rafmacalaba/datause-encoder-data huggingface dataset:
34 of developing countries' textile exports face restrictions while the coverage ratio for clothing is about 63 percent. In contrast, under 5 percent of OECD intra-trade in these goods encounter restrictions (mainly goods shipped from Japan). The Multifiber Arrangement, special textile quotas, bilateral quotas, and voluntary export restraints account for these major differences. Nontariff barrier coverage ratios for developing countries' footwear exports are about 20 points higher than on OECD intra-trade of these goods. "Voluntary" export restraints imposed by the EU and EFTA largely account for these differentials.'<sup>6</sup> Table 7 shows OECD coverage ratios are not always higher for developing countries' exports. Twenty five percent of OECD intra-trade in foods encounter NTBs compared to the 17 percent coverage ratio for shipments of these goods from developing countries. The difference is due to the fact that tropical food products like tea, coffee, and cocoa -- which account for approximately 15 percent of all developing countries' food exports - - face relatively few OECD nontariff barriers. Most agricultural NTBs are applied to temperate zone products (particularly grains and dairy products) which are mainly exported by other OECD and some developing countries, like Argentina, outside the tropics. Sugar, which is produced in temperate zone countries (from beets) as well as in the tropics (from cane) is an exception. Some African and other developing country oilseeds and vegetable oils, which can be substitutes for temperate zone products, encounter NTBs in the form of European variable import levies.

Table 7 indicates the profile of nontariff protection against sub-Saharan African exports differs somewhat from other developing countries. First, only about 11 percent of African non-fuel exports face NTBs as opposed to the 17 percent average for all developing countries. The lower NTB coverage ratio is largely accounted for by the fact that most African countries' textile and clothing products are not affected by MFA restrictions. Mauritius is a noteworthy exception with $116 million, or 88 percent of its textile and clothing exports to the United States, covered by quotas. Only 19 percent of African textile exports face NTBs, as opposed to 53 percent for all "<sup>6</sup> Evidence shows that textile and footwear restrictions have major distorting effects on the exports of developing countries who face the measures. For example, the US International Trade Commission estimates of tariff plus NTB protection for 54 broad classes of textile and clothing products. The estimates range to over 100 percent with the nontariff barrier component of total protection generally being far higher than that of tariffs. It is generally held that levels of nontariff protection against textiles and clothing in Europe are of a similar magnitude to that of the United States,
**Adams,** Charles **and Jeremy Oreenwood** (1985), 'Dual **Exchange Rate System and** Capital Controls: An Instigation,' Journal oi18 43-63.

**Shagwati,** J&gdish and Sent Hansen (1973), **'A** Theoretical Analysis of Smuggling,' Omst.ly Jounal2 o Eonomi, & may, reprinted in **Shagwati, ad.** (1974), Illegal Trstions in 04tLanal TrAde (Amterdam, NorthHolland).

uilter, Will.s (1988), "Some Thoughts en **the** Role of Fiscal Policy In Stabilization and Structural Adjustment in Developing Countries," *lmo. Devaraj an, Shantayanan and Jaim de Melo (1987), "Adjustment With a Fixed Exchang Rate: Camroon, Coto d'Ivoire, and Senegal," World Bank EcoLi Rtviex 1, 1o. 3: 47-487.

Dornbusch, Rudiger (1983), **"Real Interest Rates,** Home Goods, and Optimal External Borrowing,' JoU=r allo PoliticLal egon 91, No. 1, February: 141153.

Gavin, Michael (1988), -Structural Adjustmen **t** o a Terms of **Trade** Dicturbanco: **The** Real Exchange **Rate,** Stock **Prices** and the Current Account,' mlmeo, Coluwbia **UnLversity.** Krui, Kathie (1987), -Adjustment in the Franc Zone: Focus on **the Real** Exchange nate,' World Sank, CPD DiLcussion Paper No. 1987-7, April.

- ----------- (1985), 'Adjustment Policies in the Ivory Coast in **the** Fromwork of UMOA,' World Bank, CPD DiscuseLon Paper No. 1985-6, January.

Macedo, Jorge Braga de (1987), 'Curreacy Inconvertibility, **Trade** Taxes and Smuggling,' Journal of International Economic 27: 109-125. MartLn, Lawrence and Arvlnd Panagariya (1984), 'Smuggling, Trade, and Prieo Dlsparity: A Crime-Theorotic Approach,' Journal of Interainl Z&onomic 17: 201-217. Obstfeld, Maurice (1986a), 'Capital Controls, the Dual **Exchange** Rate, **and** Devaluation," Journal gf International Economics Vol. 20: 1-20.

--- -- (1986b), 'Capital Flown, the Current Account, and **the Real** Exchange Rate: Some Consequencos of Liboralization and Stabilization,' in Edwards and Ahmed, *ds, Economic dustmnt d Ercanga **Rates** in D oIg f&mz=;aa (Chicago, University of Chicago Press). ......----..----- (1981), 'Capital Mobility and Devaluation in an Optimizing Model With Rational Expectations,' rican Economic Review, Papers _nd 1r;J.gdUnga Vol. 71: 217-221.

46
# **Figure 1**

<!-- Start of picture text --> Migrant in NZ Succ Bal in Tn Migrant in NZ Succ Bal in Tn<br> Unscucc Bal Unscucc Bal<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>15 16 17 18 19 20 21 22 23 24 25 15 16 17 18 19 20 21 22 23 24 25<br>Mental Health Index (25 = Best) for Men >=15 Mental Health Index (25 = Best) for Women >=15<br> Migrant in NZ Succ Bal in Tn Migrant in NZ Succ Bal in Tn<br> Unscucc Bal Unscucc Bal<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>15 16 17 18 19 20 21 22 23 24 25 15 16 17 18 19 20 21 22 23 24 25<br>Mental Health Index (25 = Best) for Men >=15 Mental Health Index (25 = Best) for Women >=15<br>Mental Health by Gender<br>Density Density<br>CDF CDF<br><!-- End of picture text -->

# **5.4. Non-linear treatment effects**

We take two approaches to estimating non-linear treatment effects. First, we examine the impact of migration on the probability of having a mental health score below various thresholds. The rationale for this is that we might expect improvements in mental health to only occur for individuals who would have poor mental health in the absence of migration. Individuals who are already happy and suffer from no signs of mental distress may not see improvements in mental health. This is clearly true in mechanical matter at the very top of the distribution, but may also occur for the individuals in the upper-middle of the distribution.

Different thresholds correspond to different degrees of mental distress. While there is no universal choice of a threshold, several studies of general populations have used a cut-off of less than 17 as an indicator of major depression, and a cut-off of less than 19 as an indicator of poor 16
- 36 -

scheme. If it is based on the shared savings arrangement, investment is funded by a contractor.<sup>30</sup> The central government often subsidizes part of investment in ESCO projects in Japan. In most cases, one-third to a half of the eligible investment is supported by the central government. The average amount of subsidy is JPY63 million, if it is granted. It is often that 70-90 percent of the total investment is eligible for the central government’s subsidies. Thus, the amount of subsidy will be JPY50-80 million if the subsidy rate is one-third.

The energy efficiency investment is expected to bring about a 20 percent reduction of energy use. This does not seem different across types of facilities (Figure 10). Notably, these levels of energy savings are not particularly high by global standards. Many public buildings in developing countries can easily achieve 20 to 40 percent of energy savings by retrofitting existing equipment (Singh et al. 2010).

It is worth noting that the Japanese economy is generally energy-efficient, and so are public buildings. The survey data used herein show that the average building-specific energy use is about 150kWh per m<sup>2</sup> , and most facilities consumed less than 200kWh per m<sup>2</sup> even before the energy efficiency projects (Figure 11).<sup>31</sup> Hence, it is very likely that the expected energy savings would be modest, regardless of the relatively large amount of investment.

The reduced energy use has two implications: On one hand, facility owners can save their energy spending. This amounts to on average JPY30 million per annum. Notably, most of these expected savings will be paid to contractors as service fees. On the other hand, the reduced energy use also means a reduction of CO2 emissions, which amount to 790 tCO2 in a

> 30 The maximum investment in our sample amounted to JPY1.7 billion in the case of a hospital in the prefecture of Kanagawa, which has 12 floors and consumes more than 40GWh.

> 31 European average building-specific energy use is 203kWh per m2 (Kingspan Insulated Panel 2011).
35. Number of children: Total number of alive children a woman had at the time of the baseline survey.

36. At least one son: Indicator variable that equals one if the woman had at least one son at the time of baseline survey.

37. Co-residence with mother-in-law: Indicator variable that equals one if a woman coresides with her mother-in-law at the time of baseline survey.

38. MIL opposed to FP: Indicator variable that equals one if a woman expressed at baseline that one of the disadvantages of using FP is that her mother-in-law is opposed to it.

39. Valued concealability of FP method: Indicator variable that equals one if a woman expressed at baseline that one of the most important features of a contraceptive method is that it can be used without anyone else knowing.

40. Found FP embarrassing to use: Indicator variable that equals one if, at baseline, a woman expressed that one of the disadvantages of using family planning is that it is embarrassing to use.

41. Lack of close peers in village that use FP: Indicator variable that equals one if none of the woman’s close peers in her village at baseline were using a family planning method at baseline.

42. Poor: Indicator variable that equals one if, at baseline, a woman’s household is below the poverty line and in the bottom two terciles of the asset index distribution, and is zero otherwise.

43
# **Table 2: OLS Estimates of Wage Determinants using Occupational versus Industry Offshoring Exposure, 1983-2002**

Dependent Variable: Log Wage

||Industry-Sp <br>|Offshoring<br>ecific Expo<br>|Measured by<br>sure,Manufactu<br>|ringOnly|Occupati<br>|Offshoring<br>on-Specific|<br>Measured by<br>Exposure,All|Sectors|
|---|---|---|---|---|---|---|---|---|
|Variable|All<br>Occupations|Most<br>Routine|Intermediate<br>Routine|Least<br>Routine|All<br>Occupations|Most<br>Routine|Intermediate<br>Routine|Least<br>Routine|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Lagged log of low income|0.001|0.002|0.001|0.003|-0.045**|-0.073***|0.010|0.062|
|affiliate employment|(0.00)|(0.00)|(0.00)|(0.00)|(0.02)|(0.02)|(0.03)|(0.07)|
|Lagged log of high income|0.014**|0.010*|0.010|0.023**|0.039**|0.053***|0.007|-0.035|
|affiliate employment|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.03)|(0.07)|
|Lagged import penetration|0.07|0.11|0.019|-0.056|-0.290***|-0.289***|-0.944|1.024|
|using 1979 weights|(0.08)|(0.08)|(0.08)|(0.10)|(0.09)|(0.10)|(0.59)|(0.65)|
|Lagged export share using|0.04|-0.01|0.012|0.077|0.234|0.636***|0.278|-0.845**|
|1979 weights|(0.06)|(0.07)|(0.06)|(0.05)|(0.16)|(0.19)|(0.19)|(0.39)|
|Number of observations|586,499|337,295|159,538|89,666|3,068,095|1,109,836|1,156,207|802,052|
|R-squared|0.46|0.39|0.41|0.38|0.50|0.42|0.54|0.40|

# _Source_ : See Table 1.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. The classification of occupations into routine categories is determined by the proportion of tasks which are routine in each occupation, with low being occupations with more than 2/3, intermediate being between 1/3 and 2/3, and high being occupations with less than 1/3 of tasks designated routine. Controls are included for lagged log price of investment, lagged total factor productivity, and lagged log real price of shipments among manufacturing workers. Wage specifications control for a worker's gender, age, race, experience, whether in a union, and include industry, year, education and state fixed effects. The occupation-specific exposure regressions also include 2-digit occupation fixed effects. Controls for lagged computer use rates are imputed by the worker's industry (columns 1-4) and by occupation (columns 5-8).
7 tends to raise the ratio of a constant amount of foreign currency denominated liabilities to GDP. The only large rises in foreign liabilities over the period were in the Transition countries, and in Thailand. And the Thai banks’ liabilities at the end of the decade were down significantly from their high levels of the mid-1990s.<sup>9</sup>

**Figure 2.  Banks'  Gross Foreign Liabilities, 1990-2000**

<!-- Start of picture text --> 45 45<br>Thailand<br>40 40<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>1989 1991 1993 1995 1997 1999 2001<br>Foreign Liabilities Ave. For. Liab.<br>Percent of GDP<br><!-- End of picture text --> Focusing only on the end-points of 1990 and 2000 obscures the cycle in gross foreign funding of developing country banks during the decade. On average, gross foreign funding rose as a percentage of GDP in nearly every year from 1990 to 1997. The only exception was 1994, when the Mexican crisis began. However, after 1997, gross foreign funding fell as a percentage of GDP, on average, in the 25 countries. Offshore banks reduced their short-term credit lines and their long-term lending as it was amortized. Meanwhile the banks in the 25 countries reduced their exposures to reduce devaluation risk. Again note that the falls in dollar funding were even larger in percentage terms, because the real depreciations increased the ratio of a given amount of foreign currency liabilities relative to GDP.

Meanwhile, the average gross foreign assets of banks in the 25 countries generally rose over the decade (Figure 3).<sup>10</sup> This rise probably reflected banks’ attempts to hedge risks off-shore, once the capital account was liberalized, particularly in countries where dollar deposit liabilities became important. Again, the movements over time also include the effect of the real depreciations.

The net result of the rise in assets and fall in liabilities was that by the end of the

> 9  The Thai data reflect a) the operation of the Bangkok International Bank facility, which operated an onshore, off-shore banking facility, in which domestic citizens could deposit in off-shore banks and funds were then lent back to on-shore banks, often in foreign currency, b) guarantees that became actual liabilities, c) the large devaluation in 1997, and d) after 1997, the takeover by the Thai government of some of the foreign currency liabilities of the banks.

> 10 See also Honohan and Shi, 2003.
<!-- Start of picture text --> 60<br>c 50<br>2<br>bb<br>= 40<br>a<br>°<br>a<br>wm 30<br>3<br>-<br>‘6 20<br><!-- End of picture text --> <!-- Start of picture text --> x<br>100 :<br>2005 2012 2005 2012<br>Vulnerability index 0.2 2x PL<br>m@ Poor mVulnerable m Middle-class<br><!-- End of picture text -->
not impacting our results), but the difference between the two groups of establishments is not statistically significant.

Figure 4: Relationship between management and productivity, internally‐ versus externally‐recruited managers

# 5. Conclusions

Despite some promising recent developments in some countries in the region, the fact remains that productivity in South America has not grown at the rates seen in other developing economies; between 1960 and 2017, annual total factor productivity growth in emerging Asian economies was 0.97 percent, while in the LAC region it contracted by 0.11 percent. This has important implications for economic growth and poverty reduction – income per capita in emerging Asian economies increased from 11 to 58 percent of the US level, while in the LAC region, the increase in per capita income relative to the US over the same period was from 20 to 24 percent.<sup>7</sup> If firms’ productivity can be boosted by improved management practices, this may be a cost‐effective way of accelerating economic growth and boosting prosperity. Previous studies have shown that improving the management practices of firms in the developing world can have large positive impacts on productivity, and that one reason for which better management practices are not more widely‐used is that managers are unaware of them. This suggests that simply 7 IDB, 2018.

28
# **Table A.3 Summary of the investment climate assessment (ICA) surveys, sorted by geographical area**

|||Year of<br>survey|Years of production<br>function (PF)<br>variables|Total number of<br>observations<sup>1</sup>|Final number of<br>observations available for<br>regression analysis<sup>2</sup>|
|---|---|---|---|---|---|
||Algeria|2002|2000–1|952|706|
|North Africa|Egypt|2004|2001–3|2,931|2,629|
||Morocco|2003|2000–2|2,550|2,422|
||Senegal|2003|2000–2|783|535|
||Benin|2004|2001–3|591|475|
||Mali|2003|2000–2|462|309|
|Economic Community of<br>|Cape Verde<sup>3</sup>|2006|2005|47|47|
|West African States<br>(ECOWAS)|Mauritania*|2006|2005|80|80|
||Burkina Faso*|2006|2005|51|51|
||Niger*|2005|2004|64|48|
||Cameroon*|2006|2005|119|118|
|Horn of Africa|Ethiopia**|2002|1999–2001|1,281|1,142|
||Eritrea**|2002|2000–1|237|179|
||Kenya|2003|2000–2|852|577|
|East African Community|Uganda|2003|2001–2|900|635|
|(EAC)|Tanzania|2003|2000–2|828|561|
||Burundi<sup>3</sup>|2006|2005|102|101|
||Malawi|2005|2004–5|320|288|
||Madagascar|2005|2002–4|870|623|
|Southern African|Zambia|2002|1999–2001|564|417|
|Development Community|Lesotho<sup>3</sup>|2003|2000–2|225|79|
|(SADC)|Botswana***|2006|2005|114|112|
||Namibia***|2006|2005|106|104|
||Swaziland***|2006|2005|70|69|
|Mauritius||2005|2002–4|636|417|
|South Africa||2003|2001–2|1,737|1,492|

_Source:_ Authors ́ calculations **;** ICA data **.** Note: 1 Total number of observations is equal to the total number of firms surveyed multiplied by the total number of years.

2 The observations available for regression analysis are the total number of observations minus the observations with any PF variable missing and/ or outlier after the cleaning process.

3 Countries for which no regression analysis was conducted.

* Countries pooled for regression analysis: Mauritania, Burkina Faso, Niger, and Cameroon.

** Countries pooled for regression analysis: Ethiopia and Eritrea.

*** Countries pooled for regression analysis: Botswana, Namibia, and Swaziland.

Table A.3 offers an initial overview of the data we use in the analysis. We have data for 26 countries from five different geographical regions. Cape Verde, Lesotho, and Burundi are special cases. The PF information for Lesotho is rather poor and it is impossible to make reliable statistical inferences 17
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ideal setting for the application of this methodology. With a wide range of agro-ecological zones, there is considerable variation in agricultural productivity and climate across the sub-continent. Also, Indian agriculture is expected to be especially hard-hit by warming temperatures in the coming decades, although not necessarily uniformly (World Bank, 2009). While agriculture contributes only about a fifth of GDP, productivity growth in this sector has been the major driver of poverty reduction in India, largely through increases in agricultural wages (Datt and Ravallion, 1998; Eswaran, et al. 2008; Lanjouw and Murgai, 2009). Data for India are relatively abundant as well, our main source being the nationally representative National Sample Surveys (NSS). The 2002-03 NSS round collects detailed farm-level information and the 2004-05 round includes both a huge household expenditure survey and an employment survey. These data are supplemented with a district-level panel of agricultural output and weather.

Past empirical literature focuses on predicting the impact of a changing climate on national or global agricultural product, largely ignoring the question of how damages will be distributed across the relevant population. There are three distinct approaches to making such predictions. Agronomists base crop damage assessments on temperature and, perhaps, precipitation responses taken from agricultural field trials. More sophisticated crop modeling (e.g., Parry et al. 1999) also accounts for certain adaptations to climate change, such as shifts in planting dates, increased irrigation, and changes in crop varieties. However, even this limited set of adaptations is _assumed a priori_ , rather than necessarily reflecting how future farmers would behave. Economists prefer to use data that reflect actual farmer behavior. The hedonic or so-called Ricardian approach, pioneered by Mendelsohn, et al, (1994), infers the impact of climate change on future agricultural productivity from the present-day cross-sectional relationship between climate and land values. This allows for as much (but no more) adaptation than is revealed in the data. Predictions based on reduced-form Ricardian regressions presuppose that the technological envelope along which farmers will adapt in the future will be the same as it was in the past.<sup>2</sup> In response to perceived empirical deficiencies of such cross-sectional analyses, Deschˆenes and Greenstone (2007) propose using panel data on farm revenue to estimate the response of agricultural productivity to year-toyear weather fluctuations. However, since there is relatively little scope for adaptation to short-run weather, damage estimates based on shocks can only provide an upper bound on the long-run impact of climate change on agricultural productivity; there is no guarantee, moreover, that this bound will be very informative.

Our Ricardian estimates indicate that warming of about 1.25<sup>_◦_</sup> C expected to occur in

> 2The Ricardian approach also ignores the costs of adjusting along the technological frontier (see, e.g., Quiggin and Horowitz, 2003; Kelly et al., 2005).

2