rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 18: Upgrading Opportunities for Penang, 2000**

|sitc4|short description|haveRCA|exporting|invdensity|Lall Class|
|---|---|---|---|---|---|
|8811|Photographic cameras, flashlight apparatus, parts, accessories, nes|0|1|3.052|HT2|
|7928|Aircraft, nes and associated equipment|0|1|3.115|HT2|
|7525|Peripheral units, including control and adapting units|0|1|3.116|HT1|
|452|Oats, unmilled|0|1|3.160|PP|
|7518|Office machines, nes|0|1|3.177|HT1|
|8710|Optical instruments and apparatus|0|1|3.255|HT2|
|5161|Ethers, epoxides, acetals|0|1|3.270|RB2|
|7511|Typewriters; cheque-writing machines|0|1|3.271|HT1|
|2519|Other cellulosic pulps|0|1|3.292|RB1|
|8821|Chemical products and flashlight materials for use in photografy|0|1|3.299|MT2|
|7762|Other electronic valves and tubes|0|1|3.314|HT1|
|5112|Cyclic hydrocarbons|0|1|3.354|RB2|
|8852|Clocks, clock movements and parts|0|1|3.363|MT3|
|6411|Newsprint|0|1|3.364|RB1|
|7591|Parts, nes of and accessories for machines of headings 7511 or 7518|0|1|3.372|HT1|
|8851|Watches, watch movements and case|0|1|3.385|MT3|
|5827|Silicones|0|1|3.394|MT2|
|344|Fish fillets, frozen|0|1|3.404|PP|
|7523|Complete digital central processing units; digital processors|0|1|3.422|HT1|
|4111|Fat and oils of fish and marine mammals|0|1|3.431|RB2|

_Note:_ PP: Primary Products, RB1: Agro-Based, RB2: Other Resource-Based, LT1: Textile, garment & footwear, LT2: Other, Low-Technology, MT1: Automotive, MT2: Process, MT3: Engineering, HT1: Electronic & Electrical, and HT2: Other, High-Technology. Source: Authors’ calculation 41
Noticeable differences can be observed in the sectoral employment patterns of men and women (Table 3). In particular, women are overrepresented in health and social work, education as well as financial intermediation. Women make up between 60 to 80 percent of those employed in these sectors. Construction, transport and communications, energy as well as mining are the sectors, which are dominated by men.

**<u>Table 3. Structure of Employment by Sector, % (population age 15 and above)</u>**

|Sector of Economic Activity|Male|Female|Total|
|---|---|---|---|
|Agriculture, hunting forestry and fishing|24.8|22.9|24.0|
|Mining and quarrying|1.5|0.5|1.1|
|Manufacturing|19.9|13.2|17.0|
|Electricity gas and water supply|2.7|0.5|1.8|
|Construction|8.1|1.4|5.2|
|Wholesale and retail trade|12.0|16.5|13.9|
|Hotels and restaurants|2.4|3.7|2.9|
|Transport, storage and communication|8.2|3.1|6.0|
|Financial intermediation|1.5|3.0|2.1|
|Real estate, renting and business activities|3.3|3.6|3.4|
|Public administration and social security|4.9|5.0|4.9|
|Education|3.9|8.3|5.8|
|Health and social work|2.2|12.8|6.8|
|Other community, social and personal services|4.4|4.8|4.6|
|Private households with employed persons|0.1|0.5|0.2|
|Extra-territorial organizations & bodies|0|0.1|0.1|
|Total|100|100|100|

Source: Serbia LFS, 2009 Transition to a market economy was characterized by emergence of the informal sector, which currently employs almost a quarter of Serbian population (26% of men and 24% of women). The definition of the informal sector used in this paper includes all those working in non-registered private companies as well as workers without employment-related pension insurance. There are no significant gender differences in the characteristics of workers employed in the informal sector. Most of the informal sector employees live in rural areas and work in agriculture (70% of men and 76% of women). The overwhelming majority of people engaged in informal activities have extremely low education levels with 55% of men and 67% of women being educated only up to primary school level. The high percentage of people employed in the informal sector may be indicative of the relatively high costs associated with business registration, social security contributions and taxes; the preference for flexible working hours of some population groups; and/or the lack of other employment opportunities for low-skilled workers.

# _<u>Flexible Forms of Employment and Average Working Hours in Serbia</u>_

Flexible forms of employment (e.g. part-time, seasonal and temporary work arrangements), which are quite widespread in the EU countries allowing young people to obtain work experience while receiving their education, helping parents to combine work with childcare responsibilities and enabling older people to stay in the workforce longer, are uncommon in Serbia. The overwhelming majority of Serbian population continues to give preference to permanent full time jobs: 88.6% of the employed have a permanent job and 91.3% work full time.

5
# **A notion of opinion pools**

Decisions on many political and public policy issues rely on the opinions of experts. An opinion pool is a method of combining a number of different opinions about some unknown quantity θ to generate a single pooled opinion about θ. The two most widely used opinion pool methods are linear opinion pools and logarithmic opinion pools. Suppose we have n experts, and let _pi(_ θ _)_ represent expert _i_ ’s probability distribution for unknown quantity θ, i =1, ..., n, and _wi_ be expert _i_ ’s weight. Then the combined probability distribution _p(_ θ _)_ is a weighted linear combination of the experts’ probabilities (weighted arithmetic mean model) in a linear opinion pool, whereas _p(_ θ _)_ in a logarithmic opinion pool is expressed as multiplicative averaging (weighted geometric mean model): Linear opinion pool _p(_ θ _)_ = ∑ _i wi * pi(_ θ _)_ Logarithmic opinion pool _p(_ θ _)_ = _k_ Π _i pi(_ θ _)_<sup>_wi_</sup> ( _k_ is a normalizing constant) The problem of opinion pools generally reduces down to determining the optimal weights _wi_ for experts. Various methods for finding the optimal models are explored, for example, in DeGroot and Mortera (1991), Bordley (1982), and DeGroot (1974). Apparently, the simplest choice of weights is assigning all experts an equal weight, _wi = 1 / n_ . A simple arithmetic averaging of experts’ assessments is used in many studies, including a U.S. Nuclear Regulatory Commission study on the frequency of accidents at nuclear reactors (NRC 1989). Cook (1991) discusses that while there are some efforts in compensating such a simplistic method by improving the elicitation procedure itself, such as those witnessed for the NRC document (Honano et al. 1990; Wheeler et al. 1989), this type of method is less than optimal as it lacks any attempt to evaluate the quality of experts’ estimates.

# **Cooke’s classical performance-based weight model**

The method proposed by Cooke (1991) is a performance-based weighted averaging model using properties of scoring rules, known as the classical model<sup>3</sup> . He emphasizes that the fundamental goal of science is to build rational consensus and, therefore, the process of collecting expert assessments must be subjected to the following five basic principles (the first and second principles are later combined as a scrutability/accountability principle (Cooke and Goossens 2000)):

1. _Reproducibility_ :  All results must be reproducible, with calculation models and data being clearly specified and made available.

2. _Accountability_ : The source of data (name and institution) must be identified, and data must correspond to the exact source from which the data are elicited.

3. _Empirical Control_ : Experts’ assessments must be, in principle, physically observable. 4. _Neutrality_ : The elicitation process must ensure that the actual beliefs of experts be collected (e.g. no punishment or rewords through a self-rating system).

5. _Fairness_ : All experts must be regarded equally before the aggregation process.

> 3 The term classical comes from the calibration measure’s close association with classical hypothesis testing.

5
invested in rsky domestic capital exceeds its share in wealth. Second, we show that, if labour income is less risky than capital income, positive output shocks raise the ratio of financial to human wealth and hence expose the investor to greater risk. This induces investors to take safer investment positions in their financial wealth, and so the share of the shock invested in domestic capital falls short of its share in financial wealth. We obtain a simple rule to determine when a positive output shock leads to a current account deficit: the country's debt has to exceed a certain threshold that depends on how attitudes towards risk vary with wealth and the size of labour income. This threshold can be either positive or negative, and is zero in the case of constant relative risk aversion and no labour income.

Our research naturally relates to existing intertemporal models of the current account. The early generation of intertemporal models, such as Sachs (1981,1982) **3** Obsffeld (1982), Dombusch (1983) and Svensson and Razin (1983), were designed to study the effects of terms of trade shocks and to develop rigorous theoretical foundations for the Harberger-Laursen-Metzler effect. We share with these models the notion that countries save transitory income shocks so as to smooth consumption over time. However, since these models abstract from capital accumulation, income shocks can only be invested in foreign loans. As a result they predict that positive transitory income shocks lead to current account surpluses in all countries.

Simply allowing for capital accumulation is not sufficient to obtain the main result of this paper, however. Subsequent contributions by Sachs (1981), Persson and Svensson (1985) and Matsuyama (1987) extended the early intertemporal models to include capital accumulation by investors with perfect foresight. These models were designed to analyze the current account response to persistent shocks 3See Obstfeld and Rogoff (1995) for a survey of these models.

4
opposite results. For example, using a CGE for Mozambique, Arndt et al. (2008) find that biofuels would have favorable effects on growth and income distribution. They show that the welfare and distributional effects of higher production of sugar cane to feed into ethanol industries are positive; the benefits would be larger if the expansion sugarcane is materialized through smallholders than through large plantations as the former employs more unskilled labor, provides higher rents to their land and also provide higher wage. Similarly, applying a CGE model, Arndt et al. (2011) show that cultivation of jatropha feedstock reduces poverty and increases GDP in Mozambique. A similar finding is reported by Hertel (2009) where developing countries with significant agricultural self-employed poverty population benefit from higher factor returns following increased production of biofuels. However, the positive impacts of biofuels on poverty reduction depend on how biofuel policies are designed. For example, HabibMintz (2010) finds that jatropha-based biofuel development in Tanzania would not help reduce poverty and food insecurity unless strong regulatory frameworks for land, investment management, and rural development are in place.

This paper aims to analyze the effects of large-scale expansion of biofuels on the global income distribution, and poverty. The model used in our study explicitly represents both biofuels and land-use changes, which are critical to fully assess overall impacts of biofuels. Our global computable general equilibrium (CGE) model includes four types of biofuels: sugar ethanol, corn ethanol, grains ethanol and biodiesel. The land use module we developed uses a multi-level constant elasticity of transformation (CET) nest of various uses of land: forestry, pasture, agricultural crops (disaggregated further to grains, oilseeds, sugar, rice, wheat and other crops). With these developments, we are able to capture the fuel vs. food demand for agricultural commodities, land reallocation, and land rent. To assess the distributional and poverty impacts, we feed the CGE results to a Global Income Distribution Dynamics (GIDD) model, a model that is based on detailed household surveys, regularly carried out by the World Bank. We find that large-scale expansion of biofuels would improve wages for unskilled labor and land rent. However, it would also increase food prices, especially in low-income countries where food share in overall household expenditure is higher. The effects on poverty are negative (i.e., increase in poverty) in some countries, while positive in others. Globally, these differences balanced out to a slight increase in poverty.

3
# **<u>Table C.3 First-stage regressions, Jordan</u>**

||**2010-**<br>**2010**|**2013**<br>**2013**|
|---|---|---|
|Head`s age|-0.002|0.004***|
||(0.00)|(0.00)|
|Head is female|0.213***|0.107***|
||(0.05)|(0.03)|
|Highest education level is primary|0.171***|0.280***|
||(0.04)|(0.03)|
|Highest education level is secondary|0.320***|0.449***|
||(0.05)|(0.04)|
|Highest education level is tertiary|0.666***|0.729***|
||(0.05)|(0.03)|
|Urban|0.022|0.037*|
||(0.03)|(0.02)|
|_cons|6.976***|6.718***|
||(0.08)|(0.06)|
|adjusted R2|0.16|0.18|
|N|1873|3437|

**Note:** ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. Regression is estimated using Weighted Ordinary Least Squares. Household heads’ ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second round. The reference groups are household with no primary education and living in rural areas.

81
_Figure 2 Backcasted trend in poverty rates and number of poor at the US$3.20 poverty line, pass-through of 1_ a) Poverty rate, % (US$3.20) <!-- Start of picture text --> b) Number of poor, millions (US$3.20)<br><!-- End of picture text --> <!-- Start of picture text --> 82.0 145.0 142.7<br>80.0<br>79.5<br>140.0<br>78.0<br>76.0 76.5<br>135.0<br>74.0<br>72.0 130.0 125.9<br>71.0<br>70.0<br>125.0<br>68.0<br>66.0 120.0<br>64.0 118.0<br>115.0<br>62.0<br>Backcast, pass-through=1 PovcalNet<br>Backcast, pass-through=1 PovcalNet<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> Note: the figure shows the backcasted poverty rates series at the US$3.20 poverty line (panel a). Using household consumption data from the 2018/19 NLSS and sectoral GDP growth rates from the World Bank-MFM-Tool, we backcast household consumption over the previous decade by applying the same growth rate to household consumption and mapping the sectoral information to the household’s head sector of employment. Number of poor is estimated using WDI population data (panel b).

# _<u>5.2. Survey-to-survey imputation</u>_

This section presents the results of the survey-to-survey imputation into the 2010/11, 2012/13, 2015/16 and 2018/19 GHS. As detailed in Section 4.2, we impute consumption using information on 23 nonmonetary indicators available in the 2018/19 NLSS and all GHS rounds.

Before moving to the results, Figure _3_ shows the average value of each of these indicators over the four rounds of data used in the analysis (see also Table _8_ Table _8_ ). Several of these indicators show an improvement in household welfare over the first three periods, but then worsen after 2015/16, suggesting a reaction to Nigeria’s 2016 oil-price-induced recession. For example, ownership of assets (TV set, generator, cars and other vehicles) was increasing up until 2015/16 and but then registered a decrease in the last wave suggesting that households might be selling their assets to offset a negative shock to their welfare. A similar trend can be seen in the consumption of imported rice. This trend in itself is consistent with a decrease in poverty rates in the first half of the decade and a worsening in more recent years.<sup>18</sup>

> 18 In section 7, we provide additional evidence using data from the DHS that supports these trends in non-monetary indicators.

13
reflect the extent to which FDI can substitute for direct exporting activity, or offer possible diversification benefits. In contrast, acquisitions in other emerging countries tend to be associated more with considerations of factor price differentials. Finally, the ease of financial access—both in the home and host economies—appears to facilitate M&A transactions, a result consistent with the notion that limitations to trade flows may be overcome by substituting capital flows for goods exports.

We also fit our empirical model of M&A deals to a set of growth assumptions for emerging and advanced economies to obtain a projection of outbound cross-border M&A by emerging market firms for the period 2010–25. Under plausible scenarios of relative growth rates, we find that M&A activity is expected to recover from the crisis-induced decline, and grow at an average of 8.2 percent annually over the period. This respectable rate nevertheless represents a moderation in the rate of growth relative to the past, where—for the decade leading up to the crisis (1998–08)—average annual growth was significantly higher, at 14.3 percent.

Our findings corroborate, and extend, the existing literature in several ways. Existing empirical papers on FDI have tended to focus on testing one theoretical framework against another (Braconier, Norb ̈ack & Urban 2005; Brainard 1997; Head & Ries 2008; Helpman, Melitz & Yeaple 2004). In contrast, we adopt an agnostic view on the different competing theories and seek instead to test a fairly eclectic set of potential hypotheses. Like several papers in the literature (Anand & Delios 2002; Blonigen & Piger 2011; Carr, Markusen & Maskus 2001; Makino, Lau & Yeh 2002), we indeed find that, depending on the circumstance, different theoretical motivations may drive FDI. Our innovation is to frame these distinct cases in terms of emerging versus advanced economy target acquisitions.

While a small number of papers have relied on cross-border M&A data (di Giovanni 2005; M ́eon & Delannay 2006; Rossi & Volpin 2004), these have tended to be relatively limited in terms of time period and/or country coverage, and none have explicitly focused on M&A _by_ emerging economies. Finally, unlike several recent papers, we eschew an explicit focus on policy- or politically-related factors driving FDI—such as political risk (Busse & Hefeker 2007) or investment agreements (B ̈uthe & Milner 2008; Neumayer & Spess 2005)—and instead embed these factors in our overall gravity framework.

The paper is organized as follows. In the following section, we describe some broad stylized patterns of M&A activity originating from emerging economies (Section 2). This is followed by an overview, in Section 3, of the different theoretical streams that have informed economists’ understanding of bilateral M&A (and FDI, more generally). Section 4 follows with a description of the dataset, econometric specification, and estimation methodology. Benchmark results and robustness checks follow (Section 5), before a final section concludes with some brief thoughts on policy and future research directions.

3
# Appendix Table 2B

|D|omain 2 Ind<br>(1)<br>Agree/Stro|ex: Ability/E<br>(2)<br>nglyAgree|ffort<br>(3)<br>Strong|(4)<br>lyAgree|(5)<br>Incl.|(6)<br>Neutral|
|---|---|---|---|---|---|---|
|Treatment|‐0.673**|‐0.651**|‐0.523**|‐0.492**|‐0.201|‐0.192|
||(0.259)|(0.249)|(0.210)|(0.200)|(0.243)|(0.249)|
|Effort Median|0.189|0.204|0.487**|0.517**|0.258*|0.285*|
||(0.164)|(0.157)|(0.210)|(0.205)|(0.134)|(0.146)|
|Treatment X Effort Median|0.568*|0.532*|0.151|0.116|0.131|0.112|
||(0.310)|(0.293)|(0.284)|(0.261)|(0.224)|(0.225)|
|Male||0.122||0.0902||0.192|
|||(0.166)||(0.157)||(0.148)|
|Part‐Time||‐0.0914||0.0180||0.0635|
|||(0.183)||(0.148)||(0.158)|
|No Training||0.117||‐0.0581||‐0.0403|
|||(0.264)||(0.364)||(0.216)|
|Less than University Degree||0.387||0.449||0.101|
|||(0.305)||(0.284)||(0.236)|
|University Degree||0.328*||0.287*||0.0849|
|||(0.181)||(0.149)||(0.157)|
|>= 60 students in class||0.265*||0.235*||0.150|
|||(0.152)||(0.132)||(0.138)|
|Age||‐0.0120||‐0.0255**||0.00321|
|||(0.0117)||(0.00943)||(0.0104)|
|Number of Subjects teaching this Term||0.0679||0.0976||0.00730|
|||(0.111)||(0.0979)||(0.133)|
|Constant|0.0970|‐0.0683|‐0.0218|0.194|‐0.0620|‐0.480|
||(0.143)|(0.565)|(0.177)|(0.428)|(0.131)|(0.450)|
|Observations|348|348|348|348|348|348|
|R‐squared|0.071|0.103|0.079|0.119|0.023|0.035|

Standard errors in parentheses, clustered by school. *** p < 0.01, ** p < 0.05, * p < 0.10.

34
measures affecting international trade in services,<sup>18</sup> the most restrictive policies are observed in the GCC countries such as Qatar, Kuwait, and Saudi Arabia, and in other Middle East countries, including Egypt and Tunisia (Figure 11). In contrast, policies are relatively liberal in Latin America, Eastern Europe and the OECD countries, with Sub-Saharan Africa somewhere in between the restrictive and the more liberal regions.

**_Figure 11.  Services Trade Restrictiveness Index, by Region, 2007/08_**

<!-- Start of picture text --> 48.2<br>41.6<br>39.4 39.3<br>29.6<br>20.7 19.9<br>18.9<br>GCC SAR EAP MENA AFR LAC ECA OECD<br>Note: W is average STRI of total 102 countries<br>W<br><!-- End of picture text --> Source: Borchert, Gootiiz and Mattoo (2010)

**Restrictiveness in service trade is prevalent in all major services sectors.** The breakdown of the STRI by major service sector shows the highest barriers to trade in the MENA region appearing in professional services, financial services, and transport services (Figure 12). Even though the markets for most services are now more competitive worldwide, they remain far from being truly contestable in most countries.  In telecommunications, governments continue to limit the number of providers and the extent of foreign ownership.  In both banking and insurance, the allocation of new licenses often remains opaque and highly discretionary.  In retail, a range of domestic regulations, such as pervasive zoning laws, severely impede entry in both developing and industrial countries.  Transport and professional services remain a bastion of protectionism.  In maritime transport, even though international shipping is today quite open, entry into cabotage and auxiliary services such as cargo handling is in many countries restricted. In air transport, most countries across all income groups restrict investment in the supply of international and domestic air passenger services.  Within professional services, accounting and the practice of foreign law tend to be more open than auditing and the practice

> 18 The STRI database contains information from a total of 102 countries, of which 78 are developing countries and 24 OECD countries; the country coverage represents all world regions and income levels.

18