rafmacalaba/datause-encoder-data huggingface dataset:
positive sign which implies that anticipations of economic growth induce more investment. Similarly, the interest rate appears to exert a negative and significant effect on private investment which is consistent with the user cost of capital theory. Both variables are highly significant, indicating that at the final stage, supply and demand considerations constitute major factors for the entrepreneurs to undertake a new investment project. However, our model fails to verify the Solow hypothesis of decreasing return to scale of physical capital accumulation. The coefficient of the GDP per capita variable, although negative, is not significant.

Finally, estimation of equation (1) confirms the natural curse hypothesis. The coefficient of the oil export variable as a percentage of total merchandise export is significant and negative. Identically, the regional dummy for MENA countries exhibits a negative coefficient. MENA countries seem to be diverging from the rest of the world in terms of private investment which is the key determinant of long-term growth. However, this dummy variable is not significant at the conventional levels. This result is likely to stem from the oil export variable in the system which significantly reduces the private investment.

In the “Administrative Quality” equation (Table 1, column 2), our estimations reveal the positive impact of several factors on the quality of the administration. This is the case of the GDP per capita which means that more developed countries entail better governance institutions. Also, private investment helps improve the administrative quality significantly at a 5 percent level. This last result justifies the use of the 3SLSQ 26
21

# _3.4 Results_

Figure 2 shows the results of our assumptions in terms of trends in stunting. The counterfactual rate of stunting among children falls at 1.5% per year. The nutrition program kicks in in 2016 reducing the rate of stunting among children below the counterfactual; the program reaches its full scale in 2025, at which point the reduction in the rate of stunting below the counterfactual reaches 20%. By 2025, stunting has fallen by 36% compared to its 2010 value – 4 percentage points below the 40% target reduction adopted by the 65<sup>th</sup> World Health Assembly. We assume the nutrition program is sustained at scale and thereafter stunting stays at 20% below the counterfactual.

<u>Figure 2: Reductions in stunting among today’s children and their effects on childhood stunting rates among the workforce in later years</u> <!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2110 2120<br>Stunting among today's under-5s (counterfactual)<br>Stunting among today's under-5s (after program)<br>Childhood stunting among today's workforce (counterfactual)<br>Childhood stunting among today's workforce (after program)<br><!-- End of picture text --> It takes much longer than 10 years for the childhood stunting rate among workers to fall by 20%. The childhood stunting rate among workers in any year is a weighted average of the childhood stunting rates that were prevalent when today’s workers were children. Given the lag between childhood and joining the labor force, and the assumed 40-year working life, the childhood stunting
The inflows of foreign capital to an open economy is likely to lead to the development of business opportunities in the country and thus to the creation of additional jobs in particular for the qualified and educated population. The coefficient capturing the development of the informal sector maintains its negative sign but does not display a statistical significance in various specifications modeling the secondary education unemployment. The political opposition variable is still insignificant similar to the initial model, suggesting that the polity in the countries has no direct effect on the distortion of the labor market as captured in our model by the growing proportion of the youth and educated job seekers.

**Table2: Secondary level educated job seekers as a dependent variable**

||(1)<br>Secondary|(2)<br>Secondary|(3)<br>Secondary|(4)<br>Secondary|
|---|---|---|---|---|
|Secondary Unemployment(t‐1)|0.926<sup>***</sup>|0.914<sup>***</sup>|0.916<sup>***</sup>|0.920<sup>***</sup>|
||(36.20)|(33.72)|(33.26)|(33.23)|
|Corruption Index(t‐1)|4.186<sup>***</sup>|3.382*|3.731<sup>**</sup>|3.504<sup>**</sup>|
||(2.94)|(1.88)|(2.08)|(2.00)|
|Education|0.226<sup>**</sup>|0.254<sup>**</sup>|0.251<sup>**</sup>|0.261<sup>**</sup>|
||(2.20)|(2.47)|(2.06)|(2.07)|
|Inflation||0.403|0.562<sup>**</sup>|0.515*|
|||(1.40)|(1.97)|(1.79)|
|FDI||0.0304|‐0.0279|‐0.0742|
|||(0.18)|(‐0.16)|(‐0.47)|
|Monetary Aggregate||‐0.739|‐1.286|‐1.354|
|||(‐0.52)|(‐0.96)|(‐0.98)|
|Political Opposition|||‐0.008|‐0.0116|
||||(‐0.79)|(‐1.00)|
|Worker’s Rights|||0.389|0.495|
||||(0.99)|(1.08)|
|Observations|966|902|897|897|
|AR(0)|0.00|0.00|0.00|0.00|
|AR(1)|0.13|0.43|0.53|0.50|

Notes: Standard errors are in parentheses. All GMM regressions use robust standard errors and treat the lagged corruption measure as endogenous. In the case of two‐step GMM, the Windmeijer (2005) finite sample correction for standard errors is employed. *, ** and *** denote significance at the 10%‐, 5%‐ and 1%‐level, respectively. The values reported for AR(1) and AR(2) are the p‐values for first and second order autocorrelated disturbances in the first differences equations.

Table 3 measures the sensitivity of the dynamic relationship between youth unemployment and corruption in various institutional contexts. Columns 1 and 2 analyze the dynamic effect in countries with different legal systems. Countries that have adopted the Common Law as a judiciary doctrine appear to suffer less from the positive impact of corruption on youth unemployment as compared to countries with Commercial Law. Our main variable of interest, corruption practices, is Page | 16
present marginal effects from Heckman two-step probit equations.<sup>11</sup> In the first stage, we estimate the probability of becoming self-employed in the years 2002-2003. Our specifications are similar to those in _Columns 2, 3, 5 and 6_ of Table 4. In the second stage we estimate the probability of remaining self-employed in the year following that of transition. The dependent variable is a dummy variable coded 1 if the respondent remained self-employed in the following year and 0 if not. As identifying restrictions in the second stage equation, we exclude the variables capturing optimism and pension receivership. Besides satisfying the statistical criteria, it seems intuitive that those two variables, captured in the beginning of the panel, will have an effect on the decision to become self-employed, but will not affect entrepreneurial outcomes in any way. Wald χ<sup>2</sup> test-statistics testing the null hypothesis of independent equations reject the null in Table 8, rendering a positive correlation coefficient between the two equations. This indicates that unobserved variables such as ability, talent etc. that positively affect the decision to become self-employed also exhibit a positive impact on the survival chances of the new business ventures.

Income as measured by household consumption at the initial year in the panel has a persistently positive effect in the survival equation, indicating that higher potential of self-financing is an essential component of self-employment activity and longevity in BiH. Among the variables capturing sources of finance and membership in financial institutions, it is the bank loan variable that exerts a significantly positive effect in the survival equation. This finding is further reinforced by the positive impact of the variable capturing transfers from NGO programs. This could be attributed to the good screening mechanisms of the financial institutions with respect to the entrepreneurial prospects of the individuals they choose to finance. Hence, while the decision to become an entrepreneur is not related to financing from banks, the ability to survive is significantly increased by an existing relationship with a bank. Consistent with our earlier findings, domestic remittances and social benefits discourage entrepreneurship and do not improve the probability of success.

> 11 We also estimated a Multinomial Probit model (self-employed/survivor, self-employed/failed, not selfemployed) and our main results proved robust (not shown).

19
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IPCC, 2006. Guidelines for National Greenhouse Gas Inventories. Intergovernmental Panel on Climate Change (IPCC). pp. 3-16.

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Se

|

7 The results displayed in Table 5.4 confirm that there is little correlation between the rankings of sectors discharging toxics by water and air. In fact, when Inter-Quartile Mean intensities are compared, the air rankings are negatively correlated with land and water rankings. These low correlations also suggest that inter-medium substitutability may be a second-order problem for toxic waste.

Table 5.4: Rank Correlation Coefficients Between Toxic Pollutants by Diff t Media: I -B i Toxic Pollution Intensity with Respect to Value Added (Gee Medium T= T=] esRee ee ee ee ee CES LEE TEE EE ES There are, however, a few industries which are highly toxic pollution intensive in all three media (See Table 5.5). These are Industrial Chemicals Except Fertilizer (3511), Plastics and Man-made Fibers (3513), Tanneries and Leather Finishing (3231), and Non-Ferrous Metals (3720). The least toxic pollutionintensive manufacturing sectors with respect to air, water and land are food-processing industries such as Bakery Products (3117), Grain Mill Products (3116), Fish Products (3114); and other industries such as Wearing Apparel (3220).

46
**Table 15. Voluntary Targeting and Re-Hiring**

|||||Dependen<br>|tVariabl|e: re-hire<br>dF/dX|Pbit||dF/dX|
|---|---|---|---|---|---|---|---|---|---|
||Probit<br>(1)||dF/dX|Probit<br>(2)|||ro<br>(3)|||
|Voluntaryage-biased downsizing|0.486|(0.189)<br>|[0.1825]|0.5438|(0.223)b|[0.20121|0.4696|(0.261)|[0.1850]|
|Voluntaryskill-biaseddownsizing|-0.230|(0.357)<br>|[-0.0793]|-0.6009|(0.433)|[-0.1778]|-0.4238|(0.506)|[-0.1571]|
|Voluntaryfemale-biased downsizing|-0.276|(0.727)<br>|[-0.0932]|-0.1348|(0.779)|[-0.0459]|-0.1241|(0.788)|[0.0323]|
|Union|0.499|(0.259)<br>|[0.1638]|0.1576|(0.292)|[0.0540]|0.8966|(0.495)|[0.2993|
|ILOConventions||||-0.0035|(0.004)|[-0.0012]||||
|LaborFiringCost|||||||-0.0772|(0.120)|[-0.0303]|
|Constant|-0.798|(0.648)||-0.8361|(1.149)||-1.3176|(1.177)||
|Observations|292|||214|||146|||
|Log likelihood|-177.33|||-125.82<br>|||-88.48<br>|||
|Waldchi2|22.8|||20.75<br>|||7.87<br>|||
|Prob>chi2|0.000|||0.000|||0.040|||

All Regression include: parbal privatization<sup>dummy, sectoraldummies,countrymacrocontrols, and continental dummies.</sup> Standard errors and marginal effects<sup>are</sup> given<sup>in parenthesesand brackets respectively.</sup> Significant at I percent; b significant at 5<sup>Dercent;</sup> '<sup>significantat10 percent</sup>

**Table 16. Compulsory Downsizing and**<sup>**Re-Hiring**</sup>

|||||Dependen<br>|tVariabl|e:re-hire<br>||||
|---|---|---|---|---|---|---|---|---|---|
||Probit<br>(1)||dF/dX|Probit<br>(2)||dF/dX|Probit<br>(3)||dF/dX|
|Compulsoryage-biaseddownsizing|0.713|(0.190)'|[0.2699]|0.726|(0.221)|[0.2695]|0.5602|(0.257)b|[0.2201]|
|Compulsoryskill-biased downsizing|-0.862|(0.353)**b**|[-0.242]|-0.583|(0.366)|[-0.1738]|-0.7775|(0.435)'|[-0.2660]|
|Compulsoryfemale-biaseddownsizing|0.738|(0.404)'|[0.2861]|1.008|(0.538)'|[0.3858]|0.3483|(0.869)|[0.1382]|
|Union|0.449|(0.252)|[0.1480]|0.094|(0.301)|[0.0323]|0.8522|(0.490)'|[0.2858]|
|ILOConventions||||-0.0056|(0.004)|[-0.0019]||||
|LaborFifing Cost|||||||-0.0496|(0.120)|[-0.0194]|
|Constant|-0.572|(0.649)||0.0014|(1.120)||-0.8201|(1.164)||
|Observations|292|||214|||146|||
|Loglikelihood|-171.05|||-121.4|||-92.4|||
|Waldchi2<br>Prob>chi2|32.59<br>0°00°0|||24.44<br>0000|||11.66<br>0.040|||

All Regression include: partial privatization<sup>dummy, sectoraldummies,countrymacro controls, and continental dummies.</sup> Standard errors and marginal effects<sup>aregiven in parentheses and bracketsrespectively.</sup> Significant at I percent; b significant at<sup>5oercent;' significant at10Percent</sup> 38
- What are the advantages and disadvantages of integrated supervision?

- What are the reasons for adopting integrated supervision?

- How powerful are the new integrated supervisory agencies in terms of the sectors they oversee and the regulatory and supervisory powers they have been given?

- How far have countries gone in harmonizing their regulatory and supervisory practices?

- What are some of the practical obstacles faced by policy-makers in adopting integrated supervision?

This paper is based on the results of a survey conducted by the authors in a group of 15 developed and developing countries that have established a single supervisory agency or, alternatively, an agency that oversees at least two of the main types of financial intermediaries in a country, including: Australia, Canada, Denmark, Estonia, Hungary, Iceland, Korea, Latvia, Luxembourg, Malta, Mexico, Norway, Singapore, Sweden and the United Kingdom. Senior officials of the new unified agencies of this group of countries kindly responded to a questionnaire sent by the authors (see Annex 1 for the questionnaire).

In addition, the authors had the opportunity to review a survey conducted among the members of the international group of integrated supervisors in 1999 and discuss with some of them their valuable experiences on integrated supervision.<sup>3</sup> In comparison to that survey, which is based on a limited group of developed economies, this survey covered both developed and developing countries. It also updated some of the data and included new areas with regard to the size of financial systems and financial conglomerates.

This paper will proceed as follows. The first section briefly reviews the arguments in favor of and against integrated supervision and examines the reasons for adopting it in the countries covered in the survey. Section two assesses how powerful the new integrated supervisory agencies are in terms of the sectors they oversee and the regulatory and supervisory powers they have been given. In the third section we try to measure and compare how far countries have gone in integrating their regulatory and supervisory practices. Finally, in the fourth section we discuss the complexity of merging different supervisory agencies, drawing useful lessons for other countries considering or planning to adopt integrated supervision, and identifying possible areas for future research.

# I Arguments in Favor **and** Against Integrated **Supervision**

Traditionally, most countries have regulated and supervised their financial intermediaries through multiple institutions, including the ministry of finance, the central bank, as well as specialized supervisory agencies, such as banking, securities and insurance commissions (or superintendencies). Over the years, some countries have established additional agencies to oversee particular market activities (such as derivatives), to supervise particular types of intermediaries (e.g. credit unions, mortgage banks, thrift companies and pension funds), or to

> 3 The results of that survey are available at: http://www.apra.gov.au/Policy/loader.cfm?url=/commonspot/security/getfile.cfin&PagelD= 1114

3
# _Main results_

To examine the sensitivity of imputation accuracy to various predictor variables, we build the estimation models on a cumulative basis, with the later models sequentially adding more variables to the basic models (Model 1 or Model 2). On the whole, we employ nine core imputation models across five countries.<sup>14</sup> Model 1 is the most parsimonious (or basic) model and consists of household size, household heads’ age and gender, household heads’ highest completed levels of schooling, a dummy variable indicating whether the head belongs to the ethnic majority group, the shares of household members in the age ranges 0-14, 15-24, 25-59 and 60 and older, a dummy variable indicating whether the head worked in the past 12 months, and a dummy variable indicating urban residence. Model 2 adds household asset variables and house (dwelling) characteristics to Model 1. Household assets include variables indicating whether the household has a car, motorbike, bicycle, desk phone, mobile phone, DVD player, television set, computer, refrigerator, air conditioner, washing machine, or electric fan. House characteristics include the construction materials for the house’s roof and wall and the type of water and toilet the household has access.<sup>15</sup> Models 1 and 2 include standard variables available in most LSMS-type surveys and other types of micro surveys.

Model 3 adds total food expenditures to Model 2, and Model 4 adds total non-food expenditures to Model 2. Models 5 to 8 add to Model 2, respectively, durables expenditures, health expenditures, education expenditures, and utilities expenditures (such as on electricity, water, and

> 14 For misspecified regressions, adding more variables may result in larger inconsistency (Snijders and Bosker, 1994; De Luca, Magnus, and Peracchi, 2018). As such, it is useful to examine imputation accuracy for different models.

> 15 For Viet Nam, house wall material is assigned numerical values using the following categories: 6 "cement", 5 "brick", 4 "iron/wood", 3 "earth/straw", 2 "bamboo/board", and 1 "others". Toilet type is assigned numerical values using the following categories: 6 "septic", 5 "suilabh", 4 "double septic", 3 "fish bridge", 2 "others", and 1 "none".

16
institutional and legal framework, providing the potential to differentiate more clearly between the impact of endowments and spatial factors on economic growth.<sup>7</sup> The vast diversity, geographic disparity and socio-economic differences across more than 450 autonomous districts and cities provide enough units of observation to test for the determinants of growth. Furthermore, Indonesia has economic data that cover several years before the East Asian crisis of 1997/98, the crisis and immediate post-crisis period, as well as the first five years of decentralization, thus making it possible to explore whether the determinants of sub-national growth have changed over time.

This paper uses districts rather than provinces as the unit of analysis. Most previous studies on Indonesia have used provincial level data (Garcia and Soelistianingsih, 1998; Wibisono, 2003), although recently some other researchers have started using district level data (Landiyanto et al, 2005; Fitrani, 2005). This paper builds on the work done by Fitrani (2005), who looked at the growth performance of Indonesia’s districts from 1993 to 2003, finding conditional growth convergence after decentralization. This paper extends Fitrani’s work in three ways. First, it covers a longer time period (1993-2005) allowing a longer period of analysis of the decentralization period which started in 2001; second, it extends the range of growth determinants to include new data on economic geography; and, finally, it briefly explores the determinants of changes in sub-national growth, i.e. “growth accelerations and decelerations” and estimates the characteristics associated with districts that experienced such accelerations.

The paper is structured as follows: In Section 2, we describe the large database of district level characteristics that has been constructed for this research. Section 3 explores the spatial pattern of economic activity in Indonesia and how it has changed over time, whilst Section 4 undertakes a descriptive analysis of Indonesia’s sub-national pattern of growth from 1993 to 2005. In Section 5 we discuss the estimation approach and provide empirical analysis of the determinants of growth at the sub-national level. We also identify the pattern of growth accelerations (or decelerations) and attempt to explain these changes. The conclusions and some policy implications are presented in Section 6.

# **2. DATA**

Most of the data in this paper are generated from various surveys and censuses conducted by the Central Bureau of Statistics (BPS). The BPS conducts the National Socio-Economic Survey (Susenas) every year, which provides comprehensive socioeconomic indicators at the district level. Every three years, the BPS also conducts the Village Potential Census (Podes). This covers all villages in Indonesia, collecting data on basic infrastructure and socioeconomic conditions. We have also drawn budget data from the Regional Finance Information System (SIKD-Ministry of Finance), investment data from the Regional Investment Coordinating Boards (BKPMD), and

> 7 Districts do, however, vary politically and this may also influence growth.

3