rafmacalaba/datause-encoder-data huggingface dataset:
# I. Introduction

<mark>Does globalization increase or reduce inequality? Intense debate surrounding globalization’s effect on wage inequality followed Wood’s (1997) suggestion that inequality fell in East Asia but rose in Latin America following liberalization. W</mark> ood’s (1997) argument inspired many prominent papers that sought to explain why liberalization in a labor-abundant country would result in rising inequality. <mark>During the 2000s, however, inequality fell throughout Latin America, suggesting that the link between globalization and inequality is not well understood.</mark> When reviewing this literature, Goldberg and Pavcnik (2007) show that the relationship between globalization and inequality is subtle and complex. <mark>Contributing factors include</mark> outsourcing (Feenstra and Hanson 1997), skill-biased technological change (Esquivel and Rodriguez-Lopez 2003), rising numbers of educated workers (Lopez-Acevedo 2006), exchange rate movements (Verhoogen 2008), income support policies (Esquivel et al. 2010), and the rise of China <mark>(Chiquiar and Ramos-Francia 2008 and</mark> Dussel Peters and Gallagher 2013). Changes in relative output prices, the variables suggested by neoclassical trade theory, remain relatively unexplored.

The goal of this paper is to assess the role of relative output price changes in driving inequality. Most trade models focus on one of two relative output price measures. The first classifies products by factor intensity. In the globalization-and-wages literature, the relative price of skill-intensive goods would be the most relevant. The Stolper-Samuelson theorem, a standard result in neoclassical trade theory, shows that an increase in the relative price of skill-intensive goods would increase the relative wages of skilled workers. The less-common second measure is the relative price of traded goods. To illustrate the potential role of the relative price of traded (to nontraded) goods, we develop and simulate a simple Salter-Swan model with firm-level heterogeneity. Firm-level heterogeneity has become almost a standard for recent trade models, including those that focus on labor-market effects of globalization.<sup>2</sup>

> 2 Helpman et al. (2014) is a prominent example a model that employs firm-level heterogeneity to illustrate the effects of trade on inequality.  Through simulations, they use data from Brazil to show that trade and inequality follow an inverted U-shaped pattern: inequality rises at first and then falls with the share of exporting firms.  In their model, the turning point for inequality emerges when 40% of the firms export.

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**Figure 5: Uruguay Round commitments, Doha offer and actual policy by sector**

<!-- Start of picture text --> 100.00<br>90.00<br>80.00 Offer improvement<br>(Uruguay Round<br>70.00 commitment- Doha<br>Offer)<br>60.00 Offer gap (Doha<br>Offer - Actual Policy)<br>50.00<br>Actual Policy<br>40.00<br>30.00<br>20.00<br>10.00<br>0.00<br>FinServ Telecom Retailing Maritime ProfServ<br><!-- End of picture text --> <!-- Start of picture text --> FinServ<br><!-- End of picture text --> <!-- Start of picture text --> Telecom<br><!-- End of picture text --> <!-- Start of picture text --> Retailing<br><!-- End of picture text --> Source: Gootiiz and Mattoo (2008) There is a reasonable prospect that offers will be improved. The report on the status of the services negotiations (WTO 2008a) noted that further discussion was needed on issues relating to participants' level of ambition, their willingness to bind existing and improved levels of market access and national treatment, as well as specific reference to Modes 1 and 4 with respect to the treatment of sectors and modes of supply of export interest to developing countries. A “signaling exercise” among a group of Ministers is being used to seek improved offers on services in parallel with discussions on “modalities” in agriculture and non-agricultural market access (NAMA). It remains to be seen how far these improvements will bridge the gap between what is currently on the table and a more ambitious outcome (Mattoo, 2005).

# **_IV. LDCs and the Doha Agenda_**

The Least Developed Countries are in an unusual situation in the Doha Agenda negotiations. This is a Round for Free for the least developed countries, so they are not required to reduce their own applied tariffs. As a consequence, the economic impacts of these negotiations on the LDCs will depend more on what other countries do than on economic reforms undertaken in the LDCs.

For most developing countries, reductions in their trading partners’ trade barriers would likely bring about gains through improvements in market access. For two reasons, the LDCs are in a more vulnerable position. The first is that they already have tariff preferences for development in a number of their trading partners, and especially in the European Union. The second reason is that many LDCs are net importers of agricultural products, whose prices might be expected to rise slightly as a consequence of global trade liberalization in agriculture (Anderson, Martin and van der Mensbrugghe 2006).

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bonds (GfBi; , foreign assets (Fa) , cash and<sup>reserves (CAb), and</sup> loans (L,. Since the rates of interest of<sup>these assets tend to</sup> move I). the sante direction, multicollinearity problem is enrountered inr estimating bank portfolio equations. In addition, there are some regtilations oii bank portfolio. For example, commerclal barnks must hold government bonds up to a certain percentage ini order to be allowed to increase the number of their branches. Not all of these rates of return<sup>are included in the</sup> estimatint equations In this block. The estimation results are shown in equations (26)-(28).

# The Identities

The identities consist of definitional<sup>equations.</sup> They comprise the following items: monetary base (B), total deposits of commercial banks (D}, notes in circulaticn (CAp), which is the balance sheet of the Bank of<sup>Thailand, the government bonds held</sup> by non-bank private sector (GBp),<sup>which is the difference between</sup> total interest-yielded financial assets (Z) and time and<sup>saving</sup> deposits. The balance sheet of commercial banks is imposed<sup>by</sup> specifying identity (33), relating commercial banks' borrowing from the central bank (Hb). Other identities include aggregate exports and imports, trade<sup>balance and current account balance,</sup> domestic absorption (A), national income identity, the excess aggregate demand (E), budget deficit (Dg), and the claims on government by the Bank of Thailand<sup>(Hg).</sup> Identity (44), explaining Bf,<sup>is in fact the government budget</sup> constraint. Bf is modeled as a residual from other means of deficit financing. This implies that the government<sup>will adjust</sup> the level of external debt to<sup>accommodate the fiscal position.</sup> In pract4ce, the Thai government prefers early retirement of external debt. to domestic<sup>debt when the budget is in surplus.</sup> The size of monetary base varies<sup>according to the movements of</sup> claims on government and commercial banks as well as the net foreign assets. Consequently, the monetary authorities can control only the domestic source<sup>of monetary base, since the net</sup> foreign asset component of the monetary base varies by balance of payments condition under the fixed exchange rate regime.

# 8. IMPLICATION OF PUBLIC DEFICIT

What is the implication of public<sup>deficit on current account and</sup> other macro variables' The relationship between public<sup>deficit</sup> and current account deficit<sup>is shown in Figure</sup> 11. Although there is a clear negative correlation<sup>between the two, the causal</sup> structure of the relationship<sup>is unclear, since current account</sup> deficit may lessen the size of public deficit if the major component of tax revenue is<sup>tariff revenue. Furthermore,</sup> there might be a third factor that<sup>is positively related to the current</sup> account deficit, but negatively related to the public deficit.

As argued earlier, the partial equilibrium analysis cari be 19
# **The micro‐simulation model setup**<sup>**10**</sup>

The micro‐simulation is divided into three steps: the baseline, simulation and assessment. It is based on Olivieri S, et al. (2014) with a major difference of accounting for internal migration or population displacement.

## **Baseline**

The first step is the process by which individual and household level information is used to estimate a set of parameters and unobserved characteristics for various equations of the household income generation model. The model behind the micro‐simulation is the household income generation model developed by Bourguignon and Ferreira (2005). This model allows accounting for multiple transmission channels and working at the individual/household level. The first component of the model is an identity that defines the per capita income in a household � as the ratio between the total household income and the total number of members (��) in that household: where � = household member � = level of education Λ = maximum level of education � = labor status � = economic sector �� ��� is an indicator function of labor status _j_ of individual � with level of education � �� ��� = earnings of individual � with level of education � in economic sector � ��� = total non‐labor income received by household � The total household income—the expression in brackets in equation (1)—results from adding two main sources of family income: _labor_ and _non‐labor_ income. At the same time, the total family labor income is the aggregation of earnings in different economic sectors across members.<sup>11</sup> So, we see not only whether an individual participates (or does not participate) in the sector, but also whether that individual receives (or does not receive) wages for that job. The labor participation model relies on the utility maximization �� approach developed by McFadden.<sup>12</sup> Assume that the utility (��� ), for individual � of household _h_ , associated with labor status _j=0,...,J_ , and level of education _L_ , can be expressed as a linear function of observed individual and household characteristics (���� ) and unobserved utility determinants of the

> 10 This section is based on Olivieri, S. et.al (2014)

> 11 Notice that although we could estimate specific models for salaried and non‐salaried workers based on the microdata from the household survey, we would not be able to use these models because this information, generally, is not available from the macro side. Macro‐economic projections are calculated mainly for aggregate economic sectors such as agriculture, industry and services instead of wage or self‐employed, formal and informal sectors.

> 12 McFadden (1974).

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increased significantly. Private formal employment fell drastically to a third of its level. Public employment also fell, but to a much smaller extent as the state tried to hold the line on formal employment. But in spite of this effort, total employment in the formal sector in 1980 was only three-quarters of the level of 1975, and the private sector share of formal employment reached its lowest level of 13.6 percent.

# **3.1.2. Wages**

The course of real earnings per worker employed in the formal sector is plotted in Figure 3.1 (reproduced from ISSER Figure 8.2, p.159). It shows that the drastic decline in real wages took place in the decade of 1974-83. If the cost of living deflator is to be believed the level of real wages in 1983 was a mere 12 per cent of its level in 1974 ( ISSER, p.151). This decline in real wage was due to the failure of nominal wage increase to keep pace with the increasing rate of inflation. It was also the result of a deliberate policy of the government as the dominant employer to choose to reduce the wage bill, as much as it could, through wage decline rather than a reduction in employment. Between 1960 and 1985 while employment in the formal sector (with the public sector providing an increasing share) increased by 40 per cent, real wage fell by more than 80 per cent. Thus in spite of the employment increase the wage bill fell _pari passu_ with the decline in GDP per capita and government revenue, but clearly the employment situation would have been much worse if the government had opted in favor of protecting real wage rather than employment.

**Figure 3.1 Average Real wages of Formal Sector Workers (1960-91)**

<!-- Start of picture text --> i 300..<br>250 .<br>o200<br>50<br>0 II Iii I _+ ,.j I , _ I ,I ,I , IA+,<br>_~~ _ ~~~~ _ _ . _ _ 0(0 I<br><!-- End of picture text --> _Source: adaptedfrom ISSER, 1994, Figure 8.2, p. 159 (original datafrom GSS. CPI was used to compute real earnings.)_ 9
# _a. Labor market costs and flexibility_

Labor market flexibility refers to the regulatory and institutional framework governing the hiring and firing of workers and the costs associated with employment. With everything else constant, most countries where labor markets are flexible are likely to experience the most immediate (and possibly largest) labor restructuring as a result of increases in energy prices. However, these are also the countries where workers will find it easier to move to cleaner and more productive jobs and sectors (McMillan and Rodrik, 2011).

We measure labor market costs and flexibility through indicators of employment protection legislation (EPL), the tax wedge, the minimum wage level and the maximum length of fixed contracts (see Annex 5 for country specific details). We divide our results into three groups of countries in terms of their relative labor market flexibility (Table 2). The countries that have high labor market flexibility such as Albania, Azerbaijan, Bulgaria, Georgia, Kazakhstan, Russian Federation and Slovak Republic have relatively low employment protection legislation (EPL) and relatively low tax wedges. The lower labor market flexibility in countries like Croatia, Latvia, Lithuania, Macedonia FYR, Romania, Slovenia, and Turkey is due to a combination of relatively higher EPL and tax wedge in these countries. For instance, Turkey has significantly higher EPL (3.49) and tax wedge (41.8) rates compared to Georgia that stands on the other end with much lower EPL (0.38) and tax wedge (26.7) rates. There is significantly less variation across countries in terms of minimum wage levels and the length of fixed contracts.

**Table 2 - Principal Component Analysis – Labor Market Flexibility Results**

|**Labor Market**<br>**Flexibility**|**Country**|
|---|---|
|Low|Croatia, Latvia, Lithuania, Macedonia FYR,<br>Romania, Slovenia, Turkey|
|Medium|Armenia, Czech Republic, Estonia,<br>Hungary,Moldova,Poland,Serbia,Ukraine|
|High|Albania, Azerbaijan, Bulgaria, Georgia,<br>Kazakhstan, Russian Federation, Slovak<br>Republic|

Source: Authors’ calculations

# _b. Skills, training and innovation_

In response to the permanent increase in energy prices, governments will need to ensure that their citizens are well-equipped to transition from employment in energy-intensive industries to other industries that are less vulnerable to an energy price shock. The role of training and skills, therefore, will be more crucial than ever for enabling workers to secure employment in new and emerging industries in a timely manner, and for firms to be able to innovate and create those cleaner jobs.

Skills, training and innovation reflect, therefore, the ability of a country’s workforce to adapt to changing conditions and move to new industries. To capture these dimensions, we use gross domestic expenditure on R&D (GERD) as a percentage of GDP, percentage of labor force (ages 15-64) with secondary education, the ratio of vocational/technical secondary enrollment to general secondary enrollment, the average years of total schooling and the latest PISA math score.

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different nutrients and leisure, and total labor time is normalized to 1.

Maximizing a household’s objective function, subject to these constraints yields a set of reduced form nutrient demand functions: where, **I** is a vector of individual characteristics, such as, age, gender, endowment, etc., and **Z** is a vector of household characteristics, such as, household size and composition.

As is well-known, the key difference between these reduced form demand function of the collective framework with those of the unitary framework is that in the case of the former, the sharing rule and thus the bargaining power of individuals become an explanatory variable for individuals’ demand for nutrients (and for other outcomes) in addition to total household income. Since income pooling implies that controlling for household income, individual bargaining power does not affect the demand functions of the household members, using measures of spouses’ bargaining power, one can test the key assumption of the unitary model – income pooling – vis-` _a_ -vis – intrahousehold bargaining – for nutrient allocation: In the case of income pooling,<sup>_<u>∂</u>_</sup> _∂a_<sup>**<u>C</u>**</sup> _i_<sup>_<u>i</u>_= 0, whereas in the presence of intrahousehold bargaining,</sup> _<u>∂</u>_ **<u>C</u>** _<u>i</u>_<sup>_<u>∂</u>_</sup><sup>**<u>C</u>**</sup><sup>_<u>i</u>_</sup> _∂ah_<sup>=</sup> _∂aw_<sup>.</sup>

# **4 Econometric Framework**

To analyze intrahousehold bargaining in nutrient allocation, I use the following empirical specification of equation 5: where _k_ indexes a nutrient (e.g., calorie, protein, iron, etc.), _i_ individual, _j_ household, _v_ village, _s_ survey location ( _v ∈ s_ ), _t_ survey round, and ln, natural log. The dependent variable, _y_<sup>_k_</sup> is an individual’s adequacy ratio of nutrient _k_ (see section 5 for variables’ definitions and descriptives). The covariates are: a vector of dummy variables indicating the age-sex group (with adult male as omitted category) and pregnancy and lactating status of an individual ( **A** _ijvst_ ), a measure of individual’s health endowment, _κijvs_ (described below), which, based on PRH, is used to 8
# **1 Introduction**

Are automation technologies an opportunity or a threat to developing countries? The literature has examined this question by studying automation in advanced economies - which have been early adopters - focusing on the impact on imports from developing countries (Kugler et al. (2020); Artuc et al. (2019); Faber (2018); Artuc et al. (2018)).<sup>1</sup> Much less is known about the impact of automation in developing countries on their own economies. This is an important gap for at least two reasons. First, given the differences in the structure of production and the type of skills in the labor force, it is not clear that the evidence on automation in high-income countries may provide useful guidance for developing countries. Second, firms in low and middle income countries have begun to invest in automation technologies, whose penetration is expected to grow over the next decades (Hallward-Driemeier and Nayyar (2017)).

To help fill the gap, this paper focuses on industrial robots, an important class of automation technology. It examines empirically the impact of robots on firms and local labor markets in Indonesia, which is a suitable context for this analysis. The number of robots in the country was very limited before the beginning of our sample in 2008 and accelerated stiffly thereafter. By the end of the sample in 2015, the penetration of robots in the most automated industries was similar to advanced economies. Therefore, the experience of Indonesia - an early adopter among developing countries - should be informative about other large developing economies, in which adoption rates are still limited today and are expected to grow. Indonesia provides a rich set of high quality data including a large panel of manufacturing plants and labor force surveys, which our analysis can leverage along with data on imports of robots.

Our key contribution is to document a positive impact of automation on employment in Indonesia (at least until 2015). Consistently with the predictions of a task-based model (Acemoglu and Restrepo, 2018b), the plant level results suggest that the positive impact on employment is driven by the productivity gains of automating plants. Our result differs

> 1A commonly tested hypothesis, for instance, is that adoption of robots in advanced economies induced re-shoring.

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in a diversified portfolio.<sup>22</sup> For its part, the OTPP used to invest all its assets in nonmarketable government bonds but it was authorized in 1990 to diversify its portfolio and has since developed an elaborate investment strategy that, like the CDPQ, includes investments in Canadian and foreign public equities as well as private equities and other alternative assets.

# **3.2 Funding Sources**

The CPPIB received the first transfer of funds in March 1999. It receives all cash flows that are not required by the CPP to pay current pensions and also retains all investment income generated from its operations. The CPPIB also received the proceeds of redeemed federal and provincial government bonds.

Under a 1996 agreement among provincial governments, each province had the option to roll over its existing bonds for one further 20-year term. Funds not rolled over were transferred to the Investment Board, unless they were needed by the CPP. Federal bonds, which amounted to 3.4 billion CAD in 1999, did not have the rollover option. The bond portfolio of CPP was managed by the Federal Department of Finance (FDF) until April 2007 when the small amount of remaining bonds was transferred to the CPPIB.

The total assets of the Plan and the Board grew rapidly in recent years, reflecting the increase in contribution rates and the higher investment returns achieved by the CPPIB (Table 3.1). The total assets of CPP rose from 44.5 billion CAD in 2000 to 117 billion CAD in March 2007. The fixed-income securities of CPP fell steadily during this period and were less than 1 billion CAD in 2007. In contrast, the assets managed by CPPIB expanded from 2.4 billion CAD in 2000 to 116 billion CAD in 2007. The total assets corresponded in March 2007 to 8.1 percent of 2006 GDP, up from 4.5 percent in 2000.

|Tabl|e 3.1:Tot|al Assets|ofCPP|and CPP|IB,2000|-2007|||
|---|---|---|---|---|---|---|---|---|
|end March<br>in billion CAD|2000|2001|2002|2003|2004|2005|2006|2007|
|Total Assets of CPPIB|2.4|7.2|14.3|17.5|32.8|58.6|88.6|115.9|
|Bonds with FDF|42.1|41.5|39.3|38.1|37.7|22.7|9.4|0.7|
|Total Assets of CPP|44.5|48.7|53.6|55.6|70.5|81.3|98.0|116.6|
|CPPIB Assets (% GDP)|0.2|0.7|1.3|1.5|2.7|4.5|6.5|8.1|
|CPP Assets (% GDP)|4.5|4.5|4.8|4.8|5.8|6.3|7.2|8.1|

Source: CPPIB Public debt in Canada is much higher than in Ireland, New Zealand and Norway and does not allow the government to make an additional contribution to building the assets of CPP. Net federal public debt is estimated at 35 percent of GDP, while including the debt of provinces the total probably exceeds 60 percent of GDP.

> 22 The CDPQ operates under a dual mandate to both earn investment returns and promote economic development in Quebec, while the CPPIB has no public policy mandate.

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