rafmacalaba/datause-encoder-data huggingface dataset:
of pre-treatment survey periods and those surveyed in the immediate aftermath), and consider the third and fourth quarters to be post-treatment. Thus, while our baseline specification averages the effects on workers surveyed from 5 weeks to 3 years after the Rana plaza collapse, we also run specifications where we explicitly consider treatment effect heterogeneity by time duration since the collapse.

# **3.3 Identification strategy**

Our main identification strategy is a triple-difference that tests for differential outcomes in apparel workers (vs non-apparel manufacturing workers), in the four districts of Bangladesh that constitute the vast majority of export apparel factories (vs the rest of the country), pre-vs post Rana Plaza. We define the apparel industry according to the ISIC-3 classification (ISIC-3 two-digit code 18), which we also refer to as the garment industry.

Our treated districts are Dhaka, Gazipur, Narayanganj, and Chittagong. As these districts represent 3229 of the 3251 (99%) export-oriented ready-made garment factories mapped as of March 2021 (Mapped in Bangladesh, 2021), we argue that they were most closely affected by the Rana Plaza reforms, given the role of international buyers described in section 2.2. Although garment employment in Bangladesh is disproportionately concentrated in the four treated districts, control districts still host 46% of garment workers in the sample, giving us sufficient control observations in our triple-difference estimation. Figure 4 shows the distribution of garment workers across treatment and control districts.

We thus estimate the following specification for an outcome _Y_ for worker _i_ in district _j_ at time 13
relaxed, parents are in a better position to make appropriate decisions regarding their child’s human capital. CCT programs are also more costly per recipient to administer than UCT programs because of the costs associated with monitoring conditions. Unconditional cash transfers act through increased income, so that health outcomes should improve as long as the income elasticity of demand for health is positive and marginal productivity of health care is positive (Parker and Wong, 1998). Mounting evidence shows both types of transfers improve child health outcomes (for CCTs in Colombia (Attanasio, Gomez, Heredia, and Vera-Hernandez 2005), Ecuador (Paxson and Schady 2008), Nicaragua (Maluccio and Flores 2005; Macours, Schady, and Vakis 2008); and for UCTs in South Africa (Case and Deaton 1998; Case, Hosegood, and Lund 2005; Duflo 2003)), but the evidence on which mechanisms–conditionality or income effects- are playing a role in driving the impacts is more limited.<sup>2</sup> In this paper, we present evidence of the impacts of a unique cash transfer pilot program in rural Burkina Faso, the Nahouri Cash Transfers Pilot Project (NCTPP), on the take-up of routine preventative health clinic visits. The NCTPP incorporated a random experimental design to evaluate the relative effectiveness of the following four cash transfer programs targeting poor households in rural Burkina Faso: conditional cash transfers given to fathers, conditional cash transfers given to mothers, unconditional cash transfers given to fathers, and unconditional cash transfers given to mothers. Our evaluation focuses on health utilization for children 0 to 59 months old, in particular their visits to health clinics for routine preventative care. Even by African standards, child health outcomes in Burkina Faso are considered to be poor. In 2003,

> 2 However, not all impact studies show consistently positive results for these programs, and differences tend to depend on the specific details about the cash transfer program, the age range of the child studied, the evaluation method, and whether the evaluations were measuring short or medium term impacts. Even for outcomes that were directly conditioned upon, such as preventative health clinic visits, evaluations for Chile (Galasso 2006), Ecuador (Paxson and Schady 2008), and Nicaragua (Maluccio and Flores 2005) show no significant change in the number of visits to the health clinic for preventative reasons, while there were positive impacts on routine health check-ups in Colombia (Attanasio et al. 2005), Honduras (Morris, Flores, Olinto, and Medina 2004), and Jamaica (Levy and Ohls 2007).

3
- 12 -

About 100 days have water supply interruption in Albania. Importantly, however, some firms in other countries _do_ experience more service interruptions than Albania. The figures include the maximum days of service suspensions in each country. There is a trend that countries having the lower average would likely have the lower maximum. However, the figures indicate that there is a significant variation in service levels received by individual firms. In general, the quality problem in telecommunications is relatively modest in all countries.

Figure 10. Number of Days without Electricity in ECA <!-- Start of picture text --> 250<br>Average (left scale) 350<br>200 Maxmum (right scale) 300<br>250<br>150<br>200<br>100 150<br>100<br>50<br>50<br>0 0<br>Source: BEEPS.<br>Figure 11. Number of Days without Water Supply in ECA<br>120<br>Average (left scale) 350<br>100 Maxmum (right scale) 300<br>80 250<br>200<br>60<br>150<br>40<br>100<br>20 50<br>0 0<br>Source: BEEPS.<br>14 Average (left scale) 350<br>Maxmum (right scale)<br>12 300<br>10 250<br>8 200<br>6 150<br>4 100<br>2 50<br>0 0<br>Source: BEEPS.<br>Albania Georgia Tajikistan Kyrgyzstan Bulgaria Uzbekistan Ukraine Romania Yugoslavia Turkey Macedonia Belarus Kazakhstan Croatia Estonia Hungary Bosnia Armenia Moldova Russia Latvia Slovenia Poland Lithuania Czech Rep. Slovakia<br>Albania Georgia Tajikistan Ukraine Bulgaria Kyrgyzstan Romania Armenia Moldova Belarus Yugoslavia Macedonia Turkey Russia Uzbekistan Czech Rep. Lithuania Hungary Bosnia Latvia Kazakhstan Estonia Slovenia Croatia Poland Slovakia<br>Albania Georgia Kazakhstan Ukraine Tajikistan Yugoslavia Kyrgyzstan Macedonia Hungary Bosnia Bulgaria Russia Croatia Armenia Slovenia Poland Lithuania Moldova Uzbekistan Romania Latvia Turkey Belarus Estonia Slovakia Czech Rep.<br><!-- End of picture text --> Figure 11. Number of Days without Water Supply in ECA Figure 12. Number of Days without Telecommunications Services in ECA
|Top 25%*<br>(10)<br>-0.380*|(0.203)<br>-0.407*<br>(0.206)<br>-0.043<br>(0.103)<br>-0.124<br>(0.094)<br>0.002<br>(0.005)<br>0.002**<br>(0.001)|57<br>0.186<br>0.819||rts the p-values of<br>ontrol (25 percent|
|---|---|---|---|---|
|Efects<br>Trimming Tail 5%<br>(9)<br>-3.896**|(1.881)<br>-4.729**<br>(1.946)<br>-0.417<br>(0.753)<br>-1.218<br>(0.930)<br>-0.085**<br>(0.039)<br>0.010*<br>(0.005)|52<br>0.389<br>.359|ine in 1959-1961.|e. (4) The last row repo<br>25 percent of famine c|
|Interaction<br> <br>(8)<br>-7.173**|(2.903)<br>-8.672***<br>(2.937)<br>-1.559<br>(1.063)<br>-1.311<br>(0.935)<br>-0.098**<br>(0.046)<br>0.009<br>(0.005)|57<br>0.408<br>0.145|Chinese Fam|ical signifcanc<br>as in the bottom|
|Full Sample<br>(7)<br>-7.093**|(2.861)<br>-9.045***<br>(2.888)<br>-2.131*<br>(1.191)<br>-2.435***<br>(0.851)|57<br>0.384<br>0.077|g the Great|and 1% statist<br>f the county w|
|(6)<br>-6.021**|(2.673)<br>-9.491***<br>(2.606)|58<br>0.302<br>0.002|h Rates durin|ndicate 10%, 5%<br>the value of 1, i|
|Top 25%*<br>(5)<br>-0.395**<br>(0.196)<br>0.032<br>(0.117)|-0.040<br>(0.102)<br>-0.141<br>(0.092)<br>0.002<br>(0.004)<br>0.002**<br>(0.001)|57<br>0.187|e on on Deat|** respectively i<br>able, which take|
|fects<br>Trimming Tail 5%<br>(4)<br>-4.214**<br>(1.915)<br>-0.108<br>(0.985)|-0.372<br>(0.754)<br>-1.393<br>(0.920)<br>-0.089**<br>(0.038)<br>0.010**<br>(0.005)|52<br>0.381|d Guerrilla Presenc|reported. (3). *, **, *<br>riable is a dummy vari<br>value of 0, otherwise.|
|Separate E<br>(3)<br>-7.798**<br>(2.936)<br>-0.077<br>(1.310)|-1.492<br>(1.038)<br>-1.688*<br>(0.914)<br>-0.105**<br>(0.046)<br>0.009*<br>(0.005)|57<br>0.396|Factions an|ercepts are not<br>dependent va<br>, and take the|
|Full Sample<br>(2)<br>-7.886***<br>(2.916)<br>-0.433<br>(1.330)|-2.101*<br>(1.204)<br>-2.925***<br>(0.830)|57<br>0.365|n Efects of|hesis. (2). Int<br>) and (10), the<br>ng 1959-1961)|
|(1)<br>-7.298**<br>(2.769)<br>-1.982<br>(1.350)||58<br>0.254|d Interactio|s are in parent<br>In Columns (5<br>e famine duri|
|_Power Structure_<br>YRD<br>Guerrilla<br>YRD×GuerrillaNo [_b_0]|YRD×GuerrillaYes [_b_1]<br>_Initial Conditions_<br>Ln_GVOPC_52<br>Ln_pop_52<br>_Geography_<br>Share of Plain (%)<br>Distance to Xiamen<br>(Km)|Obs.<br>_R_2<br>_H_0 :_b_0 = _b_1|Table 12: The Separate an|Note: (1). White Standard Error<br>the hypothesis that_b_0 = _b_1. (5)<br>counties sufering the most sever|

37
contracts are from sellers that produce intermediate goods), indicating that the buyers are mostly at the end of the value chain.

Our data is a snapshot of outstanding receivables as of December 1, 2005. Importantly, this snapshot is before PrimeRevenue started factoring the receivables.<sup>6</sup> Also, PrimeRevenue allows firms to post whatever trade credit contract they choose, and does not limit the choice to a set of standardized options for firms. Buyers generally post invoices for all ̳important‘ suppliers, which is estimated by PrimeRevenue to capture over 90% of total inputs to the buyer. Our database includes information for 29,019 contracts, the full set of contracts in PrimeRevenue at the time, which includes 56 large buyers and 24,140 suppliers. The data includes complete information on contract terms: the contract size, net days, discount days, discount, and currency in which the contract is denominated. Most buyers interact with most sellers only once, though there is a fraction of repeat buyers and sellers who have multiple buyers.

For buyers, we can control for firm size using buckets based on their total sales<sup>7</sup> , location (North America or Europe), sector, and whether the buyer is investment grade. For suppliers, we know their size (sales buckets) and whether the supplier is investment grade. Information is not provided on supplier location or sector, since 95% of buyers purchase inputs via local distribution centers to avoid any import duties, such as tariffs and taxes. Apart from missing information about net days for 832 out of 29,019 contracts, we have complete information on contract terms.

Empirical work on trade credit thus far has been hampered due to a lack of firm-level data on trade credit contract terms. Most studies have used the Federal Reserve‘s Survey of Small Business Finances (SSBF) database of U.S. firms, which has only limited data on credit terms and firm characteristics (e.g., Petersen and Rajan, 1997; and Giannetti et al., 2011). Ng et al. (1999), instead, uses survey level data on 950 listed U.S. firms to study the determinants and characteristics of trade credit contracts.

> 6 Unfortunately, we were unable to obtain repeated cross-sections or panels of data from Prime Revenue, because these are proprietary data. We obtained a single snapshot of data for the year 2005, prior to Prime Revenue starting factoring the receivables in 2006.

> 7 Buyer and supplier size buckets based on total sales are (in U.S. dollars): less than $0.1 billion; $0.1-2 billion; $2-7 billion; $7-10 billion; larger than $10 billion.

7
Table 5: Capital control announcement and forecast error reduction(FER)

|Mean GDP growth<br>std.|GDP growth|FER,1st month|FER,2nd month|FER,3rd month|
|---|---|---|---|---|
|3.99%|2.67%|0.63%|0.57%|0.69%|

My sample period is between 2004Q1 and 2013Q4.

a given month becomes more accurate by about 0.55%-0.7% in real-time, depending on which month in the quarter is considered.

A caveat of the above result is that unlike other economic data used, capital control announcements can be made at different points in time in a given month instead of at a given time. To use these announcements in the current framework, I need to make a decision where to put them in the calendar of data release. I position capital control announcements such that this decision does not exaggerate the information conveyed by them. For instance, if half of the announcements were made on November 20th in 2009 and the other half made on December 10th 2009, capital controls would come into the forecasting model on the 10th of a month and the November 20th announcements would be treated as if they were made on December 10th.

# **5.4.4 Robustness checks**

I complete two robustness checks of my nowcasting results: first, my empirical results are robust to the month I consider. When quarterly GDP is projected on factor estimates of the second month or the third month of the current quarter, capital controls is still helpful in obtaining a more precise nowcast<sup>10</sup> . Second, I compare my results for the capital controls dummy against random dummies of similar structure. For this purpose, I generate 500 random dummies of the same length and the same proportion of -1s and 1s in simulation. In each iteration, a random dummy is placed in exactly the same place as the capital control dummy and used alongside the same macroeconomic data to generate a GDP-nowcast. The capital control dummy outperforms 81% of random dummies in terms of MSFE reduction.<sup>11</sup> These exercises show that

> 10Table 9 in the appendix presents these results in detail.

> 11Figure 10 in the appendix plots the distribution of MSFE reduction of random dummies alongside that of capital controls, shown with the solid red line. It is not surprising that some randomly generated dummies does better than the capital control dummy for this particular sample period.

36
positive correlation with wages in 1999 but not in 1993 (see Table 21). Furthermore, productivity was negatively correlated with quality control in 1993.

The correlation of quality control with productivity varies by firm size. For example, the introduction of quality control in 1999 increased productivity in small firms, but it decreased productivity in medium-size firms. Quality control did not have a correlation with productivity in micro or large firms in 1999. However, when comparing statistically significant productivity results for small firms in 1993 and 1999, productivity seems to increase over time.

**Table 21: Quality control**

|**Variable**||**19**|**93**||||**19**|**99**||
|---|---|---|---|---|---|---|---|---|---|
|**Qc**|**Wage**|P>Izl<br>|**Pro**|**ductivity**|**P>Izl**|**Wage**|**P>zl**<br>|**Productivity**|**P>zl**|
|ooled|0.04|0.27||-0.23|0.00|0.08|0.05|_0.15_|0.16|
|icro|0.20|0.49||0.84|0.12|0.21|0.23|_0.05_|0.91|
|**Small**|-0.18|0.67||-2.59|0.00|0.05|0.63|1.55|0.0l|
|Medium|0.06|0.35||0.06|0.59|-0.19|0.02|-0.78|0.00|
|Large|0.03|0.51||-0.54|0.00|0.05|0.37|0.05|0.74|

_Source_ Author's calculatons based on ENTRAM-ENESTYC.

# **iv. Sectors**

Industry-specific factors were measured by including dummy variables for each of the nine major manufacturing sectors: (1) food, beverages, and tobacco; (2) textiles, clothing, and leather; (3) wood and wood products; (4) paper and paper products; (5) chemicals, oil derivatives, and coal; (6) non-metallic mineral products; (7) basic metal industries; (8) metal products, machinery, and equipment; and (9) other manufacturing industries.

Controlling for other firm and individual characteristics, the estimations show that in 1993 employees working in sectors other than the food, beverages, and tobacco sector earned around 10 percent more than them, except for the wood and products and the other manufacturing industries (see Table 22). However, only basic metal industries showed superior productivity compared to the food sector by 21 percent more. In 1999 workers in all other sectors -other than food, beverages, and tobacco- received higher wages, 22
# **1. INTRODUCTION**

Good quality of education is critical in the new era of global competition and technological change. Mexico’s future development depends on its ability to take advantage of new opportunities quickly and decisively. Good basic education that can be accessible to all is a necessary element for a sustainable, poverty-reducing development strategy.

This paper is part of a series that examines teachers’ incentives and professional development in Mexico, in pursuit of the long-term goal of improving student learning performance.<sup>1</sup> The paper is divided into the following sections: the background succinctly places the objectives in context. Section 3 describes the data and methodology used in this paper. Section 4 compares school factors and management that are correlated with learning achievement in highly effective and ineffective schools. Section 5 measures the impact of school factors on learning achievement, particularly _Carrera Magisterial_ , teachers' salaries and training. Section 6 offers conclusions.

# **2. BACKGROUND**

Mexico is a federal country with a population of almost 97.4 million people spread unevenly over nearly 2 million square kilometers. About three-fourths live in urban areas. The country is relatively young—24 percent of the population is between 5-14 years old. The share of this age group in the total population is the highest among OECD countries, which have an average of about 14 percent. The pace of demographic growth has been dropping dramatically in recent times. As a result, the population under 6 years old has been decreasing at the rate of 0.5 percent a year, while the 6-14 age group has been increasing by no more than 0.1 percent a year. By the end of the century, the total number of persons in this age group will have virtually stabilized.

The structure of Mexico’s educational system has the following main characteristics. Basic education is the Mexican government’s highest priority. The basic education system consists of: (a) early childhood education (or pre-school), which is optional for children 3 to 5 years old; (b) mandatory primary education,

> 1 Lopez-Acevedo and Salinas (2000a) Teacher’s Salaries and Professional Profile in Mexico. The World Bank Mimeo. Lopez-Acevedo and Salinas (2000b) Factors that Affect Learning Achievement in Mexico: The Case of Mexico D.F., Nuevo Leon and Tabasco. The World Bank. Mimeo.

3
Tax Evasion: Evidence from a Revealing Experimental Representative Survey of Indonesian Firms<sup>∗</sup> Christopher Hoy<sup>†</sup> Filip Jolevski<sup>‡</sup> Anthony Obeyesekere<sup>§</sup> Originally published in the Policy Research Working Paper Series on _July 2024._ This version is updated on _August 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** Tax Evasion; Experiment; Firms; Public Finance, Compliance

**JEL Codes:** H25, H26, E26, D22, O17, O53, C83, C93

> ∗The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The authors are incredibly grateful for the support received throughout this study from Jorge Luis Rodriguez Meza, Habib Rab, Alexandre Laure, Rong Qian and Stephen Davenport. We also want to thank Revocatus Paul for his outstanding support with data analysis. The authors are also very appreciative of comments received from Lars Christian Moller, Emilia Skrok, Anne Brockmeyer, Jonathan David Timmis, Mahvish Shaukat, Khudadad Chattha, David C. Francis, Norman Loayza, Joshua Wimpey, Davide Mare, Attique Ur Rehman, and Nazım Tamko ̧c. In addition, we are very thankful for comments and suggestions provided by staff at the Indonesia Directorate General of Taxation, participants in the World Bank DECIG Seminar. The work was made possible by funding support from European Union, SECO and Canada under Indonesia PFM MDTF Phase III Program, together with funding from the World Bank Global Tax Program and the Gates Foundation.

> †Christopher Hoy (corresponding author, choy@worldbank.org) is an Economist with the Poverty and Equity Global Practice at the World Bank.

‡Filip Jolevski (fjolevski@worldbank.org) is an Economist with the Enterprise Analysis Unit, Development Economics at the World Bank and with the Department of Economics at George Mason University.

> §Anthony Obeyesekere (aobeyesekere@worldbank.org) is a Country Economist in the Indonesia team of the Macroeconomics, Trade and Investment Global Practice.
**Table 5: Regression-based Estimates Accounting for Temporal and PSU Fixed Effects**

|Dependent var. (Survey):|One if<br>have<br>outage<br>1 day|One if<br>have<br>outage<br>2 days|Per-day<br>hours<br>of<br>outage<br>1 day|Per-day<br>hours<br>of<br>outage<br>2 days|Per-day<br>hours<br>of<br>outage<br>1 day|Per-day<br>hours<br>of<br>outage<br>2 days|One if<br>have<br>outage<br>over|No. of<br>days<br>having<br>outage<br>10|Per-day<br>hours<br>of outage<br>on|
|---|---|---|---|---|---|---|---|---|---|
||ago|ago|ago|ago|(6pm &|(6pm &|10 days|over<br>days|average|
||||||10pm)|10pm)||||
||(a)|(b)|(c)|(d)|(e)|(f)|(g)|(h)|(i)|
|(A) With no control of frequenci|es of messages|sent in the r|eference da|y & time||||||
|Boxes||||||||||
|one if have 1 day ago|0.070***<br>(0.008)|-|-|-|-|-|-|-|-|
|one if have 2 days ago|<br>-|0.067***<br>(0.008)|-|-|-|-|-|-|-|
|per-day hours 1 day ago|-|<br>-|0.057***<br>(0.007)|-|-|-|-|-|-|
|per-day hours 2 day ago|-|-|-|0.060***<br>(0.008)|-|-|-|-|-|
|per-day hours 1 day ago|-|-|-|-|0.012**|-|-|-|-|
|b/w 6pm and 10pm|||||(0.005)|||||
|per-day hours 2 days ago|-|-|-|-|-|0.008|-|-|-|
|b/w 6pm and 10pm||||||(0.006)||||
|One if have|-|-|-|-|-|-|0.020**|-|-|
|over the last 10 days|||||||(0.010)|||
|No. of days|-|-|-|-|-|-|-|0.013**|-|
|over the last 10 days||||||||(0.006)||
|per-day hours,|-|-|-|-|-|-|-|-|0.021|
|<br>average over 10 days|||||||||(0.013)|
|R-squared|0.412|0.387|0.563|0.534|0.072|0.074|0.071|0.066|0.081|
|<br>No. of obs.|12928|12940|12928|12939|12941|12937|12954|12954|12953|
|**Interview date FE**|YES|YES|YES|YES|YES|YES|YES|YES|YES|

12
# **<u>Table 10. Shocks in rural Haiti</u>**

|%|% affected<br>by this shock|Income<br>only|Assets<br>only|Both|% reporting<br>this shock as<br>the worst<br>shock*|Income<br>only|Assets<br>only|Both|
|---|---|---|---|---|---|---|---|---|
|Increase in food prices|70.7|50.9|0.7|37.1|10.1|53.2|1.8|30.1|
|Cyclone, Flood|63.9|31.7|1.3|63.2|11.4|37.9|0.9|58.6|
|Drought|54.6|40.3|1.2|55.2|4.8|45.8|0.0|50.8|
|Irregular rainfall|49.6|37.9|0.3|56.7|1.7|27.3|2.3|63.6|
|Disease/Accident of a household member|47.6|42.0|1.1|54.1|30.8|41.2|1.0|51.8|
|Animal diseases|47.1|4.4|4.1|90.7|9.5|3.1|1.5|90.8|
|Crop diseases|37.6|45.1|0.5|51.6|4.5|38.8|0.9|54.3|
|Rarity of basic foodstuffs on the market|29.1|42.6|1.1|43.9|2.1|25.5|0.0|61.8|
|Increase in seed prices|27.7|48.5|0.8|42.0|1.0|51.9|0.0|29.6|
|Drop in relative agricultural prices|25.3|60.3|0.6|35.5|1.1|52.9|0.0|35.3|
|Drop in wages|22.6|48.6|0.6|47.9|1.6|25.5|0.0|63.8|
|Human epidemia|22.1|47.8|0.5|40.5|2.2|41.5|0.0|45.3|
|Death of an household member|21.9|33.0|0.4|63.3|11.7|30.6|0.7|64.2|
|Increase in fertilizer prices|12.9|43.9|1.0|44.5|0.9|56.3|0.0|31.3|
|Drop in demand|12.7|54.9|2.1|38.7|0.3|28.6|14.3|42.9|
|Insecurity (theft, kidnapping)|11.1|23.5|6.9|64.2|2.1|27.8|1.9|68.5|
|New household member|10.0|47.1|0.7|36.8|0.5|50.0|0.0|37.5|
|Cessation of transfers from relatives/friends|4.7|38.9|0.0|54.3|0.3|33.3|0.0|16.7|
|Loss of job or bankruptcy|3.9|39.0|1.5|57.6|0.9|35.0|0.0|50.0|
|Equipment, tool breakdown|2.7|49.7|1.6|27.5|0.0|100.0|0.0|0.0|
|Others|2.7|32.0|0.0|64.3|1.0|41.7|0.0|20.8|

Source: Own computations using _Haitian Vulnerability and Food Security Survey_ , 2007.

Notes: The sum of the three columns "income only", "assets only" and "both" do not sum to 100% due to non response or don't know or no impact. *Do not sum to 100% due to non response or don't know.

28