rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 5. Global activity**

## **A. Growth by country groups**

<!-- Start of picture text --> B. Contribution to global growth forecast errors<br>Percent Percentage point<br>6 Start of year estimates 0.6<br>EMDE oil exporters<br>0.4 EMDE oil importers<br>4 Advanced economies<br>World<br>0.2<br>2 0.0<br>-0.2<br>0<br>-0.4<br>World Advanced EMDE oil EMDE oil -0.6<br>economies importers exporters 2014 2015 2016 2017<br>2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017<br><!-- End of picture text --> Source: World Bank.

A. B. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP weights. Country classification is presented in Appendix 1.

A. Purple diamonds correspond to growth forecasts at beginning of each calendar year from _Global Economic Prospects_ . Bars represent actual data up to 2016 and estimates for 2017.

B. Forecast errors computed as the difference between actual global growth and forecasts at the beginning of each calendar year. Sample includes the 153 countries with forecast data available in 2014.

19
relatively unskilled, labor-intensive sectors, which conforms to a finding by Hanson and Harrison (1999) for Mexico and Goldberg and Pavcnik for Colombia (2001). In fact, the Spearman correlation between tariffs and the share of unskilled workers in an industry (measured by the share of workers with less than complete secondary education) is .4 (p-value of .08) in 1987.

From 1988 to 1994, however, Brazil underwent a significant trade liberalization that gradually reduced its tariffs and NTBs. The liberalization proceeded in several stages. In 1988 and 1989, the reforms reduced the average tariff levels from about 60 in 1987 to 39 percent in 1989. Kume (2000) and Hay (2001) argue that the initial 1988-1989 tariff reduction had no significant bearing on the exposure of domestic industries to increased foreign competition due to continuous reliance on substantial NTBs. The NTBs such as import licenses, special import programs, and administrative barriers to trade were eliminated in the second stage of the reforms that started in 1990 as the Collor government endeavored to instigate productivity improvements by domestic firms through increased foreign competition.<sup>6</sup> The gradual tariff reductions implemented from 1990 to 1994 lowered the average tariff from 34 percent in 1990 to 11 percent tariff in 1995. The government partially reversed these trade reforms in 1995 following the real appreciation of the _real_ that lowered the competitiveness of the manufacturing sector and widened the current account deficit. Nevertheless, the average tariff climbed only slightly between 1995 and 1998.

**6** Unfortunately, the information on NTBs is not available. This might not be very problematic. First, unlike in many developed countries, tariffs were an important policy instruments in countries such as Brazil. Second, NTBs are inherently hard if not impossible to measure. The common wisdom in the field is that the agencies collecting NTB data take great care in making the data comparable across sectors in a given year, but are less concerned with consistency of the numbers across years. This makes the use of time series data on NTBs troublesome. Finally, we control for import penetration, which partially accounts for the effect of NTBs on various labor market outcomes.

7
Perotti, R. 1996. “Growth, Income Distribution and Democracy.” Journal of Economic Growth 1 (2): 149–87.

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Ramey, G. and V. Ramey (1995). “Cross-Country Evidence on the Link between Volatility and Growth”. American Economic Review 85 (5): 1138-1150.

Ravallion, M. 1997. “Can High Inequality Development Countries Escape Absolute Poverty?” Economics Letters 56 (1): 51–7.

———. 2004. “Pro-Poor Growth: A Primer.” Policy Research Working Paper 3242, World Bank, Washington, DC.

Rodrik, D. 1999. “Where Did All the Growth Go? External Shocks, Social Conflict, and Growth Collapses.” Journal of Economic Growth 4 (4): 385–412.

Wolf, Holger. 2003. “Volatility: Definitions and Consequences.” Chapter 1 in “Managing Volatility and Crises: A Practitioner’s Guide.”. World Bank Regional Study Ruiz-Castillo J., E. Ley, and M. Izquierdo. 2002. “Distributional Aspects of the Quality Change Bias in the CPI: Evidence from Spain.” Economic Letters 76 (1): 137– 44.

World Bank. 2006. World Development Report: Equity and Development. Washington, DC: World Bank.

39
37 percent improvement is needed for Sri Lanka to achieve the trade flows at the South Asia average tariff rate. The tariff rates of these countries were below the South Asia average in the studied period, applying the South Asia average rate to those countries implies a reduction of trade flow. On the other hand, India’s tariff rates were lower than the South Asia’s average rate, and hence, applying the South Asia average rate to this country will result in a trade flow that exceeds the trade flow at the South Asia average tariff rate. This exercise can be repeated for the rest of trade facilitation measures.

Figure 10: Trade Gains (percentage) from Collective Capacity Building in the Context of Intra-regional Trade in South Asia <!-- Start of picture text --> %<br>80<br>70<br>60 Bangladesh<br>50 India<br>40 Pakistan<br>30 Sri Lanka<br>20 South Asia<br>10<br>-<br>Customs Regulation Services all<br>Maritime)<br>Ports (Air and<br><!-- End of picture text --> Source: Authors’ calculation based on Wilson, Mann, Otsuki database 2004

# _Global Trade and the South Asia Region_

Now we turn to the analysis of gains from collective action in capacity building between the South Asia region as one group and the rest of the rest of the world as another group. The rest of the world is represented by 76 countries in the WMO data set. In the previous section we estimated the gains to trade from action only by South Asia and the implications for intra-regional trade. Capacity building in trade facilitation by countries in the region is also expected to generate gains from trade with the rest of the world _even if the rest of the world does not reform or invest in capacity building measures_ . If the rest of the world would upgrade capacity in trade facilitation simultaneously, however, the estimated gains to South Asia would rise further. For example, consider development assistance and reform undertaken through obligations in the World Trade Organization in trade facilitation by all WTO members.

A similar simulation scenario as in the previous section is used here to examine the question: “How would trade change if South Asia and the rest of the world acted together 24
around 10 percent or more growth in two of the other most populous countries: India and Indonesia.

**Table 5: Passenger Car Fleet in Selected Asian Countries (‘000 cars)**

||**1990**|**1995**|**2000**|**2005**|**Average Annual**<br>**Growth Rate**|
|---|---|---|---|---|---|
|Bangladesh|42|51|70|90<sup>a</sup><br>|5.6%|
|India|2,694|3,841|6,143|8,619<sup>b</sup><br>|9.4%|
|Pakistan|559|773|1,066|1,167<sup>b</sup><br>|5.8%|
|Sri Lanka|174|229|335|444<sup>b</sup>|7.5%|
|Indonesia|1,313|2,107|3,039|5,494<br>|10.0%|
|Malaysia|1,715|2,609|4,213|5,500<sup>b</sup><br>|9.4%|
|Philippines|1,471|2,333|2,156|3,982<sup>b</sup>|8.0%|
|Thailand|1,222|1,913|2,665|2,993<sup>a</sup>|6.6%|
|Vietnam|33|26|44|n.a.|2.9%|
|China|1,622|4,179|8,537|17,359<sup>a</sup>|18.5%|
|Korea|2,075|6,006|8,084|10,621<sup>a</sup>|12.4%|
|Mongolia|8|24|44|80<sup>a</sup>|17.8%|

Source: ESCAP (2007b) Note:<sup>a</sup> data are for 2004;<sup>b</sup> data are for 2003 While all these countries presently have low levels of personal motorization (mostly 2-wheelers in many cases), it is highly probable that these levels will increase dramatically as they achieve economic growth and given the expected increase in urban population (ADB, 2006). The World Energy Outlook 2007 (IEA, 2007h) warns that India is getting closer to the tipping point of US$3000 GDP per capita (based on purchasing power parity, or PPP), after which vehicle ownership rates accelerate swiftly. It also estimates that per capita GDP (PPP) in India will rise to US$13,000 by 2030, and greater buying power of that magnitude can be anticipated to assert itself partly in higher demand for 4 wheel vehicles, such as, cars and sports utility vehicles (SUVs) (ESCAP, 2007a). The Asian Development Bank (ADB, 2006) projects the active population of cars and SUVs in China to grow to 15 times its 2005 size to 193 million in 30 years under a business-as-usual (BAU) scenario; the analogous figure for India is 13 times to 80 million in 2035.

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<!-- Start of picture text --> 80<br>70 BNational @Urban GRural }<br>i"G ; way<br>eee<br>Poest 2003 0 4 = 5 66 67 BD Richest<br>Market Income + Pensions decile<br>Source: Authors’ estimates basedon LCMS 2015.<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>70 National @Urban Rural<br>i3<br>:a | ,<br>é | VA<br>2 1 AVA<br>Poorest 23 4 5 GTB COD Rchest<br>Market Income + Pensions decile<br>Source: Authors’ estimates based on LCMS 2015.<br><!-- End of picture text -->
Table 2: CAFTA-DR results

||(1)<br>Allgoods|(2)<br>Manufacturing|(3)<br>Agriculture|(4)<br>Services|
|---|---|---|---|---|
|RTA|0.13<sup>_∗∗∗_</sup>|0.13<sup>_∗∗∗_</sup>|0.12<sup>_∗∗∗_</sup>|0.06<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.03)|(0.01)|
|RTA_×_ CAFTA|-0.25<sup>_∗∗∗_</sup>|-0.25<sup>_∗∗∗_</sup>|-0.18<sup>_∗∗∗_</sup>|-0.13<sup>_∗∗∗_</sup>|
||(0.04)|(0.04)|(0.04)|(0.02)|
|Total CAFTA|-0.12<sup>_∗∗∗_</sup>|-0.12<sup>_∗∗∗_</sup>|-0.06<sup>_∗∗_</sup>|-0.07<sup>_∗∗∗_</sup>|
||(0.03)|(0.04)|(0.03)|(0.02)|
|Observations|425984|425984|425984|425984|

**Note:** All goods refer to manufacturing and agricultural products. All regressions use the PPML estimator with exporter-time, country-pair and importer-time fixed effects. Border-time controls are also included in all regressions. Robust standard errors, clustered at the country-pair level, are in parentheses. _∗ p <_ 0.10,** _p <_ 0.05,*** _p <_ 0.01

by 31 percent as a result of the agreement. This is an important finding since trade liberalization within the region is critical to enhance regional integration and support the development of regional supply chain linkages.

Even within a given country pair, an RTA does not necessarily affect trade in both directions symmetrically (Baier et al., 2019). We, therefore, further differentiate the effects of CAFTA-DR on Central America’s exports to and imports from the U.S. Column 3 shows that the negative effect is much stronger and more robust for Central American imports from the U.S., which decreased by 25.9 percent. On the other hand, the CAFTADR impact on Central America’s exports to the U.S. is statistically insignificant. While the baseline results are presented for trade in goods, columns 4-6 show the estimates by broad sectors. The positive effect of CAFTA-DR for intra-regional trade is largely driven by the increased trade in the manufacturing sector, which also represents a large share of trade flows for Central American countries. The effect on agriculture and services is not statistically significant.

We now turn to explain these results in which the intra-CAFTA effect is positive, but the one for Central American exports to the US is not significant and negative for US exports to Central America. Rojas Rodríguez and Matschke (2023) discussed the existence of a common trend among CAFTA-DR members to diversify trade from the United States to the rest of the world, explaining their shift away from the U.S. as their main export destination and import source that may have contributed to these results. From our point 14
<!-- Start of picture text --> skilled unskilled<br>1.00 skilled to unskilled:<br>skilled to unskilled: skilled to unskilled: 7.1%<br>19.0%<br>26.5%<br>0.75 stays unskilled:<br>stays unskilled: stays unskilled: 58.9%<br>72.0% 67.7%<br>0.50 stays skilled:<br>stays skilled: stays skilled: 92.9%<br>81.0%<br>73.5%<br>0.25 unskilled to skilled:<br>unskilled to skilled: unskilled to skilled: 41.1%<br>28.0% 32.3%<br>0.00<br>Afghan Iran-matched Iran Afghan Iran-matched Iran<br>Change in occupation type skilled to unskilled stays unskilled<br>for who stays employed stays skilled unskilled to skilled<br>proportion<br><!-- End of picture text --> _Figure 5 - Change in occupation type among people who stay employed in two consecutive years_ <!-- Start of picture text --> 0.75<br>nationality<br>0.50<br>Afghan<br>Iran<br>0.25<br>0.00<br>12 14 16 18<br>monthly per capita expenditure (log)<br>density<br><!-- End of picture text --> _Figure 6 - The distribution of log monthly per capita expenditure by nationality (in fixed IR rials)_ 20
member and records their data in ProGres. The PA is also the person who receives assistance from UNHCR although he/she may not be the only recipient of humanitarian assistance.<sup>8</sup> Although income data are available, we use expenditure data from JD-HV. The existing literature leans toward computing poverty estimates based on expenditure rather than income because (i) income does not capture the possibility that there are people who can live off their savings or have assets to meet their immediate needs; (ii) consumption is smoother and less variable than income, especially in agricultural economies; and (iii) income reporting appears to be more misleading, especially for those with more resources who tend to underreport income more (Carletto et al, 2021; Cutler & Katz, 1991; Deaton & Zaidi, 2002; Meyer and Sullivan, 2003, 2011, 2012).

Aggregations of expenditure categories were used to compute total expenditure; however, the main challenge was constructing comparable poverty estimates across waves given a change in the number of spending categories included in the questionnaire. In the 2013 wave, there were six expenditure categories, namely, rent, utilities, food, health treatment, education, and others. In addition to these categories, the 2018 home visit survey reports expenditure data on water, transportation, infant needs, essential household items, hygiene items, debt payment, and telecommunications. This difference in expenditure categories could inflate reported expenditure in the second wave. This is because survey respondents tend to reveal more accurate information when asked item by item, than if asked for an expenditure or income aggregate. In part, this may be due to an intention to withhold information to obtain the monetary assistance, and in part, it has to do with the recall error that this type of questionnaire elicits.

To aggregate expenditure, we restrict the number of categories to those in both waves which represent a sizeable portion of a household expenditure; that is, rent, utilities, food, water, and transportation. These five categories average 97.5 percent and 77 percent of household expenditure in the first and the second waves, respectively. While spending on healthcare, education, and household items were also recorded in both waves, the amounts were not large and were excluded because health care and education expenditures may reflect a vulnerability (for example, caused by illness or disability in the case of health care), or a luxury for those who can access the service, and

> 8 The monthly per capita cash assistance Syrian refugees receive is roughly 60%-65% of the Survival Minimum Expenditure Basket (SMEB) and is intended to cover rent, water and sanitation costs. Assistance levels are mainly determined by family size. Since 2014, PAs often make the withdraws or their allocated cash assistance using iris scan enabled ATM machines (UNHCR, 2018b).

10
– provision of the road in itself will alter the villagers’ disease environment unless, for example, the excavation work leaves behind fine breeding grounds for mosquitoes, or the easier access to towns and markets increases their exposure to communicable diseases. It remains the case, however, that any attempt to measure the effect of providing an all-weather road on morbidity and mortality must address the question of whether villages with all-weather roads have the same disease environment as those without. This is particularly important when there are no ‘before’ and ‘after’ data, so that comparisons are perforce cross-sectional ones. If, for example, villages that are infested with malaria are specially favored with the provision of all-weather roads by way of compensation, or, on the contrary, neglected as hopeless cases, then comparing villages with and without such roads will be hazardous where drawing inferences is concerned. As in Section 4, we begin with the data from the household survey, which cover individuals’ life histories (births and deaths), but their ailments only in the _kharif_ season of 2009.

# **5.1 The household survey: Morbidity**

In the absence of ‘before’ and ‘after’ data, the only available measure of the disease environment where morbidity is concerned is the incidence of disease among the 1,292 villagers comprising the sample, 455 of whom were residents of the 11 villages with an all-weather road. The simplest index of morbidity is the fraction of the population reporting not a single episode of sickness during _kharif_ 2009: in villages with and without such a road, respectively, this fraction was 55.4 and 56.4 percent. A small refinement is to break down the category of the sick into those suffering one, two and three or more episodes, respectively; but this alters the finding not at all ( _χ_<sup>2</sup> (3) = 2 _._ 30). A further alternative is to break down episodes of sickness by ailment: viral fever, malaria, influenza and colds, and others, whereby sick individuals are classified by their first ailment in _kharif_ 2009 (about one-half of them had two or more episodes). With ‘healthy’ as the complementary status, the null hypothesis that there is no difference in the two patterns of incidence is not quite rejected at the 10 percent level ( _χ_<sup>2</sup> (4) = 7 _._ 33). We conclude that, based on the observed incidence of resulting episodes of illness, there are no differences in the disease environments where morbidity is concerned.

Although the environments appear to be equally hazardous to health, it could be that better and more timely treatment shortens the individual episodes of illness. The 455 residents of villages with an all-weather road had, on average 5.77 days of sickness; the average for the 837 residents of those without was 5.63. Discarding those who were 26
Heerink, Nico, and Henk Folmer, 1994. "Income Distribution and the Fulfilment of Basic Needs: Theory and Empirical Evidence," Journal of Policy Modeling, 16:625-652.

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Jamison, Dean T., Jia Wang, Kenneth Hill, and Juan-Luis Londoflo, 1996. "Income, Mortality and Fertility Control in Latin America: Country-Level Performance 1960-90," mimeo, LAC Technical Department, The World Bank. Washington, DC.

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underscores the importance of carefully measuring variety growth when making price and welfare calculations. The measure also accounts for the importance of different varieties to the consumer budget decision by using expenditure shares as weights. Lower lambda ratios mean there are more varieties, and more is spent on new varieties.

Table 3: _Descriptive Statistics of Lambda Ratios_

|||_Statistics_|||
|---|---|---|---|---|
||_Country_|_Percentile 5_|_Percentile 50_|_Percentile 95_|
||||_(Median)_||
|1|Bhutan|0.180621|1|4.873188|
|2|Brunei Darussalam|0.2329797|0.9998763|3.409473|
|3|Cambodia|0.2241164|0.9998575|3.127603|
|4|China|0.753171|0.9998782|1.271071|
|5|Congo, Rep.|0.1689567|0.9998815|4.86562|
|6|Fiji|0.2912371|1|2.729448|
|7|Hong Kong SAR,|0.73206|0.9999413|1.296693|
||China||||
|8|Indonesia|0.6388264|0.9999301|1.502139|
|9|Japan|0.8844972|0.9999906|1.127803|
|10|Kenya|0.2560976|0.9989868|3.221963|
|11|Korea, Rep.|0.8347616|0.9999483|1.178565|
|12|Lao PDR|0.2689466|1|2.75684|
|13|Macao SAR, China|0.3357269|1|2.712074|
|14|Malawi|0.1913071|1|4.847844|
|15|Malaysia|0.6697937|0.9998229|1.391351|
|16|Mauritius|0.3905521|0.9999442|2.413027|

18
16 and Khan and Knight (1984) among others), and a second model where we assume that the money market continuously clears. For the partial adjustment model we estimated: (13) mt - raO + **_Tal_** it + (1-T) mt_l + t P/t where **_T_** - speed of adjustment of the money market.

- For the market clearing model (mt mtd, for all t) we estimated:

(14) mt - ao + a, it + pt The results from estimating (13) and (14) for the 'tablital period are presented in table 110 11, The Durbin-Watson statistic is reported for the market clearing model and Durbin's (1970) h-statistic is reported for the partial adjustment model. We observe that the assumptions regarding the :orrelation if interest rates and money market disturbances as well as the speed of adjustment of the money market do not significantly affect the estimated values of the money demand's structural parameters. However, the 10 Two methods were employed to correct the model for seasonal correlation in the residuals. The first one was to assume a seasonal 12th order moving average process. The second one was to introduce a seasonal dummy fcr the aO that is equal to 1 every December and zero otherwise. We did not use a seasonal auto-regressive representation for the pt's or a full fledged X-ll methcd due to the small number of observations (25).

11 The data employed, all from DATAPIEL, are the end of period interest rates on 30 day deposits in Buenos Aires (measured in percent per month), the end of period real stock of money is the ratio of the end of period stock of nominal Ml to the end of period consumer price index (1974-100). End of period prices were taken to be the mean of the monthly average price (CPI) in t and in t+l - i.e. Pt - .5(AvPt + AvPt+1).