|47.2X|

42