rafmacalaba/datause-encoder-data huggingface dataset:
If this is right, it is important information for policymakers to have to be able to target compensation programs effectively.

We take the focus on occupations in a new direction. Building on earlier work (Artu ̧c, Chaudhuri and McLaren (2010)) (henceforth ACM) in which we estimated the costs to workers of switching industries in a dynamic model in order to measure the welfare effects of trade shocks on workers in different industries, we expand our framework to allow workers to change _both_ their industry _and_ their occupations, estimating the costs of doing so in an integrated dynamic structural econometric model. Our strategy is to specify a rational-expectations model in which industry and occupational switching is a forward-looking investment decision by long-lived workers; estimate the key structural parameters (particularly means and variances of moving costs) on worker data; and then simulate the effects of trade shocks using these estimates to analyze welfare and the time-path of the labor market’s adjustment.

This approach has a number of advantages. First, it allows us to incorporate a real dynamic analysis into the effect on different occupations. Workers can and do change occupation, but it is costly to do so, and the degree of cost will affect the wage effects of a trade shock as well as how those wage changes translate into welfare changes. Importantly, a dynamic analysis allows us to identify the role of option value, which has been shown to have a large effect on the welfare analysis of trade shocks (Artu ̧c, Chaudhuri and McLaren (2010)). If one’s wage in one’s own industry and occupation is reduced by a policy change, but wages in other occupations and industries to which one might consider switching are increased, then the positive option-value effect brought about by the latter may dominate the negative direct effect of the former. One needs a dynamic model with option value built in in order to out what the net is.

Second, we will argue that a full account of occupational choice can have a significant effect on the whole pattern of gains and losses from trade shocks. Take a simple thought experiment as an example. Consider an economy with two goods and two types of worker, skilled and unskilled. Each good is produced by workers doing either of two tasks; output is a function of how many hours of each task are done, and the two goods differ in their task intensity. A worker’s ‘occupation’ is defined by which task he or she performs. Consider three cases.

_Case 1._ If skilled workers can all do task 1 but unskilled workers can do only task 2 and 3
21 exports, nor does a shortage of capital serve as the constraint it is in other developitag countries.

# V. Summary and Conclusions

Economic theory postulates that developing countries should have a comparative advantage in labor intensive products in their trade with developed market economy countries. This study examined the validity and strergth of this proposition by comparing indices of labor intensity for three, four and fivedigit SITC products with matched indices of developing countries' revealed comparative advantage. The findings have important implications, ranging from a test of factor proportions theory to the establishment of a methodology for ide-.tifying potential "successful" developing country export. The results also provide quantitative evidence on the extent to which labor intensity, and several other production and market characteristic variables, influence the product composition of South-North trade.

The results show that products in which developing countries have a revealed comparative advantage are in fact highly concentrated . ithin the labor intensive group. This provides a strong verification of factor proportions theory. In particular, where relatively low RCAs occur in the labor intensive group human and physical capital requirements, as well as natural resource requirements appear to be explanatory factors. Regressions were tested which explained over 50 per cent of the variation in RCA indices. The explanatory variables in these tests largely related to differences in production functions and it appears the results could be improved by testing factors relating to demand -- particularly, measures that reflect trade barriers or the market power of domestic firms. Three (or five) firm concentration ratios for sales or production have been extensively employed as measures of market power in the
||||(0.01590)||||(0.01617)||||(0.01590)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Remittances & Others|||0.00920||||0.01209||||0.00628||
||||(0.01513)||||(0.01560)||||(0.01478)||
|Property income and investments|||0.00520||||0.00679||||0.00408||
||||(0.01385)||||(0.01409)||||(0.01377)||
|Transfers and other benefits|||0.01537||||0.01238||||0.01786||
||||(0.01484)||||(0.01519)||||(0.01495)||
|Own > median size of land (2.5 acres)|||0.01488||||0.01669||||0.01200||
||||(0.01410)||||(0.01453)||||(0.01413)||
|Observations|5,896|5,893|4,128|4,125|5,896|5,893|4,128|4,125|5,896|5,893|4,128|4,125|
|Root MSE|0.563|0.563|0.611|0.593|0.567|0.567|0.614|0.596|0.571|0.571|0.618|0.599|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status,|||||||||||||
|education, occupation, other income sources,<br>land ownership)|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

20
11

# **3.7 Macro-economic Background**

Since the start of the adjustment program in 1986, a growing debate has emerged as to the efficiency of these programs to bring about sustainable growth, and as to their short and long term impact on poverty and basic needs fulfillment. It is much less clear what the impact has been on the poor and the social sector in particular. Initial claims of a strong negative impact were based on inadequate empirical evidence and a too limited conceptual approach. This section presents some macro-economic indicators to evaluate their change over the period 1985 to 1993.

**Table 3.1: Nominal and Real GDP Growth, 1985-92**

||**1985**|**1986**|**1987**|**1988**|**1989**|**1990**|**1991**|**1992**|**Average**|
|---|---|---|---|---|---|---|---|---|---|
|Nominal GDP at market prices<br>(N billion)|72.4|73.1|108.9|145.2|224.8|260.8|324.0|549.8|244.3|
|Real GDP at market prices<br>(N billion)|108.3|110.3|108.9|119.3|128.4|138.4|145.9|150.0|128.8|
|Real Growth rates|9.8|1.8|-1.3|9.6|7.6|7.8|5.4|2.8|3.8|
|(in percent)||||||||||
|Real per capita private consumption<br>(in 1987 Naira)|1117|1003|852|864|893|878|877|851|917|

Source: Nigeria Unified Survey, March 1996

1. Table 3.1 shows that in 1986 nominal GDP grew by 1.8 percent, but the growth rate of real GDP was negative in 1987 at -1.3 percent. However, GDP growth picked up and reached a high of 9.6 percent in 1988 and has stabilized at 7.8 percent, 5.4 percent and 2.8 percent in 1990. 1991, and 1992 respectively. In 1993, the relative constancy in the growth rate from the prior year is attributed to the recovery in the services sector, which offset the steady but not increasing oil production levels (World Bank, 1994).

# **4. EVOLUTION OF POVERTY IN NIGERIA**

# **4.1 The Evolution of Mean Expenditure in Nigeria**

It is daunting to try to analyze income distribution in Nigeria. More so than in many developing countries, the availability of relevant data constitutes one of the most serious obstacles to analysis. We do not have an accepted population distribution for Nigeria by age and sex. Almost no national aggregate data exists for the rural areas that would allow us to make a confident judgement about intra-sectoral income distribution. Furthermore, Nigeria is an extremely heterogeneous society. We cannot expect that culturally associated understandings of what constitutes wealth and poverty and the ways people are grouped will necessarily be the same throughout the country. Thus if we want to understand the import of interpersonal, interstate and intersectoral income distinctions, this necessitates sensitivity to the variety of meanings attached to wealth and poverty in different parts of the country.

A number of tentative studies do give various measures of concentration of income in Nigeria or parts of Nigeria. Teriba and Philips (1971) using data from the income tax returns of
Figure 2.5

# **Import Tariffs All Products**

<!-- Start of picture text --> 45.0<br>40.0 -<br>019839<br>__.0 ___ - _- : a 02000<br>3ao.0 --<br>F-25.0<br>10.0<br>5.0 ___ __ __<br>0.0<br>0< + < 4' / ,a 4' 4' CX .6'a 4 'SZ<br><!-- End of picture text --> NOTE: Eariy year, shown as 1989, actually varies by country<sup>between1988 and1994,andlater year, shownas2000,varies between</sup> 1995 and 2000. Data not available for countries not shown.

Source: The World Bank, World Development Indicators<sup>2002, Table 6.6,pp348-350.</sup> In the early 1990s, import tariffs for East Asian economi were much higher than those in NAFTA and the EU reflecting the region's<sup>developing-countrystatus' 50and</sup> the EU and NAFTA'sl<sup>51</sup> earlier trade liberalization.<sup>152By 2000,however,averagetariffs</sup> in East Asia had declined sharply, particularly in the ASEAN countries<sup>1</sup> " partly due<sup>to</sup> progress under AFTA and APEC's Bogor Declaration. As a result of<sup>China's</sup> preparations for WTO accession, its average tariff dropped<sup>from 41percentin 1992to</sup> 16.3 percent in 2000 in all products (primary and manufactured.) The level of private capital flows into a country indicates the strength of its investment climate and reflects the degree of liberalization of<sup>its financialmarkets.As</sup> seen in Table 1, this level as a percent of GDP for East Asia<sup>remains quite low(13.3</sup> percent) compared to, for example, 49 percent for the EMU. This reflects<sup>the effectof</sup> the crisis and East Asia's comparatively lesser developed financial markets.

The most significant capital flows for developing countries, including<sup>East Asia,</sup> are foreign direct investment flows. But even these<sup>were very small(3.9 percent of GDP)</sup> for East Asia and the Pacific in 2000. However, in 2001,<sup>theAsia-Pacificregion</sup> introduced the largest number of changes in FDI regimes designed to create<sup>a more</sup> favorable investment climate to attract more FDI. Out of 194 such<sup>favorable regulatory</sup> changes made worldwide, the region's share was 43<sup>percent.154</sup> East Asia's liberalized trade and FDI were instrumental in the region's<sup>growth in</sup> the 1 980s and early 1 990s. During that time, East Asia had opened<sup>up toother typesof</sup> 42
-44-

instead of 6 percent). If the fiscal adjustment comes from public sector investment in addition to the small decline with respect to GNP assumed here, growth performance will fall short of the base case projections.

Third, no further real depreciation of the exchange rate is projected beyond 1988. Exports are predicted to grow at 7 percent in real terms in this case.<sup>16</sup> If instead a policy of real exchange rate depreciation would be followed, the debt-output ratio would increase faster due to the capital losses incurred after a real depreciation.17 Fourth, the scenario assumes that the foreign financing necessary to cover a current account deficit of 2.5 percent of GNP will indeed be forthcoming. This will require additional financing, since Turkey has just entered a period of substantially increased repayment obligations. The implicit assumption is that these can be refinanced, and that additional funds will be available to allow a current account deficit of 2.5 percent of GNP. Of course, in the current external environment it is conceivable that additional funds cannot be raised. Section IV.D(ii) therefore considers what will happen if this additional financing will in fact not materialize.

- 1j/ Output in Turkey's trading partners is projected at 3.5%. This is an export-weighted average of growth rates assumed in the World Development Report for the OECD and the Middle East. Econometric evidence presented in Annex II shows an income elasticity of Turkish exports of about 2, which explains the 7 percent real growth rate for exports.

- 17 Of course, by virtue of its design, the debt-resource ratio would not increase after such a policy. The debt-export ratio would decrease enough to exactly offset, at the weights chosen, the increasing debt-output ratio.

ChhibberDisk/chhibber-Turkey Paper/ac:07-28-88: c
3 for central government finances and exteumal debt which eome from the IM and the World Bank respectively. Ihese historical data must be consistent within and across sectors, and conform to standard macroeconomic accounting rules. Both of these requirements obtain in the flow of finds famework. Accordingly, the model reconciles historical data for flow of fumds consistency and then uses them as the basis for projections.

# Behavioral Relationships, Accoundng Rules and Flow-of-Funds Consistency

2.3 Macroeconomic accounting rules and flow of funds consistency set limits within which economic behavior takes place in the model. The accounting rules and flow of funds consistency requirements are the same as those set out in Holsen (1989), Khadr et al. (1989) and Easterly et al. (1990). The behavioral relationships are the projection rules for variables. Some originate in econometric analysis that has yielded coefficients linking the variable of interest to its determinants. Others are constant ratios of other vaiables with which they had, or are exp -- ted to have, a stable long run relationship. Yet others are projected on the basis of their relationship to variables whiclt are exogenous to the model.

2.4 To simplify the exposition of the model, this section only specifies and discusses the main behavioral relationships in the monetary sector, the central government accounts, the external sector and the national accounts. A more comprehensive notational presentation of the behavioral relationships, with their flow-of-fimds accounting identities, is provided in Annex I.

# **The** Monetar Sector: the demand for financial assets

2.5 The model identifies demand functions for three financial assets-currency, demand deposits and time deposits (quasi-money). The demand for currency is a positive fniction of real income growth and a negative function of the real deposit interest rate and the rate of inflation.
Malaysia, projects like the expansion of the public transport system in Kuala Lumpur, and airport and port upgrades, are expected to proceed through 2030 with a significant share of investment going toward renewable energy and green infrastructure. The Philippines ranks particularly low for transport and trade-related infrastructure; although it rose two places in the World Economic Forum’s 2022 global infrastructure ranking to 57th place, this remains the country’s lowest-ranked competitiveness factor. By contrast, it ranks quite high on measures of health and education infrastructure and the quality of its seaports and airports. In Cambodia and Lao PDR, investment in basic road infrastructure is a priority.

Education and health care. The region has made great progress in human development outcomes, including child survival, nutrition, and education, but still faces serious humanresource shortfalls.

- Health care. EMDEs in EAP reduced child mortality rates by an average of onefourth between 2010 and 2020. However, child mortality rates in Kiribati, Lao PDR, Myanmar, Papua New Guinea, and Timor-Leste are still well above global averages. The region has historically faced a high incidence of infectious diseases, some of which have spread globally (for example, SARS, pandemic influenza, and COVID-19; Lee and Pang 2015). Rates of non-communicable diseases are expected to rise, and infectious diseases are expected to remain a risk associated with high population mobility and environmental degradation (Anbumozhi and Intal 2015). Adjusting to these trends will require public investment in basic infrastructure, education, health, and environmental protection.

- Education. Although enrollment in primary education in the region is almost universal, there are deficiencies in student retention (Cambodia, Lao PDR, Myanmar), quality of education (Cambodia, Lao PDR, Malaysia, Thailand, Vietnam), and literacy rates (Cambodia, Lao PDR, Papua New Guinea, TimorLeste). Extended school closures during the pandemic led to substantial further learning losses, especially for the poor.

Environmental challenges. Many countries in the region face environmental problems that threaten to undermine not only economic growth and stability but living standards, lives, and livelihoods. The main challenges include water management, deforestation and land degradation, air pollution, and climate change. According to the Verisk Maplecroft Global Risk Analytics Dataset, which ranks the world’s 576 largest urban centers on their exposure to a range of environmental and climate-related threats, 99 of the world’s 100 riskiest cities are in Asia, including 37 in China, where air and water pollution presents a growing health risk. The worst-performing city in the ranking, Jakarta, also suffers from severe air pollution, but added to this are perennial threats from seismic activity and flooding. These have prompted the government of Indonesia to initiate relocating the capital.

# 5.4 Regional policy priorities
**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

the wake of the crisis. The conclusion states the case that firms in the developing world will, in one or all of the ways described, soon come to play a more central role in driving the innovative trajectory of the industry by leveraging the full complement resources that have become available in GVCs.

# **The Electronics Industry’s Role in Global Value Chain Formation**

Each year, the electronics industry generates a mushrooming array of products and services increasingly used in nearly every human endeavor.<sup>2</sup> Now deeply entwined in our social fabric, electronics products and systems now support critical aspects of communication, education, finance, recreation, and government. Thousands of companies from dozens of countries contribute to the industry on a daily basis. Even a single product can contain work carried out by dozens of firms in multiple countries. Because there is less need for co-location of engineers than in other technology-intensive sectors, such as with the co-location of design with manufacturing, it is relatively easy for electronics firms to engage in the twin strategies of outsourcing and off-shoring. Global sourcing is common. Factories can be relocated with relative ease and produce a wide variety of end products. As a result, GVCs in the electronics industry are more geographically extensive and dynamic than in any other goods-producing sector.

Evidence of the importance of the electronics industry in GVC formation can be found in statistics on intermediate goods trade. Trade in intermediate goods is indicative of GVCs because fragmented production processes require that parts, components, and partially manufactured subassemblies cross borders—sometimes more than once—before finished goods are shipped to final markets (Feenstra 1998; Dean, Fung, and Wang 2007; Brülhart 2008). Table 1 shows the relative importance of various goods-producing industries in GVCs: intermediate electronics and automotive goods dominate total trade in the top-50 manufactured intermediate products (a combined 64.7 percent in 2006). Next important is a group of undifferentiated materials including metal stock (copper, aluminum, and steel), wood, and paper (8.4 percent in 2006), followed by chemicals and plastics, manufactured metal parts, gold and diamonds, aircraft parts, and so on. The share of electronics intermediates (including semiconductors, printed circuit boards, and so on) has grown dramatically since 1988, from 24.4 percent of the top 50 products to 43.3 percent in 2006. The share of automotive intermediates fell from the top spot in 1988 (25.1 percent) to the number two spot in 2006 (21.4 percent). As a result, the growth rate of electronics intermediates was the highest in the top-50 product groupings (13.8 percent per year).

> 2 This section draws from Sturgeon and Memedovic (forthcoming).

3
**Proposition 8.** _The market-access benefits of tariff reductions in export markets are inversely related to the degree of market power exercised by the domestic trade and distribution sector in the export market._

**Proposition 9.** _The benefits of market access concessions can be offset by increases in the degree of market power exercised by the domestic trade and distribution sector in the export market._

The first of these propositions formalizes the dependence of goods market integration in the European Union (recall the EU autos exemption) on distribution sector competition. The second goes directly to the heart of the Fuij-Kodak dispute. To use a technical GATT/WTO term, _nullification and impairment_ can follow from changes in domestic regulation of the distribution sector.

# **5 Empirics**

We turn next to an empirical exercise, focusing on whether the basic effects we have discussed, imperfect competition in distribution affecting market access in goods, matters in an empirical sense.his involves estimating reduced form gravity equations of bilateral trade flows, based on tariffs, distance, and exporter-specific dummies for a cross-section. (See for example Disidier and Head 2003 and Anderson and van Wijncoop 2003.) We include measures of distribution sector competition, as a check on our theoretical results developed above.

Our basic data for this exercise are summarized in Table 1. From the OECD (2000), we work with two estimates of the degree of competition in the road freight and retail distribution for some, but not all, OECD members. This includes an index of barriers to entry in the sector, and also what can be interpreted as an overall or composite index of 13
<!-- Start of picture text --> Consumption of Consumer Electronics:<br>Model Baseline<br>1200<br>1000<br>»<br>2800<br>a<br>g<br>5< 600<br>z<br>= 400<br>200<br>0<br>2020 2040 2060 2080 2100<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> Consumption of Silver as End<br>Product: Model Baseline<br>8<br>6<br>Ss7<br>g<br>a4<br>z°<br>2<br>0<br>2020 2040 2060 2080 2100<br>Year<br><!-- End of picture text -->