rafmacalaba/datause-encoder-data huggingface dataset:
||||**Appendix**|**Table A5: C**|**ausal Medi**|**ation Analysi**|**s**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**_Outcome_**|||**Sales**|||||**Profits**|||
|**_Mediator_**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
||Record|Planning|Stocking-Up|Marketing|Joint|Record|Planning|Stocking-Up|Marketing|Joint|
||Keeping||||Decision<br>Making|Keeping||||Decision<br>Making|
|ACME|46.89|58.94|132.17*|214.06**|52.98|8.66|10.43|6.56|18.95*|5.76|
|ADE|1216.13***|1204.15***|1131.04**|1049.3**|1210***|187.97**|186.22**|190.07**|177.7**|190.86**|
|% of ATE Mediated|3.7%|4.6%|10.4%|16.9%|4.2%|4.3%|5.2%|3.3%|9.4%|2.9%|

**Notes:** This table presents analyses to measure the average causal mediation (ACME) and the average direct effects (ADE) of the _All Three_ treatment on two outcomes; namely, 5% winsorized sales (columns 1-5) and 5% winsorized profits (columns 6-10). ACME isolates the impact of particular business practice chanels (mediators) that treatment effects get transmitted through (record keeping (columns 1 and 6), planning (columns 2 and 7), stocking-up (columns 3 and 8), marketing (columns 4 and 9) and joint decision making (columns 5 and 10)). ADE represents all other pathways. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
have benefitted from recent climate change. This implies that the indigenous people of Bolivia are on average more adversely affected from recent climate change than the nonindigenous population, simply because they happen to live in areas where the climate has turned less beneficial for human development (colder).

# **6. Conclusions**

This paper has analyzed the direct evidence of climate change in Bolivia over the past 60 years. Contrary to expectations, and contrary to the predictions of most General Circulation models, this evidence shows a consistent cooling trend of about 0.2oC per decade over all highland areas, and only slight, scattered evidence of warming in the lowland areas. No systematic trends in precipitation were detected, only decade-long cycles. There are indications that the decrease in average temperatures in the highlands is hiding an increase in daytime and summer temperatures but a decrease in nighttime and winter temperatures, although much more detailed daily temperature records would be needed to confirm this.

Using municipality level data, models of the relationships between climate, life expectancy and consumption levels were estimated (controlling for other factors that might affect life expectancy and consumption levels). The results suggest that consumption possibilities in Bolivia increase with temperature, but at a decreasing rate. Consumption levels are almost twice as large in the warmest parts of Bolivia compared to the coldest parts (when controlling for differences in education levels, urbanization rates, and precipitation). Precipitation, however, was not found to have any systematic effect on consumption levels. Neither temperature nor precipitation was found to have any systematic effects on life expectancy.

When simulating the impact of recent climate change in Bolivia (1oC reduction in temperatures in the already cold highlands, and a 0.25oC increase in temperatures in the already hot lowlands over the last 50 years), we found an adverse impact on national consumption levels of 1.3 percent. Since the predominantly poor and indigenous population in the highlands experienced a negative effect due to recent cooling and the much richer population in the lowlands experienced slightly positive effects of modest warming, the overall effect of recent climate change would be an increase in inequality between Bolivian municipalities and an increase in poverty.

Notice that this is due to the direction of recent climate changes in Bolivia, and not due to any inherent characteristics of the poor which might make them more vulnerable to climate change. If recent climate change had showed warming across the country (as climate models typically suggest), then the poor in the highlands would have benefited much more than the rich in the lowlands, thus reducing poverty and inequality.

The magnitudes of the estimated impacts are not large, however. A reduction in consumption levels of 3-4 percent (the most adverse effects encountered) over a 50 year period corresponds to just one year of low growth.

16
- 34 -

different prices just because they belong to different industries. And heavily charged industries will be losing competitiveness, if their products are tradables.

|Table 8.|Price ela|sticity of energy de|mand b|y industry||
|---|---|---|---|---|---|
||Pooled|data with industry-sp<br>fixed effects|ecific|Sepa<br>regr<br>|rate SUR<br>essions<br>|
||SU|R<br>SF|A|byi|ndustry|
|All industries|–0.403|(0.021) ***<br>–0.366|(0.168)|**<br>...||
|By industry:||||||
|Mining|–0.451|(0.017) ***<br>–0.532|(0.099)|***<br>...||
|Construction|–0.165|(0.052) ***<br>0.145|(0.538)|–0.446|(0.118) ***|
|Manufacturing|–0.205|(0.038) ***<br>1.584|(2.266)|–0.348|(0.047) ***|
|Transport|–0.467|(0.014) ***<br>–0.529|(0.082)|***<br>–0.237|(0.044) ***|
|Trade|–0.416|(0.022) ***<br>–0.443|(0.156)|***<br>–0.340|(0.103) ***|
|Real estate|–0.473|(0.014) ***<br>–0.561|(0.075)|***<br>–0.742|(0.099) ***|
|Hotels & restaurant|–0.459|(0.015) ***<br>–0.524|(0.089)|***<br>–0.895|(0.151) ***|
|Otherservices|–0.431|(0.021) ***<br>–0.473|(0.131)|***<br>0.071|(0.225)|
|The standard errors are s<br>1% level significance, re<br>Figure 15. Price e<br>-0.8<br>-0.6<br>sticity of demand|hown in<br>spectively<br>lasticity<br>-0.|parentheses. *, ** and<br>.<br>of energy demand<br>45<br>-0.4|*** rep<br>by pool<br>7<br>|resent the 10<br>ed SUR reg<br>-0.47<br>-0.46|%, 5% and<br>ression<br>|
|-0.40<br>04<br>ela|||-0.42||-0.43|
|-.<br>02<br>ergy price||-016<br>-0.20||||
|-.<br>0.0<br>imated en||.||||
|All industries<br>Est|Minin|g<br>Construction<br>Manufacturing<br>Transport|Trade|Real estate<br>Hotels &<br>restaurant|Other services|

# **_Conditional energy demand elasticity with respect to output_**

The implied conditional factor demand elasticity with respect to output can also be calculated by evaluating the SUR results at the sample means. The average response of nonresidential energy demand to a marginal shock in output is estimated at 0.056 in the pooled model but ranges from 0.07 to 0.17 in the country-specific models (Table 9). Hence, if the output demand declines exogenously by 10 percent, the nonresidential demand for energy would decrease by 0.7 to 1.7 percent. Recall, again, that the majority of enterprises in the economy normally spend 5 to 9 percent of total costs for energy consumption. Therefore, the marginal impact of changes in any single factor price is of necessity partial. Also it is noteworthy that
fall in savings, associated with rising capital inflows, also took place in Latin American and East Asian countries during the 1990s (Reinhart and Talvi, 1998).

**Table 1.a - Real Variables**

|_Period average_|**1987-90**|**1991-98**|**1999-04**|**2005-08**|
|---|---|---|---|---|
|||_Percentage_<br>|_change_<br>||
|**GDP growth**|**5.4**|**4**|**4.1**|**6.4**|
|Imports of goods and services|3.0|3.2|3.7|25.2|
|Gross Fixed Capital Formation|-3.0|4.9|0.4|16.6|
|Private consumption|4.6|3.5|3.4|6.0|
|Public consumption|5.5|5.0|2.9|2.1|
|Exports of goods & services|11.5|3.3|10.2|23.4|
|**Shares**||_Percent_|_of GDP_||
|Imports of goods & non-financial|||||
|services|35.1|28.6|24.1|34.4|
|Gross fixed capital formation|27.4|19.8|18.1|19.9|
|Private consumption|72.2|75|74.2|71.7|
|Public consumption|9.7|10.8|12.1|11.8|
|Exports of goods & non-financial<br>services|23.9|22.9|19.6|30.9|
|**Gross domestic savings**|**16.3**|**14.3**|**13.9**|**16.5**|

_Source: Authors’ calculations based on the Ministery of Economic Development and Central Bank of Egypt data._

**Table 1.b - Fiscal Variables**

|_Period average_|**1987-90**|**1991-98**|**99-2004**|**2005-08**|
|---|---|---|---|---|
|**Budget Sector**|_Percent_|_of GDP (un_|_less otherw_|_ise stated)_|
|Primary Balance|-12.0|3.2|-1.4|-2.0|
|Change in primary deficit (percentage point)|-1.7|1.9|-1.2|0.5|
|Overall Balance|-15.8|-3.8|-7.3|-8.0|
|Change in overall deficit (percentage point)|-2.3|1.8|-1.4|0.7|
|Net Public Sector Debt|na|100.5|92.3|91.2|

_Source: Authors’ calculations based on the Ministry of Finance data_

**Table 1.c - Nominal Variables**

|_Period average_|**1987-90**|**1991-98**|**99-2004**|**2005-08**|
|---|---|---|---|---|
||_Pe_|_rcent (unless_|_otherwise sta_|_ted)_|
|CPI inflation|18.8|11.5|4.2|9.6|
|Average exchange rate (LE/US$)|1.8|3.4|5.2|5.6|
|Average tariff rate|na|11.5|10.3|4.3|
|Maximum tariff rate|na|30|28.1|12.3|

7
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* Percent of <40 years old faculty<br>* Percent of faculty with 5yrs+ industry experience<br>Collect data on employment or employers’ satisfaction more than once per year @<br>Program has staff assigned to collect grads' employment data @<br>HEI has an employment center @<br>Offers remediation classes during the program @<br>Teaches numerical competencies @<br>Has enough equipment or tools for practice<br>-10 0 Percentage points. 10 20<br>Infraestructure @ Training and curriculum @ Link with productive sector Faculty<br><!-- End of picture text -->

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* Percent of governing body that belongs to: current students e@<br>General or specific knowledge test is an admission requirement e@<br>* Percent of faculty that are women<br>* Percent of faculty with BA degree<br>* Number of faculty (log)<br>Offer non-class-based remediation @<br>Offers remediation classes during the program e<br>HEI perception of labor market is very important to update program e<br>Teaches numerical competencies e<br>Offers credits for longer degrees @<br>5 0 5 10<br>Percent<br>@ Training and curriculum Faculty @ Other Practices<br><!-- End of picture text -->
2. New entrants are mainly drawn from the most productive set of _OR_ firms. The extensive margin of trade composition.

3. Product standards harmonization decreases export sales at existing exporters. The intensive margin of trade.

The model presented above is fairly flexible in terms of the assumption on the level of stringency of international norms in country _R_ . Note that hypotheses 1 and 3 remain unchanged if I assume that country _F_ and country _R_ have different but equally costly product standards to begin with. In this case, harmonization involves a different composition of the extensive margin of trade (hypothesis 2). It is represented now by entry from the most productive non-exporter firms because they can now access both markets upon the payment of a single fixed cost. Alternatively, I can relax the assumption that country _R_ uses international norms and get the same theoretical results. In this scenario the only required assumption is that country _R_ has lower fixed costs to export than country _F_ and that harmonization entails some reduction on the cost to export to country _F_ due to, for example, the elimination of standards aimed to protect domestic firms.

# **3 Data**

This analysis uses the U.S. linked/Longitudinal Firm Trade Transaction Database (LFTTD), which links individual U.S. trade transactions to U.S. firms in the Longitudinal Business Database (LBD)<sup>10</sup> , in conjunction with firm level information from the Censuses of Manufactures (CM) of the Longitudinal Research Database (LRD) of the U.S. Census Bureau. A key contribution of this study is the linking of firm level U.S. manufacturing data to industry level measures of EU product standards and their relationship with international norms. This section outlines the main features of the datasets.

## **3.1 U.S. Manufacturing firms Across Industries and Time**

The CM is conducted every five years and the empirical part of this paper makes use of CM information from 1992, 1997, and 2002.<sup>11</sup> The unit of observation for the Census is a manufacturing establishment, or plant, and it contains detail information on inputs and output of all establishments.<sup>12</sup> For 1992 and 1997, plants are classified at the four-digit Standard Industrial Classification level (SIC4). In 2002, industry classification changed to the 6-digit North American Industry Clas-

> ones for which differentiation and variety is important.

> 10See Bernard et al. (2009) for a complete description of the LFTTD and its construction. For an extensive discussion of the LBD see Jarmin and Miranda (2002).

> 11Though CM data are available for earlier periods, I cannot use them because export information on the LFTTD is not available.

> 12The CM imputes input usage data for small manufactures, referred to in the data as “administrative records”. As it is customary in the U.S. microdata research —see Bernard, Redding and Schott (2010)—, these observations are excluded from the analysis.

10
## **By granular region**

## **8c. Median asset growth rates of SOFIs**

<!-- Start of picture text --> Benchmark Europe WEST ECA EAST ECA<br>2007/2011 2011/2015<br>300%<br>200%<br>200%<br>150%<br>100%<br>50%<br>0%<br>-50%<br><!-- End of picture text -->

## **8d. Median loan growth rates of SOFIs**

<!-- Start of picture text --> Benchmark Europe WEST ECA EAST ECA<br>2007/2011 2011/2015<br>300%<br>200%<br>200%<br>150%<br>100%<br>50%<br>0%<br>-50%<br><!-- End of picture text --> Comparing the median asset and loan growth rates of SOFIs against the overall banking sector over the same periods,<sup>13</sup> the trends are broadly in line, but some differences do emerge. Growth rates are higher overall for SOFIs in both periods under observation. Median asset and loan growth rates in East ECA for the overall banking sector are higher post 2011, while for SOFIs were higher in the period 2007/2011 compared to 2011/2015. For median loan growth rates, whilst SOFIs in Benchmark Europe experienced a contraction post 2011, the overall banking sector experienced positive (yet low) growth – possibly SOFIs scaled back post GFC as the private sector picked up or had to reduce their assets following State Aid decisions from the European Commission because of capital injections by their governments following the GFC. In West ECA, however, median loan growth by SOFIs post 2011 continues to be significantly higher than that observed for the overall banking sector that is subject to a growth slowdown.

Finally, considering the question of whether SOFIs with a countercyclical policy exhibited higher median asset and loan growth rates, this does not appear to be the case at the outbreak of the GFC – every SOFI expanded its balance sheet. However, SOFIs with an explicit countercyclical policy appear to have maintained high growth rates in the 2011-15 period, unlike SOFIs that lacked such a policy.

# 5.3.4 _Lending sectors_

Figure 9 reports the ranking of lending by economic sector. Overall, both Commercial and Development SOFIs concentrate heavily on manufacturing and energy. Commercial SOFIs do,

> 13 These results must be interpreted with caution, as the overall banking sector includes SOFIs.

24
22 large establishments that resulted in the takeover of those firms by their workers. In fact, this procedure became the institutionalized way in which the government seized a large number of firms. The government made use of a 1932 Decree-Law that provided that whenever a labor dispute generated a serious disruption in supply, the authorities could take over the firm in question.

Table 1 Chile: Main Macroeconomic Indicators

||1970|1971|1972|1973|
|---|---|---|---|---|
|Inflationa|34.9|34.5|216.7|605.9|
|Growth|2.1|9.0|-1.2|-5.6|
|Real Wages (1970:3-100)|<br>98.4|115.1|103.5|70.3|
|Gov't. Revenueb|23.7|20.4|18.2|20.2|
|Gov't. Spendin b|26.4|31.1|31.2|44.9|
|Budget DeficitV|2.7|10.7|13.0|24.7|
|Money Growtha|52.9|99.3|100.9|264.4|
|Int'l. Reserves ($Mill.)|<br>320|129|95|36|
|Trade Balance($Mill.|246|73|-161|-73|
|Black Market Premium|99|358|898|2349|

a Percent of GDP b Percent, Dec.-Dec.

Sources: Yanez (1978); Banco Central de Chile; International Monetary Fund; Edwards (1986); Edwards and Edwards (1987), Solimano (1988).

By the end of 1971, the government had used this channel to expropriate more than 40 large firms. Naturally, this process disrupted production not only in the firms that were seized but also in the rest of the industrial sector where uncertainty was quickly mounting. Moreover, the level of productivity in the nationalized firms began to drop rapidly, contrary to the thinking of the architects of the UP plan, these firms
|||(0.156)|(0.170)|(0.170)|
|---|---|---|---|---|
|Industry: Social services||0.081|0.080|0.083|
|||(0.074)|(0.079)|(0.079)|
|government worker/employee||-0.011|-0.035|-0.034|
|||(0.136)|(0.147)|(0.147)|
|private worker/employee||0.087*|0.118**|0.120**|
|||(0.045)|(0.048)|(0.048)|
|In urban area|||0.089|0.092|
||||(0.063)|(0.063)|
|Status as household head: 1=yes; 0=no|||<br>0.183**|<br>0.184**|
||||(0.088)|(0.088)|
|Married: 1=yes; 0=no|||0.087*|0.086*|
||||(0.048)|(0.048)|
|Muslim: 1=yes; 0=no|||<br>0.147|<br>0.146|
||||(0.207)|(0.207)|
|House ownership: 1=owned; 0=not owned|||-0.079|-0.079|
||||(0.054)|(0.054)|
|Electricity in house: 1=yes; 0=no|||0.129|0.128|
||||(0.113)|(0.113)|
|Toilet in house: 1=yes; 0=no|||-0.015|-0.014|
||||(0.046)|(0.046)|
|Farm business: 1=hh with farming business; 0=no|||<br>0.082|<br>0.084|
||||(0.053)|(0.053)|
|Non-farm business:1=hh with non-farming business; 0=no|||-0.001<br>(0.040)|-0.002<br>(0.040)|
|Work history data incomplete||||-0.522|
|||||(0.438)|
|Observations|11,546|11,507|9,450|9,450|
|R-squared|0.557|0.561|0.564|0.564|
|Number of individuals|9,801|9,773|7,912|7,912|
|Standard errors in parentheses|||||
|<br>*** p<0.01, ** p<0.05, * p<0.1|||||
|A constant was included in all estimations but is not|||||
|reported, as were time fixed effects.|||||

Our main interest however, is in the coefficients for the experience variables. In relation to these variables it is important to note that “Years mostly self‐employed” includes all types of self‐employment and so we cannot separate the effects of different types of self‐ employment experience on wages. While self‐employment with a permanent work may be very different to the other forms of self‐employment, it makes up a very small proportion of observations (only 2% of youth and adults are in this type of self‐employment). This means that while the coefficient for the self‐employment experience variable is the weighted average of all types of self‐employment, it mostly reflect the return to self‐employment without permanent staff. Strikingly, an extra year spent mostly in self‐employment is related to a 3.6% lower current wage for youth, compared to the baseline of years not working. This is evidence of scarring for youth. Given the measurement error issues with this variable, a “false positive” result for scarring for this variable is unlikely, unless it is related to omitted 28
De Mel, Suresh, David McKenzie and Christopher Woodruff, 2009. “Are Women More Credit Constrained? Experimental Evidence on Gender and Microenterprise Returns”, _American Economic Journal: Applied Economics_ 1(3): 1-32.

Dolfman, Michael, Solidelle Fortier Wasser, and Bruce Bergman, 2007, “The Effects of Hurricane Katrina on the New Orleans Economy, _Monthly Labor Review_ , June 2007.

Eisensee, Thomas, and David Sromberg, 2007, “New Droughts, News Floods, and U.S. Disaster Relief,” _Quarterly Journal of Economics_ , Vol. 122(2), pp.

Ferreira, Francisco and Norbert Schady. 2009. “Aggregate Economic Shocks, Child Schooling, and Child Health”, _World Bank Research Observer_ 24(2): 147-181.

Ferguson, Roger, 2006, “Insurance Solutions for Emerging Markets,” paper prepared for London Economic Forum, December 2006.

Government of Sri Lanka, 2005 (May), “Post Tsunami Recovery and Reconstruction Strategy.” Government of Sri Lanka, 2006, “DAD Date Report: Tsunami-Related projects as of 15 May 2006,” Reconstruction and Development Authority, Sri Lanka.

Harvey, Paul, 2005, “Cash and Vouchers in Emergencies,” Humanitarian Policy Group Discussion Paper.

Jayasuriya, Sisira, Paul Steele and Dushni Weerakoon, 2005, “Post-Tsunami Recovery: Issues and Challenges in Sri Lanka,” Asian Development Bank working paper.

Kahn, Matthew 2005. “The Death Toll from Natural Disasters: The Role of Income, Geography, and Institutions”, _The Review of Economics and Statistics_ 87(2): 271-84.

Lustig, Nora L. (2000) “Crises and the Poor: Socially Responsible Macroeconomics”, _Economia_ 1(1): 1-30.

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McKenzie, David J. 2006. The Consumer Response to the Mexican Peso Crisis. _Economic Development and Cultural Change_ 55(1): 139-172.

<u>Morduch, Jonathan, 1999. “Between the state and the market: can informal insurance</u> patch the safety net?”, _The World Bank Research Observer_ 14(2):187–207 31
I start by estimating: where the outcome variable, _Pijt_ is the log mode price of soybean in _mandi i_ located in district _j_ at month _t_ . Since prices are likely to change proportionally rather than by a fixed rupee amount, it is sensible to transform the variable using logs.<sup>36</sup> The _γi_ ’s represent a full set of _mandi_ fixed effects that control for unobserved time invariant heterogeneity in _mandi_ characteristics arising from a differences in infrastructure, soil quality and market size.<sup>37</sup> The _μt_ ’s represent a full set of month fixed effects (there are 65 indicator variables for the sample going from April 2000 to September 2005) that control for any time varying aggregate factors affecting the price of soybean across all the _mandis_ in the state, such as world prices, common demand shocks or common climate shocks.

To control for differential price trends across districts during the period of interest, District-specific time trends are also included represented by _tφj_ . The standard errors are clustered at the district level. _εijt_ is a _mandi_ -district-month specific error term. _Kjt_ is an indicator variable for whether there is a kiosk in district _j_ in month _t_ . Similarly, _Hjt_ is an indicator variable for whether there is a hub in district _j_ at month _t_ . Since hubs were set up after kiosks, _β_ 3 measures the marginal effect of adding a hub to a kiosk.

Using the sample of 144 _mandis_ in 23 districts of the state, the results indicate a positive and significant effect of kiosks on the average price of soybean as presented in Table 4. The presence of kiosks in a district is associated with an increase in the average monthly mode price of soy in the _mandis_ located in that district by 1.7 percent (columns (1)-(2)). This result lends support to the theoretical prediction that the the availability of price information to farmers increases the competitiveness of traders in local output markets, leading to an increase in the price of soybean in the _mandis_ . The effect of a hub on the _mandi_ price of soybean is very small and insignificant. Since quality is scientifically tested at the ITC hubs, there might be sorting of farmers based on the quality of their

> 36Moreover, the mode price appears to be positively skewed whereas the log mode price is much more symmetrically distributed. Results are qualitatively similar when estimated in levels.

> 37District fixed effects are essentially incorporated by including _mandi_ fixed effects.

18