rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 8 Triangular Trade Index Figure 9 Intraindustry Trade within East Asia<br><!-- End of picture text --> <!-- Start of picture text --> Textiles, garment 70 vertical intra-industry trade<br>Autos 60<br>Electrical<br>50<br>Office, telecom 2000-04<br>1990-94 40<br>Industrial, metal working<br>inter-industry trade<br>30<br>Power generator<br> M etal 20<br> Wood, paper, furniture<br>10<br>horizontal intra-industry trade<br> Chemicals<br>0<br>0.0 0.1 0.2 0.3 0.4 1990 1991 1992 1993 1994 1995 1996 2000 2001 2002 2003 2004<br>index index<br>Source: UNCOMTRADE Source: UNCOMTRADE and Urata 2006<br>Note: The indices are aggregated at the 6-digit HS level and<br>computed using the method proposed by Fukao, Ishido, and<br>Ito (2003).<br><!-- End of picture text --> Production sharing, by definition, incorporates the back-and-forth nature of trade: the importation of inputs for assembly or additional processing, as well as the exportation of intermediate goods for assembly or additional processing by third countries. This is distinguished from pure intraindustry trade, one-way production sharing, and the trade in intermediate goods. Ando and Kimura (2003) point out that the East Asian success story is mainly a vertical intraindustry trade phenomenon within which transactions are characterized by back-and-forth trade links whereby several countries in the region participate in various stages of single production chains.<sup>9</sup> A sizable share of the exports of Malaysia, the Philippines, Thailand, and Vietnam are accounted for by vertical specialization (VS). Indonesia is a poor performer in this area: its share of exports involved in vertical specialization declined dramatically, from 27 percent in 1997 to 15 percent in 2001. However, Indonesia’s vertical specialization 1 (VS1) contribution to export growth (see figure 10) is much more significant. Countries such as Brazil, Chile, and South Africa have achieved high levels of VS1, indicating that they are increasingly engaged in the intermediate stage of the manufacturing value chain as opposed to the final stage.

**<u>Figure 10 Vertical Specialization in East Asia</u>**

<!-- Start of picture text --> a. As a share of exports b. Contribution to export growth,1992–2000<br>share of VS in export s (%)<br>60 South Af r i ca<br>T ur key<br>50 Costa Ri ca<br>40 19972001 Sr i Lanka Br azi l Chi l e Share of VS in export growt hShare of VS1 in export growth<br>30<br>Indi a<br>Chi na<br>20<br>Indonesi a<br>Phi l i ppi nes<br>10<br>T hai l and<br>0 M al aysi a<br>China Indonesia Korea Malaysia Philippines Thailand Viet nam 0 10 20 30 40 50 60 70 80 90 100<br>percent<br>Source: Based on the Global Trade, Assistance, and Source: Pitigala 2006; UNCOMTRADE; production data from<br>Production Database, Center for Global Trade Analysis, the United Nations Industrial Development Organization.<br>Purdue University, data versions 5 and 6. Note: Exports are aggregated at the 3-digit HS level.<br><!-- End of picture text --> 11
Table 8: Pre-Shipment Inspections (PSI): Inventory and Sales Behavior

||(1)<br>Raw invent|(2)<br>Sales|(3)<br>Sales (t+1)|
|---|---|---|---|
|Pre-shipment inspections|(output)<br>0.003|-0.042|-0.075|
||(0.079)|(0.093)|(0.094)|
|Pre-shipment inspections|(input)<br>0.582**<br>(0.285)|0.336<br>(0.291)|0.519**<br>(0.243)|
|Observations|40,102|40,102|38,309|
|R-squared|0.927|0.908|0.906|
|Plant FE|yes|yes|yes|
|2-dig ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|5-dig HH-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of pre-shipment inspections (PSI), applied either to output or inputs, on plant’s raw materials inventory (col. 1), sales (col. 2) and next period’ sales (col. 3) in the years between 2008 and 2015. _Output PSI_ is the share of HS-10 digits products subject to at least one PSI within the corresponding five digit industry. For each five digit industry, _Inputs PSI_ is the weighted sum of output PSI in all five digit supplying industries. The coefficients are obtained by the estimation of Equation 1. All specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

29
Table 7: U.S. long firm-level panel data 1960-2016: Sector-specific identification of inflation effects on R&D

|||R&D/asset|s|
|---|---|---|---|
||sector volatility|sector growth|sector R&D intensity|
|infation _∗_sector-volatility|-.001<sup>_∗∗_</sup>|||
||(-2.67)|||
|infation _∗_sector-growth||-.002<sup>_∗∗_</sup>||
|||(-2.86)||
|infation _∗_sector-R&D|||-.001<sup>_∗∗_</sup>|
||||(-5.38)|
|ln(total assets)|-.016<sup>_∗∗_</sup>|-.016<sup>_∗∗_</sup>|-.016<sup>_∗∗_</sup>|
||(-9.79)|(-9.79)|(-9.85)|
|ln(retained earnings)|.004<sup>_∗∗_</sup>|.004<sup>_∗∗_</sup>|.004<sup>_∗∗_</sup>|
||(3.38)|(3.37)|(3.36)|
|Firm FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|Obs|37,949|37,949|37,949|

U.S. firm-level balance sheet panel data from Compustat, 1950-2016. Median inflation: 3.0% (average 2.6%). Heteroscedasticity robust s.e. clustered at year level t-statistics in parenthesis. **,* significant at 5%, 10%.

# **5 Conclusion**

Our paper makes two contributions to the literature concerned with the effects of inflation on innovation, productivity and growth. We first present a simple model proposing a negative causal effect of inflation on growth, which is transmitted not via aggregate investment, but instead via technology choice. Hence, inflation affects the composition of investment, tilting it away from innovative activities towards safer but return-dominated ones. The primary channel along which adverse effects of inflation materialize is therefore not in the volume of aggregate investment, but in the dynamics of aggregate productivity growth.

Our theory is based on the idea of costly liquidity provision in the presence of financial frictions and makes predictions about how the severity of the adverse inflation effect depends on firm (or industry) characteristics. Specifically, we show that the liquidity mechanism is particularly relevant in fast growing and volatile industries, and in those with high income 28
In 2006, developing countries supplied nearly 450 MtCO2e of primary CDM credits for a total market value of US$4.8 billion. The carbon market and associated emerging markets for clean technology and commodities have attracted a significant response from the capital markets and from experienced investors, including those in the United States. Analysts estimated that US$11.8 billion had been invested in 58 carbon funds as of March 2007 compared to US$4.6 billion in 40 funds as of May 2006, half of which is managed in the UK (Bulleid, 2006; New Carbon Finance, 2007). By August 2007, the CDM Board had registered 760 projects expected to deliver about 1 billion CERs by 2012. In addition, 1,500 projects were at the validation stage or ready for registration. Together, these 2,260 projects could deliver close to 2.2 billions CERs by 2012.

Joint Implementation shares the same origins as CDM, since early treaty negotiations concerning project credits made no distinction between projects located in transitional or developing countries. Consequently, some exploratory project investments hosted in Annex I countries were made in 2001 -- before the first CDM transaction -- by public buyers. Nevertheless, as separate mechanisms evolved, JI project development stalled -- due in large part to a decision to allow early crediting only under CDM. Delays among Annex I countries to meet JI eligibility requirements along with uncertainty together with delays associated with establishing track-two project-cycle rules further dampened investment. Nevertheless, as UNFCCC and host country domestic rules evolved, projects began to enter the JI pipeline, beginning in late 2006 and by February 20008, pipeline projects represented a potential of 188 MtCO2e by 2012 (UNEP RISOE, 2008).

# <u>Evaluations of mitigation potential and project investment</u>

As discussed earlier, bottom-up and top-down methods have been employed to provide an understanding of how emission trading might affect abatement activities. To start, the viability of a given offset project has to do with basic physical and economic characteristics. Bottom-up studies in particular take stock of the potential for different types (asset classes) of investment over a range of potential market prices for carbon. For example, reducing emissions by switching fuels might be economically viable at lower prices for carbon, while investing in alternative sources of energy may be viable at higher prices. In the aggregate, the distribution of viable technologies also has implications for the geographic distribution of investments. Both the geographic and asset class distribution of potential projects are important for policy makers. For one, different types of abatement activities are associated with different costs and the relative potential supply of low or high cost mitigation opportunities will affect the costs of meeting the policy emission goals. Different types of abatement activities also have different implications for additional spillover benefits such as health or biodiversity co-benefits and technology transfer. Where projects locate is important as well, 43
twice in the previous decade. After peaking around 2010, the REER was already gradually dropping (Figure 16) before the collapse of the Lira in 2018. Peak current account deficits in the period prior to 2002 have tended be symptomatic of and precipitate an economic crisis, which occurred in 1994 and in 2001. However, since then, larger deficits have been sustained, particularly as financing inflows have increased and the exchange rate has been free to adjust.

Figure 15: Interest rates and inflation (percent) <!-- Start of picture text --> 100% Real interest rate<br>Inflation<br>80%<br>60%<br>40%<br>20%<br>0%<br>Source: Haver Analytics; author’s estimates<br>Figure 16: Real effective exchange rate (index, 2010=100)<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Source: Haver Analytics; author’s estimates<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

**A rapid expansion of private sector credit, especially to corporates, is also likely to have contributed to higher investment and CADs.** The large increase in private sector investment over the past 15 years has been associated with a rapid scale-up in private sector credit (Figure 17). Most of this credit went to the corporate sector, rather than to households, with corporate credit rising exponentially from almost nothing in 2001 to over 60 percent of GDP in 2017. At the same time, elevated investment was associated with a steadily falling incremental capital-output ratio (ICOR) (Figure 18), implying that positive effect of investment on the CAB weakened over time. This expansion was facilitated by a period of increasing global liquidity, especially in emerging markets and developing economies (EMDEs). Net capital inflows to EMDEs grew from a low level in 2000 to reach a peak of US$1 trillion in 2007 and 2010 before falling again as U.S. monetary policy began tightening (Figure 19). Increased availability of external financing

13
# _Parametric Regressions_

Our main results are shown in tables 2a and 2b. We pooled the surveys of 1999 and 2001 and estimated the regressions with a six-month bandwidth.<sup>24</sup> Tables A.1 and A.2 in the appendix show only the results with the PNAD of 1999. The results are very similar, though less precise.

Tables 2a and 2b show the results with three different specifications of the smooth function, linear and quadratic polynomials, and piecewise linear. We add mothers’ years of schooling as controls. In addition, the tables present the mean values of the outcomes of the comparison group.

Table 2a shows a decrease in paid-work incidence of 4.1 percentage points (p.p.) for all males or 27 percent. The result is very stable across the three specifications and is statistically significant at a 5-percent level. The same results are obtained when only using the 1999 PNAD and are shown in table A.1 in the appendix. For 1999 only, the child-labor decline is mostly driven by the decrease in paid formal work.

To shed more light on the time allocation in work and schooling activities, we look at the proportions only working, working and studying, and neither working and studying. We decompose working into working in paid and unpaid activities respectively. The results are shown in table 2.b.

There is fall of 3.4 p.p. (about 28 percent) in the proportion of boys doing paid work and studying and an increase of 5.6 p.p. (about 8 percent) in the proportion of boys only studying. Both these results are statistically significant, different from 0 at 5 percent. It seems that some affected boys returned to school after the ban. Although not shown in the table, we estimate that the affected cohort is 2 p.p. more likely to be delayed in school just after the ban. Given that 45 percent of 14-year-old boys were delayed in school in 1999, we do not have enough statistical power to detect an impact on age-grade distortion statistically different from zero.<sup>25</sup> This is, in fact, consistent with the reduction in the proportion of boys only working for pay from 2002 onwards, as will be shown below.

> 24 We estimated the pooled model with four and five-month bandwidth. The results are almost identical and are available upon request. We used the algorithm proposed by Calonico et al. (2014) that was designed to select the optimal bandwidth in non-parametric fuzzy design settings and found very similar bandwidth sizes to the ones used in our pooled estimates.

> 25 Using the _sampsi_ command in Stata, we would need about 20,000 observations to observe a statistically significant result with power of 80 percent and significance level of 5 percent.

17
faced a double handicap in future poverty reduction: they had lower subsequent growth and the poor shared less in the gains from that growth.

Other factors point to the same conclusion. It appears that aggregate economic growth in China is increasingly coming from sources that bring more limited gains to the poorest. The low-lying fruit of efficiency-enhancing pro-poor reforms are possibly getting scarce. Inequality is continuing to rise and poverty is becoming more responsive to rising inequality. At the outset of China’s current transition period to a market economy, levels of poverty were so high that inequality was not an important concern. That has changed.

It also appears that perceptions of what “poverty” means are evolving in China. It can hardly be surprising to find that the standards that defined poverty 20 years ago have lost relevance to an economy that quadrupled its mean income over that period. China could well be entering a stage of its development in which relative poverty emerges as a more important concern than in the past. Economic growth will then be a blunter instrument for fighting poverty in the future.

# **_India_**

As in the case of China, it is clear that economic growth has tended to reduce poverty in India. And, as in China, the poverty impact of accelerated growth in the 1990s has been dulled by rising inequality.

Assessing what has been happening to inequality in India has been clouded by a comparability problem between the two main surveys available for the 1990s (Deaton, 2001; Datt and Ravallion, 2002). Figure 8 gives three estimates of the GIC for the 1990s. The “unadjusted” estimate is based on the actual surveys with no attempt to correct for the comparability problem. One of the other two is based on comparable distributions of 24
# Figure 5: Predicted TFR, 1987 births per woman enrolled in 1965

<!-- Start of picture text --> 8<br>TFR<br>6<br>4<br>2<br>0<br>0 10 20 30 40<br>Secondary enrollment rate (%), 1965<br><!-- End of picture text --> Note: results based on regressions controlling for spouse effect (all LDCs, n-45) Source:.. Subbarao and Raney, 'Social Gains from FemaleEducation,- forthcoming, PHRWD
Table 13 provides some partial results with respect to “background variables” that might have affected experts’ expected WTP answers. Few of these effects turn up as significant; not surprising considering that only 49 experts are involved in the survey. The most striking effect is related to the regular reading of and familiarity with two central journals, the Journal of Environmental Economics and Management (JEEM), and the American Journal of Agricultural Economics (AJAE). Readership of JEEM strongly and significantly increases average WTP, while readership of AJAE has the opposite effect. It is unclear how this should be interpreted. We know that JEEM is the primary publication outlet for environmental CV/CE studies, while AJAE readers typically are more conservative and more skeptical toward SP methods (and may tend to place lower values on the environment). High stated WTP guesses among the JEEM readership could represent a combination of being better informed, and bias in the direction of preferring SP methods over other methods (and, perhaps, believing that the true environmental values among the respective populations are high).

Higher fractions of experts’ research time spent working on CV and CE methods also affect stated WTP assessments positively, and significantly given a log-linear specification. Experts who perceive a greater difficulty in implementing a CV study on biodiversity in their own country were found to provide significantly lower WTP assessments under a linear specification. The experts’ familiarity with the CV literature was found to be more important for expressed average WTP values, than having done many SP studies. On the other hand, more time spent on CE and CV research significantly increases stated WTP. These results could be interpreted “positively” (those who know SP methods well understand better how environmental goods are to be valued), or “negatively” (the sample of experts who are particularly familiar with SP methods is self-selected and contains individuals with a propensity to value the environment highly; or tend to think that the population values it highly).

There are few differences between the WTP results from Rounds 1 and 2. Familiarity with CV studies has however a significantly negative effect on WTP in Round 2 (in the log-linear specification), but not Round 1. This could indicate that after experts were presented with the WTP answers from other experts, and allowed to adjust their responses, the pattern that those that were more familiar with CV studies stated lower mean WTP for their own country became clearer. Note also that if these experts thought that the Amazon CV survey would be difficult to implement in their own country, they stated significantly lower than otherwise WTP (in the loglinear specification) in Round 1, whereas this effect disappeared in Round 2 when they saw that other experts had also managed to provide their estimates.

23
significantly.<sup>34</sup> The results here indicate that the closer the state’s congressional delegation was to the president, the more likely the state bank was to be liquidated or privatized. This suggests that the executive might have found it easier to do away with state banks in states where he had stronger ties to the congressional delegates, perhaps because they could be compensated with political currency more readily. The executive simultaneously wielded greater punitive powers within the party as well if party members opposed the elimination of the state bank.

These results have to be interpreted with an important caveat. The small number of observations does not allow us to control for many different factors simultaneously.

Idiosyncratic differences between the states are not captured in our regression. Finally, the time dimension, the fact that the states did not take the decision simultaneously is not explicitly modeled in our regressions.<sup>35</sup>

# **4. Bank-level regressions: data and methodology**

In order to assess the performance and efficiency effects of the different PROES options, we utilize an unbalanced panel of 207 banks with quarterly data over the period January 1995 to September 2003, with a total of 4,864 observations. Our sample contains 19 of the 35 state banks. These banks were federalized, privatized and restructured at different points during our sample period. Liquidated state banks are not included. Further, we had to drop several banks for which we have incomplete data.

> 34 In regressions we do not report here, we also tried measures of the share of a state’s delegation in the House that belonged to the PSDB (the government coalition).  Further, we used a dummy variable indicating whether a state’s governor was member of the PSDB (one of the parties of the government coalition).  In line with the results in Table 3, we find that states with a higher share of PSDB deputies or a governor belonging to one of the parties supporting the Cardoso government were more likely to liquidate their state banks or have it federalized. 35 Compare Clarke and Cull (2002) on Argentina.

16
figures are obtained. Overall,<sup>the elimination of all deaths</sup> believed to be directly due<sup>to malaria would reduce the</sup> probability of dying before<sup>age 5 to about 70% of pre-</sup> intervention levels.

Table 5: Comparison of changes<sup>in childhood mortality following</sup> the elimination of malaria deaths<sup>and the use of bed-</sup> nets

|PHC VILLAGE|S|||
|---|---|---|---|
||IMR|**4qq**|sqo|
|With malaria|109.6|156.7|249.1|
|No malaria<br>Bed-nets|91.0<br>72.4|88.4<br>44.6|171.3<br>113.8|
|Ratios||||
|No malaria/malaria|0.83|0.56|0.69|
|Bed-nets/malaria|0.66|0.28|0.46|
|NON-PHC VILLA|GES|||
|1988-9 with malaria|93.0|100.3|184.0|
|1989-90 with malaria|116.1|102.9|207.0|
|Mean 1988-90|104.5|101.6|195.5|
|No malaria (2 yr average)|102.2|66.0|161.4|
|Ratios||||
|No malaria/malaria 1988-90|0.98|0.65|0.74|

Much more exciting is the comparison<sup>between the pre-</sup> intervention levels and the actual<sup>mortality outcomes as measured</sup> in the post-intervention year.<sup>As we see from the middle section</sup> of Table 5, the mortality of<sup>the 1-4 year olds was cut to 28% of</sup> pre-intervention levels and<sup>the probability of dying before age 5</sup> fell to 46% of its pre-intervention<sup>level. Quite clearly, the</sup> 16
<!-- Start of picture text --> cll |<br>Eee es<br>Se 8 |<br>sees ? AK &<br>aoe aad &)<br>* ale of<br>a in ie?<br>Ee remd wd<br>are Sap<br>2<br><!-- End of picture text -->

<!-- Start of picture text --> oa |<br>a.<br>See may |<br>=== ¥ a :<br>mwoe a a<br>fee “aed<br>oF. F aah 4<br>a He<br>2 . ee<br>Bae<br><!-- End of picture text -->