rafmacalaba/datause-encoder-data huggingface dataset:
bility across countries and across time by Gr ̈un and Klasen (2008); a measure of ethnic fractionalization<sup>16</sup> as proxy for social fragmentation from Alesina et al. (2003) which is constant over time<sup>17</sup> . Since education is also a factor influencing the performance of democracy, literacy rates are also used as an explanatory variable in our panel analysis for life expectancy. As control variables we consider as most important whether a country experienced some conflict in the period under observation and whether the population suffers from HIV/AIDS. To measure war we take data from the UCDP/PRIO intrastate conflict onset dataset, 1946-2006. We choose the variable warinci2 that measures the incidence of intrastate war and is coded 1 in all country years with at least one active war.<sup>18</sup> For HIV/AIDS we take HIV/AIDS adult infections prevalence rates estimated by UNAIDS. Data coverage over time and countries leads us to the decision to create a variable that takes the value 1 when a country has a prevalence rate over 10 per cent in the year 1999. Taking the availability of all variables as criteria, we decide to analyze the period from 1970 to 2003, though some of the variables are available for longer time spans.

Unfortunately, the available data on public expenditures were not sufficient for our purposes. Such data would have enriched our analysis as we could have examined the channels that democracy takes to affect human development. We suspect that democracy causes different priorities in public expenditures compared to autocracies. Therefore increases in public expenditures on health and education can be decomposed into two components: An increase due to higher total expenditures and an increase due

> 16 The ethnic fractionalization measure renders the probability that two individuals selected at random from a population are members of different groups. It is calculated with data on language and race using the following formula _FRACj_ = 1 _−_<sup>�</sup><sup>_N_</sup> _i_ =1<sup>_s_2</sup> _ij_<sup>,where</sup><sup>_sij_istheproportionof</sup> group _i_ = 1 _, . . . , N_ in country _j_ going from complete homogeneity (an index of 0) to complete heterogeneity (in index of 1). For more details see Alesina et al. (2003: 159f.).

> 17 According to Alesina et al. (2003: 160f) the assumption of stable group shares is not a problem, as examples of changes in ethnic fractionalization are rare. At least over the time-horizon of 20 to 30 years time persistence can be assumed.

> 18 War is defined by more than 1000 battle deaths. As intrastate wars are more frequent than interstate wars we decide to take the intrastate war variable.

17
|Dependent variable-->||Extensive|Margin|||Intensive|Margin||
|---|---|---|---|---|---|---|---|---|
|Total factor productivity|0.093***<br>(0.024)|0.113***<br>(0.031)|0.224***<br>(0.077)|0.094***<br>(0.024)|0.035**<br>(0.014)|0.047***<br>(0.018)|0.032<br>(0.029)|0.035**<br>(0.014)|
|Logged age|0.091|0.087|0.228|0.108|-0.014|-0.018|-0.255**|-0.013|
||(0.123)|(0.123)|(0.300)|(0.123)|(0.073)|(0.073)|(0.121)|(0.073)|
|Small-sized firm|-0.384***|-0.384***|-0.081|0.049|0.141**|0.142**|= 0.142|0.211|
||(0.098)|(0.098)|(0.381)|(0.221)|(0.058)|(0.058)|(0.127)|(0.154)|
|Mid-sized firm|-0.287***|-0.288***|-0.424***|-0.206|0.011|0.010|0.006|-0.049|
||(0.060)|(0.060)|(0.158)|(0.140)|(0.032)|(0.032)|(0.059)|(0.085)|
|Logged goods exports|0.018***|0.018***|0.014|0.018***|0.002|0.002|0.002|0.002|
||(0.004)|(0.004)|(0.012)|(0.004)|(0.002)|(0.002)|(0.004)|(0.002)|
|Logged services exports|0.064***|0.064***|0.063***|0.064***|0.016***|0.016***|0.014***|0.016***|
||(0.007)|(0.007)|(0.011)|(0.007)|(0.002)|(0.002)|(0.003)|(0.002)|
|Logged imports|0.030***|0.030***|0.061***|0.030***|0.008***|0.008***|0.007|0.008***|
||(0.005)|(0.005)|(0.019)|(0.005)|(0.003)|(0.003)|(0.006)|(0.003)|
|Exposure to imports|0.132***|0.123***|0.182|0.158***|0.094***|0.088***|0.132**|0.091***|
||(0.038)|(0.038)|(0.174)|(0.042)|(0.018)|(0.018)|(0.053)|(0.019)|
|Highproductivity firm||0.004||||0.004|||
|*exposure to imports||(0.013)||||(0.005)|||
|Low productivity firm||0.020||||0.013**|||
|*exposure to imports||(0.014)||||(0.005)|||
|LoggedR&D intensity|||0.025||||0.019||
||||(0.106)||||(0.038)||
|LoggedR&D intensity|||-0.000||||-0.002||
|*exposure to imports|||(0.026)||||(0.008)||
|Small-sized firm||||-0.124**||||-0.020|
|*exposure to imports||||(0.057)||||(0.037)|
|Mid-sized firm||||-0.023||||0.016|
|*exposure to imports||||(0.036)||||(0.019)|
|Number ofobservations|29168|29168|6519|29168|36128|36128|9407|36128|
|Numberoffirms|3137|3137|699|3137|4314|4314|1138|4314|

Notes: Services revenue includes all services offered by firms ever classified as manufacturing. This include firms that offer construction and other services. Services estimations at the extensive margins are done with logit function, while those at the intensive margins use the poisson pseudo maximum likelohood (PPML) estimator. All estimations include firm and year fixed effects and report standard errors clustered at the firm-level. Significance levels are indicated with *** (at 1 % level), ** (at 5% level) and * (at 10% level).
- 36 -

Table 13

# Probability of POST-PROBECAT Employment in a Larger Firm (Men)

|Dependent Variabl|e: Firm Size C|ategories 1-1|0|
|---|---|---|---|
|Explanatory Variables|Maximum Lik|elihood Ordere<br>Estimates|d Logit|
||(1)|(2)|(3)|
|Schooling Attainment|I|||
|No education|-.262|-.163|-.229|
|Primary-incomp.|-.014|.102|-.051|
|Secondary-incomp.|.289**|.236*|-.092|
|Secondary-compl.|.202*|.189|.095|
|High School|.517**|.633**|.541**|
|College|.482**|.986 *|.845**|
|PROBECAT Trainee||||
|Trainee (1,0)|.786**|||
|x Schooling||.246**||
|x Schooling-squared||-.017**||
|x No education|||-.236|
|x Primary-incomp.|||.827|
|x Primary-comp.|||.707*|
|x Secondary-incomp.|||1.314**|
|x Secondary-comp.|||.893.**|
|x High School|||.688**|
|x College|||-.057|
|Lamda (sel. coor'd)|-.354**|-.282**|-.338**|

NOTES: 1. Sample of 2330 observations. Regressions included quadratic specification of general experience, the duration of previous unemployment, and time dummy variables.

2. * and ** denotes statistical significance at the 5 percent and 1 percent levels, respectively.
A14 Thailand Table **A14: Regional disparity** trernds in **Thailand**

|**Year**|Max/Min<br>(MMR)|Simple<br>coefficient<br>of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CVw)|Relative<br>mean<br>Unweighted Weighted<br>Deviation gini index<br>gini index<br>(Rw)<br>(Gu)<br>(Gw)|Theil<br>index (T)|
|---|---|---|---|---|---|
|1991|9.320|0.880|1.002|0.765<br>0.451<br>0.457|0.389|
|1992|_9.077|0.865|0.986|0.756<br>0.446<br>0.452|0.379|
|1993|9.774|0.894|1.020|0.789<br>0.463<br>0.468|0.406|
|1994|9.262|0.861|0.989|0.772<br>0.454<br>0.459|0.387|
|1995|8.696|0.834|0.959|0.754<br>0.443<br>0.449|0.368|
|1996|8.529|0.816|0.944|0.751<br>0.442<br>0.447|0.361|
|1997|8.273|0.797|0.921<br>5|0.745<br>0.438<br>0.442|0.351|
|1.200<br>1.000<br>s 0.800<br>|__,''_|**Figure A1**<br>___|**4: Regionald **|**isparitytrends in Thailand**<br>'!___||
|0.600<br>0.400<br>||||||
|0.200<br>0.000||||||
||1991|1992<br>1|993<br>1994<br>Year|<br>1995<br>1998<br>1997||
|Simp<br>@Unw|le coefficient of<br>eighted<br>gini inde|variation (CVu)<br>x (Gu)|4Weighted co<br>+<br>Weighted gin|efficient of variation (Cvw)<br>Relative mean Deviati<br>i index (Gw)<br>-Theil<br>index (T)|on (Rw)|

49
The current study confirms that even when mortality risk is higher among malnourished children, this has only a minor impact on the measured nutritional status of the child population or on that status by gender with evidence from a country with high rates of malnutrition and moderately high mortality. We show this, first, by illustrating the degree to which the nutrition results reported in various surveys form India would have changed had mortality rates differed. While our initial simulations impute results using global evidence on the relationship of nutrition and risk of mortality we also bolster these simulations with additional simulations based on the estimated hazard of mortality from survey data.

The paper is organized as follows: The next section describes the data and presents some descriptive statistics. Section II outlines the theoretical framework and the empirical strategy. Section III discusses the main results, and section IV presents some implications of the findings.

# **II. Data and Descriptive Statistics**

This analysis uses data from three waves of India‘s National Family Health Survey (NFHS; 1992/93, 1998/99 and 2005/06), a survey of representative households in states and territories covering some 99 percent of the population<sup>3</sup> and similar in structure to demographic and health surveys conducted in several other countries. The NFHS follows the pattern of a standard Demographic and Health Survey. The main sample of NFHS contains information on 45,279 children in 33,032 households from the 1992/93 round, 30,984 children in 26,056 household from the 1998/99 round, and 48,679 children in 33,968 households from the 2005/06 round.<sup>4</sup> Because the NFHS does not collect information on household income or consumption, a household wealth index was constructed from the data on household assets using the method based on principal components (see, for example, Filmer and Pritchett 2001; Rutstein and Johnson 2004).

The NFHS provides height and weight data for children under age 48 months in 1992/93, under age 36 months in 1998/99, and under age 60 months in 2005/06. The NFHS contains no anthropometric information for deceased children at the time of their death. For comparability between NFHS rounds, the sample was restricted to children under age 36 months. The analysis focuses on the age-adjusted measure of height-for-

> 3 Kashmir, Sikkim, and some remote territories were not covered in NFHS-1. Detailed information on NFHS methodology and sample design is available at www.nfhsindia.org/. The data are available from MEASURE DHS, Macro International Inc. at www.measuredhs.com.

> 4 The number of observations in the 1998/99 round is smaller, as it collected height and weight information only for the last two children under age 3 of ever-married women who were interviewed. In the 1992/93 round, measurements of height were not collected in Andhra Pradesh, Himachal Pradesh, Madhya Pradesh, Tamil Nadu, and West Bengal.

4
# **Table 4:  Regression Results:  Myanmar**

All variables in logs except the dummy variable for protected status Dependent variable: Log odds ratio, cumulative forest cleared %, 2014

||Rando|m Sample (10,000|obs.)|20% Rand|om Sample|All Obse|rvations|
|---|---|---|---|---|---|---|---|
||IV<br>(1)|Bootstrap IV<br>(2)|Spatial IV<br>(3)|IV<br>(4)|Bootstrap IV<br>(5)|IV<br>(6)|Bootstrap IV<br>(7)|
|Distance from road|-0.188<br>(21.82)**|-0.188<br>(15.53)**|-0.186<br>(21.67)**|-0.181<br>(224.06)**|-0.181<br>(26.62)**|-0.181<br>(502.01)**|-0.181<br>(30.26)**|
|Distance to nearest|-0.349|-0.349|-0.340|-0.335|-0.335|-0.336|-0.336|
|urban center (DU)|(17.84)**|(12.41)**|(17.50)**|(184.49)**|(26.95)**|(413.39)**|(21.88)**|
|DU x Primary road<br>share|0.063<br>(10.45)**|0.063<br>(7.29)**|0.060<br>(10.10)**|0.060<br>(105.20)**|0.060<br>(13.29)**|0.060<br>(238.00)**|0.060<br>(13.45)**|
|Slope (Std. dev. of<br>elevation)|-0.444<br>(14.99)**|-0.444<br>(11.85)**|-0.440<br>(14.97)**|-0.467<br>(169.14)**|-0.467<br>(20.26)**|-0.468<br>(379.01)**|-0.468<br>(20.54)**|
|Slope x Elevation|0.043<br>(11.81)**|0.043<br>(9.23)**|0.042<br>(11.70)**|0.044<br>(131.35)**|0.044<br>(17.32)**|0.044<br>(293.71)**|0.044<br>(16.16)**|
|Protected area|-0.775<br>(24.83)**|-0.775<br>(20.00)**|-0.761<br>(24.52)**|-0.780<br>(266.24)**|-0.780<br>(34.58)**|-0.782<br>(596.76)**|-0.782<br>(34.92)**|
|Agricultural<br>opportunity value|0.080<br>(11.50)**|0.080<br>(7.27)**|0.080<br>(11.55)**|0.075<br>(113.94)**|0.075<br>(13.67)**|0.074<br>(254.01)**|0.074<br>(14.96)**|
|Constant|1.335|1.335|1.431|1.319|1.319|1.324|1.324|
||(12.53)**|(8.80)**|(13.27)**|(133.43)**|(19.79)**|(299.47)**|(16.68)**|
|Observations|10,000|10,000|10,000|1,153,615|1,153,615|5,768,082|5,768,082|
|R-squared|0.21|0.21||0.20|0.20|0.21|0.21|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 25
# _3.1 Baseline Scenario for Public Debt Dynamics_

In what follows we use the FS tool to develop a baseline scenario for public debt dynamics over the projection period. The derivation of public debt dynamics is based on a debt updating equation (see eq. 5, section 2 for the continuous time version)<sup>10</sup> . Key inputs used to derive public debt dynamics are taken from macroeconomic projection framework.<sup>11</sup> As discussed in section 2, implicit in this approach is the proper consolidation of the general government accounts with the Central bank’s profit and loss account.<sup>12</sup> Public debt-to-GDP ratio can change as a result of primary deficits, _pd,_ net of seigniorage revenues, _σ_ ; “automatic debt dynamics” (the difference between real interest and real growth rates), and changes in the real exchange rate, _e–hat_ , resulting in capital gains or losses on net foreign-currency denominated debt. Baseline projections of these debt determinants are summarized in Table 1: _Table 1. Key Macroeconomic and Fiscal Assumptions_

||**Actual**|||**Proje**|**ctions**|||
|---|---|---|---|---|---|---|---|
||2005|2006|2007|2008|2009|2010|2011|
|Real GDP growth (in||||||||
|percent)|4.8|5.0|5.0|5.0|5.0|5.0|5.0|
|Average real interest rate<br>on foreign debt in percent|7.6|8.0|8.4|8.4|8.4|8.4|8.4|
|Average real interest rate<br>on domestic debt in||||||||
|percent|11.7|5.3|10.2|11.2|11.1|11.1|11.1|
|Change in the real<br>exchange rate (Local<br>currency per US dollar)|5.1|-1.0|-0.5|-0.4|-0.8|-0.8|-1.0|
|Inflation rate (in percent)|6.8|6.0|4.5|3.5|3.5|3.5|3.5|
|Primary deficit, percent<br>ofGDP|-6.5|-6.5|-6.5|-6.5|-6.5|-6.5|-6.5|

_Source: World Bank and IMF staff estimates_ To derive public debt dynamics for the consolidated public sector, the FS tool also provides an estimate of the revenue from seigniorage during the projection period. Key inputs in the calculations of seigniorage revenue are projections for average inflation rate, average price level

> 10 Note that we use continues time formulas for the easiness of exposition. The FS tool, however, uses the discrete time version of the debt updating equation. For the exact discrete time formulas see the Technical instruction manual that accompanies this paper.

> 11 See the technical instructions manual, section III.

> 12 For detailed derivation and exposition see Anand and van Wijnbergen (1988).

18
- The stringency of regular employment regulations appears to be what really discriminates between the provision of training and the use of temporary workers (Table 7c). Firms facing stringent employment regulations for regular workers are more likely to use both instruments. In addition, there is some evidence that firms in these countries tend to prefer training to employing temporary workers. There is also some evidence that increasing the stringency of temporary employment regulations push firms towards making greater use of training instead of employment workers under temporary contracts. (Table 7c).

Overall, these results suggest that liberalizing temporary employment regulations while leaving in place strict regulations for regular employment may not lead to a substitution of training for more temporary employment. Most likely firms will expand both. In other words, at least in our sample of developing and transition economies, training seems to be the main reaction of firms to strict regulations on regular contracts, while the use of temporary employment is a complement for larger firms, while an alternative solution for smaller firms that have difficulties in providing training.

# **_6. Concluding remarks_**

In this paper we look at how employment protection legislation is perceived by employers in a wide range of industrial, developing and transition economies. We draw from harmonized firm surveys compiled by the World Bank and compare the results from these surveys with indicators of _de jure_ labor laws. We also assess how employers react when constrained by strict labor regulations. In particular, we look at whether they make greater use of on-the-job training instead of hiring and firing workers to upgrade the skills of the workforce, or whether they use more temporary employment to enhance the adaptability of the workforce to the evolution of demand.

Our main results can be summarized as follows:

- _Employers’ perceptions are broadly consistent with de jure labor regulations._ In a large sample of developing, transition and industrial countries, employers’ concerns about labor market regulations are closely matched by the relative stringency of _de jure_ labor laws.  In countries with labor regulations are more stringent from an international perspective, a larger number of employers report that these legislations are a major or severe constraint to their operation, even after controlling for factors that may influence the varying degree of law enforcement.

- _Not all firms are affected in the same way by strict labor regulations._ Mediumsize firms are the ones most severely affected.  This is probably because in many countries where enforcement of regulations is limited small firms do not comply, remaining invisible to regulators and inspectors.  And for large firms, hiring and firing costs play a smaller role in total expected costs of adjusting the workforce. By contrast, firms of medium size are sufficiently large to be visible to the authorities and thus unable to avoid regulations, but not large enough to use

24
WPS4248

# Monetary policy, structural break, and the monetary transmission mechanism in Thailand<sup>*</sup>

Heiko Hesse<sup>†</sup> World Bank

## Abstract

The paper studies monetary policy and the monetary transmission mechanism in Thailand in light of the Asian Crisis in 1997. Existing studies that adopt structural VAR approaches do not give a clear and agreed-upon view how monetary shocks are transmitted to the Thai economy that is subject to structural breaks. This study explicitly models a pre-crisis and post-crisis cointegrated VAR model. This analysis supports the arguments of Corbett and Vines (1999) as well as Phongpaichit and Baker (2002) that the trinity of open capital markets, pegged exchange rate regime and monetary policy autonomy is inconsistent in the pre-crisis period. In contrast, the model points to an effective monetary policy in the post-crisis period. Further, the author analyzes the common driving trends of the model.

Keywords: Thailand; Monetary Policy; Monetary Transmission Mechanism; Asian Financial Crisis; Cointegration; Common Trends JEL classification: C32; E41; E58; F41

## World Bank Policy Research Working Paper 4248, June 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*I am very grateful to Bent Nielsen for his help and suggestions during the process of the paper. I am also thankful to Heino Bohn-Nielsen, Soren Johansen, Katerina Juselius and Anders Rahbek for providing excellent support during a summer school on cointegrated VAR methods and comments on an earlier draft at the University of Copenhagen. In addition, my thanks are due to Ronald Babula, Roy Bailey, Steve Bond, Chris Bowdler, Julia Giese, David Hendry, Patrick Honohan, Takamitsu Kurita, Roland Meeks, John Muellbauer, Jonas Stulz, David Vines, Ilknur Zer, as well as seminar participants at the U.S. International Trade Commission, Bank of Thailand, Chulalongkorn and Thammasat University for helpful comments. † hhesse@worldbank.org.
|4|(10)|-0.001<br>(0.031)|Yes<br>Yes|566|0.03|0 months..|
|---|---|---|---|---|---|---|
|3|(9)|0.070***<br>(0.023)|Yes<br>Yes|1230|0.08|between 49-6|
|DPT<br>2|(8)|0.027<br>(0.022)|Yes<br>Yes|1037|0.08|onths; 4 is|
|0<br>1|(6)<br>(7)|0.033<br>0.051**<br>(0.042)<br>(0.025)|Yes<br>Yes<br>Yes<br>Yes|561<br>1016|0.05<br>0.10|tchment areas.<br>; 3 is between 37-48 m|
|4|(5)|0.011<br>(0.037)<br>0.87***<br>(0.03)|No<br>No|566|0.001|within ca<br>36 months|
|3|(4)|0.081**<br>(0.039)<br>0.79***<br>(0.03)|No<br>No|1230|0.011|vel.<br> are clustered <br>s between 25-|
|DPT<br>2|(3)|0.023<br>(0.041)<br>0.78***<br>(0.03)|No<br>No|1037|0.001|percent le<br>ance terms <br>months; 2 i|
|1|(2)|0.056<br>(0.044)<br>0.73***<br>(0.04)|No<br>No|1016|0.004|he 1 [5] (10)<br>sis. Disturb<br>ween 13-24 <br>able A.8.|
|Dependent<br>variable<br>Age<br>0|Speci...cation<br>(1)|Program impact<br>0.017<br>(0.050)<br>Constant<br>0.56***<br>(0.04)|Controls<br>No<br>District ...xed e¤ects<br>No|Observations<br>561|R2<br>0.001|a. *** [**] (*) denote signi...cance at t<br>b. Robust standard errors in parenthe<br>c. Age: 0 is below 12 months; 1 is bet<br>d. Control variables: see note (d) in T|

49
observe that communities that gained access to pre-primary education and those that did not have similar pre-intervention trends. Reassuringly, the results remain robust when controlling for differential trends at various geographic aggregation levels and introducing time-varying attributes. The estimated impacts on adequate progression are found to increase in the average pre-primary take-up rate of the community. Although these effects are important, they suggest that complementary actions need to be undertaken in order to generate sizable improvements in adequate progression.

We proceed by exploring if and how pre-primary access had heterogeneous impacts across subgroups of the population. We find larger effects for communities with higher levels of schooling among adults. This finding is not fully explained by higher take-up rates since these rates do not differ between communities with low and high average education. There are several possible explanations behind this difference in impacts. For example, teacher quality may be higher in high-education communities, or there may exist a complementarity between pre-primary attendance and parental education. We do not find differential effects of pre-primary attendance by gender, percentage of indigenous population or chronic malnutrition.

The paper proceeds as follows. Section 2 provides background before Section 3 describes the data employed in the empirical analysis. Section 4 outlines the research design, Section 5 presents the main results, and Section 6 examines their robustness. Section 7 concludes the paper.

# **2 Background**

With an income per capita of $2,623 in 2009, Guatemala is considered to be a low middleincome nation. About half of its 14 million inhabitants live in rural areas. A similar proportion is indigenous. There is significant inequality in the country in a context of high poverty and extreme poverty rates (51% and 15%, respectively), concentrated in the indigenous rural populations. Life expectancy at birth reached 70 years in 2009, compared to 73.3 years in the other Central American nations. The under-5 mortality rate is significantly higher than in the rest of Latin America and the Caribbean (39.8 and 22.5 deaths per 1,000 live births, respectively).

Illiteracy is high: 23.7 percent of individuals aged 18 to 49 have no formal education (Living Standards Measurement Survey, 2006). Primary school coverage is virtually universal, though the quality of education is low. Alvarez and Schieflbein (2007) find that teachers use inadequate instruction methods given the cultural barriers and sociodemographic context. Primary school repetition rates are high, especially during the initial grades (for example, they reach 30% in first grade). As a result, Guatemala has one of the 4