rafmacalaba/datause-encoder-data huggingface dataset:
adverse environmental consequences. Onyeji and Fischer (1994) in a study for Egypt find that adverse climate change will lead to a decline in agricultural production and in GDP. However, they argue that large instruments in adaptation are required to make significant gains in avoiding the adverse impacts of climate change on the economy. Yates and Strzepek (1998) argue that global warming is likely to have adverse consequences for the Egyptian economy. Benson and Clay (1998) in a study involving a number of African countries argue that developing countries in Africa may be less prone to climate change shocks than industrial countries.

# **_4.4 Overview of the literature_**

The above review of the literature shows that in spite of the weaknesses of the production function approach, the findings of studies based on it concur in some ways with those of studies using the Ricardian approach. Nevertheless, most of the production function approach studies seem to argue more strongly that climate change may be expected to have positive impacts on agriculture, while most Ricardian studies predict a negative impact. In addition, the production function studies show that farmers can overcome the adverse impact of global warming by implementing adaptation measures as the climate changes.

# **5. Methodology**

Most studies of the impact of climate change on agriculture employ the Ricardian analysis (see for instance Mendelsohn et al. 1994) while traditional studies have used the production function approach (for example Rosenzweig & Iglesias 1994). As noted in the literature review, it has been criticized for having an inherent bias and tending to overestimate the damage climate change causes to farming because of failing to take into account the enormous variety of substitutions, adaptations and old and new activities that may displace obsolete activities as climate changes. The Ricardian approach is based on the observation by David Ricardo (1772–1823) that land rents reflect the net productivity of farmland and it examines the impact of climate and other variables on land values and farm revenues. This approach has been found attractive because it corrects the bias in the production function approach by using economic data on the value of land. By directly measuring farm prices or revenues, the Ricardian approach accounts for the direct effects of climate on the yields of different crops as well as the indirect substitution of different inputs, the introduction of different activities and other potential adaptations to different climates (Mendelsohn et al. 1994). It is also attractive because it includes not only the direct effect of climate on productivity but also the adaptation response by farmers to local climate.

The Ricardian approach is a cross-sectional model applied to agricultural production. It takes into account how variations in climate change affect net revenue or land value. Following Mendelsohn et al. (1994), the approach involves specifying a net productivity function of the form: R = Σ Pi Qi (X, F, Z, G) - Σ Px X 16
itself may in some cases be unconscious. These could include the way job ads are framed, where they are posted, the way interviews are conducted and how applicant status is gleaned. A large number of studies have documented discrimination along lines of race, ethnicity and gender but increasingly, studies are also documenting the more “invisible” axes of discrimination – along lines of neighborhood or residence, good or plain looks, skin color, family background, elite or non-elite status, the way candidates speak English or the lingua franca. These mechanisms are _screening or filtering devices_ that exclude some groups while including others. Figure 1 shows that discrimination can occur along a number of processes – some are real, overt, legal and conscious, while others are not. In some cases, the sheer invisibility and intangibility of the processes makes people wonder if they are real or perceived.

In periods when the labor market is tight and “good jobs” are few, potential employers may try and maximize their own utility by imposing additional filters. For instance, the Washington Post reported that in the currently tight labor market, pregnant women tend to lose their jobs more frequently than do other workers. The United States Equal Employment Opportunity Commission (EEOC) saw a growing number of charges from FY 2009, which culminated in close to a record 100,000 charges in FY 11 (EEOC, 2012)<sup>3</sup> . This increase is likely to be related to the shrinking job market. Similarly, a question in the World Values Surveys asks respondents whether in periods of jobs shortage, men should be given precedence over women. Most countries display more gender equal norms in this respect. But countries where people say men have more right to jobs are those that also have lower labor force participation rates for women (2005-2008), suggesting a societal preference for gender inequality in hiring (World Bank, 2013).

As mentioned earlier, many firms have a vision of the culture they want in the workplace and may value homogeneity and an “upper class” ethos in management jobs (see Loury 1999). The term “ _homophily”_ has been used in the sociological literature to describe the propensity of people who are similar to each other to bond together. As McPherson et al

> 3 <u>http://www.eeoc.gov/eeoc/statistics/enforcement/charges.cfm</u>

16
# Notes to Table 5

Sources: Downriver Kulik, Smith, and Stromsdorfer (1984: Table 3.4); Buffalo: Corson, Long, and Maynard (1985: Table IV.4); Texas WAD: Bloom (1990: Table 8.2); and New Jersey: Corson, and others (1989: Tables 2, 3, and VII.1).

- ***, **, and * signify that the program effect is statistically significant at the 1%, 5%, and 10% levels, respectively.

- a. Phase I net impact estimate for BASF workers using Lear-Siegler as the comparison plant.

- b. "Target plant" net impact estimates.

c. Measured weekly for Downriver and Buffalo, annually for Texas WAD, and quarterly for New Jersey Ul. New Jersey estimates are measured for the second quarter after initial Ul claim.

- d. Measured over 30 weeks for Texas WAD and over the benefit year for New Jersey.

- e. Cost estimates are per worker who received services.

**0**
for larger and more efficient firms, or are they efficient as a reflection of the problems of information flow and control span that prohibit larger firms from entering? If artificial barriers exist, what exactly does it take to encourage the emergence of large investors or “servicers” in the rental market that could drive down rents or enhance service quality?

**Figure 2. Building Structure and Rental Tenure in Some Market Economies**

<!-- Start of picture text --> 70<br>Apartment & hostel buildings Non ow ner-occupied tenure<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>IrelandUnited Kingdom Netherlands United States Denmark France Germany Spain<br><!-- End of picture text --> _Source_ : Authors’ calculations using National Agency for Enterprise and Housing (Denmark): Housing Statistics in the European Union (2003), UN-ECE (2004a), Joint Centre of Housing Studies (2004).

Social choice theories may also help in understanding the supply side of renting. Why do some mature market countries maintain extensive social rental housing programs (as in The Netherlands), while the programs of their immediate and culturally close neighbors are much more modest (as in Belgium)? This situation may arise from preferences of the median voter, who in The Netherlands is a renter and in Belgium is an owner. Social choice could help explain the preponderance of social renting in transition countries before 1989. The dismal housing situation of the working class was a major issue in the revolutions of the early twentieth century that produced Soviet socialism. But it may also explain the inertia in dealing with many of the current housing policy problems in the region. For example, the median voter in ECA transition countries is a former renter turned owner (tenant-owner) in a M-F building privatized almost for free (give-away privatization). The median voter in the United Kingdom or United States is an owner who bought a housing asset on the market through considerable effort that continues over the term of mortgage loan repayment. And homeownership in some countries focuses mostly on homes rather than apartments, which does not have to deal with a difficult issue of jointly-owned common-property management, maintenance and repairs. In other countries, where apartment ownership is more significant, this issue gains more importance and affects the way M-F buildings are owned and managed. Not surprisingly, housing policies and instruments differ in societies with such fundamentally different experiences and political economies. These widely differing circumstances have contributed to communication problems in comparative housing policy research and debate.

7
**Figure 2:  Durban Cargo Dwell Time Frequency**

<!-- Start of picture text --> 50%<br>45% 45%<br>40%<br>35%<br>30% 30%<br>25%<br>20%<br>15% 15%<br>10%<br>6%<br>5%<br>3%<br>1%<br>0%<br>Day 1 Day 2 Day 3 Day 4-8 Day 8-28 Over 28<br><!-- End of picture text --> Source: Interviews with TPT and with major shipping lines and warehouses operators.

Therefore, Durban appears to be a good benchmark for Southern Africa, but even more importantly, for SSA ports. Durban is, indeed, by far the port where cargo dwell time is the lowest in Southern Africa and SSA in general (see Table 2 which presents data collected for the Africa Infrastructure Country Diagnostic (AICD) for Southern Africa and Table 3 for selected ports in SSA).

**Table 2: Dwell time in Southern Africa**

||Cape<br>Town|Durban|East<br>London|Port<br>Elizabeth|Walvis<br>Bay|Luanda|Beira|Maputo|
|---|---|---|---|---|---|---|---|---|
|||South|Africa||Namibia|Angola|Moza|mbique|
|Container dwell|||||||||
|time—average|||||||||
|(days)|6|4|7|6|8|12|20|22|

_Source: Africa Infrastructure Country Diagnostic, 2009_ 8
investors would mechanically display poor performance. To evaluate this possibility, we use the sample of market entrants and study the relation between an individual’s stock selection and the stock selection decision made by her experienced classmates. The regression methodology follows the one applied by Hvide & Ostberg (2015) who study social interactions and stock selection at the workplace. We create a variable that represents the fraction of total purchases in stock _s_ by a new ii,cc,tt+∆,ss investor _i_ during ∆ months after the course start date. The dependent variable, ff , is defined for all ii,cc,tt+∆,ss stocks traded by individuals during the measurement period, and ff= 1 by construction. The ss ii,cc,tt+∆,ss main explanatory variables are the fraction of purchases made by the experienced classmates in ∑ff ∆ months prior to the course, , and ∆ months after the course start date, . We consider stock purchases cc,tt−∆,ss cc,tt+∆,ss separately for pre and post training to examine whether direct spillover effects in stock selection are related NN NN to past and/or future trades by peers. Since groups are formed exogenously and most courses last less than a month, the correlation between classmates’ stock selection decision might be more pronounced in the short term. Alternatively, some classmates could develop long-lasting friendships and they might continue to discuss their trading activity well beyond the course end date. To account for these possibilities, we evaluate stock selection in different time windows (i.e., ∆ is three or 12 months). There was a total of 70 different stocks traded by individuals in the CSE courses during the sample period and the mean fraction of total purchases invested in a stock by new investors in their first year of trading was 0.92% (Table 7).

To relate an individual’s stock selection to that of her experienced classmates, we estimate the following regression: following regression: Ω Ψ ii,cc,tt+∆,ss cc,TT,ss cc,tt−∆,ss tt,ss cc ff = αα+ ββNN + γγPPE ΓEE RREER EEaass + QQ<sup>′</sup> + MM<sup>′</sup> (4) ii,ss ss,tt pp ll,tt ii,cc,tt+12,ss In equation (4), _T_ is either _t-_ ∆ or _t+_ ∆ and +ZZ<sup>′</sup> + μμ + δδ + γγ are the average trading returns + εε cc,tt−∆,ss among experienced students calculated for each stock during PPE EE RREER ∆EEaass months before the course start date. We control for time-varying stock characteristics (Ω ) by including stock returns and variance in addition to tt,ss 30
Table 3: Determinants of Absolute Rolling Correlations - Linear Regression Analysis

|||||Sample Per|iod 1970M0|2-2013M0|5||||
|---|---|---|---|---|---|---|---|---|---|---|
|||Re|gressor: St|andard Dev|iation of S&|P500 Ret|urns (_σSP_500_,_|_t_)|||
||Crude oil|Coal|Natural|Soybean|Barley|Maize|Sorghum|Rice|Wheat|Sugar|
||||gas|oil|||||||
|Coal|0.011||||||||||
|Natural gas|0.003|0.015***|||||||||
|Soybean oil|0.009|0.013|0.003||||||||
|Barley|0.019*|0.020*|0.005|0.018**|||||||
|Maize|0.016|0.015|0.013***|0.015***|0.006||||||
|Sorghum|0.009|0.009|0.011***|0.013**|0.004|-0.001|||||
|Rice|0.007|0.000|0.007|0.011**|0.004|0.002|0.009||||
|Wheat|0.007|0.013|0.003|0.006|0.005|0.007|0.007|-0.006|||
|Sugar|0.005|0.008**|0.002|0.003|0.008|0.005|0.008|0.000|0.017***||
|Cofee|0.017**|0.013*|0.006|0.015**|0.021***|0.005|0.005|-0.007|0.009|0.009|
||||R|egressor: R|eal Interest|Rate (_RR_|_t_)||||
||Crude oil|Coal|Natural|Soybean|Barley|Maize|Sorghum|Rice|Wheat|Sugar|
||||gas|oil|||||||
|Coal|-0.002||||||||||
|Natural gas|-0.002|0.000|||||||||
|Soybean oil|-0.001|-0.002***|0.001||||||||
|Barley|-0.001|-0.001|0.001|-0.001|||||||
|Maize|0.001|-0.001|0.000|-0.002**|0.000||||||
|Sorghum|-0.001|-0.001|-0.001|-0.001|0.001|0.000|||||
|Rice|0.002|0.000|0.000|0.000|0.001|0.002|0.001||||
|Wheat|0.000|0.001|0.001|-0.002|0.000|-0.002|0.000|0.000|||
|Sugar|0.001|0.000|-0.001|-0.001|-0.002|-0.001|0.000|-0.001|0.002||
|Cofee|-0.001|-0.002|-0.001|-0.001|0.000|0.000|0.001|0.001|-0.001|-0.001|

Note. This table reports the OLS estimates of _δ_ and _γ_ in the equation The model parameters are estimated over the full sample, 1970M02-2013M05.<sup>�</sup> _ρ_ � _xz,w,t_ is the estimated value of the rolling correlation between the nominal price returns of commodities _x_ and _z_ at time _t_ , where _w_ is the length of the window used to compute the rolling correlations ( _w_ = 24 months, in this case). _L_ = 12 is the maximum lag allowed in the model; _L_ is determined using the Schwarz criterion. A Newey-West correction is used to produce reliable standard errors. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels.

17
# **1. Introduction**

A large body of research finds a positive connection between capital market development and national growth rates (Levine and Zervos, 1996, 1998; Demirguc-Kunt and Maksimovic, 1998; Rajan and Zingales, 1998; Henry, 2000; Levine, 2002; Beck and Levine, 2004; Bekaert et al., 2005). This macrolevel work, however, does not necessarily imply that firms use the funds raised in these markets to increase their productive capabilities—human capital, physical capital, and intangible capital—and grow. Firms have access to multiple sources to finance their growth, including bank credit. Moreover, capital markets can foster aggregate growth indirectly, by facilitating risk diversification, enhancing information dissemination, and boosting savings, without necessarily being the conduits of capitalaugmenting funds for corporations (Levine, 1991, 2005; Holmstrom and Tirole, 1993; Obstfeld 1994; Acemoglu and Zilibotti, 1997; Aghion et al., 2018).

To contribute to the study of capital markets and growth, we use firm-level analyses to assess the relation between firms issuing securities and changes in their productive capabilities and overall growth rates. Using firm-level data, we examine how firms that issue equity and bonds grow before, during, and after they raise new capital compared to firms that do not issue securities. We examine both overall firm growth, as measured by sales and assets, and growth in productive capabilities, as measured by human capital, physical capital, and intangible capital. Furthermore, we complement an extensive literature that suggests that the relation between securities issuances and firm growth might depend on (a) whether firms are issuing equity or bonds, (b) the severity of financing constraints on firms, and (c) the extent to which firms are issuing securities in more market-based or more bankbased financial systems. We do so by studying how the relation between issuing securities and growth varies across each of these dimensions.

To conduct the study, we assemble a comprehensive dataset of 150,165 firm-level issuances of equities and bonds in domestic and international markets over the period 1991-2016. We match 1
|12|Fixed efects estimation results: GDP<br>. . . . . . . . . . . . . . . . . . . .|. . . . . .<br>55|
|---|---|---|
|13|Granger causality estimation results<br>. . . . . . . . . . . . . . . . . . . . .|. . . . . .<br>55|
|14|Fixed efects and FGLS estimation results: net FDI infows<br>. . . . . . . .|. . . . . .<br>56|
|15|Estimation results by income level and region: net FDI infows<br>. . . . . .|. . . . . .<br>58|
**Figure 6 – ECA Countries are More Likely to Employ Workers in Energy-Intensive Sectors Compared to Other EU Countries**

Share of employment in energy-intensive industries as a percentage of total employment (2007) <!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br><!-- End of picture text --> Source: United Nations Statistics Division Note: The Western European countries are marked with lighter-colored bars. Energy-intensive industries comprise the following sectors: mining, quarrying, other utilities, construction, transport (air and land), storage and communications..

In the event of an energy price introduction or increase, the employment effect is likely to go beyond those workers directly employed in energy-intensive industries. As discussed in our conceptual framework, two other channels will also be very relevant in the short to medium term: the effect of the energy price shock on the capacity of the energy-intensive sectors to generate further employment and to contribute to the overall aggregate demand (and therefore, to the creation of jobs in other sectors); and, second, the effect of the energy price shock on otherwise considered clean industries that depend on energy-intensive inputs.

As of 2007, Azerbaijan, an important oil producer, was the country whose output was most dependent on energy-intensive industries (close to two thirds). Kazakhstan, Belarus, Russia, and Romania all had at least one third of their output derived directly from energy-intensive industries.<sup>14</sup> On the other hand, Montenegro presented the lowest dependency of output on energy-intensive industries within ECA – at 14 percent – followed by Albania, Armenia, Latvia, Turkey, and Moldova, all below 25 percent (Figure 7). As one can see, dependency on energy-intensive industries in terms of value-added does not necessarily translate into dependency in employment. Since sectors have varying labor (and capital) intensities, a sector can represent a large share of value added while making up a small share of overall employment. This is the case, for example, of large oil and gas producers such as Azerbaijan.

> 14 Albania and Romania have a very large portion of value added and employment in construction while Bulgaria has a very large transport sector.

11
6 Banefits in Russia suggests that firm provided benefits may not exceed those supplied by local authorities. Indeed, in our sample for housing, child care and health facilities, firm expenditure at local level was comparable or in excess of local .shority or non-firm expenditures in only 10-30% of cases, depending on the function (Table 3). As these items constitute the major expenditure charges on the benefits side, this cautions against a simplistic view of the scale of firmlevel functions.

From the survey we are also able to get some handle on the response of firms to the current and future provision of benefits. Several results stand out. First, larger firms offeiing wider benefits and higher aggregate benefit costs ha'7e begur to scale back these obligations. By end-1992 cver 35% of firms with housing prcgrammes had either reduced or suspended those programmes and a further 10% intanded to reduce them in 1993. For other social functions -- kindergartens, access to polyclinics -- between 10-20% of firms had started to reduce availability. In a significant number of cases in the survey, firms had begun to increase cost recovery by raising user fees, generaliy by applying differential tariffs for non-employee access to services. Thus, while there is a clear preference in all firm size classes to dispense wiT.h, or curtail, the range of benefits, most of this has yet to happen; being expressed as a projection for 1993. More strikir.g is the generally sluggish adjustment of benefits by firms, though, of course, we have no way to correct for quality of services or rationing. Part of the likely reason behind this inertia is the reasonably buoyant financial position of most sample firms in 1992 4. And indeed we find that there is a loose correlation between current profitability and benefits provision. However, most of this is captured -- as we discuss below -- by changes to housing programmes. A furthe? set of reasons concern the institutional difficult les associated with transfer, especially where municipal administrations, themselves often facing tighter budgets than firms, have been unwilling to take over the functions, exerting moral and other suasion on firms to maintain social activities (see Box 1).

In summary, preliminary indications from both the survey and other evidence See Commander et al (1993) for more details
|Age of household head|4138|42.17|13.13|18|90|
|---|---|---|---|---|---|
|Respondent single|4195|.07|.26|0|1 (Yes)|
|Respondent polygamous|4195|.20|.40|0|1 (Yes)|
|Respondent Muslim|4198|.52|.50|0|1 (Yes)|
|Respondent Catholic|4198|.24|.43|0|1 (Yes)|
|Household income < 30,000 CFA Francs|4074|.39|.49|0|1 (Yes)|
|House has brick walls|4175|.20|.40|0|1 (Yes)|
|House has cement floors|4172|.29|.45|0|1 (Yes)|
|House has more than 5 rooms|4120|.19|.39|0|1 (Yes)|
|House has a television|4200|.08|.27|0|1 (Yes)|
|House has a mobile phone|4200|.27|.44|0|1 (Yes)|
|Population of the village|4120|2055.91|1386.06|115|8205|
|Distance from nearest urban center|4160|23.39|18.71|0|145|
|Distance from nearest bus/train stop|4120|23.31|29.31|0|150|
|Village has a functional private school|4180|.07|.26|0|1 (Yes)|
|Village chief has primary education|4200|.25|.43|0|1 (Yes)|
|Village chief has secondary education|4200|.20|.40|0|1 (Yes)|
|Village has a paved road|4200|.1|.30|0|1 (Yes)|
|Village has a secondary school|4200|.17|.38|0|1 (Yes)|
|Village has a literacy center|4180|.48|.50|0|1 (Yes)|
|Village has a health center|4200|.54|.50|0|1 (Yes)|
|Village has a potable water source|4200|1.14|2.32|0|17|
|Most common language in village is also most<br>common in commune|4200|.85|.35|0|1 (Yes)|
|Probability two households in village speak<br>the same language at home|4200|.75|.23|.27|1|

44