rafmacalaba/datause-encoder-data huggingface dataset:
Note: The left panel plots average effectively applied import tariff imposed by China during 1992-2005. The right panel plots average effectively applied export tariff faced by China during 1990-2005. Data based on WITS-TRAINS database.

Table 5 reports the findings when tariff shocks are controlled for. Panel A shows the effect of FDI inflows on structural transformation, and Panel B the effect on school enrollment. Increased FDI presence has an even larger impact on structural transformation: a one percentage point increase in FDI shock now reduces change in agricultural employment share by 3.1 percentage points, and raises the change in industry and services employment share by 1.8 and 1 percentage points respectively. Import and export tariff shocks do not seem to have significant impact on the pace of structural transformation. They do affect school enrollment strongly, lower export tariff and higher import tariff lead to higher growth in college enrollment. FDI inflow shock still promotes high school and college enrollment. These results suggest that the effect of FDI entry on local structural transformation and school enrollment is not driven by China’s concomitant trade liberalization.

Table <u>5. Robustness check: control tariff shocks</u>

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Panel A:<br>|structural transf<br>|ormation<br>|Panel B: schoo<br>|l enrollment<br>|
|Dependent var|∆agri_emps|∆indu_emps|∆serv_emps|∆Highschool|∆College|
|FDI_shock|-3.105***|1.825***|1.036**|0.754**|0.523*|
||(0.930)|(0.678)|(0.416)|(0.304)|(0.306)|
|L.FDI_empshare|-0.0480|0.106***|-0.0637|-0.0508*|0.00758|
||(0.0382)|(0.0329)|(0.0415)|(0.0283)|(0.0265)|
|Observations|637|637|637|637|637|
|R-squared|0.532|0.532|0.442|0.810|0.475|
|Prov-Year FE|Y|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|Y|

Note: This table reports the effect of FDI shock on city level structural transformation and school enrollment when export and import tariff shocks are controlled for. Lagged city characteristics include population, average age, share of Han ethnicity, log GDP per capita in both panels and average years of schooling in panel A. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# 4.3 SOE reform

Another major event – the state-owned enterprise (SOE) reform – may have affected structural transformation and school enrollment during our study period. China gradually transformed its economy from a state-dominated one to a market-driven system. The process culminated in a huge economic restructuring of SOEs in the mid-1990s. Following China’s FDI liberalization in 1992, SOEs faced increasing competition from domestic private enterprises and foreign enterprises. As SOE workers were guaranteed lifetime employment, SOEs could not shed staff to improve efficiency. Moreover, SOEs were encumbered by hefty welfare expenditure, ranging from staff dormitories, child education, health care, and retirement benefits (Meng 2000). By 1994, 40 percent of SOEs were making losses. To improve the 19
**Table 2b:  Community Radio and Village Exposure to Education Programs** ( _Commune fixed effects, commune-clustered robust p-values in parentheses_ )

|Dependent variable: Education programming|Community<br>radios|Including total<br>number of<br>other radios|Disaggregating<br>by type of<br>radios|
|---|---|---|---|
|Community radio received by village (# stations)|40.46<br>(0.00)|20.52<br>(0.04)|38.61<br>(0.00)|
|Within-commune community radio received by<br>village (# stations)||||
|Out-of-commune community radio received by<br>village (# stations)||||
|All radio received by village (# stations)||19.28<br>(0.00)||
|Public radio received by village (# stations)|||0.522<br>(0.97)|
|Commercial radio received by village (# stations)|||6.438<br>(0.39)|
|Religious radio received by village (# stations)|||67.08<br>(0.01)|
|Observations||210|210|
|_R_<sup>_2_</sup>||0.90|0.91|

Note: Standard errors clustered at commune level. Each column regresses the number of education or health programs received by a village, as determined by information supplied by the radios that broadcast to the village, on the number of different types of radio stations received by the village. In addition to the radio controls in the table, the regressions control only for commune fixed effects and therefore capture only intra-communal variation in village access to education and health programming.

Despite the fact that individual community radio stations are small (have weak signal strength), collectively they are a significant media alternative for nearly all villages in our sample. At least one community signal is accessible to 93 percent of our sampled villages. In contrast, while private commercial radio stations are generally larger (greater signal strength), they are concentrated in the southern communes of Benin, outside our study area. Only a few villages in our sample (23 percent) are able to access their signals. Government-owned public radio has several relay transmitters and covers most of the villages in our sample (95 percent) with uniform programming.

Survey respondents also report a strong preference for community radio. Of the 3,828 households that listen to some radio, 64 percent report listening to at least one community radio 11
# <u>1. Introduction</u>

The relationship between young children’s literacy skills, future human capital formation, and subsequent economic development is an increasingly researched theme in economic development. The importance of literacy to individual productivity including the diffusion of technology is well established in developing countries (Basu and Foster 1998; Rosenzweig 1995), and literacy has been described as a threshold for economic development (Azariadis and Drazen 1990). Gaps in reading skills persist as children age (Butler et al. 1985); as a result, early literacy skills are an important determinant of a child’s future education outcomes including future literacy skills (Marteleto et al. 2008; Entwisle et al. 2005; Jimerson et al. 2000; Alexander et al. 1997).

Despite their importance for development, developing countries struggle to provide children with basic literacy skills, even after substantial progress towards the 1990 Education for All goals. For example, in a recent regional assessment of 10 countries in Francopone Africa, the _Programme d’analyse des systèmes éducatifs de la confemen_ (PASEC), 71.4 percent of 2<sup>nd</sup> grade students and 57.3 percent of 5<sup>th</sup> grade students on average do not achieve minimum proficiency in literacy (PASEC 2015:36,50). In the Pacific, assessments of early age literacy in Tonga in 2009 and in Vanuatu in 2011 found that after three years of schooling, only 30 percent of students in Tonga and 25 percent of students in Vanuatu were able to read fluently for comprehension (World Bank 2012a, 2012b, 2012c). In Kiribati and Tuvalu, 20 percent of 3<sup>rd</sup> grade children achieved minimum reading comprehension proficiency (World Bank 2017a, b). In Papua New Guinea, early grade reading assessments conducted in four provinces between 2011 and 2013 found that students lagged two years behind curriculum targets for fundamental pre-reading skills (World Bank 2014a, 2014b, 2014c, 2014d).

International research has identified basic skills that young children need in order to read alphabetic languages (Linan-Thompson and Vaughn 2007; Wolf 2007; Sprenger and Charolles 2004; Chiappe et al. 2002; see also: Gove and Cvelich 2011 and National Reading Panel 2000). Among these are an understanding of the relationship between printed letters and sounds (Scarborough 2002), the speed at which a child can read (Abadzi 2006), and oral reading fluency (Fuchs et al., 2001).

2
<!-- Start of picture text -->
- ee e<br>«© e<br>ee<br>xBao)© .%ee ee e e. eo? . e e . °<br>Noa e had e<br>= oe PS . . . .o ee<br>aL e . . e oe<br>=t e e<br><x ee e e e<br>e<br>N e<br>oO e eo e<br>-40 -20 0 20 40<br>© Sample average within bin ———_ Polynomial fit of order 1<br><!-- End of picture text -->
are variables such as education, age, sex, and experience<sup>5</sup> . Variables controlled by the employers, the so-called demand factors, have not often been<sup>used. Without direct</sup> measures of productivity, gender or ethnicity, for<sup>example, cannot establish</sup> discrimination, established correctly. Additionally, it is generally<sup>the case that studies</sup> report positive coefficient estimates on the age of an employee, conditional<sup>on a variety</sup> of covariates. These estimates neither imply that older workers are<sup>more productive than</sup> younger ones, nor that wages rise faster with productivity (see Hellerstein, Neumark,<sup>and</sup> Troshe; 1996). These problems may be overcome by estimnating<sup>individual wage and</sup> productivity equations jointly, and, thus, comparing wages and productivity<sup>for various</sup> groups of workers.

The general-to-specific methodology is used in the regression analysis. First, we formulate a general model that contains all relevant explanatory variables. Second, eliminating statistically insignificant variables one at the time reduces this model<sup>to a</sup> more parsimonious model. In this specification, only statistically significant variables are included.

The statistical models we use are simultaneous single level models.<sup>This may cause</sup> aggregation biases equivalent to problems produced in the learning<sup>achievement</sup> literature. Here workers are the unit of observation and firns are included<sup>in the</sup> individual vector of variables. It is worth noting that in general<sup>this methodology may</sup> cause aggregation biases; it may: (1) overestimate the group effect,<sup>that is, the firm effect,</sup> namely productivity; and (2) underestimate the individual effect<sup>on wages and</sup> productivity. Multilevel estimation takes aggregation biases iinto account.<sup>The firm level</sup> effect impacts average production but also the individual slope of,<sup>for example, gender.</sup>

> 5Groshen (199la) mentions that education, age, occupation, ethnicity, gender and union variables<sup>only</sup> account for 51 percent of the variation in the log of wages, analyzing (US)<sup>data from the Current</sup> Population Survey One Quarter Earnings Sample, 1986. Introducing demand<sup>related factors is likely</sup> to explain parts of the 49 percent of the variation in wages that workers'<sup>characteristics cannot</sup> account for.

6
reduced costs by 30 percent or more (“Reform_30%”) and so on. For each of the cutoffs, we classify countries as treated (reformers, defined as change below the cutoff) or controls (nonreformers, change above the cutoff).

Our empirical strategy here is to use a difference-in-difference estimation to test the effect of one indicator of reform (e.g. “Reform_20%”) on new firm registration. We then compare results for different cutoff points: If the cutoff point is correctly classified (i.e. it is close to the “true” magnitude of reform seen in the data), the difference-in-difference estimation will produce the most significant and largest in magnitude estimates of the impact. However, we should observe a lower impact of misclassified reforms.<sup>4</sup> In other words, this methodology allows us to identify which cutoff points lead to the largest and most significant increases in formally registered businesses.

# **3. Data and Descriptive Statistics**

# **_3.1 Data on New Firm Registrations_**

For our dependent variable, we use data on the number of new firms registered in the calendar year. The data is collected directly from the local Registrar of Companies, which is the entry point for entrepreneurs joining or transitioning to the formal sector in most countries, or other statistical offices, and are not based on surveys or estimations.<sup>5</sup> The data includes only new corporations, which are defined here as _private companies with limited liability_ . Notably, this is

> 4 Thus, if a true reform, i.e. a change that makes cost fall below benefits is a reduction in 20% of costs, but we classify a country as a reformer only if a reduction of 40% or more is observed, several countries with actual reforms, i.e. those in the range of 20-40%, will be classified as non-reforms and hence the difference between reformers and non-reformers will be muted. This is an example of Type I error, when true reformers are misclassified as non-reformers. On the other hand, if a true reform is 40%, but we set our reform dummy for 20%, a number of countries in the range of 20-40% will be classified as reformers, when in reality they are not. This is an example of Type II error. In either case, we should observe the most significant results the closer our definition of the reform dummy is to the magnitude of the actual reform.

> 5 For additional information on the methodology, see Klapper and Love, 2010.

10
Table 1 characterizes the final sample. As will be explained later, our analysis focuses on children ages 1-3 and 5-7. The original sample of children in this age range in rural communities is 3,500 and in urban communities is 4,849. Some cases are dropped from the analysis due to lack of measurement, and only a few additional cases are lost due to measurement error or change of residence.<sup>8</sup> As a result, 86 percent of the rural sample and 79 percent of the urban sample are used in the analysis that follows. We analyzed in detail lack of measurement in children’s height and found that, though missing data is not random, interactions of main parental characteristics with the relevant groups suggest there are no differential effects across the cohorts or locality groups used in the analysis.

Summary statistics (not shown) of the 2002 and 2005 height-for-age z-scores show that children 1 to 8 years old in Mexico are, on average, 0.56 standard deviations below the reference median in 2002 and 0.42 below the reference median in 2005 (standard deviations are 1.26 and 1.47, respectively). The percentage of stunted children, that is, children that are more than two standard deviations below the reference median, is 12 percent in both years.

As expected, the nutritional status of children in the rural areas is worse than that of children in the urban areas. Rural children are on average 0.75 and 0.59 below the reference median in 2002 and 2005 respectively, whereas the corresponding numbers for urban children are 0.42 and 0.29. With respect to stunting, the overall incidence of 12 percent is a combination of an incidence of stunting among rural children of 16 percent and an incidence of stunting in urban children of 10 percent.

> 8 Children that moved between 2002 and 2005 are eliminated from the 2005 sample because treatment status based on the place of residence does not correspond to actual treatment.

10
|P152898|2015|2017|12/15/2016|4 Closed|Lebanon|
|---|---|---|---|---|---|
|P152898|2015|2017|6/23/2017|5 Closed|Lebanon|
|P152898|2015|2018|12/27/2017|6 Closed|Lebanon|
|P152898|2015|2018|6/29/2018|7 Closed|Lebanon|
|P152903|2015|2016|8/17/2015|1 Closed|Congo,Democratic Re|
|P152903|2015|2016|3/10/2016|2 Closed|Congo,Democratic Re|
|P152903|2015|2017|11/23/2016|3 Closed|Congo,Democratic Re|
|P152903|2015|2017|6/28/2017|4 Closed|Congo,Democratic Re|
|P152903|2015|2018|1/24/2018|5 Closed|Congo,Democratic Re|
|P152903|2015|2019|12/6/2018|6 Closed|Congo,Democratic Re|
|P152903|2015|2020|7/9/2019|7 Closed|Congo,Democratic Re|
|P152910|2016|2017|9/22/2016|1 Active|Congo,Republic of|
|P152910|2016|2017|5/22/2017|2 Active|Congo,Republic of|
|P152910|2016|2018|12/3/2017|3 Active|Congo,Republic of|
|P152910|2016|2018|6/28/2018|4 Active|Congo,Republic of|
|P152910|2016|2019|2/1/2019|5 Active|Congo,Republic of|
|P152910|2016|2019|6/28/2019|6 Active|Congo,Republic of|
|P152910|2016|2020|12/24/2019|7 Active|Congo,Republic of|
|P152910|2016|2020|5/21/2020|8 Active|Congo,Republic of|
|P152925|2015|2021|7/21/2020|1 Active|Tuvalu|
|P152932|2016|2016|6/29/2016|1 Active|Zimbabwe|
|P152932|2016|2017|12/27/2016|2 Active|Zimbabwe|
|P152932|2016|2017|5/15/2017|3 Active|Zimbabwe|
|P152932|2016|2018|12/1/2017|4 Active|Zimbabwe|
|P152932|2016|2018|5/23/2018|5 Active|Zimbabwe|
|P152932|2016|2019|12/3/2018|6 Active|Zimbabwe|
|P152932|2016|2019|6/10/2019|7 Active|Zimbabwe|
|P152932|2016|2020|10/19/2019|8 Active|Zimbabwe|
|P152932|2016|2020|4/11/2020|9 Active|Zimbabwe|
|P152936|2016|2016|1/13/2016|1 Active|Myanmar|
|P152936|2016|2017|7/26/2016|2 Active|Myanmar|
|P152936|2016|2017|1/27/2017|3 Active|Myanmar|
|P152936|2016|2018|7/26/2017|4 Active|Myanmar|
|P152936|2016|2018|1/18/2018|5 Active|Myanmar|
|P152936|2016|2019|7/16/2018|6 Active|Myanmar|
|P152936|2016|2019|1/20/2019|7 Active|Myanmar|
|P152936|2016|2020|7/19/2019|8 Active|Myanmar|
|P152936|2016|2020|1/13/2020|9 Active|Myanmar|
|P152936|2016|2021|7/10/2020|10 Active|Myanmar|
|P152959|2017|2017|5/16/2017|1 Active|China|
|P152959|2017|2018|11/19/2017|2 Active|China|
|P152959|2017|2018|5/24/2018|3 Active|China|
|P152959|2017|2019|10/23/2018|4 Active|China|
|P152959|2017|2019|5/17/2019|5 Active|China|
|P152959|2017|2020|12/11/2019|6 Active|China|
|P152959|2017|2020|6/21/2020|7 Active|China|
|P153012|2017|2018|9/21/2017|1 Active|Brazil|
greater risk of conflict. Otherwise, exceeding the threshold does not constitute a signal of conflict. In many cases there is a natural intuitive orientation -- for example, it seems reasonable to suppose a priori that a greater incidence of conflict among neighbors raises the risk of conflict at home. But in other cases the orientation might be unclear. To address this issue systematically, we orient each of the explanatory variables to be consistent with the estimated sign in the corresponding probit regression. That is, if a variable enters positively in the probit regression (such that higher values are associated with a higher risk of conflict), then we do not reorient the variable. If on the other hand a variable enters negatively in the probit regression, then we reverse the orientation of the variable. To facilitate the interpretation of results, we reorient variables in this way (i.e. based on the signs of the slopes in the probit regression) for all variables and for all the classification rules, even though this step is of course not necessary for the other prediction algorithms.

# **2.3 Implementation Issues**

Implementing the probit classifier is straightforward, since it simply requires the estimation of a probit regression, followed by a one-dimensional grid search over cutoff probabilities to find the optimal cutoff probability �<sup>∗</sup> that minimizes the prediction loss function. The grid search itself is automated in the `lroc` command in Stata. To implement the random forest classifier, we rely on the `randomForest` package in R.<sup>9</sup> Implementing the linear classifier and the threshold classifier is more challenging because it involves minimizing an objective function that is flat in much of the parameter space, and otherwise jumps discontinuously. To see why, consider for example the threshold classifier, and consider what happens when we search over candidate values of the threshold for explanatory variable �� that happen to fall strictly between two adjacent observations ��� and ����,� (assume observations have been ordered by increasing values of �� ). For any threshold in this range, the conflict/no conflict prediction does not change for any observation, and so the prediction loss function also does not change. However, when the candidate threshold moves just outside this range by crossing either ��� or ����,� , the classification of observations changes and the objective function changes discontinuously. The size of the “flat” regions of the parameter space and the size of the discontinuous “jumps” in the objective function depend on how large the sample is and how evenly the covariates are distributed over their

> 9 - <u>https://www.rdocumentation.org/packages/randomForest/versions/4.6 12/topics/randomForest</u>

16
proxies for the more volatile cross-border lending conducted primarily by the parent banks of foreign affiliates in the ECA region. We interact the foreign participation variable with year dummies to test whether firms were better able to weather the crisis in countries with wellestablished foreign banks. The pattern of results that we find is robust to using different measures of foreign bank participation and different years between 1999 and 2002.<sup>21</sup> S is a matrix of sector dummy variables because financial constraints and the probability of survival could vary across industries. Finally, T represents variables that control for the timing of surveys. In the case of the financial constraints, these are simple dummy variables corresponding to the round of the survey. For the survival analysis, we use time dummies indicating the month that the firm was surveyed in the 2008-9 BEEPS survey. Although the FCS were conducted within a tight two-month window (June and July 2009), most of the 2008-9 BEEPS were conducted over a three to six month window in individual countries. The dummies indicating the month of the 2008-9 BEEPS are included to control for differences in survival possibilities for firms first interviewed before the start of the global crisis and those that were first interviewed in the early part of the crisis.<sup>22</sup>

# **V. Regression Results**

This section presents results from the survival analysis and the financial constraints analysis. The results from the firm survival regressions are discussed first.

# **A. Firm Survival**

Table 3 shows results from the firm survival regressions. The first column presents base results omitting the financial and performance variables, the second and third include the financial variables and the fourth and fifth include both financial and performance variables.

> 21 For example, although we use the 1999 share of total banking assets held by foreign-owned banks from Claessens and Van Horen (2008a), we derive similar results when we use the foreign claims on the countries in the ECA region (as a share of GDP) as reported by the Bank for International Settlements.

> 22 We also included the timing of the 2008-2009 BEEPS survey in a simple cross-sectional regression of the 20082009 financial constraints, although it was not significant. This could be because perceptions of financial constraints are forward looking and thus all respondents had a reasonable sense of the coming crisis, regardless of the month in which they were interviewed.

18