rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 5. EAP: Investment growth**

_Investment growth in the EAP region stabilized at moderate levels in 2015-16 following a gradual decline in 2010-13. This decline reflected a steady slowdown in China and a sharp deceleration of investment growth in commodity exporters through end-2013. Since early-2014, investment growth has begun to recover in major commodity exporters as their terms-of-trade bottomed out and major central banks embarked on easing cycles. FDI to the EAP region remained buoyant and supported investment growth_ .

# **A. Investment growth**

# **B. Investment growth**

<!-- Start of picture text --> Percent, year-on-year Percent<br>1990-2008 avg 2003-08 avg<br>16<br>16 EAP EAP ex. China EMDE<br>12<br>12<br>8<br>8<br>4<br>4<br>0<br>0<br>EAP EAP ex. EMDE China<br>China<br>C. Term of trade change D. Monetary policy rates<br>Percent Percent 2000-05 2006-08<br>6 Commodity exporters 16 2011-12 2013-15<br>Commodity importers H1 2016 H2 2016<br>4 12<br>2 8<br>0 4<br>-2 0<br>-4<br>-6<br>2010 2011 2012 2013 2014 2015<br>Commodity exporters Commodity importers<br>E. FDI: Groups F. FDI: Countries<br>Percent of GDP Percent of GDP<br>Commodity exporters 12 2015<br>4 Commodity importers (ex. China)1990-2008 Difference from 1990-08 average<br>2003-08 7<br>3<br>2<br>2<br>-3<br>1<br>-8<br>0<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1 2013Q3 2014Q1 2014Q3 2015Q1 2015Q3 2016Q1 2016Q3<br>Mongolia Indonesia Malaysia China Vietnam Philippines Thailand<br>LAO KHM VNM MMR MYS THA IDN PHL MNG PNG PLW<br>2011 2012 2013 2014 2015 2011 2012 2013 2014 2015<br><!-- End of picture text --> Sources: Haver Analytics; International Monetary Fund; United Nations Conference on Trade and Development; World Bank, World Development Indicators, World Bank.

A. GDP-weighted averages.

C. Investment-weighted averages. Commodity exporters include Indonesia, Malaysia, Myanmar, and Papua New Guinea. Commodity importers include Cambodia, the Philippines, Thailand, and Vietnam. An increase denotes an improvement in terms-of-trade.

D. Policy rates are the average of end-of-period data.

E. FDI inflows. Weighted averages.

F. For difference from 1990-2014 average, positive values indicate improvement of FDI inflows. LAO = Lao, PDR, KHM = Cambodia, VNM = Vietnam, MMR = Myanmar, MYS = Malaysia, THA = Thailand, IDN = Indonesia, PHL = Philippines, MNG = Mongolia, PNG = Papua New Guinea, PLW = Palau.

11
|_Lending to Private Firms_|**Table A1- Variable Definitions (Cont’d)**|
|---|---|
|Volumef,b,m|Volume of loans firm_f_has from bank_b_in a month_m_(in logs).<br>|
|Investmentf,b,m|Volume of loans destined to investment projects that a firm_f_has from bank_b_in a<br>month_m_(in logs).|
|Working Capitalf,b,m|Volume of loans destined to working capital that a firm_f_has from bank_b_in a<br>month_m_(in logs).|
|Interest Ratef,b,m|Annualized interest rate of the loan that firm_f_has from bank_b_in a month_m_<br>(percent)_._|
|Collateralf,b,m|Fraction of loan that firm_f_has from bank_b_in a month_m_that is guaranteed by<br>firm’s assets_._|
|Maturityf,b,m|Outstanding maturity of the loan that firm_f_has from bank_b_in month_m_(in logs)_._|
|_Firm Outcomes_||
|Liabilitiesf,y|Total liabilities of firm_f_in year_y_(in logs).|
|Assetsf,y|Total assets of firm_f_in year_y_(in logs).|
|Fixed Assetsf,y|Total fixed assets of firm_f_in year_y_(in logs).|
|Salesf,y|Operational revenue of firm_f_in year_y_(in logs).|
|_Bank Variables_||
|Loansb,m|Volume of commercial loans extended by bank_b_in month_m_(in logs)_._|
|Loans – Govtb,m|Volume of loans to local governments extended by bank_b_in month_m_(in logs)_._|
|Loans – Non-Govtb,m|Volume of loans to private firms extended by bank_b_in month_m_(in logs)_._|
|IntRate – Govtb,m|Average interest rate of loans extended to local governments by bank_b_in month<br>_m_(percent)_._|
|IntRate – Non-Govtb,m|Average interest rate of loans extended to private firms by bank_b_in month_m_<br>(percent)_._|
|BankExposureGovb,Mar16|Ratio of lending volume by bank_b_to all local governments over its total lending<br>volume in March 2016.|
|BankExposureGovs,b,Mar16|Ratio of lending volume by bank_b_to local governments in state_s_over its total<br>lending volume in March 2016.|
|_Other Variables_||
|Postm|Indicator that takes value 1 if month_m_if after April 2016.|
|Tradable Sectori|Indicator that sector_i_produces tradables following Mian and Sufi (2014).|
|Government Exposurei|Measure of the dependence of firms in industry_i_on government spending<br>following Belo, Gala, and Li (2013).|
|Norths|<br>Indicator of northern states according to the National Statistics Agency (INEGI).<br>Companies classified in the_primary_sector mainly extract and harvest resources,|
|Primary, Secondary and|like agriculture, mining, or forestry. The_secondary_sector comprises businesses|
|Tertiary Sectors|that are involved in processing, manufacturing, and construction. Businesses in|
||the_tertiary_sector provide services, such as retailers or financial companies.|

59
**Table 6a:  Brazil - Log hourly wages for the labor force as a whole and with Heckman correction***

||**Hour**<br>|**ly wage esti**|**mation**|**Hec**<br>|**kman correc**|**ted**|
|---|---|---|---|---|---|---|
||All|Men|Women|All|Men|Women|
|**_Wage earn_**|**_ers_**<br>||||||
|_Female_|-0.26<br>(-40.73)|---|---|-0.11<br>(-11.77)|---|---|
|_Age1525_|-0.59<br>(-61.75)|-0.63<br>(-52.34)|-0.51<br>(-32.6)|-0.57<br>(-58.55)|-0.61<br>(-49.77)|-0.52<br>(-31.85)|
|_Age2645_|-0.14<br>(-15.74)|-0.12<br>(-10.9)|-0.16<br>(-11.13)|-0.22<br>(-22.89)|-0.24<br>(-20.03)|-0.17<br>(-10.42)|
|_Nonwhite_|-0.24<br>(-39.64)|-0.24<br>(-32.68)|-0.21<br>(-22.07)|-0.24<br>(-39.77)|-0.24<br>(-32.56)|-0.21<br>(-22.11)|
|_Nosch_|-0.97<br>(-83.01)|-1.0<br>(-72.19)|-0.88<br>(-40.01)|-0.85<br>(-64.95)|-0.94<br>(-64.84)|-0.84<br>(-25.45)|
|_Pri_|-0.44<br>(-68.67)|-0.46<br>(-54.23)|-0.41<br>(-41.63)|-0.38<br>(-53.21)|-0.43<br>(-49.04)|-0.39<br>(-25.37)|
|_Home_|-0.22<br>(-17.23)|-0.11<br>(-4.61)|-0.26<br>(-17.31)|-0.21<br>(-16.39)|-0.11<br>(-4.49)|-0.26<br>(-17.18)|
|_constant_<br>|-2.51<br>(-256.3)|-2.49<br>(-207.9)|-2.8<br>(-187.1)|-2.39<br>(-211.54)|-2.3<br>(-164.1)|-2.75<br>(-90.05)|
|R<sup>~~2~~</sup>|57057|0.25|0.19|---|---|---|
|Sample<br>size|0.24|36002|21055|111548|53223|58325|

* t- and z-values in parentheses

36
36 increase in wages in the private and public sectors effected in December 1968 and in December 1969 did not appear to have had an effect on inflation.17 In both countries this was possible because the economy maintained two strong nominal anchors, the fixed exchange rate and price controls.

The eventual failure of the programs of the sixties in Argentina and Uruguay can be tied to the lack of persistence in maintaining fiscal discipline. This can be seen from figure 4-b, which shows the behavior of inflation, devaluation and budget deficits over the period. In both cases the budget deficits display a U shape. There was an important fiscal adjustment at the beginning which lasted for approximately two years which was critical for the initial positive results of the anti-inflation effort. This was followed, however, by a relaxation at a later stage in the fiscal effort which led to a resurgence of inflation, although this was partly postponed by delaying exchange rate devaluations. In the two programs, the deterioration in the fiscal balance preceded the resumption of inflation. The fiscal cycle is a feature observed in many stabilization programs. In Argentina, for example, all major stabilization efforts during the last three decades started with significant reductions in the budget deficit, which in all cases were later abandoned (see Kiguel and Liviatan 1988). B. Output and Unemployment The heterodox approach is primarily used as a way to limit the costs of reducing inflation usually encountered in orthodox programs. The available evidence indicates that in most cases the programs have been 17 For details on these increases see data in Viana (1988).
<!-- Start of picture text --> A. Small States<br>Climatic dis.<br>0.020 a<br>0.010 fr ~es ===}<br>0.000} —~“\<br>-0.010 \<br>aay<br>Z0.020 cn ———<br>-5 (9) 5 10<br>Geological dis.<br>0.200<br>0.150 puro c<br>0.100 /<br>0.050 __ ff]<br>0.000 SP es<br>-0.050 \7<br>-5 O 5 10<br>Other dis.<br>.02 f ———<br>“pe Z<br>-0.020 \<br>-0.040 Nt<br>-0.060 =<br>-0.080<br>-0.100<br>-5 O 5 10<br>Wind Storms<br>one ~<br>gi,<br>es0.020 \<br>\,<br>-0.040 \cians ieee<br>-5 O 5 10<br><!-- End of picture text --> <!-- Start of picture text --> B. Rest of the World<br><!-- End of picture text --> <!-- Start of picture text --> Climatic dis.<br>enn<br>0.010<br>0.000 poccccscoo<br>‘a<br>-0.010 \<br>eue0 ee<br>-5 (@) S) 10<br>Geological dis.<br>0.200<br>0.150<br>0.100<br>0.050<br>0.000 ———<<br>a refedna homliraiwderar<br>-5 O 5 10<br>Other dis.<br>0.0200.000<br>Fe el<br>-0.020 *<br>-0.040 ,<br>-0.060 <<br>-0.080 Smear<br>-0.100<br>-5 O 5 10<br>Wind Storms<br>0.020 74S<br>a<br>\a | ee |<br>-0.020<br>6i6K6<br>-5 O 5 10<br><!-- End of picture text -->

Time in Years
- <u>B. Technology Vs. Labor Intensive Industries</u>

- The share of labor-intensive industries in Latvia is larger than the share of technology-driven industries (See Annex 1 for a definition of industrial taxonomies.) This is the reverse of the situation in current EU member states as well as in the most dynamic transition economies (Hungary, Czech Republic, Slovakia and Estonia).<sup>10</sup>

**Figure 4 Technology Driven & Labor Intensive Industries' Share in Manufacturing Production, 1999**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Bulgaria Czech Estonia Hungary Latvia Lithuania Poland Romania Slovak Slovenia<br>Rep. Rep.<br>Technology-Driven Industries Labor-Intensive Industries<br><!-- End of picture text --> _Source_ : WIIW, 2001

- A recent FIAS analysis<sup>11</sup> suggests that only 2% of the Latvian workforce is employed in high tech sectors, a figure that is far lower than the EU average in general and below the level in the NMS’s in particular.  Similarly, the Latvian Innovation Survey<sup>12</sup> reports that high tech sectors generate only 3%-4% of manufacturing output and 6% of exports, compared with 20%-30% in developing countries.<sup>13</sup>

- 10  Unfortunately, sectoral data at the level of detail required to determine the technology and skill intensity of production and exports is available only until 1999.

- 11  Paragraph 16 of draft FIAS report

- 12  Chapter 3, P. 18

> 13 Data about employment in high tech sectors and high tech exports needs to be interpreted with care. Until recently, for example, Mexico was the world’s largest high tech exporter.  But its exports consisted primarily of products assembled in Mexico with imported components.  Mexican value added was very low and Mexico’s contribution to these exports consisted primarily of low wage labor working in foreign-owned assembly plants.  Most of the high wage, high value added design, R&D, and marketing operations were conducted elsewhere.  As a result, productivity, wages and incomes in

14
**12**

The housing bubble was the result of a long period of low interest rates by the US Federal Reserve. The Federal Reserve cut interest rates by a total of 550 basis points in a series of steps between 2001 and 2004. The easing, subsequent tightening and current easing are shown in chart 3.2. Some believe (for example the ‘Austrian school’ and John Taylor<sup>9</sup> ), that monetary policy was too loose for too long and this is what gave rise to the asset price bubble and commodity price spike. Taylor argues that had the Federal Reserve followed the Taylor rule (actually the Henderson-McKibbin-Taylor rule), interest rates would have risen much sooner and the bubbles n would not have appeared to the same extent (chart 3.2).

# 3.2 **Federal funds rate** Actual and counterfactual

<!-- Start of picture text --> 9<br>8<br>7<br>Actual<br>6<br>Taylor rule<br>5<br>4<br>3<br>2<br>1<br>0<br>Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08<br>Federal funds rate (%)<br><!-- End of picture text --> Note: The daily effective federal funds rate is a weighted average of rates on brokered trades. Weekly figures are averages of 7 calendar days ending on Wednesday of the current week; monthly figures include each calendar day in the month. Annualised using a 360-day year or bank interest.

_Data source:_ US Federal Reserve Statistical Release, http://www.federalreserve.gov/releases/h15/data/Monthly/H15_FF_O.txt, Accessed 5 March 2009.

While low interest rates were due to fears of deflation<sup>10</sup> and led to a boom in US housing, low interest rates were not just the result of the Fed’s actions. US bond yields were also low because of low world rates (with Japanese bond yields at a little over 1 per cent and short term interest rates at zero). There was also an international aspect to low US interest rates with Japan and Europe only recovering very slowly from the 2000-01 downturn and in turn placing pressure on the US to keep interest rates low. In Japan there were fears of re-emergent deflation. That is the principal reason why interest rates were kept low in the US for an unusually long term — until mid-2004 when the Fed began a very sharp tightening cycle<sup>11</sup> . The low interest rates through 2003-04 —

> 9  Taylor, J.B. 2008, _The Financial Crisis and the Policy Responses: An Empirical Analysis of What Went Wrong_ , p. 2.

> 10 For example, see Alan Greenspan’s account in T _he Age of Turbulence,_ Allen Lane, 2007, pp228-229.

> 11 The sharpest, in fact, since the Volker deflation of the early 1980s.
Policy Research Working Paper 8625

# **Abstract**

Is the variation in bilateral trade flows across countries primarily due to differences in the number of exporting firms (the extensive margin) or in the average size of an exporter (the intensive margin)? And how does this affect the estimation and quantitative implications of the Melitz (2003) trade model? The benchmark Melitz model with Pareto-distributed firm productivity and fixed costs of exporting, predicts that, conditional on the fixed costs of exporting, all variation in exports across trading partners should occur on the extensive margin. This paper subjects this theoretical prediction to a reality check drawing upon the World Bank’s Exporter Dynamics Database (EDD) which has firm-level exports from 50 developing countries to all destinations. Around 50 percent of the variation in exports across trading partners is shown to be along the intensive margin, contradicting the benchmark Melitz-Pareto model. The paper finds that moving from a Pareto to a lognormal distribution of firm productivity allows the Melitz model to successfully match the role of the intensive margin evident in the EDD. The paper then studies the implications of our findings for quantitative trade theory. Using likelihood methods and the EDD, a generalized Melitz model with a joint lognormal distribution for firm productivity, fixed costs and demand shifters is estimated, and exact hat algebra is used to quantify the counterfactual effects of a decline in trade costs on trade flows and welfare in the estimated model. Finally, these effects are compared to those that would be predicted by the Melitz-Pareto model, with the Pareto shape parameter chosen to match the average trade elasticity implied by the estimated Melitz-lognormal model. The paper shows that the effects on welfare turn out to be quite close to those in the standard Melitz-Pareto model even though the effects on trade flows remain different.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at afernandes@worldbank.org, Klenow@stanford.edu, smeleshchuk@imf.org, mpierola@IADB.ORG, and andres@econ.berkeley.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Figure 7: Returns Patterns over Time**

<!-- Start of picture text --> Returns to schooling<br>1970 1980 1990 2000 2010<br>Year<br>25<br>20<br>15<br>10<br>Returns to schooling<br>5<br>0<br><!-- End of picture text --> The returns to schooling have declined significantly since the 1980s, when they were above 13 percent, to just over 9 percent in recent years (Table 4). This is due, at least partly, to the unprecedented expansion in schooling since the 1980s and, especially, since the late 1990s. Schooling has expanded by almost 50 percent since 1980. Over a 30 year period the returns to schooling have declined by 3.5 percentage points, or 0.1 percent a year. At the same time, schooling increased by more than 3 years, or 2 percent a year. On average, another year of schooling leads to a reduction of the returns to schooling by one percentage point.

**<u>Table 4. Returns to Schooling and Average Years of Schooling by Period</u>**

||**Returns to**<br>**Schooling**|**Average**<br>**years of**<br>**schooling**|**Number of**<br>**surveys**|
|---|---|---|---|
|**1980-1985**|13.3|6.6|12|
|**1986-1990**|12.7|8.1|23|
|**1991-1995**|11.0|8.0|58|
|**1996-2000**|10.1|8.8|109|
|**2001-2005**|9.9|10.1|228|
|**2006-2010**|9.6|10.9|238|
|**2011-2013**|10.0|11.6|149|

15
26 the economic isolation imposed by ISI policies. Interestingly, low income countries experience a similar increase in growth following trade liberalization as middle income countries. Since there is a positive relation between the quality of institutions and income per capita, this implies that supply response from trade opening was not limited by weak institutions. Furthermore, countering frequent criticisms, the evidence in this Appendix shows that countries in Sub-Saharan Africa have indeed benefited significantly from an outward orientation of economic policies<sup>2829</sup> . Finally, we divide the sample according to pace of the trade reforms, either fast or gradual, based on a classification established at Jinjarak et al (forthcoming), and we observe that gradual reformers obtain more robust benefits than fast liberalizers.

**Figure 4: Evolution of GDP Per Capita Growth by Country Subgroups**

<!-- Start of picture text --> AFR EAP LAC<br>1.2 7 2.5<br>1 6<br>0.8 5 2<br>0.6 4 1.5<br>0.4<br>0.2 3 1<br>0 2<br>0.5<br>-0.2 1<br>-0.4 0 0<br>[-12,-5] [2,9] [-12,-5] [2,9] [-12,-5] [2,9]<br>Years Since Reform Years Since Reform Years Since Reform<br>MNA SAR Low Income<br>2 3 1.4<br>2.5 1.2<br>1.5 2 1<br>1 1.5 0.8<br>0.6<br>1<br>0.5 0.4<br>0.5 0.2<br>0 0 0<br>[-12,-5] [2,9] [-12,-5] [2,9] [-12,-5] [2,9]<br>Years Since Reform Years Since Reform Years Since Reform<br>Middle Income Large Small<br>3 2.65 2<br>2.5 2.6<br>1.5<br>2 2.55<br>1.5 2.5 1<br>1 2.45<br>0.5<br>0.5 2.4<br>0 2.35 0<br>[-12,-5] [2,9] [-12,-5] [2,9] [-12,-5] [2,9]<br>Years Since Reform Years Since Reform Years Since Reform<br>GDP Per Capita Growth (%) GDP Per Capita Growth (%) GDP Per Capita Growth (%)<br>GDP Per Capita Growth (%) GDP Per Capita Growth (%) GDP Per Capita Growth (%)<br>GDP Per Capita Growth (%) GDP Per Capita Growth (%) GDP Per Capita Growth (%)<br><!-- End of picture text --> 28 These patterns are robust to the inclusion of a time trend.

29 We cannot make strong inferences about MNA and SAR due to the small number of countries in our sample.
# **I Introduction**

This paper explains the methodology and results of a survey to survey imputation exercise that was used to generate headcount poverty estimates for India. India was selected as a pilot case because of its size and importance, as well as the lack of recent data on household living standards. The latest survey-based estimates of poverty are from 2011-12. The most recent nationally representative survey that could be used to impute poverty into a later year is the NSSO expenditure on services and durables survey of 2014-15. The resulting poverty estimates from the imputation informed the 2015 poverty estimates, which are the most recent that are currently available, marking the first time that the World Bank used this type of method as an input into its global and regional poverty estimates.

Understanding trends in poverty in India is not only vital to Indian policy makers to measure changes in the welfare of the poor, but also has major implications for regional and global estimates of the prevalence of poverty. In 2013, an estimate of the number of extreme poor in India, defined as those consuming less than $1.90 per day per person, numbered 250 million<sup>1</sup> . This means that India accounted for over a quarter of the global total of 783 million extreme poor. Although Nigeria has now likely passed India as the nation with the largest number of extremely poor persons, accurately monitoring India’s progress in the fight against extreme poverty remains critical to assess progress towards the goal of reducing poverty to less than 3% by 2030.

The second motivation for undertaking a modeling exercise is that the typical method used by the World Bank to adjust poverty to a later year tends to overestimate the pace of poverty decline. When estimating global and regional poverty rates, the World Bank usually adjusts or “lines up” survey data from a variety of years to a common year, by scaling up the measured levels of household per capita consumption to account for growth in the intervening years. For example, to “line up” a survey from 2012 to 2015, each household’s per capita consumption, measured in 2012, would be assumed to grow at the same rate. In most countries, this common rate of growth is assumed to be equal to the growth in household final consumption expenditure (HFCE) during the intervening period.<sup>2</sup> Because it is a component of the national accounts, HFCE is published annually and can therefore be used to line up the survey means to any later year.

> 1Estimate is based on the traditional line-up method explained below.

> 2In some countries, growth in per capita GDP is used instead of final household consumption expenditure.

2