rafmacalaba/datause-encoder-data huggingface dataset:
2010; Aggarwal, 2018), it also reduces poverty (Khandker et al., 2009), and contributes in local market development (Mu and van de Walle, 2011) and welfare gains (Kebede, 2024).<sup>1</sup> The second area of contribution is to the literature on rural employment and structural transformation. Road improvement and travel time reduction contributes in easing access to jobs. High transportation costs lead to increased size of the agricultural workforce and employment in subsistence farming (Gollin and Rogerson, 2014; Adamopoulos, 2011). On the other hand, improved accessibility to jobs, through improved roads, benefits structural transformation. Asher and Novosad (2016) and Mu and van de Walle (2011) find that the rural roads programs in India and Viet Nam, respectively, increase wage labor participation and the share of households mainly relying on the service sector as their main source of income. Nakamura et al. (2020) show that improvement of rural roads in Ethiopia led to a 4.6 percentage point increase in women’s employment and 14.1 percentage point increase in youth employment. In India, Herrera Dappe et al. (2021) found that the construction of new all-weather rural roads resulted in a 9.5 percent increase in overall employment rates. This effect was more pronounced in villages located farther from urban centers. Improved rural roads have also been shown to increase household income by specializing in agriculture by using more fertilizer and newer technologies, and hiring more labor, as shown by Qin and Zhang (2016) and Shamdasani (2021) in the cases of Chinese and Indian rural roads, respectively.

And finally, the paper also contributes to the growing body of literature that use historical settings to account for endogeneity. Duranton and Turner (2012) use a historical highway plan of the US highway system to estimate a structural model of city growth and transportation, Baum-Snow et al. (2012) use Chinese rail and road networks from 1962 as a source of identifying variation in rail and road networks after 2000, Volpe Martincus et al. (2017) use historical Inca routes for Peru, Garcia-L ́opez et al. (2015) and Holl (2016) use Roman roads and the 1760 Bourbon postal routes as sources of exogenous variation of highway extension in Spain, and M ̈oller and Zierer (2018) rely on an 1890 plan of railroad network in Germany and 1937 map of planned autobahns to study regional employment.

> 1For an overview on the impacts of infrastructure improvements on various economic outcomes, please see Redding and Turner (2015).

4
11 implementation, and that we have quarterly data prior to UC and monthly data thereafter, the constrained version of eqn (1) includes six dummies: the first equaling one when the quarter is three or more years before implementation ( _UC_<sup>-3</sup> ); the second equaling one when the quarter is two years before UC implementation ( _UC_<sup>-2</sup> ); the third equaling one when the month is between zero and 12 months after implementation ( _UC_<sup>0</sup> ); the fourth equaling one when the month is between 12 and 24 months of implementation ( _UC_<sup>1</sup> ); the fifth equaling one when the month is between 24 and 36 months of implementation ( _UC_<sup>2</sup> ); and the sixth equaling one when the month is 36 months or more after implementation ( _UC_<sup>3</sup> ). If UC has a causal effect on _y_ , we would expect  3 and  2 to be zero, and at least some of  0 ,<sup></sup> 1<sup>,</sup>  2 and  3 to be nonzero, depending on the time profile of UC impacts.

# IV. **DATA**

Our data are from Thailand’s Labor Force Survey (LFS) conducted by the National Statistics Office of Thailand (NSO). The data were collected quarterly from 1985 to 2000, and then monthly from January 2001 (UC was launched in the first provinces on April 2001) until the present. Each round of the survey covers all provinces and all industries in the entire country. The sample size is between 300,000 and 460,000 persons per year, with some rotation across surveys. The LFS gives a weight variable which allows us to gross up to the population. In this paper, we use the data from the start of 1997 to the end of 2005. This time frame covers the four years and three months prior to the earliest implementation of UC, the nine months of staggered implementation across the remaining provinces, and four years after the last province acquired UC. This gives us a sample size of
# **Cross-Border Spillover Effects of the G20 Financial Regulatory Reforms: Results from a Pilot Survey**

Clive Briault Erik Feyen* Ines Gonzalez Del Mazo Brian Kwok Chung Yee Jan Rademacher Ilias Skamnelos JEL: G15, G18, G2, G21, G28 Keywords: Financial reforms, Financial supervision and regulation, Basel III. *Corresponding author (efeijen@worldbank.org).
households in each. To select a starting point the enumerator used a code of the day<sup>16</sup> and chose every second house in rural areas and every fifth house in urban areas. The selection of individuals within the household to answer the questionnaire was conducted as follows: the head of household (male or female) was selected to answer the first part of the questionnaire dealing with general questions about the households. Using the roster of household members which was compiled during the first part of the interview, another member of the household aged 18 or above was selected randomly to answer the second part of the questionnaire in which perception questions were asked. Alternation between male and female was ensured. The survey thus generated data that are reflective of the opinions of those aged 18 and above in northern Mali.

To assess the representativeness of the data, which were collected under rather challenging circumstances, the ethnic composition of the sample was compared with the ethnic composition in the North as reported by the 2009 Census.

<!-- Start of picture text --> Figure 2: Ethnic composition of the Figure 3: Ethnic composition of<br>North, 2009 Census sample<br>Other<br>Other Arab Peulh/Foul ethnicities Arab<br>ethnicities 3% be 3% 5%<br>13%<br>7%<br>Peulh/Foulbe<br>7%<br>Tamashek<br>32% Tamashek<br>36%<br>Songhai<br>Songhai 49%<br>45%<br><!-- End of picture text --> Similarly, regional representation of the sample was found to be in line with actual regional distribution of the population in the north.

> 16 The code of the day is the sum of the two figures of the date, i.e. if it’s the 25th of August the code of the day is 2+5=7. The enumerater will chose house number 7 as a starting point.

8
inclusiveness in school decision making. The manual was provided to all schools participating in the study.

## **3.1.2. The Management Training**

The management training and capacity building are the centerpiece of the WSD intervention. The principals, teachers, and representatives of parents and students received training in six areas of school management, also described in the school management manual. The six areas were (1) community participation, (2) learner’s welfare and school environment, (3) curriculum management, (4) teaching and learning resources, (5) teachers’ professional development, and (6) leadership and management. In the course of this training, participants developed a local school development plan addressing various areas with guidance from the trainers and the supervision of the WSD unit within the Ministry of Education.

The training used a cascade method. First, the experts who developed the SMM trained twenty people (“master trainers”) at the national level. Second, the master trainers conducted regional trainings of “cluster monitors” (school inspectors over a cluster of schools), school directors, and some senior teachers. Then those regional trainees carried out a local training with the school management committee, senior teachers, and – in some cases – a student representative. The training lasted between 10 and 20 days, with sessions split across several periods. The initial sessions of the local trainings were supervised by experts who had developed the SMM. Since most parents do not speak, read, or write English, the training put emphasis on local languages and drawings (See Figure 1) to convey the messages more effectively.

## **3.1.3. The Grant**

Some of the activities suggested in the manual and included in the school development plans, like workshops, might require financial resources. Over time, the funding for these activities was expected to come from the school budget and locally raised funds. However, during the first year, the intervention schools were provided with a grant to serve as a catalyst for school improvement. A grant of US$500 was given to all the schools in the WSD and the Grant-only groups after a school development plan was presented. The schools were required to spend the funds on activities pertaining broadly to learning and teaching. The schools informed the regional office about their spending plans and submitted the receipts. This grant represents about 16 months worth of salary for a first grade teacher without experience or about 14.5 months worth of salary of a first grade teacher with five years of experience. It represents less than 5% of the average annual school budget.

# **3.2. Sampling**

The sample in this study is the census of lower basic public and government-aided schools in regions 2, 3, 4, and 6 (276 schools) in The Gambia (Figure 2). The two regions that were excluded from the study were Region 1, which is essentially only the capital city and was 8
Figure 1: BRAC’s Ultra-poor Graduation Program – Timeline <!-- Start of picture text --> Graduation<br>Community Mobilization<br>Healthcare Support<br>Coaching<br>Savings<br>Consumption Allowance<br>Asset Transfer<br>Refresher Refresher Refresher<br>Technical Skills Transfer<br>Training Training Training<br>Targeting<br>0 Months 12 Months 24 Months<br>Implementation start Implementation end<br><!-- End of picture text -->

**Source:** Adapted from `http://www.brac.net/images/index/tup/brac_TUP-briefNote-Jun17.pdf` _Notes:_ The figure shows the typical timeline of BRAC’s graduation program in Bangladesh. The specific timeline may vary depending on the local conditions, as well as beneficiary requirements.

35
East Asia and the Pacific (EAP) region. Computing the growth benefits of closing the gap relative to a specific benchmark uses the values of the different dimensions of trade integration over the last five-year period (2010-14) using the following formula: Growth benefit�β���� ����������z��������� �z����������

**Table 8** reports the potential benefits on economic growth, physical capital accumulation, and TFP growth of increasing manufacturing trade in SSA (including the different Sub-Saharan Africa country groups by natural resource abundance) and narrowing the gap relative to other benchmarks. Panel A of Table 8 reports the potential economic growth benefits of closing the gap in terms of manufacturing trade using the coefficient estimates in column [4] of Table 5 (0.067 for manufacturing trade). Our calculations reveal that growth per worker in Sub-Saharan Africa would accelerate by 3.85 percentage points per year if the region were to closet its gap in manufacturing trade volumes relative to the SSA leader. Note that the estimated gap for the region as a whole differs from the potential gains across country groups classified according to their degree of natural resource abundance. For instance, in SSA, the largest potential growth benefits of rising trade volumes are likely to be accrued by non-resource abundant SSA countries (3.87 percentage points per year) while the smallest growth benefits are attained by resource abundant SSA countries in the region (3.72 percentage points per year).

**–** **<u>Table 8. Comparative Statistics, Manufacturing Trade (2010 2014)</u>**

|||<br>Region<br>Leader|<br>World excl.<br>SSA (90th<br>percentile)|Industrial<br>countries<br>(median)|East Asia &<br>the Pacific<br>(median)|
|---|---|---|---|---|---|
|Panel A.<br>|SSA|3.85%|4.17%|0.76%|0.63%|
|Growth<br>0.067|Resource Rich|3.72%|4.03%|0.63%|0.50%|
||Non-Resource Rich|3.87%|4.19%|0.79%|0.66%|
|Panel B.||||||
|Capital|SSA|3.51%|3.79%|0.70%|0.58%|
|0.061|Resource Rich|3.39%|3.67%|0.57%|0.46%|
||Non-Resource Rich|3.53%|3.81%|0.71%|0.60%|
|Panel C.||||||
|TFP|SSA|2.13%|2.30%|0.42%|0.35%|
|0.037|Resource Rich|2.05%|2.23%|0.35%|0.28%|
||Non-Resource Rich|2.14%|2.31%|0.43%|0.36%|

_NOTE: The estimations are calculated based on the results from regressions in Table 5 Columns [4] [5] and [6]._ Similarly, we look at the channels of transmission of closing the gap in manufacturing trade by assessing their benefit in terms of (potential) growth of physical capital per worker and total factor 25
**Table 5: Heterogeneity of Experiment Questionnaire Treatment Impact on Pooled Binary Poverty Proxies Across Sample Comparisons & Questionnaire Modules**

||**A**|**ll**|**F**|**ood**|**Non**|**-Food**|**S**|**hocks**|
|---|---|---|---|---|---|---|---|---|
|_Sample Comparison_|_Experiment vs._<br>_Standard_|_Same_<br>_Households:_<br>_Experiment vs._<br>_Standard_|_Experiment vs._<br>_Standard_|_Same_<br>_Households:_<br>_Experiment vs._<br>_Standard_|_Experiment vs._<br>_Standard_|_Same_<br>_Households:_<br>_Experiment vs._<br>_Standard_|_Experiment vs._<br>_Standard_|_Same Households:_<br>_Experiment vs._<br>_Standard_|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||0.023***|0.023***|0.026***|0.031***|0.029***|0.032***|0.014**|0.006|
|_Overall_|(0.005)|(0.004)|(0.008)|(0.007)|(0.006)|(0.006)|(0.006)|(0.004)|
|**Observations**|197,513|107,080|62,082|33,658|70,529|38,235|64,902|35,187|
|_1st Half of_|0.023***||0.022*||0.035***||0.010||
|<br>_Fieldwork_|(0.008)||(0.013)||(0.010)||(0.010)||
|**Observations**|98,466||30,952||35,156||32,358||
|_2nd Half of_|0.022***||0.025**||0.023**||0.019**||
|<br>_Fieldwork_|(0.007)||(0.012)||(0.009)||(0.008)||
|**Observations**<br>**Note:**The reported co|99,047<br>efficients and stan|dard errors are thos|31,130<br>e associated with|the binary variable|35,373<br>identifying whethe|r a household was|32,544<br>subject to the exp|eriment questionnaire|

**Note:** The reported coefficients and standard errors are those associated with the binary variable identifying whether a household was subject to the experiment questionnaire treatment. The estimations are based on Logit regressions, using the pooled data at the household level for all 70 binary poverty proxies from food, non-food and shocks modules. The control variables, as specified in Equation 1, are included but not reported. The regressions are weighted and take into account clustering and stratification. The results are robust to varying the set of control variables and/or including interviewer fixed effects. ***/**/* indicate statistical significance at the 1/5/10 percent level.

21
between the lines of the number of recipients and non-recipients at each test score.<sup>20</sup> Because the scholarship program affects enrollment decisions, there are in total approximately 25 percent more CSP recipients than non-recipients in 7<sup>th</sup> grade at the time of the June 2006 school visit, and 30 percent more recipients than non-recipients enrolled in 8<sup>th</sup> grade at the time of the household survey (after rescaling). The question is where these additional CSP recipients are found in the distribution of test scores.

Figure 5 clearly shows that the additional children brought into school by the CSP are not distributed proportionately at all values of the test score. For the school-based mathematics test, the ratio of recipients to non-recipients is larger than 1.2 at every value of the test 13 and below. (The median score for the sample as a whole is 11). In the household-based mathematics test, the ratio of nonrecipients to recipients clearly slopes downwards from left to right. For the vocabulary test, finally, the number of CSP recipients and non-recipients is almost exactly the same at the highest values of the test score, implying that _no_ children with very high test scores were kept in school by the program, while at the lowest values of the vocabulary test score there are at least twice as many recipients as non-recipients.

The second approach we take uses only data from the June 2006 in-school mathematics test. Because we visited CSP schools in both June 2006 and in June 2007, the sample of children who took the test in June 2006 can be divided into children who will continue from 7<sup>th</sup> grade to 8<sup>th</sup> grade (“stayers”) and those who will leave school over the period (“drop-outs”). We can then generate histograms for the June 2006 test scores of “stayer” and “drop-out” children, separately for recipients and non-recipients. As before, the point of the analysis is to see how the distribution of June 2006 test scores among stayers differs for recipients and non-recipients, after weighting to ensure equal numbers of recipients and nonrecipients in June 2006. Figure 6 shows that virtually all of the CSP recipients who were kept in school between 7<sup>th</sup> and 8<sup>th</sup> grade had test scores in 7<sup>th</sup> grade that placed them below the median; proportionately,

> 20 For the school-based and household-based mathematics tests, each “bin” corresponds to a given score, ranging from 1 to 20 for the household test, and 1 to 25 for the school-based test—see above. For the vocabulary test, each “bin” corresponds to the number of children within 10-point brackets, with the number of potential correct answers on the test running from 40 to 142.

19
engaging in that activity even if it falls slightly out of the specified time period. Asking about the longer time period first could make the respondent more willing to state that she did not engage in that activity more recently.

# **Within-question order effects**

Order effects occur _within_ questions, as well. That is, the order in which responses to a question are presented can affect the frequency with which those responses are chosen or rated. For example, Boardman, et al. (1996) note "a study that asked some respondents to value preserving seals and then whales, while others were asked to value preserving whales and then seals. Seal values were considerably lower when the seal question was asked after the whale question." Unfortunately, empirical findings on within-question effects are ambiguous. Sudman, Bradbum, and Schwarz (1996) note that "despite the considerable empirical evidence for the emergence of response order effects, we know relatively little about the conditions that determine their emergence and direction, and the area is characterized by a large number of apparently inconsistent findings .... Because of the shortcomings in the available data, it is currently impossible to draw strong conclusions about the processes that underlie response order effects in survey measurement." Survey designers may be able to minimize this type of bias by randomizing across respondents the order the responses are presented.

# **Sensitive Questions and Truth-Revealing Mechanisms**

Often researchers wish to study behavior that society (or the respondent) considers inappropriate or illegal. For these or other reasons, the respondent may have an incentive to be untruthful when responding to questions. Social scientists typically worry about this problem in the context of drug use or sexual behavior (Fowler 1995, Tourangeau and Smith 1996). World Bank surveys face similar problems when they ask about firm behavior regarding issues such as corruption, regulation, and taxation. A firm may understandably be reluctant to admit to engaging in illegal activities or to avoiding regulations. While there is no way to guarantee truthful responses, research has demonstrated that the mode of data collection and the method of asking the question can affect whether respondents admit to embarrassing OT illegal behavior.

33
to be large and of similar magnitude, though significance is somewhat less.

# **Distinguishing extensive and intensive margins of FAI**

A disadvantage of the PPML estimator, noted above, is that it elides investment effects across the extensive and intensive margins.<sup>46</sup> Given the prevalence of zeroes in the data, the distinction is evidently one of some importance and interest. In the framework of Section 2, such zeroes arise when the (group-wide) marginal revenue product of the first unit of investment in some country is negative. An alternative modeling approach is to suppose there to be some fixed cost _Fj_ of investing in country _j_ . Denoting after-tax profit in country _j_ by _πj_ ( _Kh,_ **_K−h_** ) _≡_ (1 _− Ti_ )� _Ri_ ( _Kh,_ **_K−h_** ) _− ρ_ (1 + _Mi_ ) _Kij − Fj_ �, where **_K−h_** denotes the ( _N −_ 1)-vector of capital invested in all jurisdictions other than _h_ (and, for simplicity, the fixed cost is assumed to be tax-deductible), a necessary condition for the multinational to invest in _h_ is then that doing so yields group profits at least as high as can be obtained by investing only elsewhere, so that Denoting by _Ki_<sup>_∗_and</sup><sup>_K_</sup> _i_<sup>_∗∗_,respectively,thesolutionstotheproblemsontheleftandright</sup> of (11), and decomposing the proportional difference in affiliate net revenue into systematic and idiosyncratic components as, in obvious notation, _Rj_<sup>_∗∗_= (1 +</sup><sup>_λR_+</sup><sup>_uR_</sup> _j_<sup>)</sup><sup>_R_</sup> _j_<sup>_∗_andsimilarly</sup> _Kj_<sup>_∗∗_</sup> = (1 + _λ_<sup>_K_</sup> + _u_<sup>_K_</sup> _j_<sup>)</sup><sup>_K_</sup> _j_<sup>_∗_,rearranging(11)givesthecondition</sup> where _Rj_<sup>_∗≡Rj_(</sup><sup>_K_</sup> _h_<sup>_∗,_</sup><sup>**_K_**</sup> **_h_**<sup>**_∗_**),</sup><sup>_R∗≡_�</sup> _j_ = _h_<sup>_R_</sup> _j_<sup>_∗_and</sup><sup>_ω_</sup> _j_<sup>_R≡R_</sup> _j_<sup>_∗/R∗_(with</sup><sup>_K_</sup> _j_<sup>_∗_,</sup><sup>_K∗_,and</sup><sup>_ω_</sup> _j_<sup>_K_</sup> defined analogously), and _uh_ absorbs the idiosyncratic elements in the comparison with the counterfactual of investment in _h_ being constrained to zero.<sup>47</sup>

> 46The distinction between the extensive and intensive margins has been made in previous work using FDI data: examples include Davies and Kristj ́ansd ́ottir (2010) and Canton, Solera et al. (2016).

47Specifically, 27
# **Finances of Egyptian Listed Firms and the Performance of the Egyptian Stock Exchange**

Erik Feyen<sup>1</sup> World Bank, FPDFS

> 1 Erik Feyen (efeijen@worldbank.org) is a Financial Economist in the Financial Systems Department of the World Bank Group. I would like to thank William Mako in the World Bank’s Middle East and North Africa Region for his very valuable comments and suggestions. I would also like to thank David Lesmond (Tulane University) for his generous advice on implementing his model to estimate transaction costs in stock markets. Lastly, I would like to thank Diego Sourouillle and Haocong Ren for research assistance.