rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 1. Aggregate Equity Market Indicators**

This figure shows aggregate equity indicators for mainland China; Hong Kong SAR, China; and Singapore. Panel A shows the total equity market capitalization of domestic listed firms in each economy. Panel B shows price indexes of domestic listed stocks (2012=1). The mainland China equity index is the average between the Shanghai and Shenzhen composite equity indexes. The Hong Kong SAR, China, index is the Hang Seng Index. The Singapore index is the Straits Times Index. Panel C shows the aggregate equity issuance activity (excluding initial public offerings) of publicly listed firms with residence in mainland China, Hong Kong SAR, China, and Singapore. Values are expressed in billions of 2011 U.S. dollars (USD). The shaded areas capture the implementation of the Qualified Foreign Institutional Investor programs (QFII and RQFII), the implementation of the Stock Connect program, and the incorporation of domestic listed stocks from China into the MSCI Emerging Markets Index. RHS: Right Hand Side. Sources: World Bank and Refinitiv.

<!-- Start of picture text --> QFII<br><!-- End of picture text --> <!-- Start of picture text --> RQFII<br><!-- End of picture text --> <!-- Start of picture text --> Stock<br>Connect<br><!-- End of picture text --> <!-- Start of picture text --> MSCI<br>Incorporation<br><!-- End of picture text --> <!-- Start of picture text --> Hong Kong SAR, China<br><!-- End of picture text -->
discussing human development policy, including progress toward the MDGs. Is it that higher average income allows a society to buy goods and services that promote health and schooling? Or does the empirical relationship found in cross-country comparisons stem from a correlation between average incomes and other country characteristics?<sup>5</sup> Can the cross-country relationship be relied upon in drawing inferences about the macroeconomics of human development in any specific country, or the performance of that country relative to another? The answers to these questions are left begging when the cross-country relationship between aggregate HD outcomes and mean income is used to predict the impacts of future growth (or the income growth needed to attain specific HD targets) or to assess country performance at a given mean income. Indeed, given the uncertainties about what “mean income” is really capturing in cross-country regressions for HD outcomes, and the high level of aggregation in the conditional means based on cross-country regressions, it can hardly be little more than a leap of faith to believe that this approach can tell us something useful about how economic growth in any specific country setting will impact on human development.

There is also a large body of work exploring some of these issues using micro data.<sup>6</sup> Typically household or individual level data on health or education attainments are regressed on a range of individual and household variables, including income or wealth indicators. This body of micro work has been a rich source of knowledge about the micro determinants of human development attainments. However, the specifications used and the analytics have not allowed this literature to effectively explore the implications for the aggregate relationship between human development attainments in a country and economic growth, the distribution of income,

> 5 For example, Anand and Ravallion (1993) argue that what might really be driving the relationship is that richer countries tend to have lower income poverty and/or higher public spending on health and education, and it is these channels that hold the key lessons for policy.

2
# Current Regulatory CAR (as of end-2010)

|Regulatory CAR held at Banks (Banking System)<br>||
|---|---|
|Capital Buffer (%) - Asset weighted mean|13.8%|
|Capital Buffer (%) - Median|16.7%|
|Capital Buffer (%) - St.Dev.|4.9%|
|Minimum Regulatory CAR|8%|
|CARInsolvencyThreshold|2%|

|Economic Risk Based<br>|Approach (as of <br>|end-2011)<br><br>|||
|---|---|---|---|---|
|EconomicRiskWeightedCAR(BankingSystem)|TTC|PIT<br>|__StressVAR_|_StressInt._|
|<br>Capital Buffer (%) - Asset weighted mean|10.3%|10.0%|<br>4.3%|<br>0.4%|
|Capital Buffer (%) - Median|10.8%|10.6%|4.2%|0.4%|
|Capital Buffer (%) - St.Dev.|3.1%|3.1%|3.5%|4.7%|
|Undercapitalization (% of Profits)|14.2%|16.0%|17.7%|129.2%|
|# of Banks with Effective Capital Buffer < 8%|2|2|9|9|
|# of Banks with Effective Capital Buffer < 2%|0|0|2|6|
|Provisions-ExpectedLosses(BankingSystem)|TTC|PIT<br>|__StressVAR|_StressInt._|
|<br>Number of under-provisioned banks|1|1|<br>9|<br>10|
|% of Expected losses|-34.0%|-38.6%|-48.4%|-63.2%|
|% of Capital (Tier 1 + Tier 2)|-10.9%|-13.4%|-51.0%|-103.8%|
|%ofCapital(Tier1)|-16.4%|-20.0%|-66.3%|-132.7%|
**Table 6:** Impacts on agricultural inputs

|**Outcome**|**Obs**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|
|---|---|---|---|
|**Traction ox use**||||
|Household used traction oxen|947|0.56|0.10<sup>_∗∗∗_</sup><br>(0.03)|
|Number of plots where traction oxen were used|947|0.91|0.27<sup>_∗∗∗_</sup><br>(0.07)|
|Share of plots where traction oxen were used|947|0.53|0.11<sup>_∗∗∗_</sup><br>(0.03)|
|Household rented oxen|947|0.16|-0.04<br>(0.02)|
|**Non-labor inputs**||||
|Share of household plots using organic fertilizer|947|0.82|0.05<sup>_∗∗_</sup><br>(0.02)|
|Share of household plots using inorganic fert|947|0.95|-0.00<br>(0.01)|
|Share of household plots using phytosanitary products|947|0.95|0.01<br>(0.01)|
|Value of non-labor inputs for household plots (USD)<sup>_⋄_</sup>|947|1267.11|164.07<sup>_∗∗_</sup><br>(77.72)|
|**Labor inputs**||||
|Household hired labor|947|0.63|0.02<br>(0.03)|
|Quantity of hired labor<sup>_⋄_</sup>|947|28.85|1.37<br>(2.49)|
|Total household plot hours in last week<sup>_⋄_</sup>|2113|219.58|-8.46<sup>_∗_</sup><br>(4.91)|
|Total household plot hours in last week/ha<sup>_⋄_</sup>|2113|21.21|-1.51<sup>_∗∗_</sup><br>(0.58)|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. Agricultural input usage only collected for half the sample. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

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Turning to calorie-based measures, Figure 2b shows the changes in food consumption between 1992 and 2009 by region with the global calorie consumption trend curve. The regional changes in calorie consumption are broadly in line with the global calorie trend line, revealing a concave relationship between income and food consumption albeit at a much lower level relative to the CE measure. Figure 2c shows the projected growth of demand for cereal equivalents and for calories on a comparable scale (Fukase and Martin 2016). The figure shows that consumption of calories levels off much earlier and at a much lower level than consumption of cereal equivalents. This is because the latter measure reflects the increasing agricultural resource requirement resulting from dietary shifts which continue after calorie consumption stabilizes.

# **_Past and Future Growth in Global Food Demand_**

In Table 1, we decompose total growth in global food demand into parts due to population growth and to per capita consumption growth. The first three columns of Table 1 report the evolution of CE food demand for our 134 sample countries, which account for 95 percent of 2009 population.<sup>8</sup> CE food demand grew at 2.3 percent in the 1980s, 2.1 percent in the 1990s and 1.9 percent in the first decade of the new millennium.

<u>Table 1. Changes in CE food demand 1980-2050</u>

|Evolution of CE Food Demand 1980-2009|
|---|

|||CE FoodDeman<br>|d<br>|Annual<br>|Average CE Fo<br>|od Growth<br>|
|---|---|---|---|---|---|---|
||Change<br>(mil. Tons)|Initial year<br>(mil. Tons)|Last year<br>(mil. Tons)|Total<br>(%)|Per capita<br>(%)|Population<br>(%)|
|1980-1991|864|2999|3863|2.30|0.55|1.75|
|1992-2000|817|4590|5407|2.05|0.69|1.36|
|2001-2009|878|5455|6333|1.87|0.72|1.15|
||Pr|ojected Chang|esinCE Food|Demand2009|-2050||
|||CE Food Deman<br>|d<br>|Annual<br>|Average CE Fo<br>|od Growth<br>|
||Change|Initial year|Final year|Total|Per capita|Population|
||(mil. Tons)|(mil. Tons)|(mil. Tons)|(%)|(%)|(%)|
|2009-2050|7049|6899|13948|1.72|1.03|0.68|

_Source:_ Authors’ calculations. _Note:_ Annual % changes are based on log-differences.

> 8 The figures for the 1980s are not strictly comparable, since we have data only for 115 countries, with most former Soviet bloc countries not reporting.

8
Gurgel et al. (2007) use the MIT Emissions Prediction and Policy Analysis (EPPA) model to analyze an expansion of second-generation biofuel based on biomass production. The EPPA model is calibrated on 1997 data from GTAP and other sources and is rather aggregated. There is only one aggregate agricultural crop sector and many large countries are aggregated in regions. For example, Brazil and Argentina are part of the Central and South America region. Land is categorized in 5 types: crop land, pasture land, harvested forest land, natural grass land, and natural forest land. The crops sector and two biomass sectors compete for cropland. Pasture land is specific to the livestock sector, and so is harvested forest land for the forest sector. The EPPA model collapses the biomass production and biofuel transformation into a single sector. Two specifications are used for land allocation. Land moves across activities according to a CET transformation -land supply is price responsive- in a first specification, and under an alternative specification, non-agricultural land enters crop land according to an explicit cost of conversion into agricultural use. EPPA’s approach is not well suited to analyzing the food-bioenergy tradeoff because of its lack or explicit representation of agriculture, its focus on second- generation cellulosic biofuel, and the high level of aggregation.

# **_2.3 Land Module for Biofuel Modeling_**

# **_2.3.1 Biofuels, feedstock, and major biofuel producers_**

Ethanol and biodiesel are the two major commercial biofuel considered in our model. The major feedstocks used for ethanol are grains, sugar beet, sugarcane, and molasses. For biodiesel, the major feedstocks are vegetable oil from rapeseed, soybean, and palm oil. The major feedstock for ethanol in warmer countries is sugarcane, whereas grains are used in more temperate climates (corn, but also wheat, and barley), and to a much lesser extent sugar from sugar beet (Koizumi and Ohga (2007); Elobeid and Tokgoz (2006), FAPRI (2009)). Other feedstock types are not yet used in commercial operations and are not expected to be for the foreseeable future. The latter include switch grass and other forms of cellulosic feedstock, which are far from being profitable (See Carriquiry and Du (2009)). Biodiesel relies on vegetable oil and not on oilseeds directly as sometimes modeled. Vegetable oil comes from crushing oilseed crops, with oilseed meal or cake as a by-product feed to livestock. The major oilseed crops are soybean, rapeseed, and palm oil. Biodiesel production exists because of policy mandates and subsidies but it is not currently 11
32

- Greenaway, D. and C. Milner, 1990, "South-South Trade: ITheory, Evidence and Policy", World ie Bank Research Observer, v. 5, #1, pp.47-68.

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- Summers, R. and A. Heston, 1988, "A New Set of International Comparisons of Real Product and Price Levels: Estimates for 130 Countries, 1950-85", Review of Income and Wealth, Vol 17, pp. 239263.

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# Introduction

The services sector was long considered “non-tradable”, primarily because services transactions were generally thought to require physical proximity between the producer and the consumer. That view changed significantly in the 1980s and 1990s, and became crystallized in the General Agreement on Trade in Services (GATS), one of the pillars of the World Trade Organization (WTO), which entered into force in 1995. Many services are now considered tradable in principle, even if they are difficult to trade because of, for example, substantial levels of trade costs in services markets (Miroudot et al., Forthcoming).

Under the GATS, there are four ways in which services can be traded, known as modes of supply. Mode 1 is pure cross-border trade in services in which the producer and consumer remain remote from one another. It corresponds most closely to the paradigm of trade in goods. An example is business process outsourcing, in which some functions such as bookkeeping are performed remotely by a service provider in a different country, who communicates with the consumer by telephone and internet. Under Mode 2, the consumer moves to the producer’s location in order to consume the service. A typical example is tourism. Mode 3 is a common mode of supply for many services, and requires commercial establishment by a foreign service provider in the consumer’s country. Sales by that service provider are then counted as exports by the service provider’s country to the consumer’s country, as recorded in Foreign Affiliate Trade Statistics (FATS). An example is establishment of a local subsidiary by a foreign bank, which then provides financial services in the consumer’s country. The final mode of supply, Mode 4, involves temporary movement by individual service providers (natural persons) from the producer’s country to the consumer’s country. An example is an information technology specialist being sent temporarily from his home country to the consumer’s country to set up a new network.

As this introduction makes clear, all services are in principle tradable under one of the four GATS modes of supply. They can no longer be regarded as a non-tradable sector. However, the paradox remains that in most countries, only a small proportion of services GDP is in fact directly exported, although recent 2
disaggregation level is presented in Table 1 along with the corresponding formula.<sup>28</sup> The Database is publicly available for download free of charge at http://data.worldbank.org/datacatalog/exporter-dynamics-database.<sup>29</sup> For the measures of firm dynamics, firm-product and firm-destination market dynamics, we use the following definitions:

- Exporter t: any firm that exports in year t;

- Entrant t : a firm that does not export in year t-1 but exports in year t;

- Exiter t : a firm that exports in year t-1 but does not export in year t;

- Incumbent t : a firm that exports in both years t-1 and t;

- 2-Year Incumbent t : a firm that exports in years t-1, t, and t+1;

- Survivor t :  a firm that does not export in year t-1 but exports in both years t and t+1;

- 2-Year Survivort: a firm that does not export in year t-1 but exports in years t, t+1 and t+2;

- 3-Year Survivort: a firm that does not export in year t-1 but exports in years t, t+1, t+2 and t+3.

To protect the confidentiality of the firms in the raw datasets at the exporter-productdestination-year level, some of the measures in the files with product or destination disaggregation (CYH2.dta, CYH4.dta, CYH6.dta, CYD.dta) are missing when the underlying

> 28 Table 1 also describes the nomenclature used to name the different measures in the stata files consistently. For example, the variable ‘B1’ refers to the Herfindahl index; the variable ‘A7’ refers to the Export Value per Incumbent exporter, etc. While not shown in Table 1, note that we used suffixes to identify means (i), medians (ii), and standard deviations (iii) of certain measures in the stata files. For example, the variable ‘A7i’ represents the mean of the Export Value per Incumbent exporter in a given country and year; ‘A7ii’ represents the median of the same statistic and ‘A7iii’ represents its standard deviation.

> 29 Although the files CY.dta, CYH2.dta, CYH4.dta, CYH6.dta, and CYD.dta are all based on the cross-country input dataset excluding oil exports in HS Chapter 27, interested users can request from the authors an alternative version of the files where the measures are calculated including exports in HS Chapter 27 (for the countries which provided such information).

15
the base scenario and adjust it so that the returns to education remain the same for sensitivity tests.

# **2.2. Capital Accumulation**

All the variables that appear in equation (3) except for the intangible total factor productivity (A) use the readily available measurement from the national data such as gross domestic output and number of workers. Measuring the level of physical capital (K), however, is not a straightforward procedure, and the data are not readily available. A frequently used method to construct the data on capital includes the Perpetual Inventory Method, since physical capital is accumulated through investment decisions.<sup>18</sup> The law of motion of capital accumulation is as follows: Kt+1 = (1 −δ)Kt + It (4) where δ is the depreciation rate and It denotes investment at time t.

Our paper uses this method taking 1960 as our initial year and capital depreciates at 5 percent per year as in Bosworth and Collins (2003).<sup>19</sup> As Nehru and Dhareshwar (1993) suggest, we assume a steady state at the initial year and use its capital/output ratio. The series of investment comes from gross fixed capital formation in the national accounts. With a sufficient data span, the impacts of the assumptions on capital at the initial year and 5 percent annual depreciation rate are expected to be little.<sup>20</sup> Information on the trend of investment also comes from the Balance of Payment of a country considering the following identity on current account balance: S−I= (Net)capital transfer + (Net)export + Income balance (5) where S and I denote domestic saving and investment, respectively. The equation implies that investment is supposedly financed by domestic saving in a closed

> 18 Some studies such as Canning et al. (1994) use proxies such as infrastructure to capture the level of capital instead of constructing data on physical capital.

> 19 The depreciation rate varies by types of assets, and different countries accumulate different types of assets. Estimating depreciation rates by countries or asset types is beyond the scope of this study, and we use a conventional depreciation rate of 5 percent.

> 20 See Nehru and Dhareswhar (1993) for the Perpetual Inventory Method for capital accumulation procedure.

8
23 Table 4-1. **GDP** and FDI For Eleven Host Countries, 1978-87 (annual average)

||1980 U.S<br>|. dollars (billions)|<br>Ra|nk|
|---|---|---|---|---|
|Country|DGEP||E|GDP|
|Argentina|0.25|49.9|6|7|
|Brazil|1.67|191.4|1|1|
|Colombia|0.24|29.4|7|8|
|Mexico|1.19|160.7|2|3|
|India|0.07|167.2|10|2|
|Indonesia|0.85|65.7|3|5|
|Korea, Rep. of|0.12|57.6|9|6|
|Malaysia|0.64|21.1|4|10|
|Thailand|0.18|28.6|8|9|
|Kenya|0.03|6.1|11|11|
|Nigeria|0.42|84.0|5|4|

Source: IFS; IMF data are not reported for India in Becsky, Lee, and Ordu (1990; table 4-2). Nevertheless, the OECD figures reported there and the IMF figures reported here are identical for thirteen of the eighteen years (1968-85) reported in table 4-2. For the eighteen year period, the IMF figures averaged 0.008 a year more than the OECD figures. Indonesia's figures are based on a composite of IMF and OECD figures for 1968-86; see table 5-3 Becsky, Lee, and Ordu (1990). Korea's figures are from the Finance Ministry for 1968-86; the 0. 12 reported here compares with the IFS average of 0.11 for 1968-87. See table 6-6 in Becsky, Lee, and Ordu (1990).

In table 4-2 the ratios of FDI flows to GDP for each of the eleven countries are presented for each five-year period from 1968 to 1987. Although those numbers are inevitably small, they nevertheless indicate the extent of the deviation in FDI flows for individual countries and time periods from what would be expected on the basis of the economic size of the host country alone. Malaysia, at one extreme, and India, at the other, are notable for the extent to which their FDI-GDP ratios differ from the mean of 0.4 percent for the group over the twenty-year period. Malaysia's FDI inflows were consistently far above and India's far below what would be expected solely on the basis of their economic size. Many countries exhibit stability in the relative sizes of their ratios over time, but others experienced significant changes in their ratios over the twenty-year period. Kenya's and Nigeria's ratios, in particular, were both quite high for the 1968-72 period before declining, while Brazil's decreased and Colombia's increased significantly by the 1983-1987 period. The ratio for Korea was surprisingly low throughout the twenty-year period, as FDI increases barely kept pace with GDP growth. Following Japan's example, Korea discouraged FDI in manufacturing sector until the 1980s.