rafmacalaba/datause-encoder-data huggingface dataset:
which would further increase employment in disrupted sectors. Fourth, increased trade and globalization have increased demand for low- and mid-skill occupations, primarily through outsourcing manufacturing tasks to these countries.

Figure 2.2 Labor markets are polarizing in the developed world, in the developing world, and in China <!-- Start of picture text --> High-skill occupation Middle-skill occupation Low-skill occupation<br>2<br>1<br>0<br>-1<br>Growth in employment share (~1995 to ~2012)<br>-2<br>LUXNORCHEUSADNKNLDDEUAUT ITACANSWEFRA AUSGBRISLFIN IRLNZLESPISRPRT GRCCZESVNKOR HUNMYSARGCHLSVKRUSMEXHRVBRB TURPOLESTZAFTHA LTUPANCRIBWA MUSJAMKAZMKD SRBEGYPERDOMNAM SLVGTMUKRLKA MNGBOLPAKHNDNIC BTNCHNGHAIND TZAUGAETH<br><!-- End of picture text --> _Source_ : WDR 2016 figure 2.15. Countries sorted by descending order of per capita income. _Note_ : The figure displays changes in employment shares between 1995 and 2012 for countries with at least seven years of data. The classification of occupations follows Autor (2014). For the United States, comparable data could be accessed only for a short period (2003–2008); consistent with Autor (2014), the observed polarization is limited in this period, with most of it taking place in earlier years.

In China, the mid-skill employment shares in broad employment categories increased from 2000 to 2010 (see Figure 2.2). However, compared with other developing countries, China has a much faster pace of technological adoption than other lower-middle-income countries, and thus job polarizing may occur sooner. Indeed, there is evidence of emerging employment polarization in China in recent years. Between 2010 and 2015, only the top 15 percent of the initial wage distribution and the top and bottom 20th percentiles of the initial education distribution saw gains in employment shares (see Figure 2.3). China’s increasing shares of employment gains at the top and bottom end of the skill distribution are consistent with the evidence for polarization in developed countries.

12
Abundant mineral resource-led exports lead to an appreciation of the real exchange rate by shifting production inputs ( _capital and labor - resource movement effect_<sup>11</sup> ) to the booming mineral sector and non-tradable sector (retail trade, service, and construction), thereby reducing the competitiveness of the non-booming sector (mostly agriculture and manufacturing) and hence participating in their collapse. The shift to the non-tradable sector accelerates domestic inflation<sup>12</sup> ( _the spending effect_ of the boom does increase the price of non-traded goods due to increased demand), which is responsible for the real exchange rate appreciation. These short-term macro-adjustment problems result in long-term effects on growth by reducing the country’s economic diversity and increasing its reliance on exports from its natural resource sector.

Without discounting the robustness of the Dutch Disease transmission mechanisms above explained, it is however useful to go back to the original context of Dutch Disease and eventually to see how far the concept could possibly explain SSA’s oil exporting countries’ development impasse. Indeed, the term Dutch Disease refers to the fears of de-industrialization that gripped the Netherlands as a result of the appreciation of the Dutch currency that followed the discovery of natural gas deposits. The appreciation of the Dutch currency that followed the gas export boom reduced the profitability of manufacturing and service exports<sup>13</sup> . Total exports decreased markedly to GDP during the 1960s. Expansion of petroleum exports in the 1960s not only crowded out other exports, actually reduced other exports disproportionately and fueled the fears of dire consequences for Dutch manufacturing.

In fact based on existing data, SSA’s oil exporting countries’ manufacturing sector almost does not exist and where it does, like it is case of Nigeria, it is neither knowledge-driven nor globally competitive. It does not perform significantly better than the natural resources sector; therefore, the positive effects of linkages are negligible. This

> 11 The marginal productivity of labor in the booming industry will increase and attract labor away from the non-booming industry. This change in the sectoral composition of labor is called the resources movement effect of the boom (Corden, 1982).  - Managing Oil Wealth: The Case of Azerbaijan. IMF, 2004. 12 As the matter of fact, the non-tradable sector supply is inelastic in the short term so, when demand expands and resources move into the non-tradable sector, this causes prices to increase thereby induces real exchange rate appreciation.

> 13 Thorvaldur Gylfason. Lessons from the Dutch Disease: Causes, Treatment, and Cures - Institute of Economic Studies - University of Iceland. August 2001.

15
major obstacle for its economic development. With respect to geographic disparity, the National Capital region (NCR) centered on Manila, accounts 13 percent of population and produces 30 percent of GDP. As for the income inequality, the richest 20 percent of the population have an income 10.7 times that of the poorest 20 percent. (2) The remittance from Filipinos overseas accounts for 2.2 to 4.5 percent of the GDP, and it was a important factor for economic growth.

## Reference

1. World Bank. _Country Brief: The Philippines._ March, 1996.

2. Economic Intelligence Unit. _Country Profile: The Philippines. 15_ Regent Street, London SWI Y 4LR United Kingdom. 1991/92, 1992/93, 1993/94, 1995/96, & 1996/97.

# **Thailand**

## **History**

The first unified Thai kingdom was found at Sukhothai in the northern part of the Central Plain on the Indochinese peninsula. The Thai kingdom flourished in the 13 century and expanded its territory down the Chao Phraya valley to the City of Ayuthaya. From this city, the Thai kingdom extended its hegemony southward to the place where is now the modern Malaysia and eastward to the Khmer Empire. During the 19th century, the European powers expanded their colonial control to Asia. Even though the boundary of Thailand was reduced by the French encroachment in Laos and Cambodia and by the British influence in the Malay states, Thailand maintained the political independence by taking advantage of the rivalry between Britain and France. In the economic terms, however, Thailand was colonized. The British controlled the exploration of the major commodities including rice, rubber, tin and teak. The economic penetration created new interest groups in the Thai society and caused conflicts, and further led to political changes. In 1932, the monarchy was overthrown and a constitutional monarchy was set up and this political structure continues to the present time. Nevertheless, the monarchy plays an important role as a stabilizing force in the domestic politics.

## **Natural Resource Based Exports** (1950s-1960s)

Till 1973, most of the time Thailand was under the control of military government. The military government undertook a "state capitalism" program for economic development during the 1950s. The Board of Investment (BOI) was established in 1959 to stimulate both domestic and foreign investment with high protection barriers. In the 1960s, the BOI encouraged the development of an industrial sector based on import substitution. And it also concentrated on infrastructure, such as roads, power stations, and irrigation projects, which opened up the large areas of new agricultural lands for crop cultivation.

## **Oil** Shocks, **Export Diversification, and Import Substitution** (1970s)

During the 1970s, Thailand increased its tariff in favor of Import substitution and kept searching for the market for its crops (such as cassava, sugarcane, and pineapples), textiles, and garments. Helped by the export diversification, Thailand managed to minimize the damage of the first oil 66
# (1) Introduction

A large economic and sociological literature provides estimates of intergenerational persistence in economic status. A higher persistence across generations is interpreted as inequality of economic opportunities for children as their life chances are tied down closely to the socioeconomic status of their parents irrespective of their own choices and eort. The bulk of the measures used for understanding the transmission of economic status from one generation to the next are based on a conditional expectation function. The focus is on estimating the expected value of an indicator of socioeconomic status of children (e.g., permanent income, education ) conditional on parent's (usually father's) socioeconomic status (for surveys, see Solon (1999), Bjorklund and Salvanes (2011), Iversen et al. (2019), and Torche (2019)).

This vast and growing literature largely neglects any information contained in the conditional variance of children's economic outcomes.<sup>2</sup> This is a reasonable approach under two conditions: (i) the conditional variance of the relevant economic outcome does not vary in any systematic way with parental economic status, geographic location, gender, race and ethnicity etc.; (ii) parents and children are approximately risk neutral. A large body of evidence accumulated over many decades rejects risk neutrality, and strongly suggests an important role for risk aversion in economic choices under uncertainty (see, for example, Eeckhoudt et al. (2005)). There is no systematic evidence in the literature on the rst condition, but there are a variety of economic mechanisms that can make the conditional variance a function of parent's economic status and geographic location, for example. Conditional variance in children's schooling may vary across the households in a village because of their dierent abilities to cope with adverse weather shocks. With better access to credit and insurance markets, the highly educated (high income) households are better able to deal with negative shocks such as ood and drought without any disruption to children's education. In contrast, such a negative income shock may force the uneducated poor parents to take the children out of school and

> 2Although largely ignored in the literature on intergenerational mobility, some studies in the related but distinct literature on inequality of opportunity (IOP) account for the fact that conditional variance is likely to depend on the circumstances a child is born into (see, for example, Bjorklund et al. (2012)). But their focus is very dierent. Please see the discussion in section 2 below. There is a small literature that exploits the information in conditional variance by estimating quantile regression models of intergenerational mobility. But the focus there is not on understanding the inuence of parental socioeconomic status on conditional variance of children's outcomes. Please see section 2 below for a detailed discussion.

1
- Lefranc, Arnaud, Nicolas Pistolesi and Alain Trannoy (2006): “Inequality of Opportunities vs. Inequality of Outcomes: Are Western Societies All Alike?”, ECINEQ Discussion Paper # 2006-54 (August).

- Mazumder, Bhakshar (2005): "The Apple Falls Even closer to the Tree than We Thought: New and Revised Estimates of the Intergenerational Inheritance of Earnings." In S. Bowles, H. Gintis, and M. Groves (eds.) _Unequal Chances: Family Background and Economic Success._ Princeton, N.J.: Princeton University Press.

- Peragine, Vitorocco (2004): “Ranking Income Distributions according to Equality of Opportunity”, _Journal of Economic Inequality_ , **2** , 11-30.

- Roemer, John E. (1998): _Equality of Opportunity_ . Cambridge, MA: Harvard University Press.

- Roemer, John E. (2006): “Review Essay, ‘The 2006 world development report: Equity and Development”, _Journal of Economic Inequality_ **4** : 233-244.

Sen, Amartya (1985): _Commodities and Capabilities_ . Amsterdam: North Holland.

- Solon, Gary (1999): "Internenerational Mobility in the Labor Market." In Orley Ashenfelter and David Card, eds., _Handbook of Labor Economics._ Amsterdam: North-Holland.

- Shorrocks, Anthony F., (1980): “The Class of Additively Decomposable Inequality Measures”, _Econometrica_ , **48** (3), 613-625.

- Van de Gaer, Dirk, Erik Schokkaert and Michel Martinez (2001): “Three Meanings of Intergenerational Mobility”, _Economica_ , **68** : 519-537.

- World Bank (2006): _World Development Report 2006: Equity and Development_ . Washington, DC: The World Bank and Oxford University Press.

33
direct and dynamic effects on the endogenous variables–is extended along two key dimensions. First, while previous work has focused on the United States, this paper looks at 54 economies including 32 advanced economies and 22 EMDEs. Second, this paper allows for (but again does not require) fat-tailed shocks. That is, it distinguishes between slow-moving and fast-moving volatility, the latter measure reducing the possibility that the estimates of policy uncertainty are contaminated by volatility induced by transitory events or data outliers, and a way to deal with including data from the COVID-19 pandemic.<sup>2</sup> Policy uncertainty is defined in this paper as in Jurado et al. (2015): the conditional volatility of policy shocks or, more specifically, the part of policy that cannot be forecasted by economic agents. What matters for households and firms that make decisions about spending and saving, or producing and investing, is not whether a particular outcome has become more or less disperse (for example, whether sales will rise or collapse) but rather whether it has become more or less predictable (that is, more difficult to predict a rise or collapse in sales).

This paper reports the following findings. First, policy uncertainty shocks have a material impact on real activity and prices. An increase in fiscal policy uncertainty (specifically real government spending uncertainty) is associated with a statistically significant drop in real GDP, private consumption, and fixed investment. Prices also increase. Monetary policy uncertainty (or real interest rate uncertainty) follows a similar dynamic, being associated with a statistically significant decrease in real GDP and increase in prices. There is no _a priori_ theoretical basis for whether uncertainty shocks should act like supply or demand shocks (see Fern ́andez-Villaverde et al., 2015; Fern ́andez-Villaverde and Guerr ́onQuintana, 2020).

Second, policy uncertainty affects advanced economies and EMDEs differently, although given the lower precision in estimating with smaller samples, these should be interpreted

> 2Carriero et al. (2021) refer to fast-moving volatility as “transient and infrequent” increases in volatility and slow-moving volatility as “highly persistent”.

2
Kremer, M., A. Onatski, and J. Stock (2001). Searching for prosperity. NBER Working Paper 8250.

Lokshin, M. and M. Ravallion (2002). Rich and powerful? Subjective power and welfare in Russia. World Bank Policy Research Working Paper 2854. Long, J. and J. Freese (2003). Regression Models for Categorical Dependent Variables Using Stata. Texas: Stata Corporation.

McBride, M. (2001). Relative-income effects on subjective well-being in the cross-section. Journal of Economic Behavior and Organization 45, 251-278. Munoz, J. (2001). Timor Loro Sa’e Living Standards Survey Sampling Design and Implementation. World Bank, mimeo. Oswald, A. J. (1997). Happiness and economic performance. Economic Journal 107, 1815-1831.

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Ravallion, M. and M. Lokshin (2002). Self-rated economic welfare in Russia. European Economic Review 46.8, 1453-1473. Ross, C. and J. Mirowsky (1992). Households, employment and the sense of control. Social Psychology Quarterly 55, 217-35.

Sen, A. (1999). Development as Freedom. Oxford: Oxford University Press. Shorrocks, A. (1978). The measurement of mobility. Econometrica 46, 10131024. Veall, M. and K. Zimmermann (1996). Pseudo-R2 measures for some common limited dependent variable models. Journal of Economic Surveys 10, 241— 259.

Winkelmann, L. and R. Winkelmann (1998). Why are the unemployed so unhappy? Evidence from panel data. Economica 65, 1-15. World Bank (2001). World Development Report 2000/2001. Washington, DC: World Bank.

- World Bank (2002a). East Timor: Policy Challanges for ington, DC: World Bank.

a New Nation. WashWorld Bank (2002b). Empowerment and Poverty Reduction - A Sourcebook. Washington, DC: World Bank.

World Bank (2003). Timor-Leste - Poverty in a New Nation: Analysis for Action. Washington, DC: World Bank.
average bias is 17.6 percent. Again, the corresponding average bias in the IGC estimates is much smaller at 10.4 percent.<sup>9</sup> Considering estimates across different age ranges and gender, the average biases in IGRC and IGC are 24.4 percent and 6.5 percent respectively in Bangladesh, and the corresponding estimates in India are 14.12 percent (IGRC) and 7.6 percent (IGC). Moreover, we provide suggestive evidence that the bias in IGC estimate is less sensitive to the variations in the coresidency rate.

We discuss explanations and intuitions for the empirical findings that the IGC estimates suffer from much lower coresidency bias. A simple intuition can be provided by using the _σ_ _<u>p</u>_ following relationship between IGRC (denoted as _β_ ) and IGC (denoted as _ρ_ ): _ρ_ = _β_ , ( _σc_ ) where _σp_ and _σc_ are standard deviations of parental schooling and children’s schooling respectively. In other words, the IGC estimate is equal to the IGRC estimate multiplied by the ratio of standard deviation of parent’s schooling to that of children’s schooling. It is wellknown that truncation biases the estimate of _β_ downward in an OLS regression (Hausman and Wise (1977)). An equally important implication of truncation in our context is that it also affects the estimate of the ratio of standard deviations in schooling of parents to children. The IGC estimate cancels out part of the downward bias in IGRC by multiplying it with an upward biased estimate of the ratio of standard deviation of parental schooling to that of children’s schooling. We also provide a plausible rationale for the finding that the IGC estimates are less sensitive to the variation in coresidency rate.

The analysis and findings presented below have important and wide ranging implications for research on intergenerational economic mobility. First, our analysis implies that much progress in understanding intergenerational mobility can be made with the household surveys available in developing countries by focusing on IGC as the measure of mobility. These data sets are currently shunned by most researchers because of the worry that the estimates from the coresident sample suffer from bias of unknown direction, and possibly of very high magnitude.<sup>10</sup> Second, the results in this paper can be helpful in sorting out often 9As we discuss later, the difference in biases between IGRC and IGC becomes smaller as the coresidency rate increases.

> 10We focus on IGRC and IGC, as they are two of the most widely used measures of mobility. But

3
37 Table 4. Average effect of the Venezuelan migration shock on educa– tional outcomes Public schools (marginal effects)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dependent Var.|Ln(Enrollment)|Promotion Rate|Dropout Rate|Math Std.<br>Score|Language Std.<br>Score|
||**P**|**anel A: Without **|**Controls**|||
|Foreign Males|0.0053***|-0.0294***|0.0128**|0.0015|0.0041**|
||(0.0013)|(0.0096)|(0.0064)|(0.0013)|(0.0020)|
|Native Males|0.0013|-0.0312***|0.0119**|0.0000|0.0003|
||(0.0012)|(0.0090)|(0.0059)|(0.0006)|(0.0005)|
|Foreign Females|0.0051***|-0.0193*|0.0126*|0.0022**|0.0022**|
||(0.0013)|(0.0114)|(0.0068)|(0.0009)|(0.0010)|
|Native Females|0.0018|-0.0176**|0.0102|0.0004|0.0007*|
||(0.0013)|(0.0088)|(0.0064)|(0.0003)|(0.0004)|
|R-Squared|0.8980|0.2539|0.2167|0.6896|0.6729|
|||**Panel B: With C**|**ontrols**|||
|Foreign Males|0.0051***|-0.0286***|0.0129**|0.0014|0.0041**|
||(0.0013)|(0.0093)|(0.0064)|(0.0012)|(0.0020)|
|Native Males|0.0011|-0.0305***|0.0120**|-0.0001|0.0002|
||(0.0011)|(0.0089)|(0.0059)|(0.0006)|(0.0004)|
|Foreign Females|0.0049***|-0.0185*|0.0126*|0.0021**|0.0021**|
||(0.0013)|(0.0112)|(0.0067)|(0.0009)|(0.0010)|
|Native Females|0.0016|-0.0170*|0.0103|0.0003|0.0006|
||(0.0013)|(0.0088)|(0.0064)|(0.0003)|(0.0004)|
|R-Squared|0.8982|0.2544|0.2169|0.6907|0.6735|
|Native Males Mean|4.706|91.76|3.961|-0.0329|-0.249|
|Native Males SD|1.787|11.32|8.874|0.743|0.751|
|Native Females Mean|4.648|94.07|3.176|-0.400|-0.262|
|Native Females SD|1.820|9.700|8.194|0.626|0.691|
|Foreign Males Mean|1.247|87.48|8.391|-0.0445|-0.231|
|Foreign Males SD|1.167|25.09|21.43|1.389|1.535|
|Foreign Females Mean|1.221|89.29|7.842|-0.527|-0.319|
|Foreign Females SD|1.147|23.81|21.09|1.319|1.475|
|Observations|203,326|203,326|203,326|81,082|81,082|

Clustered standard errors by municipality in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All columns include School FE, Year FE and Department _×_ Year FE. Selected controls include the following variables interacted with year dummies: N. of Terrorist Attacks _×_ 2012, 2014 and 2016, Informal Labor _×_ 2018, UBN _×_ 2013.
controls for a large number of potential determinants of household consumption that are briefly described below.

- i) _Household demographics:_ This includes linear and quadratic terms in family size, and household composition variables including number of adult (ages 15-60) male and female members, number of children below 7 years of age, and number of children in the age group 7-15.

- ii) _Characteristics of household head:_ This includes indicator variables for female headship and marital status (being single, divorced or widowed), as well as linear and quadratic terms in age of the household head.

- iii) _Education:_ This includes average completed years of schooling of adult household members and a quadratic term in the same. We experimented with separating out male and female average years of schooling and also using dummy variables for each successive year of schooling. This did not significantly improve upon the simple quadratic specification in average years of schooling, and hence we decided to stick to that specification in the interest of limiting the number of interaction terms with the shock variables in an augmented model discussed later.

- iv) _Occupational characteristics:_ Occupational background of household members is represented by a set of binary variables for different occupational sectors they were employed in. The binary variables take a value of unity if there is at least one household member working in a given sector, zero otherwise.<sup>2</sup> Ten sectors are distinguished:

- agriculture, fishery and forestry; mining and quarrying; manufacturing; electricity, gas

> 12 We prefer this to a specification in terms of the number of members in each occupational category because the latter is more likely to be responsive to a shock than the affiliation of at least one member to an occupational category. All occupational variables relate to a household member's primary job or business during the first three months of the survey, i.e. April-June 1998.

10