rafmacalaba/datause-encoder-data huggingface dataset:
6-

# III. Case Studies

III.A Country case-studies In this sub-section, we briefly review some country cases, both successes and failures, based on the Bank's country studies or sector reports.

# A. Thailand's education segtor: a successful story

The implementation of education policy in Thailand has been quite successful. Public expenditure on education in Thailand rose to about 20 percent of the national budget during 1976-1984. Thailand has now achieved universal primary education, and the adult literacy rate rose from 68 percent in 1961 to 91 percent in 1985 with an average enrollment ratio of about 20 percent at the tertiary level.

One of the central reasons attributed to the success in education development is Thailand's overall macroeconomic environment during the past few decades. The Thai economy has been growing steadily at an annual rate of 7.3 in 1965-80 and 7.0 in 1980-89. Along with the industrialization and employment shifting away from agriculture, there is a surge in demand. for better educated labor, which is reflected by job vacancy trends and employers' report of difficulty in recruiting and keeping such workers. The unemployment rates have been very low (1.3 percent in 1987 and 0.8 percent for 1988). This surge in demand for educated labor has created great private and public incentives to invest in education and better utilization of existing facilities.

The second reason is the lower rate of population growth in the last 20 years, resulting from a general income growth and a highly successful family planning program. The total fertility rate<sup>5</sup> declined dramatically from 6.31 in 1960s to 2.48 in 1989. The dependency ratio has been declining steadily since 1970. This reduced the relative size of the school-age population, and correspondingly lightened the 5 Total fertility rate is the average number of children that would be born alive to a woman during her lifetime, if she were to bear children at each age in accordance with the prevailing age-specific fertility rates.
**Table A.2 : Regression analysis on the determinants of education levels**

|Dependent variable|Preschool/<br>primary|Secondary|Tertiary|Preschool<br>/primary|Secondary|Tertiary|
|---|---|---|---|---|---|---|
|Distance|0.004|0.006|-0.010|0.002|0.003|-0.005|
|from CBD|(0.005)|(0.006)|(0.005)**|(0.004)|(0.005)|(0.004)|
|Building density|-0.010|0.017|-0.007|-0.018|0.034|-0.017|
|(log transformed)|(0.023)|(0.027)|(0.024)|(0.022)|(0.024)|(0.019)|
|Precarious|-0.019|-0.098|0.118|-0.005|-0.022|0.027|
||(0.037)|(0.049)**|(0.062)*|(0.038)|(0.044)|(0.054)|
|Flood risk - high|0.068|-0.080|0.012|0.010|-0.040|0.030|
||(0.059)|(0.076)|(0.048)|(0.074)|(0.075)|(0.057)|
|Age||||0.003|-0.008|0.005|
|||||(0.001)***|(0.002)***|(0.002)***|
|Female||||-0.171|-0.013|0.184|
|||||(0.033)***|(0.039)|(0.035)***|
|1 dependent per||||-0.011|0.142|-0.131|
|working-age person||||(0.024)|(0.063)**|(0.067)*|
|1−2 dependents per||||0.009|0.160|-0.169|
|working-age person||||(0.040)|(0.076)**|(0.080)**|
|> 2 dependents per||||0.084|0.141|-0.225|
|working-age person||||(0.082)|(0.108)|(0.076)***|
|Consumption quintile||||-0.023|-0.114|0.136|
|(20–40 percentile)||||(0.056)|(0.071)|(0.044)***|
|Consumption quintile||||-0.072|-0.071|0.144|
|(40–60 percentile)||||(0.063)|(0.070)|(0.054)***|
|Consumption quintile||||-0.077|-0.138|0.214|
|(60–80 percentile)||||(0.076)|(0.064)**|(0.052)***|
|Consumption quintile||||-0.100|-0.327|0.427|
|(80–100 percentile)<br>(Richest)||||(0.057)*|(0.068)***|(0.059)***|
|Adjusted R-squared|0.01|0.01|0.03|0.10|0.12|0.20|
|Observation|1,792|1,792|1,792|1,526|1,526|1,526|

_Note:_ Standard errors in parentheses and clustered by primary sampling units. Each of these regressions conducts a linear probability model testing the effects of each covariate on the likelihood that household heads have preschool/primary (the first and fourth columns), secondary (the second and fifth columns), and tertiary education (the third and sixth columns).<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

31
Nehru, Vikram, and Ashok Dhareshwar. 1993. "A New Database on Physical Capital Stock: Sources, Methodology, and Results," Revista de Análisis Económica, Vol. 8, No. 1, pp. 37-59, Junio.

Quijada, Jose Alejandro. 2012. Econometric analysis of human development. Unpublished note. January 26.

Reinert, Kenneth A., and David W. Roland-Holst. 1997. “Social Accounting Matrices,” pp. 94-121 in Editors Joseph F. Francois and Kenneth A., Reinert. Applied Methods of Trade Policy Analysis. Cambridge University Press.

Robinson, Sherman, Andrea Cattaneo, and Moataz El-Said. 2001. “Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods,” Economic Systems Research, Vol. 13, No. 1, pp. 47-64.

Round, Jeffery I. 2003. “Constructing SAMs for Development Policy Analysis: Lessons Learned and Challenges Ahead,” Economic Systems Research, Vol. 15, No. 2, pp. 161-183.

UBoS. 2007. The 2005/06 Uganda National Household Survey. Kampala Uganda Bureau of Statistics, Ministry of Finance, Planning and Economic Development.

van der Mensbrugghe, Dominique. 2010. The Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) Model. Version 7.1. Technical Reference Document. December. The World Bank.

World Bank. 2007a. World Development Report 2008: Agriculture for Development. World Bank, Washington, D.C.

World Bank. 2007b. Fiscal Policy for Growth and Development: Further Analysis and Lessons <u>from Country Case Studies. Background report for the April 15, 2007, Development Committee</u> Meeting.

World Bank. 2011a. EdStats. November. Downloaded from http://data.worldbank.org/data- <u>catalog.</u> World Bank 2011b. World Development Indicators. November. Downloaded from <u>http://data.worldbank.org/data-catalog.</u> World Bank. 2012. The International Income Distribution (I2D2) database.

World Bank and UN. 2012. World integrated trade solution -- WITS. World Bank Development Data Group and United Nations. Statistical Division <u>(http://library.worldbankimflib.org/ElectronicResources/eresourcelist.asp?catkey=728263;</u> 21
Table 3: Pool Composition

||Full S|ample|Housek|eeper|Na|nny|Genera|l Work|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Foreign|-0.0882<sup>_∗∗∗_</sup>|-0.0554|-0.0764<sup>_∗∗∗_</sup>|0.0361|-0.135<sup>_∗∗∗_</sup>|-0.191<sup>_∗∗_</sup>|-0.0403|0.0450|
||(0.0139)|(0.0324)|(0.0171)|(0.0555)|(0.0321)|(0.0669)|(0.0382)|(0.0652)|
|Foreign Share (_θF or_)|0.0899<sup>_∗∗_</sup>|0.122<sup>_∗∗_</sup>|0.0144|0.124|0.107|0.0456|0.0639|0.166|
||(0.0334)|(0.0458)|(0.0472)|(0.0718)|(0.0900)|(0.117)|(0.0733)|(0.104)|
|Foreign x Foreign Share||-0.0636<br>(0.0551)||-0.185<sup>_∗_</sup><br>(0.0856)||0.167<br>(0.160)||-0.202<br>(0.127)|
|Profle Syntax|Y|Y|Y|Y|Y|Y|Y|Y|
|Suburb|Y|Y|Y|Y|Y|Y|Y|Y|
|Control Var|Y|Y|Y|Y|Y|Y|Y|Y|
|_R_<sup>2</sup>|0.265|0.265|0.211|0.212|0.316|0.316|0.182|0.184|
|N|5338|5338|3217|3217|1133|1133|988|988|

_Note:_ Robust standard errors in parentheses. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05 , _p <_ 0 _._ 01 , _p <_ 0 _._ 001 second necessary condition is that employers compare applicants locally, i.e. the ones presented to them at a given time on the computer screen. One implication of this assumption is that the share of foreigners presented on a previous or following search result page should matter less than the share presented on the same screen ( _θFor_<sup>1</sup><sup>_−_5).Itestthisbyestimatingequation3withtheimmigrantshare</sup> of the applicants posted 6 to 10 places above and below a given profile ( _θFor_<sup>6</sup><sup>_−_10).The coefficient drops</sup> from 0.089 to 0.024 and is not statistically significant (not reported, p-value: 0.525).

# **5 Mechanisms**

What may explain the preference for South African job seekers? There are at least three channels: i) employers risk paying a fine if they are caught hiring an immigrant who is undocumented, ii) employers may receive disutility from interacting with foreigners, and iii) employers may believe that South Africans are more productive with respect to unobservables (Appendix C offers a simple theoretical framework). To shed light on these questions I administered an anonymous phone survey with 208 domestic workers in Cape Town who posted a profile on gumtree (for more information on survey procedures, see Appendix D).

17
# **4. Data and Methodology**

This section describes the definition and sources of the data for our empirical assessment and the strategy to identify credit booms. Then we show the descriptive statistics for the episodes and duration of credit booms and identify the duration of credit booms.

To proceed with the duration analysis, we collected quarterly data for 71 countries (23 industrial economies and 48 emerging market economies) from 1975q1 to 2010q4 on real credit. The measure of credit considered in our analysis is the deposit money bank claims on the private sector taken from the line 22d of the IMF's International Financial Statistics (IFS). We express the amount of credit in real terms by dividing the nominal credit by the CPI index (at the end of the quarter). Other measures of credit considered in this study are the ratio of real credit to GDP and the leverage of the banking system. The latter indicator is computed as the ratio of private credit to bank deposits where deposits are measured as the sum of demand and time deposits (IFS lines 24 and 25, respectively).

However, our aim is to identify credit booms to compute the respective duration. Defining a credit boom is not easy because there is no consensus in the literature on the best methodology to identify them. Some studies use the amount of real credit provided by the banking system; others use the bank lending normalized by either total population or the amount of goods produced in the real economy. In this study we decided to focus on the criteria used by Calderón and Kubota (2012) for their analysis on the effects of surges in private capital inflows over credit booms. In their paper they consider the following criteria from the literature on credit booms: (i) Mendoza and Terrones (2008) or MT-criteria; and (ii) Gourinchas, Valdes and Landarretche (2001) or GVL-criteria, which is later implemented and updated by Barajas, Dell'Ariccia and Levchenko (2009).

In the criteria defined by Mendoza and Terrones (2008) to identify credit booms, an episode of credit boom takes place whenever the amount of credit extended by the banking system to the private sector grows by more than its experience during a typical cyclical expansion. The amount of real credit per capita,<sup>_l_</sup> _it_<sup>, is the key variable to identify a boom in</sup> lending. They denote<sup>~</sup> _lit_ as the deviation of (the log of) real credit per capita from its long-run 16
No less contentious is the role played by urban economic growth—whether it helps absorb surplus rural labor and unemployed urban workers or merely benefits urban elites.<sup>8</sup> Past research on these issues has relied mainly on cross-country comparisons, often in single cross-sections (such as in the many tests of the Kuznets hypothesis) but sometimes using panel data, though the typically short time-series has meant that the cross-country variability is dominant. (While one can track economic growth annually for almost all countries, the household surveys needed to monitor living standards are far less frequent.) In addressing these issues, it is clearly desirable to have a reasonably long time series of surveys; a short series can be deceptive for inferring a trend.

Among developing countries, India has the longest series of national household surveys suitable for tracking living conditions. The surveys are reasonably comparable over time since the basic survey instruments and methods have changed rather little (though we note, and address, some comparability problems). India thus provides rich time series evidence—uniquely so among developing countries—for testing and quantifying the relationship between living standards of the poor and macroeconomic aggregates.

All the substantive issues about growth and poverty in the general development literature have been prominent in the literature and policy debates on India. Famously, India's postindependence planners hoped that the country's urban-based industrialization process would bring longer-term gains to poor people, including through rural labor absorption.<sup>9</sup> However, that hope was largely shattered by the evidence of the slow pace of poverty reduction in the period from Independence until the 1980s.<sup>10</sup> In explaining this, a number of observers pointed to the slow pace of labor absorption from agriculture associated with the more inward-looking and capital-intensive development path of this period.<sup>11</sup> The urban population share has been rising steadily over time in India, from 17% in 1950 to 31% today. However, India’s pace of population urbanization (proportionate increase in the urban population share) has been less than either South Asia as a whole or lower middle-income countries as a whole, and markedly slower than for (say) China.<sup>12</sup> The trend rate of growth in

> 8  See, for example, the discussion in Eswaran and Kotwal (1994).

> 9  For a review of these debates see Ravallion (2016, Ch. 2).

> 10  See Datt and Ravallion (2002, 2011).

> 11  See, for example, Bhagwati (1993) and Eswaran and Kotwal (1994).

> 12 Urban population shares can be found in World Bank (2015 and past issues). The urban population shares of China and India were about the same around 1990, but the share now exceeds 50% in China.

3
Figure 1. Legal status of Syrian refugees upon arrival <!-- Start of picture text --> Female Principal Applicants Male Principal Applicants<br><!-- End of picture text --> <!-- Start of picture text --> No data Smuggled Born in No data Born in Smuggled<br>0.02% 0.09% CoA 0.03% CoA 0.27%<br>0.04% 0.03%<br>Legal<br>43.81% Legal Informal<br>Informal 48.86% 50.81%<br>56.04%<br><!-- End of picture text --> _Source_ : Own calculations using Home Visits data for Syrian refugees that arrived in Jordan after 2011. _Note:_ CoA = City of arrival.

# Demographic Characteristics

<mark>Women and children left Syria in large numbers and therefore, as is often the case for refugee populations, the demographic characteristics of the refugee population differ from that of pre‐crisis Syria. Figure 2 shows that before the refugee crisis, the Syrian population was very young, with the largest population cohorts being children under 15 (Panel a), but the Syrian refugee population in Jordan is even younger (Panel b). Children under 15 accounted for 36 percent of the population in Syria in 2010 but represent 45 percent of the refugee population living in Jordan. In pre‐crisis Syria, 3.5 percent of the population was 65 years or older, but among the Syrian refugees, this group only accounts for 2 percent of the population.</mark> Figure 2. Age pyramid by sex of Syrians in Syria and Syrian refugees in Jordan <!-- Start of picture text --> a. Syrians pre‐crisis b. Syrian refugees in Jordan<br>75‐79 75‐79<br>60‐64 60‐64<br>45‐49 45‐49<br>30‐34 30‐34<br>15‐19 15‐19<br>0‐4<br>0‐4<br>20 10 0 10 20<br>20 10 0 10 20<br>% Male % Female<br>% Male % Female<br><!-- End of picture text --> _<mark>Source:</mark>_ <mark>Own calculations based on United Nations, Department of Economic and Social Affairs, Population Division</mark> 2017 and ProGres data.

<mark>The sex‐ratio of Syrian refugees in Jordan differs from that of the pre‐crisis population in Syria. Women account for 52 percent of the refugees in Jordan, whereas in Syria in 2010 they represented 49 percent of the population. This is mainly explained by the increase in the share of women under 20 from 46.2 percent in 2010 to 53.9 percent in the female refugee population. In contrast, the proportion of women in all the</mark> 7
_22_ Table 4—: British Standards Institution: Categories of Work Intensity: By Sector

|Sector|Intensity|Category|
|---|---|---|
|Agriculture, forestry and fshing|355|Moderate/high (4)|
|Other industry|295|Moderate (3)|
|Manufacturing|240|Light/moderate (2)|
|Construction|355|Moderate/high (4)|
|Wholesale and retail trade|240|Light/moderate (2)|
|Information and communication|180|Light (1)|
|Financial and insurance activities|180|Light (1)|
|Public administration and defense|240|Light/moderate (2)|

_Note:_ Adopted from Costa et al. (2016) using a classification of different job activities by work intensity used by the British and European standards on heat stress, BS EN 27243:1994, and based on the International Organization for Standardization 7243:1989 (see British Standards Institution 1994).

# Sector

The second dimension of heterogeneity is the sector classification encompassing manufacturing and retail and services. Exposure to excess heat has varying effects on workers, depending on work intensity, which varies with the nature of economic activities. Work intensity in the manufacturing sector is generally considered light to moderate by the British and European standards for heat stress. Table 4 presents the level of work intensity of jobs by sector. The classification is based on the 1989 International Organization for Standards estimation of the effect of heat stress on working men (Costa et al., 2016). Work intensity in the agriculture, forestry, fishing and construction sectors is generally considered moderate to highly intensive. Manufacturing and wholesale and retail trade industries are considered to have light to moderate work intensity. We unpack the potential heterogeneity in the effects of exposure to excess heat by industry by estimating the model for two broad industry classifications: manufacturing and services, where services include retail and other services. Manufacturing firms account for about 57 percent of the WBES data and comprise a variety of firms engaged in productive activities, from wood and furniture products to manufacturing machinery, electronics, and communications equipment. Retail and services account for the remaining 43 percent of our sample. Firms in retail are those engaged in construction, retail, hotels, and hospitality services. Other services account for about 21 percent of the sample and include firms engaged in wholesale and other services.

Table 5 presents the results by broad sector classification of manufacturing, and services (+retail). The effect varies by sector, with negative and statistically significant effects on the productivity of manufacturing firms located in both temperature categories where the WBT is above 24.9, while the results for services are not
18 PLDT was ensured of receiving reasonable return on its investments for several reasons. First, the U.S. influence meant that the company could count on the legal and political leverage at its disposal to prevent its profitability from falling too far below the competitive rate of return on capital. Second, the probability of a currency devaluation, which could have hurt PLDT, was low, given the fixed exchange rate stipulated by the independence agreements between the Philippines and the U.S. Moreover, any planned devaluation would have been known well ahead of the time because of the requirement for prior U.S. approval. Third, PLDT's customers were mainly the foreigners and the Filipino elite. They were concerned about the availability and quality of service, and were not interested in a confrontation with PLDT that could jeopardize service expansion at a time when there was still a great deal of excess demand (figure 4.10). Essentially, GTE's control of technology and rate of investment allowed it to capture part, though not all, of the surplus of PLDT's operations.

These same factors influenced the government's forthcoming attitude in meeting PLDT's foreign exchange needs during the country's balance of payments difficulties in the second half of 1950s. Many other businesses were rationed. A The government did impose some restrictions on remittances of dividends to foreign shareholders, but that was a uniform policy affecting all businesses, and the fixed exchange rate imposed by the U.S. government meant that any losses due to the delay in remittance would be small. In addition, since the government did not restrict debt amortization, the company could easily circumvent remittance restrictions by increasing its foreign exchange debt in place of raising equity (figure 4.11).

On the whole, U.S. influence implied ownership security, institutional continuity, and economic stability, all factors that made the investment environment relatively safe. In addition, GTE's control of technology and investment and the excess demand for telephones among the elite ensured the company that confrontation with the government was unlikely. Telephone service grew fast.

# _Currency devaluation and conflict, 1960-63_

By the late 1950s, the U.S. role in the Philippines had changed, as the Philippine elite grew more independent and established new relationships around the world. The U.S. government lost its influence as a source of commitment for domestic policies. Sensing the growing weakness of the commitment mechanisms and lacking close ties to those in control of the government, PLDT sharply 'This point is clearly reflected in PLDT's _Annual Reports_ during those years. PLDT was even spared the exchange rate tax instituted in the 1950s to effectively devalue the peso.
**Table 4B: Trade balance as a share of GDP and household and nonfinancial corporation credit as a share of GDP**

|Dep. Var.:|2SLS|2SLS|2SLS|2SLS|2SLS-FE|
|---|---|---|---|---|---|
|netexport|(1)|(2)|(3)|(4)|(5)|
|ln_hhcredit|-3.1984***<br>(0.006)|-4.6973***<br>(0.001)|-4.2055***<br>(0.008)|-2.7021**<br>(0.020)|-2.5965**<br>(0.042)|
|ln_nfccredit|6.1132**|9.7092***|11.8835***|8.4584***|8.4563***|
||(0.013)|(0.002)|(0.001)|(0.001)|(0.002)|
|fdi|0.1940**|0.1926**|0.1601**|0.1620**|0.1658**|
||(0.013)|(0.018)|(0.033)|(0.023)|(0.024)|
|ln_initialgdppc|1.1283|0.8744|-0.1018|0.1052|-0.3430|
||(0.263)|(0.405)|(0.910)|(0.901)|(0.659)|
|financialopenness|0.0006**|0.0004|0.0004|0.0005**|0.0006**|
||(0.015)|(0.136)|(0.159)|(0.017)|(0.011)|
|tot|0.0828**|0.0474|0.0113|0.0475*|0.0102|
||(0.014)|(0.210)|(0.720)|(0.082)|(0.736)|
|commodityexporter||4.4719***|4.3796***||3.8140***|
|||(0.009)|(0.010)||(0.007)|
|financialcrisis|||-2.0288|-2.1450|-1.9610|
||||(0.205)|(0.157)|(0.299)|
|Constant|-24.2524***<br>(0.009)|-30.7479***<br>(0.002)|-34.7481***<br>(0.000)|-28.9522***<br>(0.001)|-24.8960***<br>(0.002)|
|Observations|141|141|138|138|138|
|R-squared|0.273|0.212|0.166|0.253|0.312|
|Prob > F|0.000|0.000|0.000|0.000|0.000|

Regressions (1) – (4) are 2SLS regressions with legal origin and religious composition as instruments. Regression (5) is a 2SLS regression with the same instruments adding with year fixed effects. Credit variables and initial GDP per capita are in logs. All the other variables are in levels. Five-year average data are used. Robust p-values are reported in parentheses. *, **, *** respectively indicate significance at the 10%, 5% and 1% level.

16
<!-- Start of picture text --> Change in agrcufturall outnut petert ial<br>(2080s as % of 2000 ponerntial)<br>rcasstteal courriiess7<br>BR war<br><!-- End of picture text --> <!-- Start of picture text --> BE ve vsicoir cosets<br>oe<br>fF Mickie East and North sbics<br><!-- End of picture text --> <!-- Start of picture text --> Es<br>= = 0 10 20<br>Source: Cline 2007.<br><!-- End of picture text -->