rafmacalaba/datause-encoder-data huggingface dataset:
is, such that _g(H) -_ 0. Estimnation efficiency can be improved by requiring that the estimated **_rj_** and _'Yh_ parameters generate smooth approximations for functions _f(.)_ and 9(.). One such method is the so-called roughness penalty method suggested by Good and Gaskins<sup>(1971)</sup> and Silverman (1982). In the case of ordinary least squares, the estimator is obtained<sup>byminimizing:</sup> where T is sample size and ), and A, are penalty parameters.' All reported standard errors are robust (White) standard eriors corrected for clustering.<sup>2</sup> The purpose of the rest of the paper is to estimate the above model using data on Nepal.

In case yil is dichotomous so that OLS is inappropriate, the sum of squared residuals in equation (??) is replaced by the required likelihood function. Penalty parameters<sup>_Ar_</sup> and A.. are adjusted accordingly. When the estimating function is a likelihood function (and provided some other conditions are satisfied), Silverman (1984) has shown that the roughness penalty approach yields a kernel estimator of _f(.)._ When the dependent variable is censored,<sup>weuse</sup> OLS instead of tobit for two reasons. First, we are interested in how distance affects the unconditional expectation cf yil, not how it affects yi' conditional on being uncensored. Second, in the presence of heteroskedasticity and clustering, tobit is inconsistent and standard errors cannot be corrected.

1 These estimates of the r's and -y's can easily be obtained using the regular OLS command by adding J _+ H -4_ artificial observations at the end of the sample such that dependent variable and regressors are zero, except<sup>for</sup> _Dn_T-_ A Dn_T_ **=-2A,.,** and **D.T+l** = A, for n = _T_ + 1 to T _+ J_ -2; and _Pn -T-1-_ A **_pn-T_** = and **npT+** -A.for _n_ = _T +J_ - 1 to _T + J + H -4._ 2 As is often the case in survey data, correcting for hetereskedasticity and clustering makes a big difference. To check the possible impact of the roughness penalty correction on standard errors, we compared reported standard errors with standard errors obtained by bootstrapping with 200 replications (with replacement).<sup>Giventhelarge</sup> sample size and low level of smoothing, bootstrapped confidence intervals are virtually indistinguishable<sup>from</sup> OLS estimates.

10
12 plant's level of technical inefficiency.11 Our objective is infer something about the changes in these efficiency measures that accompany trade liberalization. Ignoring econometric and data problems for the moment, imagine we are able to obtain consistent estimates of the following Cobb-Douglas production function for each industry and year: Here D1 through D3 are dummies that control for things like type of firm ownership, ul is a disturbance term, and all other variables are assumed to be measured in logarithms. (More detailed variable definitions are provided in the appendix.) Then inferences regarding scale and technical efficiency can be based on estimates of a, Po. P1. and var(ul).

To see how, refer to figure 1. Here we represent f(K*,E) indirectly with its dual, the technically efficient cost function g(V). This allows us to work in two dimensions. All variables are in logarithms, so l/g'(V) reflects returns to scale, and gt ) follows the usual pattern of increasing returns in low output ranges, then constant or diminishing returns. Dots and asterisks are hypothetical data points; they represent cross-sectional observations on plants in a givenindustry. All data points lie above g(V) to indicate that the

> 11/ This definition, which began with the work of Farrell (1957), is fairly standard for empirical work. For literature surveys, see Forsund, Lovell and Schmidt (1980) and Schmidt (1985).
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alternative evaluations of expected outcomes. Section 5 discusses market-based domestic policies in Australia, the European Union and the United States. Section 6 looks at the current state of carbon markets. Finally, Section 7 concludes and indicates areas for future research.<sup>1</sup>

# **2 Science and policy**

Motivation for a treaty limiting greenhouse gas emissions stems from evidence that the global climate is warming and the strengthening inference that human activity is a significant contributing factor. In turn, the degree to which interventions are required depends firstly on an evaluation of both points, and secondly on an assessment of the damages caused by global warming and the costs and benefits of altering its mutable components.

The conceptual foundation for the contribution of human activity to global warming is not controversial in itself and relates to the greenhouse effect. Briefly, as the earth constantly receives energy from the sun and radiates energy back into space, water vapor, clouds and long-lived gases, including carbon dioxide, work to reduce the outflow of radiated light, creating an energy imbalance known as the greenhouse effect. In 1861, John Tyndall speculated that the accumulated release of carbon dioxide from combustible fossil fuels might increase the energy imbalance, resulting in a warming of the earth’s surface. Later, the Swedish physicist, Svante Arrhenius (1896) provided a formal model of the phenomenon. Arrhenius predicted a gradual warming of the climate, but did not view the consequences as threatening. Later, as global consumption of fossil fuels increased and the earth’s cooling and warming mechanisms became better understood, the subject was further revived. In 1957, an important paper by Revelle and Suess (1957) suggested that the oceans’ capacity to absorb carbon dioxide from the atmosphere was more limited than previously thought. The authors went on to stress the potential risks and uncertainties associated with a continuing buildup of greenhouse gases. In the ensuing years, measurements of atmospheric carbon dioxide taken at the Mauna Loa Observatory starting in 1958 revealed increasing concentration of carbon dioxide in the atmosphere. Later on, corroborating evidence from ice-core analysis suggested a trend of building accumulations going back to the Nineteenth Century (Siegenthaler and Oeschger 1987).

Nevertheless, predicting the consequences of increased concentrations of atmospheric carbon dioxide and induced global warming proved a difficult task. Hurdles included inexact historic climatic measures and an incomplete understanding of the complex natural relationships among the mechanisms that heat and cool the earth’s oceans and atmosphere.<sup>2</sup> By the close of the 1970s, no

> 1 Language describing the objectives and workings of the Climate Change Treaty is ladened with acronyms and we include a glossary in the annex to this paper to help frustrated readers.

> 2 See, for example, Lindzen (1990) for a discussion.

3
legal structure, but also other city characteristics that can affect firms’ performance, such as human capital or agglomeration economies.<sup>5</sup> The same firm in a different environment will probably experience different challenges that can affect its productivity levels and outcomes.

Air pollution has also been considered an important factor affecting workers and firms through two main channels. On the one hand, pollution levels can affect workers’ health and productivity. Recent studies on the effects of air pollution on worker productivity have quantified the negative effects on different production sectors (Graff-Zivin and Neidell 2012, T. Chang, J. S. Graff-Zivin, et al. 2014, T. Chang, J. Graff-Zivin, et al. 2016, Adhvaryu, Kala and Nyshadham 2014). On the other hand, environmental regulations can impact firms’ costs and influence their location. For example, studies such as Henderson (1974) and Kahn and Mansur, (2013) have shown that manufacturing firms cluster in areas where environmental regulations are not too strict. In developing countries, cities often limit such regulations, offering “pollution havens” that can attract major factories and contribute to development (Birdsall and Wheeler 1993, Cole 2004, Eskeland and Harrison 2003), while exacerbating the negative consequences on the environment.

We build upon related work by Harrison, Lin and Xu (2014), and Reyes, Robert and Xu (2017). Harrison et al. (2014) use the World Bank Enterprise Survey data to provide evidence that key factors for improving African firm performance are political competition, infrastructure, and access to finance. Reyes et al. (2017) offered a more comprehensive list of the business environment factors and add measures of BE elements such as taxes, labor regulation, entry and exit, along with the agglomeration environment. They conduct a global analysis while controlling for country fixed effects, offering better control of country heterogeneity. They also explore how the effects of BE variables are different depending on firms’ characteristics (such as age, size) and country level of development.

The literature on the effects of pollution in Africa is less developed. Although the topic has gained attention in recent years, the scarcity of pollution data for the region had limited long-

> 5 There are many studies on the effects of agglomeration economies on long-term growth. See for example Glaeser and Gottlieb (2009); Jones and Romer (2010); Clarke, Li and Xu (2016); Li, Long and Xu (2017)).

3
**Table 1** – Determinants of growth per capita: Panel fixed effects estimations

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Log(GDP per capita)|-4.558<sup>_∗∗∗_</sup><br>(0.000)|-3.407<sup>_∗∗∗_</sup><br>(0.000)|-3.442<sup>_∗∗∗_</sup><br>(0.000)|-3.571<sup>_∗∗∗_</sup><br>(0.000)|-3.538<sup>_∗∗∗_</sup><br>(0.000)|
|Education|0.0683<sup>_∗∗_</sup>|0.0510<sup>_∗∗∗_</sup>|0.0451|0.0577<sup>_∗∗_</sup>|0.0492<sup>_∗_</sup>|
||(0.029)|(0.009)|(0.115)|(0.046)|(0.087)|
|Governm. cons.|-0.102<sup>_∗∗_</sup>|-0.143<sup>_∗∗∗_</sup>|-0.0991<sup>_∗∗∗_</sup>|-0.0947<sup>_∗∗∗_</sup>|-0.0923<sup>_∗∗_</sup>|
||(0.018)|(0.000)|(0.009)|(0.010)|(0.015)|
|Investment|0.162<sup>_∗∗∗_</sup>|0.144<sup>_∗∗∗_</sup>|0.150<sup>_∗∗∗_</sup>|0.153<sup>_∗∗∗_</sup>|0.153<sup>_∗∗∗_</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Openness|0.0147<sup>_∗∗_</sup>|0.0176<sup>_∗∗∗_</sup>|0.0134<sup>_∗∗_</sup>|0.0107<sup>_∗_</sup>|0.0133<sup>_∗∗_</sup>|
||(0.037)|(0.005)|(0.029)|(0.082)|(0.033)|
|Infation|-0.00271<sup>_∗∗∗_</sup>|-0.00185<sup>_∗∗∗_</sup>|-0.00159<sup>_∗∗∗_</sup>|-0.00154<sup>_∗∗∗_</sup>|-0.00154<sup>_∗∗∗_</sup>|
||(0.008)|(0.000)|(0.000)|(0.000)|(0.000)|
|Rule of law|0.0159<sup>_∗∗_</sup>|0.0193<sup>_∗∗∗_</sup>|0.0168<sup>_∗∗_</sup>|0.00479|0.0145<sup>_∗∗_</sup>|
||(0.020)|(0.007)|(0.014)|(0.571)|(0.033)|
|Natural res.||0.0272<br>(0.316)||||
|Oil rents|||0.0627<br>(0.144)|0.106<sup>_∗∗_</sup><br>(0.038)||
|Non-oil rents|||0.0267|-0.121<sup>_∗∗_</sup>|0.0189|
||||(0.437)|(0.022)|(0.544)|
|Rule of law x oil rents||||-0.000843<br>(0.325)||
|Rule of law x non-oil rents||||0.00236<sup>_∗∗∗_</sup><br>(0.000)||
|Oil rents, 1st qu.|||||6.753<sup>_∗∗∗_</sup><br>(0.000)|
|Oil rents, 2nd qu.|||||0.757<sup>_∗∗∗_</sup><br>(0.002)|
|Oil rents, 3rd qu.|||||0.151<sup>_∗∗_</sup><br>(0.012)|
|Oil rents, 4th qu.|||||0.0766<sup>_∗_</sup>|
||||||(0.075)|
|Observations|1031|1004|1000|1000|1000|
|Countries|150|150|150|150|150|
|_R_<sup>2</sup>|0.384|0.210|0.293|0.304|0.308|

Each specification includes country and period fixed effects, as well as a constant. _p_ -values in parentheses. _∗ p <_ 0 _._ 1, _∗∗ p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

Another reason for the lack of a clear relationship for natural resources and growth could be a missing link to institutional quality. For natural resources to improve growth rates, a stable institutional environment might be a prerequisite that guarantees a fair distribution of revenues associated with the rents. When allowing for such an interaction in column (4), it appears that on 11
(4) We test whether a central bank organizes its reserve management operations in one department.

(5) We further assess whether the reserve management operations are arranged in separate departments.

(6) Finally, we probe the impact of the Ministry of Finance’s obligation to cover the central bank’s negative equity.

Relatedly, we further probe transparency on the risks and returns of reserve management operations to the board as a continuous variable.

We use RAMP survey data collected as continuous data on the number of eligible currencies and asset classes and the allocation to nontraditional asset classes as dependent variables as a reflection of a central bank's investment policies. We also deploy RAMP data to compute the measures of risk as reflected in a central bank's current asset allocation: (1) estimated risk of the portfolio, (2) investment horizon (measured at the total portfolio level for both tranched and untranched portfolios), and (3) duration (measured at the total portfolio level and at the liquidity or investment tranche level).

# **5. Descriptive Statistical Results**

We run simple correlations for the various independent governance variables to identify patterns in the data. The correlations are run to develop context and show empirical relationships between critical elements of reserve management. The full results of the correlation analysis are found in Appendix I.

**_Independent Investment Committees._** We test whether there is a positive statistical correlation between the independence of the investment committee and the characteristics of the investment guidelines.  As explained above, we define an investment committee as independent if it is responsible for approving the investment guidelines. Table 3 indicates that an independent investment committee is negatively correlated with the number of eligible currencies and asset classes. Similarly, duration is negatively correlated with an independent investment committee.

**Table 3  Independence of the Investment Committee and Reserve Portfolio Metrics**

|Variables|Allocation to<br>nontraditional<br>assets|Number of<br>eligible<br>currencies|Number of<br>eligible assets|Duration –<br>Total portfolio<br>(months)|Duration –<br>Liquidity<br>tranche<br>(months)|Duration –<br>Investment<br>tranche<br>(months)|
|---|---|---|---|---|---|---|
|Independent investment committee|-0.233*|-0.102|-0.148|-0.282**|-0.108|-0.14|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._ 11
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| POLICY RESEARCH WORKING PAPER 2734

# Summary findings

Drawing on original surveys of agricultural traders, Fafchamps and Gabre-Madhin examine how traders operate in two Sub-Saharan African countries, Benin and Malawi. They find the following:

* The largest transaction costs for traders are search and transport. Search methods rely principally on personal visits by the trader, which raises search costs. And since enterprises are very small, transport represents a large share of marketing costs.

* Brand recognition, grading, and quality certification are nonexistent.

Quantities are not pooled for transport and storage so as to achieve returns to scale.

* Interseasonal and interregional arbitrage is not feasible for most traders, who prefer to operate day to day in a small territory.

This information provides some important insights into how agricultural trade could be improved. It suggests possible policy interventions in four main areas: increasing traders' asset base, reducing transaction risk, promoting more sophisticated business practices, and reducing physical marketing costs.

* Brokers and agents are not organized in commodity exchanges.

This paper-a product of Rural Development, Development Research Group-is part of a larger effort in the group to understand the operation of commodity markets in rural areas. The study was funded by the Bank's Research Support Budget under the research project "Markets for Agricultural Inputs in Sub-Saharan Africa" (RPO 683-48). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Pauline Kokila, room MC3-305, telephone 202-473-3716, fax 202-522-1151, email address pkokila@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at marcel.fafchamps@economics.oxford.ac.uk or e.gabre-madhin@cgiar.org. December 2001. (68 pages) _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 view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
The task of the assessor is to make two assessments for each issue: first, does the paper report adequately on the matter, providing the reader with enough information to assess the risk of bias, and second, is the paper at low risk of bias from the relevant threat? The assessor circles either a yes or a no for each question and, if possible, provides a page number and/or explanation in the comment and quote boxes to the right of the question to justify each assessment. We decided on the following rule for assessment of risk of bias: if a paper did not report adequately on the issue, it could not be assessed as having a low risk of bias. This decision reflects our judgment, mentioned earlier, that the burden of proof of the unbiasedness of a study’s results rests with the author(s). The landmark meta-analysis assessing study quality in medicine uses a similar rule (Schulz et al. 1995). We present results on reporting and risk of bias for each individual issue as well as aggregated to the bias level under a simple rule – if a study is inadequately reported or not at low risk of bias for one issue, it is inadequately reported or not at low risk of bias for the relevant bias. We do not create an overall study-level assessment<sup>7</sup> , as expectations on both the sign and magnitude of bias vary across issues.

We selected studies for assessment using the following process:

1. We searched EconLit for journal articles published between 2000 and 2009 that contained either the word randomized or randomization (or their alternative UK spellings) in the title or abstract. A search conducted on July 6th, 2010 generated 527 results. This was amended on September 5th,

> 7. Several meta-analyses of the risk of bias in medicine follow this practice as well (Spiegelhalter and Best 2003).

23
output, and import price indices were obtained from the Ministere du Plan et de l'Industrie. These series were only available from 1980. The intermediate goods index was calculated by applying the output price indices to the input-output matrix for production in Cote d'Ivoire. It was available only at the two digit level. To ensure conformability with the other indices, we calculate a labor cost index as the median nominal wage of all firms in the sous-branche in year t relative to the median nominal wage in year t-1. The median was chosen to reduce the effect of outliers. Additional details on the price indices are contained in the data appendix. Data on the value of imports within the sous-branche classification are also obtained from the the Ministere du Plan et de l'Industrie. The imports refer to imports of final goods within this classification, not the imported inputs used by domestic firms within the sous-branche.

All other data on domestic sales, exports, expenditure on labor, and expenditure on intermediate inputs was calculated from information provided to the Banque des Donnees Financieres by individual firms in the manufacturing sectors. The value of domestic sales was measured by the value of gross sales minus the value of exports. Capital was measured as net cumulated investment, deflated by an aggregate capital goods price index. All differences are real differences, expressed in prices prevailing at time t - 1 and deflated by the apr ropriate price index.

This information was available going back to 1976, but we could not consider a longer sample period owing to the limitation on the price data. Details on the construction of the data and a brief description of the data set is contained in the data appendix.

# _4. ESTIMATION AND RESULTS_

We estimated models with and without the demand side equations and with and without the restrictions of a constant returns to scale technology for two different samples<sup>9</sup> . The models with only the output supply and input demand equations were estimated using iterative seemingly unrelated regression. The models with the demand side were estimated using iterative three-stage least squares. The results are presented for the sample obtained from an aggregation over all firms in the sous-branche in Table 9 All models were estimated using SAS's PROC SYSNLIN. - 14 -
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In line with the distinction, labor costs, which are considered another important determinant of investment flows, may exert different impacts as well. It is argued that vertical FDI, by taking advantage of comparatively low production costs in the host country, shall be negatively related to it. In Bedi and Cieslik (2002), evidence shows a strong negative correlation between FDI and wage levels in Poland. In case of horizontal FDI, generally a negative sign would be expected as well because cost considerations, although they may no longer be the top priority in investors’ decision making, still play a role. However, this does not exclude the possibility that a positive relationship would show up in some instances. One would expect a positive sign on the wage rate to reflect the purchasing power and labor quality of the local market. To control for this factor, we use the wage rate in manufacturing as a proxy. The International Labor Organization’s LABORSTA provides the data for individual country manufacturing wages in local currency. We convert the wage rates to US dollars using official average exchange rates drawn from World Development Indicators. As for labor quality, we include the illiteracy rate among the host population as the proxy. The illiteracy rate data are from United Nations Education, Scientific and Cultural Organization (UNESCO)’s Institute for Statistics and are calculated as the percentage of people aged 15 and above who are illiterate.

In terms of institutional variables, it is widely accepted that the host country’s political as well as financial risks are critical to its overall economic well-being. This is more so in our case because almost all CEECs have experienced political instability and economic slump at the beginning of the transition. As pointed out in Navaretti and Venables (2004), “political risk and instability seems to be an important deterrent to 15