rafmacalaba/datause-encoder-data huggingface dataset:
This paper, in contrast to previous research, estimates the half-life of RER deviations from a theoretically-based equilibrium level while modeling RER misalignments. The average estimate of the half-life is approximately equal to 2.8 years —which is close to the lower bound of the consensus interval estimated by Rogoff (1996). In addition, 42.5 percent of our half-life estimations are less than 2.5 years that falls into nonlinear half-life estimation as suggested by Taylor et al. (2001) for large deviations from the equilibrium. In this sense, our unrestricted VECM estimations —which account for adjustment in fundamentals— are able to capture estimates of half-life that reconciles both strands from the empirical literature. Our estimates show a normally distributed histogram of α coefficients for the full sample of countries. Consequently, α coefficients are statistically significant and support the evidence of our empirical modeling of RER misalignments.

The IRFs obtained from the dynamic system estimation supports the predictions of the theoretical model of RER determination; therefore, an equilibrium real appreciation follows a positive and permanent shock in TOT, the ratio of NFA to GDP and HBS productivity differentials.

Further avenues of research in this area —and beyond the scope of this paper— include the characterization of the persistence of RER deviations (as proxied by the α coefficient) and its linkages to the macroeconomic policy framework in place. For instance, one would test whether countries with higher α ( _i.e._ lower speed of mean reversion) display more flexible exchange rate arrangements, have more rigid labor or output market regulations, or have substantial dollarized liabilities, among other factors.

13
but not of girls. The higher the sibling rank of a boy, the more likely he is to work. The same is not true for girls--lower sibling rank girls are as likely to work as higher ones. There is an interesting effect of female headship of household on child labor. In rural areas, female headship results in lower child labor among boys but no difference for girls. Similarly, the education of the oldest prime-age female in the household tends to have a beneficial effect on the child labor of boys but not girls. In rural areas, household wealth is negatively associated with the child labor decision of girls but not of boys. Thus as households get richer, there is a positive effect on the time use of girls, but not of boys. Interestingly, the age composition of the household does not affect the propensity of either boys or girls to work. However, the size of the household does tend to lower child labor. As household size increases, the propensity for work of children falls, with the effect for boys being statistically significant at the 5% level. This result is counter to what one would expect given that larger households also tend to be poorer in Peru (World Bank, 1998). Neither the presence of prime-age females in the household nor female unemployment rates at the cluster level have any effect on child labor. Household access to water services has a significantly negative effect on child labor in urban areas. This suggests that there may be complementarities between work and obtaining water. No such effect appears in the rural areas. The results for the effect of sickness and disease of children and adults on child labor are also interesting. In rural areas, the sickness of a child in the household forces girls to withdraw from work (presumably to take care of the sick) with no effect on boys. On the other hand, the presence of a sick adult in the household forces girls into child labor more than it does boys. Both these results suggest that young girls in rural Peru may be particularly vulnerable to sickness in the household.

# V. Policy Lessons

30 Recall in this paper sickness is considered endogenous at the household level. Instrunents of sickness 22
|**Non-Ferrous**|0.088|0.040|0.022|
|---|---|---|---|
|**Metal Products**|**0.011**|**0.011**|0.042|
|**Machinery**|0.147|0.021|0.086|
|**Electrical Apparatus**|0.023|0.009|0.010|
|**Transport Equipment**|0.004|0.003|0.007|
|**Professional Equipment**|**0.000**|0.002|0.021|
|**Other Manufacturing**|0.001|0.003|0.010|

Source: SNIFF (INE/SEMARNAP) Emissions intensity is measured so far by the physical volume of emissions per employee. A true measure of pollution intensity, however, must also consider the impact of emissions on air quality. Technical models of atmospheric dispersion treat large and small facilities separately, because of differences in average stack heights. Large plants have higher stacks, producing substantially lower pollutant concentration per unit of emissions. The standard dispersion model used for World Bank project analysis<sup>4</sup> implies that each unit of particulate emissions from a small plant increases air pollution approximately 14 times more than a unit of emissions from a large facility because of the difference in stack size. In combination with the broad pattern noted in the regressions, and even relative to almost all of the sector specific patterns in Table 1, this implies much greater pollution per employee for small plants.

# **4.2 Small Plants, Dirty-Sector Location and Level of Development: Evidence from Brazil**

Numerous studies have identified six industry sectors as exceptionally pollution-intensive: Iron and Steel, Petroleum and Coal Products, Metal Products, Pulp and Paper, Chemicals, and Food Products (Robison 1988, Tobey 1990, Mani 1996, Mani and Wheeler 1997). In this section, the industry share

> 4 This model is embedded in the environmental Decision Support System (DSS), which is available at the Website: http:/lwww-esd.worldbank.org/pollution/dss

10
_50_ Table 3 Correlation Matrix of Residuals of the Intrinsic Bubble Model 1980-2001

||RINTRINBUB<br>ARG|RINTRINBUB<br>BRA|RINTRINBUB<br>CHL|RJNTRINBUB<br>COL|RINTRINBUB<br>MEX|<br>RINTRINBUB<br>US|
|---|---|---|---|---|---|---|
|RINTRIJNBARG|||||||
||1.00|0.03|0.29|-0.21|<br>0.09|<br>-0.16|
|RINTRINBUB<br>BRA|0.03|1.00|0.37|0.47|0.23|<br>40.24|
|RINTRINBUB<br>CHL|0.29|0.37|1.00|0.33|0.37|<br>-0.29|
|RINTRINBUB<br>**COL**|-0.21|0.47|0.33|1.00|0.26|<br>-0.09|
|RINTRINBUB<br>MEX|0.09|0.23|0.37|0.26|1.00|<br>0.08|
|RINTRINBUB_US|-0.16|-0.24|-0.29|-0.09|0.08|<br>1.00|
**Table 8: A comparison of actual and expected profit maximizing nitrogen application rates for maize in Nigeria for risk averse farmers**

||**Percenta**<br>**ge of**<br>**plots**<br>**using**<br>**fertilizer**|**Mean of**<br>**the**<br>**Actual**<br>**applied**<br>**Nitrogen**<br>**(kg/ha)**|**Mean of the**<br>**estimated**<br>**expected profit**<br>**maximizing**<br>**level of**<br>**Nitrogen**<br>**application**<br>**(kg/ha) at**<br>**acquisition cost**|**Percentage**<br>**of plots**<br>**whose**<br>**applicatio**<br>**n rate is**<br>**less than**<br>**optimal**<br>**(%)**|**Mean of the**<br>**estimated**<br>**expected**<br>**profit**<br>**maximizing**<br>**level of**<br>**Nitrogen**<br>**application**<br>**(kg/ha) at**<br>**marketprice**|**Percentage**<br>**of plots**<br>**whose**<br>**applicatio**<br>**n rate is**<br>**less than**<br>**optimal**<br>**(%)**|
|---|---|---|---|---|---|---|
|Cereal-root<br>crop farming<br>system (C-<br>RCFS)|67.16|76.01|37.75|**16.71**|103.2|**44.76**|
|Tree crop<br>farming system<br>(TCFS)|34.18|49.59|28.95|**35.51**|60.07|**67.94**|
|Root crop<br>farming system<br>(RCFS)|18.07|45.13|39.03|**38.67**|83.09|**62.03**|

_Source: Authors estimations from the LSMS-ISA data. Values are means over the two survey years_ We also compare the effect of taking into account full acquisition costs versus the market price. Table 8 shows how using the market price without consideration for transportation costs would lead to much higher optimal rates of nitrogen application. Properly reflecting the high acquisition costs reveals optimal rates closer to the application rates than when using the observed market price. Figure 4 supports Table 8 and indicates that there is a better correspondence between observed nitrogen application rates when full acquisition costs are used compared to when market prices are used. This is seen by the tighter fit around the 45 degree line when acquisition cost is considered.

26
# **Table 10**

**Drivers of Gross Capital Inflows: NATURAL RESOURCE ABUNDANT COUNTRIES, 1980‐99 vs. 2000‐17** _Dependent Variable: GROSS Inflows, Total and By Type (% GDP) Sample: 104 countries, 1980‐2017 (annual) Estimation method: Instrumental Variables (IV)_

|Variables|Total<br>1980-99|Total<br>2000-17|FDI<br>1980-99|FDI<br>2000-17|PI<br>1980-99|PI<br>2000-17|OI<br>1980-99|OI<br>2000-17|
|---|---|---|---|---|---|---|---|---|
|_Domestic Factors_|||||||||
|GDP Growth|0.00979|-0.0506|0.00649|0.0134|-0.000404|-0.00165|0.00371|-0.0581|
||(0.0258)|(0.0347)|(0.0119)|(0.0152)|(0.00396)|(0.00249)|(0.0220)|(0.0382)|
|GDP per capita|0.689|-0.824|0.327|0.185|0.0115|-0.0300|0.350|-0.903|
|_(in logs)_|(0.842)|(0.565)|(0.386)|(0.292)|(0.129)|(0.0464)|(0.716)|(0.623)|
|CPI Inflation|0.00165|0.000702|0.000521|-8.78e-06|-4.47e-05|-0.000130|0.00117|0.000880|
||(0.00103)|(0.00272)|(0.000473)|(0.000910)|(0.000158)|(0.000154)|(0.000876)|(0.00300)|
|Primary Balance|-0.00329|0.00483|0.000900|-0.00546***|-0.000150|0.000210|-0.00404|0.00502|
|_(% of GDP)_|(0.00898)|(0.00440)|(0.00412)|(0.00106)|(0.00138)|(0.000181)|(0.00764)|(0.00486)|
|Exchange rate flexibility|-0.0111|-0.0116|-0.000572|0.00326|0.000162|-0.00246*|-0.0107|-0.00927|
||(0.00990)|(0.0152)|(0.00454)|(0.00868)|(0.00152)|(0.00146)|(0.00842)|(0.0168)|
|Investment Profile|0.00696|0.0166|0.0134|0.00649|0.000746|0.00251**|-0.00717|0.00547|
||(0.0197)|(0.0172)|(0.00905)|(0.00674)|(0.00302)|(0.00123)|(0.0168)|(0.0189)|
|Trade Openness|-0.0321|0.312|0.0135|-0.0956|0.0162|0.0186|-0.0617|0.351|
|_(% of GDP, logs)_|(0.112)|(0.233)|(0.0515)|(0.0939)|(0.0172)|(0.0164)|(0.0954)|(0.257)|
|Financial Openness|0.00798|-0.0176|0.00113|-0.00529|-0.00257|-0.00304|0.00942|-0.00635|
|_(Chinn‐Ito Index)_|(0.0295)|(0.0404)|(0.0136)|(0.0131)|(0.00452)|(0.00224)|(0.0251)|(0.0446)|
|_External Factors_|||||||||
|Foreign Growth|0.0130|-0.00301|-0.000937|0.00827|-0.00503*|-0.00317**|0.0189|-0.00282|
||(0.0176)|(0.0170)|(0.00809)|(0.00883)|(0.00270)|(0.00145)|(0.0150)|(0.0188)|
|VIX Index|-0.0588|-0.384*|-0.00399|0.0775|-0.00540|-0.0278*|-0.0494|-0.392*|
||(0.0499)|(0.203)|(0.0229)|(0.102)|(0.00765)|(0.0162)|(0.0425)|(0.224)|
|US Economic Policy|0.00170|0.00490**|0.000684|-0.000951|-0.000163|0.000230|0.00118|0.00503*|
|Uncertainty|(0.00388)|(0.00241)|(0.00178)|(0.00100)|(0.000594)|(0.000164)|(0.00330)|(0.00266)|
|Commodity Terms of|-0.276|0.0157|-0.228|0.0257|0.0466|0.00354|-0.0950|-0.00337|
|Trade (in logs)|(1.384)|(0.0927)|(0.635)|(0.0367)|(0.212)|(0.00604)|(1.178)|(0.102)|
|Oil Price|0.0933|-0.328|0.00481|0.0656|-0.00990|-0.0200|0.0984|-0.323|
|(in logs)|(0.104)|(0.239)|(0.0478)|(0.101)|(0.0160)|(0.0169)|(0.0886)|(0.264)|
|<br>Minerals and Metals|-0.153|0.363|-0.0493|-0.0268|0.0160|0.0154|-0.120|0.335|
|Prices (in logs)|(0.162)|(0.295)|(0.0745)|(0.135)|(0.0249)|(0.0210)|(0.138)|(0.326)|
|Observations|66|244|66|270|66|274|66|244|
|No. Countries|9|17|9|18|9|18|9|17|

_Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1_ 28
Policy Research Working Paper 10671

# **Abstract**

Healthy diets have been characterized as responding to four universal principles—nutrient adequacy, dietary diversity, macronutrient balance, and moderation. With rising incomes, diet concerns globally have shifted from inadequacy of nutrients and lack of diversity, to lack of balance and moderation. This has occurred alongside declining rates of stunting and wasting in children under five and increasing rates of overweight and obesity across a broad age span. Calculations undertaken for low- and middle-income countries for policy and advocacy purposes of the economic cost of unhealthy diets have used nutritional status as a proxy or have made estimates of the impact of noncommunicable diseases by simply adding up known risks of individual diet factors. Both these methods have problems. This paper proposes a new methodology, taking advantage of recent, more holistic, measures of diet quality. Preliminary regression results are presented using cross-country data and the Global Dietary Recommendations Score and Minimum Dietary Diversity for women. The results suggest that diet quality variables generally have the expected signs, but there are also clear limitations of using cross-country data. The methodology could be applied in future to a limited number of broadly representative low- and middle-income countries data sets containing both diet recall data as well as measures of noncommunicable disease risk status. The analysis suggests that this work could inform policies such as the repurposing of existing agrifood policies to complement existing public health policies, to reduce the economic and health burdens imposed by unhealthy diets.

This paper is a product of the Agriculture and Food Global Practice. 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/prwp. The authors may be contacted at sehotrton@uwaterloo.ca.

_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
December 2007) are concentrated among their top ten borrowers. Consistent with what ex-ante spreads reveal, net interest margins in Jordan also exceed those of the majority of neighboring countries (see Table 5).

# **4. Computing a Direct Measure of Competition in the Banking Sector**

Looking only at spreads can be problematic since spreads can reflect countries’ macroperformance, the extent of taxation of financial intermediation, the quality of the contractual and judicial environment, and bank-specific factors such as scale and risk preferences. The recent banking literature analyzes more direct measures of competition based on models of industrial organization that emphasize market contestability. A large number of studies have focused on measuring bank competition via the Panzar and Rosse (1982, 1987) H- statistic, which captures the elasticity of bank interest revenues to input prices.<sup>5</sup> Under perfect competition, an increase in input prices raises both marginal costs and total revenues by the same amount and, hence, the H-statistic will equal 1. Under a monopoly, an increase in input prices results in a rise in marginal costs, a fall in output, and a decline in revenues leading to an H- statistic less than or equal to 0. Panzar and Rosse (1987) show that when H is between 0 and 1 the system operates under monopolistic competition. In general, the H-statistic is interpreted as a measure of the degree of competition in the banking market. The H-statistic is only valid if the market is in long-run equilibrium (if return on bank assets is not related to input prices).

Based on the Panzar and Rosse (1982, 1987) methodology and following the empirical strategy pursued by Claessen and Laeven (2004), the H-statistic is calculated by estimating equation (1): where i denotes banks and t denotes years. P is the ratio of gross interest revenues to total assets (proxy for the output price of loans), W1 is the ratio of interest expenses to total deposits and money market funding (proxy for input price of deposits), W2 is the ratio of personnel expenses to total assets (proxy for input price of labor) and W3 is the ratio of other operating and

> 5 For example, Bikker and Haaf (2002), Gelos and Roldos (2002), Claessens and Laeven, (2004), and Levy-Yeyati and Micco (2007).

- 6 -
41

**Table 7**

**Robustness: financial development and exchange rate arrangements** _<u>(dummy remittances: 75th percentile)</u>_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||ED4|ED4|BP5|BP5|
||financial|exchange rate|financial|exchange rate|
||development|arrangements|development|arrangements|
|CA|-2.852|-2.807|-1.791|-1.792|
||[0.230]***|[0.225]***|[0.216]***|[0.213]***|
|REM|0.743<br>|0.091<br>|0.530<br>|0.110<br>|
||[0.606]|[0.516]|[0.501]|[0.437]|
|RES|-0.570|-0.763|-1.264|-1.052|
||[0.468]|[0.395]*|[0.466]***|[0.442]**|
|EXTD|0.162|0.213|0.163|0.199|
||[0.083]**|[0.092]**|[0.073]**|[0.082]**|
|SHORTD|-0.044|-0.056|-0.029|-0.047|
||[0.150]|[0.146]|[0.111]|[0.108]|
|CREGRO|0.000|-0.001|-0.000|-0.002|
||[0.002]|[0.002]|[0.001]|[0.002]|
|PPPGDP|-0.016|-0.015|-0.011|-0.006|
||[0.026]|[0.025]|[0.026]|[0.025]|
|OPEN|0.183|0.219|0.204|0.245|
||[0.106]*|[0.107]**|[0.096]**|[0.097]**|
|OT|-0.631|-0.733|-0.449|-0.518|
||[0.345]*|[0.345]**|[0.298]|[0.297]*|
|CONCD|0.016|-0.032|0.034|0.015|
||[0.117]|[0.113]|[0.106]|[0.102]|
|RES^2|1.564|1.211|1.997|1.820|
||[0.852]*|[0.766]|[0.997]**|[0.990]*|
|EXTD^2|-0.044|-0.035|-0.050|-0.045|
||[0.021]**|[0.022]|[0.017]***|[0.018]**|
|RES*DREM|<br>0.572|<br>0.604|<br>0.794|<br>0.759|
||[0.333]*|[0.317]*|[0.320]**|[0.307]**|
|EXTD*DREM|-0.111|-0.089|-0.097|-0.085|
||[0.052]**|[0.051]*|[0.043]**|[0.042]**|
|RES*FD|-0.539||0.140||
||[0.354]||[0.274]||
|EXTD*FD|<br>0.117||<br>0.080||
||[0.069]*||[0.064]||
|FD|-0.015||-0.061||
||[0.062]||[0.057]||
|RES*ARR||-0.533||-0.218|
|||[0.352]||[0.327]|
|EXTD*ARR||-0.049||-0.056|
|||[0.047]||[0.041]|
|ARR||<br>0.119<br>||<br>0.096<br>|
|||[0.043]***||[0.040]**|
|Observations|1180|1214|1157|1191|
|R-squared|0.31|0.31|0.23|0.23|

_Notes_ : see note to Table 5. DREM is a dummy variable that takes the value 1 if in a given year the level of workers’ remittances over GDP of a country is above the 75<sup>th</sup> percentile value of that year’s (cross-country) distribution. significant at 10%; ** significant at 5%; *** significant at 1%
# **Appendix Figure A.  WTO Membership Use of Antidumping (AD), 1995 - 1Q 2009**

# **AD Historically, by year: 1995-2008**

<!-- Start of picture text --> 400<br>350<br>300<br>Antidumping<br>250<br>Initiations<br>200<br>Antidumping<br>150 Measures<br>100<br>50<br>0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->

**AD During the Crisis, by quarter: 1Q 2007- 1Q 2009**

<!-- Start of picture text --> 80<br>70<br>60<br>Antidumping<br>50 Initiations<br>40 Antidumping<br>Measures<br>30<br>20<br>10<br>0<br>1Q- 2Q- 3Q- 4Q- 1Q- 2Q- 3Q- 4Q- 1Q-<br>2007 2007 2007 2007 2008 2008 2008 2008 2009<br><!-- End of picture text --> Sources: data in panel a taken from the WTO member reports to the Committee on Antidumping and comprehensively covers the full WTO membership. Data in panel b compiled by the author from the _Global Antidumping Database_ and covers only 20 using countries. According to data from the WTO, these 20 Members initiated 90 per cent (89 per cent) of all antidumping investigations (new measures imposed) by the WTO membership during 1995-2007. Unlike figure 1, the unit of observation is a WTO member country’s AD action over a given product from a single exporting country.

25
time, to population-based ones by two particular time lines assumed, namely 2050, and 2100. The C&C 2050 alternative implies the more drastic redistribution in favor of low-income and low-emission countries, as is also seen from the table. But since low-income countries are phased into the carbon trading scheme more rapidly under the C&C 2050 alternative than under the C& C 2100 alternative, the average implementing carbon price per ton C is higher in the latter alternative (as mitigation needs to be stronger in those countries that actually mitigate). We see that, in the C&C 2050 alternative, substantial finance flows from the OECD area to low-income countries. Under the (more realistic) 500 ppm target, we see that the net revenue outflow (2020 to 2100) is US$8.2 trillion, or close to US$100 billion annually (but starting at a far lower level in 2020, for two reasons: mitigation starts out as being less strict; and the starting point is one with much less interregional transfers). Among gainers, Asia (being the largest emitting area) is seen to gain less than the rest. In the C&C 2100, Asia in fact has a net liability in particular as incomes, and baseline emissions, keep growing at a rapid pace in this region.

**Table 2.5: Net volumes (in GtC), values (in US$trillion), and average GHG emissions prices (US$/ton C) up to 2100, by major regions, under two C&C convergence alternatives and two carbon concentration targets.**

|Scenario<br>alternative|Trading levels|OECD|Asia|LA, Africa,<br>ME|Former SU,<br>EastEurope|Carbon<br>price|
|---|---|---|---|---|---|---|
|C&C 2050,|Volume|-37|6|31|0|225|
|target 500|Net value|-8.2|1.6|8|-0.2||
|C&C 2050,|Volume|-42|10|31|0|300|
|target 450|Net value|-12.5|4.0|8.5|0||
|C&C 2100,|Volume|-8|-7|5|10|350|
|target 500|Net value|-3.0|-2.5|1.8|3.6||
|C&C 2100,|Volume|-23|-1|18|6|365|
|target 450|Net value|-8.3|-0.4|6.7|2.2||

Source: WBGU (2003).

An uncertain factor not touched upon here is how much of climate-related finance we are indicating, is truly additional, in the sense that it would not have been forthcoming in the absence of climate change or threats.

# **3. Short-Run Macroeconomic Effects of Additional Climate Finance**

# **3.1 Preliminary considerations**

When considering the technical options available to an economy facing increased financial inflows, any fundamental analysis will need to be modified and supplemented when considering the specifics of climate-related finance. Four issues will be focused on here:

1. To what degree does the effect of climate-related finance differ by _fund source_ (such as public versus private) or _type_ (such as pure financial transfers versus financing of particular projects or goods purchases)?

2. To what degree do effects differ by _type of financial-fund recipient_ , distinguishing in particular between public and private recipients?

10
**Figure 4: Death probability by educational background of the family. From siblings' mortality, Demographic and Health Survey 2000, Rwanda.**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>1 to 5 6 to 10 11 to 15 16 to 20 21 to 25 26 to 30 31 to 35 36 to 40 41 to 45 46 to 50 Above 50<br>Age in 1994<br>Sister with no education Sister with at least some primary education<br>Death probability<br><!-- End of picture text --> 24