rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 1 continued.</u>

||**Panel B: Indust**<br>|**ry Coverage**<br>|||
|---|---|---|---|---|
|**Industry Groups**|**# of**<br>**Observations**|**% of**<br>**Observations**|**# of Unique**<br>**Firms**|**% of Unique**<br>**Firms**|
|Automobiles & Components|567|1.68|142|1.52|
|Banks|3,430|10.17|898|9.59|
|Capital Goods|2,881|8.55|709|7.57|
|Commercial Services & Supplies|1,376|4.08|385|4.11|
|Consumer Durables & Apparel|1,492|4.43|369|3.94|
|Consumer Services|567|1.68|216|2.31|
|Diversified Financials|1,023|3.03|279|2.98|
|Energy|1,258|3.73|335|3.58|
|Food & Staples Retailing|365|1.08|91|0.97|
|Food Beverage & Tobacco|975|2.89|239|2.55|
|Health Care Equipment & Services|2,173|6.45|563|6.01|
|Hotels Restaurants & Leisure|376|1.12|204|2.18|
|Household & Personal Products|313|0.93|78|0.83|
|Insurance|849|2.52|215|2.30|
|Materials|2,120|6.29|522|5.58|
|Media|1,055|3.13|267|2.85|
|Pharmaceuticals & Biotechnology|1,465|4.35|464|4.96|
|Pharmaceuticals, Biotechnology & Life|||||
|Sciences|381|1.13|381|4.07|
|Real Estate|1,384|4.11|374|3.99|
|Retailing|1,423|4.22|376|4.02|
|Semiconductors & Semiconductor|||||
|Equipment|712|2.11|198|2.11|
|Software & Services|2,606|7.73|724|7.73|
|Technology Hardware & Equipment|2,555|7.58|752|8.03|
|Telecommunication Services|643|1.91|178|1.90|
|Transportation|854|2.53|209|2.23|
|Utilities|871|2.58|194|2.07|

9
- 31 -

Table 6: Projections of Mexican Sugar Production, Consumption, Trsde and World Prices, 1985-2004 (Free Trade Scenario)

|Year|Production|Consumption|Net Exports|World Price|Cane Price|
|---|---|---|---|---|---|
||-----------|-(Million Tons|)------------|(1984 USC/lb)|(1980<br>Pesos/Ton)|
|1985|3.42|3.31|0.71|7.62|280|
|1986|3.61|3.43|0.14|6.45|237|
|1987|3.66|3.50|0.16|9.72|357|
|1988|4.05|3.60|0.38|11.22|412|
|1989|4.61|3.66|0.83|14.48|532|
|1990|5.47|3.79|1.50|12.39|455|
|1991|6.11|3.94|2.03|9.37|344|
|1992|5.83|4.03|1.86|11.90|437|
|1993|5.83|4.02|1.81|21.94|806|
|1994|7.23|4.16|2.76|21.24|780|
|1995|9.03|4.41|4.22|12.52|460|
|1996|8.73|4.59|4.21|9.28|341|
|1997|6.92|4.71|2.60|10.29|378|
|1998|5.72|4.80|1.18|13.31|489|
|1999|5.79|4.92|0.85|14.46|531|
|2000|7.12|5.10|1.73|13.50|496|
|2001|8.01|5.23|2.59|16.88|620|
|2002|8.60|5.38|3.09|18.38|675|
|2003|8.90|5.57|3.25|15.55|571|
|2004|8.80|5.76|3.06|13.75|505|
|Mean of|1200 Observat|ions||13.60|485.30|
|Coeffici|ent of Variat|ion||0.44||
|Range Mi|n-Max of 60 s|imulations||3.2-42.6||
|Z of Obs|ervations in|the Range||8.0-18.6=67||

Source: IECCM, World Bank
The 1985-1994 Global Real Estate Cycle. Page 19 <!-- Start of picture text --> Housing Price Boom: AngloSaxon Countries<br>(nomonal refdental real state pries 19iO8-100)<br>300 -<br>240 --- -- -- -- - -- - - -- -- - - -- - -- - - - - - - - - - _- - - - -_ - - _-_ - - - _- -_<br>1980 19e1 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992<br>2 -United-Slate - -- Unted Kingdom -- Can-d- -- Au-- l- i |<br>220 -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - = -L<br>|Mourning Price fleom Nordic Countries<br>(Znomkralresident1lalreal neaaeprices 1980.100)l<br>400<br>1980 -i--83 1901 1982 1984 1985 1986 1987 1988 1989 1990 1991 1992<br>-- Oenmarl- o Finland - -No--ay | -Sweden<br><!-- End of picture text -->

Source: BIS, _Annual1 Report 1992/93_
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41
Rural Household Survey in Dercon and Porter (2014) suggests the observed height gains in the treatment group would result in approximately 3 to 4 percent increases in annual earnings as an adult.<sup>2</sup> To better understand the underlying mechanisms behind the effects, we simultaneously collected measures of fine airborne particulate matter with a diameter of 2.5 micrometers or less (PM2.5) in a random subsample of study households. These concentrations of matter, averaging approximately 1250  g/m<sup>3</sup> , or 50 times more than the World Health Organization’s (WHO) threshold for healthy exposure (WHO, 2006b), decreased by 10 percent on average for all households in the _Mirt_ treatment arm, relative to the control group.<sup>3</sup> Meanwhile, households with young children, which also cook more frequently, experienced reductions of 24 percent on average. When examining whether measured health outcomes relate directly to particulate matter, we demonstrate a clear gradient between reduced household air pollution and increased child growth, although we do not find a statistically significant or otherwise meaningful relationship between the level of particulate matter and respiratory symptoms or adult physical functioning.

These results advance our understanding of the health impacts of hazardous air pollution, while also refining design and implementation options for interventions geared toward mitigation in similarly situated environments. Health benefits for adults and older children do not arise in the data, but, given the link between early-life health and education, cognition, adult health, and earnings in later life, the demonstrated advancement for young children represents a substantial and meaningful impact.

# **2. Study Design**

# **2.1 Ethiopian context and Mirt improved cookstove technology**

Despite enjoying economic growth over the preceding decade, Ethiopia is known as a “hotspot” for forest depletion, and it is one of the top four countries in the world to simultaneously maintain a high level of nonrenewable fuelwood consumption per capita and disease burden from household air pollution (Bailis et al., 2015). Neonatal disorders and lower respiratory infections, both linked to household air pollution (Amegah et al., 2014; Smith et al., 2000; Perez-Padilla et al., 2010), are two of the top three leading causes of death in Ethiopia (GBD, 2018), and improved cooking technology is virtually nonexistent in rural areas.

> 2 Dercon and Porter (2014) estimate a 1cm increase in adult height results in a 1% increase in earnings. Our observed 0.3 to 0.4 standard deviation impact is approximately a 2cm increase in height at age 3, or 3 to 4cm as an adult.

> 3 Bluffstone et al. (2018) report on the details of the air pollution aspect of this project. Similar levels of PM2.5 have been observed in traditional open-fire cooking environments in India (Dutta et al., 2007) and Nepal (Chen et al., 2016).

4
uncertain. Increases in the restrictiveness of import rationing can reduce private investment in two ways. It can reduce private<sup>invetment because of</sup> longer queuing time and/or greater uncertainty of<sup>access to capital goods</sup> imports. It can also reduce investment demand because<sup>the expected</sup> profitability of investment is lowered by similar uncertainty<sup>of access to</sup> intermediate imports. Billsborrow (1977) finds evidence of<sup>the effect of</sup> import allocations proxied by reserves on firm investment<sup>in Colombia.</sup> Real depreciation affects private investment through several<sup>channels</sup> not all of which work in the same direction. First, real<sup>devaluation raises</sup> the cost of capital goods and acts like an adverse supply shock on investment, the size of which depends on the import content of investment (Buffie 1986, Branson 1986). Second, it raises the price of imported<sup>intermediate inputs.</sup> Both lower profitability of investment in the nontradable sector<sup>and thus slow</sup> down the adjustment response of private investment. On the other<sup>hand real</sup> devaluation can also raise investment by raising profitability of<sup>private</sup> investment in the tradable sector because it reduces the<sup>real product wage in</sup> terms of traded goods. Third, it raises demand for domestic<sup>output. Thus the</sup> net impact of real depreciation on private investment could go either way.10 This ambiguity is evident in empirical results. Most econometric studies find a negative impact of real depreciation on investment<sup>in the</sup> short-run but a positive impact in the long-run. For example,<sup>a short-run</sup> adverse effect is found by Musalam (1989) for Mexico, Solimano<sup>(1989) for</sup> Chile, Chhibber and Shafik (1990) for Indonesia. Faini<sup>and de Melo (1991)</sup> 10If capital is not wholly immobile between sectors, then total investment demand may fall or rise (Lizondo and Montiel<sup>1988) depending upon</sup> whether disinvestment in non-tradeable sectors is larger or smaller<sup>than the</sup> increase in investment in the tradeable sector.

10
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Cameroon > Telecom investments > Macro results**

||||Reduction in<br>h bli|Te|lecom<br>I|Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|5,409,614.00|0.15|0.52|0.60|0.82|0.47|
|EV|Equivalent variation||0.15|0.48|0.55|0.85|–1.08|
|s|Wage|1.00|0.02|0.83|1.01|1.50|0.73|
|yg|Government income|1,500,596.00|0.55|5.53|5.53|5.53|5.53|
|g|Total government expenditure|656,249.00|–6.01|5.38|5.38|5.38|5.38|
|ye|Firm income|2,118,815.00|0.87|–0.07|–0.27|–0.83|0.06|
|It|Total private investment|1,213,585.00|1.43|0.18|–0.07|–0.76|–0.51|
|GDP|GDP|6,395,129.00|0.41|0.41|0.41|0.41|0.41|
|e|Nominal exchange rate|1.00|0.32|0.31|–1.55|–5.51|–0.02|

# **Cameroon > Telecom investments > Sectoral results**

||||Reduction in<br>||Telecom<br>|||
|---|---|---|---|---|---|---|---|
||||other ublic||Imort|||
|Variable|Sector|Base|p<br>expenditure|VAT|p<br>duties|Foreign aid|Income tax|
||Crop agriculture|1.088.074.00|0.87|–0.07|–0.19|–0.43|–0.69|
||Export agriculture|419.908.00|1.16|0.19|0.08|–0.07|0.22|
|Va|Mining and gas|623.479.00|0.29|0.26|0.19|0.04|0.26|
|Value added or|Industries|1.266.964.00|0.96|–0.21|–0.01|0.18|0.27|
|output|Construction|108.698.00|8.28|6.49|6.19|7.75|7.54|
||Private services|2.265.916.00|0.71|0.07|0.11|0.03|0.00|
||Public services|622.090.00|–4.33|2.99|2.87|3.17|3.13|
||Crop agriculture|1.00|–0.22|0.48|0.60|0.68|–0.02|
||Export agriculture|1.02|–0.05|0.81|1.29|0.80|–0.14|
||Mining and gas|1.04|0.44|0.39|0.63|–5.42|0.02|
|pq<br>Market prices|Industries|1.11|0.24|1.96|2.23|–0.68|0.02|
||Construction|1.00|0.26|1.63|1.82|–0.17|0.20|
||Private services|1.03|–0.39|0.62|0.31|0.10|–0.44|
||Public services|1.00|0.01|1.02|1.16|0.93|0.46|
||Crop agriculture|1.00|0.66|0.28|0.30|0.48|–0.62|
||Export agriculture|1.00|1.42|0.30|0.26|0.44|0.25|
|r<br>|Mining and gas|1.00|0.78|0.33|–1.99|–6.75|0.31|
|Rental rate of<br>capital|Industries|1.00|1.74|–0.40|0.29|1.25|0.72|
||Construction|1.00|8.93|7.65|7.47|9.90|8.81|
||Private services|1.00|0.16|–0.31|–0.07|0.25|–0.56|

51
_Intertemporal Adjustment and Fiscal Policy in a Fixed Exchange Rate Regime_

## Figure 1.8 Argentina’s public debt, 1992–2003

Millions of dollars <!-- Start of picture text --> 200.000<br>180.000<br>160.000<br>140.000<br>120.000<br>100.000<br>80.000<br>60.000<br>40.000<br>20.000<br>-<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Source: Ministry of Economy, Republic of Argentina.<br><!-- End of picture text --> Looking at these results, one might conclude that the currency board produced a degree of macroeconomic stability and may have protected the country temporarily from effects of exchange rate volatility, with a positive impact on growth. However, the nation’s economic system proved incapable of making sound fiscal adjustments and restoring competitiveness. In the end, the explosion of external debt led to an acute recession.

# **2. A model of intertemporal adjustment in an open economy with a fixed exchange rate**

The model analyzes the process of intertemporal adjustment in a small to medium size open economy operating with a fixed exchange rate regime. Individuals live in an infinite horizon world with perfect foresight, and the home country produces and consumes a single good, referred to in the paper as the domestic good, while also consuming a good imported from abroad. Since the nominal exchange rate is pegged, the real exchange rate is the relative price of domestic and imported goods.

Individuals face sequential decisions in order to optimize their welfare. First, within each period, they choose an allocation of consumption between domestic and imported goods. This process determines the relationship between the consumption price index and the real exchange 9
findings are robust to controlling for a range of country-level institutional and policy variables, such as the strength of bank regulation and supervision, exchange rate flexibility and restrictions on capital flows.

Our main contribution in this paper is to investigate the role of the structure of the international banking market in the international transmission of monetary policy. The literature on the effect of monetary policy on cross-border lending builds on several papers investigating the bank lending channel domestically. Bank balance sheet strength (Jiménez et al., 2012a; Jiménez et al., 2014b; Gambacorta, 2005) and bank risk (Altunbas et al., 2010) have been shown to affect the impact of monetary policy on bank credit supply. Further, low monetary policy rates induce risk taking (e.g. Jiménez et al., 2014a; Ioannidou et al. 2015), and there is evidence of a portfolio rebalancing channel as well (den Haan et al., 2007).

The paper most closely related to ours is Cerutti, Claessens and Ratnovski (2014), who study drivers of cross-border bank flows using aggregate, bilateral credit flow data published by the BIS. They find that these flows are largely driven by global factors (e.g. VIX volatility and the slope of the US yield curve). These authors also find that the transmission of monetary policy to cross-border credit is affected by certain borrowing country policies, such as exchange rate flexibility, capital controls and bank regulation. Our paper differs from Cerutti et al. (2014) in several ways. First, we control for credit demand at the borrower firm level, so our findings are more likely to reflect supply side conditions. Second, our main interest is the effect of monetary policy in the relevant lender countries on cross-border lending. Finally, we provide a more comprehensive picture of cross-border lending by also studying non-volume loan terms.

Our paper is also related to the literature showing how banks reduce cross-border lending in response to non-policy funding shocks at home. Peek and Rosengren (1997), in particular, exploit the Japanese stock market crash in the 1990s, while various papers look at 4
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25
be using energy consumption data as a proxy for transportation services<sup>3</sup> although passenger and freight kilometers (or any equivalent units) would be the desired measurement for transportation services.

**Figure 3: Modal Mix for Freight Transport**

<!-- Start of picture text --> Railway Roads Aviation Shipping Subway<br>(a) China (1990-2006) (b) Korea (1994-2000)<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>(c) Vietnam (1990-2005) (d) Pakistan (1990-2004)<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>96 98 00 02 04 06<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 1994 1995 1996 1997 1998 1999 2000<br>1990 1992 1994 1996 1998 2000 2002 2004 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> Source: National Bureau of Statistics of China (2007); Korea National Statistical Office (2008); General Statistical Office of Vietnam (2005); Accountancy (2007); United Nations Statistics Division (2008a).

3 In energy literature, this is a common practice to measure modal mix in the transportation sector (see e.g., EIA, 2007; IEA, 2004) 11
limiting their ability to reinvest their way towards a high steady-state.<sup>6</sup> Nevertheless, these same types of small firms do appear to grow over time in a growing economy (de Mel et al, 2013), suggesting that any behavioral constraints on firm growth do not prevent the owners responding over time to rising demand.

# **4.6 Micro Meets Macro: Geographic Poverty Traps**

It is notable that the evidence most consistent with poverty traps comes from poor households in remote rural regions – whether it is the work among East African pastoralists, or the suggestion of breaking individuals out of poverty traps in some of the ultra-poor programs. In these remote rural areas, isolation reduces the number of available production technologies, so that Figure 3 may be a closer approximation than Figure 6 to the productive choice set. This brings us back to the idea that poverty is much more persistent in some places than others, and of the existence of “geographic poverty traps”. Jyotsna Jalan and Martin Ravallion (2002) define these to be formed when the characteristics of a geographic region are such that a household’s consumption cannot rise over time, while an otherwise identical household living in a different, better-endowed, area, would enjoy a rising standard of living. They use farm household panel data from rural China to estimate a dynamic consumption model which allows fixed or slowly changing geographic characteristics to influence household consumption and find evidence to support the idea that, all else equal, living in a poor area lowers the productivity of a farmer’s own investments, with areas with insufficient geographical capital, such as few rural roads, potentially in geographic poverty traps.

The key question is then why more people do not move out of poor areas? In the Chinese context, mobility restrictions such as the household registration ( _hukou_ ) system, along with thin rural land markets and uncertain land property rights might explain low mobility (Jalan and Ravallion, 2002). But many people remain in poor rural areas throughout much of the world, despite high returns to internal migration. Cost can be one reason – the same credit market failures that prevent investing in more productive assets can also prevent households financing the costs of moving. Gharad Bryan et al. (2013) conduct experimental work in Bangladesh to try to understand why more households don’t undertake seasonal migration, which they show to have high returns, and conclude that another part of the explanation seems to be that households close to subsistence are unwilling to take the risk of migration, but will do so if insured against this risk.

> 6 See also Michael Kremer et al. (2013) and Esther Duflo et al. (2011) for additional examples of small business owners not undertaking small investments with high marginal returns.

20