rafmacalaba/datause-encoder-data huggingface dataset:
growth and social justice in an Islamic economy would require universal education and employment generation (Naqvi 1981: 85). Laldin and Furqani (2012) identify the specific goal of Islamic finance as preservation of wealth, which can be done by acquiring and developing assets, by circulating wealth and protecting its value, and by protecting ownership and preserving wealth from damage.

From the perspective of an individual firm ( _micro maqasid_ ), commercial transactions are subject to rules that prohibit engaging in harmful activities such as selling products that harm consumers, dumping toxic waste harmful to the environment or residential areas, engaging in overly speculative ventures, etc. _Shari’ah_ compliance also implies fulfillment of the objectives of contracts (Kahf 2006), including upholding property rights, respecting the consistency of entitlements with the rights of ownership, linking transactions to real life activity, the transfer of property rights in sales, prohibiting debt sales, etc.

These principles have several implications for the financial transactions that are permissible under Islamic law. The two broad categories of prohibitions related to economic transactions recognized in _Shari’ah_ are excess in loan contracts _(riba_ ) and excessive uncertainty and ambiguity in contracts _(gharar_ ).<sup>8</sup> _Riba,_ which means usury, is prohibited by Islamic law. Although it is common to associate _riba_ with interest, it has much wider implications and can take different forms. The common premise in the prohibition of _riba_ lies in the unequal trade of values in exchange (Siddiqi 2014). One of the implications of _riba_ is that debt cannot be sold at a discount and can be transferred at its par value only. Debt in Islamic finance would entail interest-free loans ( _qard hassan_ ) or those created through real transactions. For example, Islamic financial institutions use debt instruments, such as a credit sale whereby the price of an asset/commodity is paid at a later date.

_Gharar_ can exist in the terms of a contract because the consequences of a transaction are not clear or there is uncertainty about whether a transaction will take place. _Gharar_ can also arise in the object of the contract arises when there is uncertainty about the subject matter of the sale and its delivery. _Gharar_ is present when either the object of sale does not exist or the seller and/or buyer has no knowledge of the object being exchanged. One of the implications of _gharar_ during contemporary times is the prohibition of derivative products such as forwards, swaps and options.

> 8For different meanings of _gharar_ see ElGamal, M. (2001) An Economic Explication of the Prohibition of _Gharar_ in Classical Islamic Jurisprudence. Islamic Economic Studies 8 at p. 32. For a detailed discussion on _gharar_ see Kamali<sup>10</sup> and Al-Dhareer, SMA (1997) Al-Gharar in Contracts and its Effect on Contemporary Transactions. Jeddah: Islamic Research and Training Institute, Islamic Development Bank.

11
in the petition, but also other products in the HS4 that are not named. The second shock, a negative import demand shock, hits roughly 3 months prior to the filing. The second shock is notably different than the first in that a) it hits the product in question but not the other products in the HS4 category, and b) it affects all imports, not only those from the countries that are subsequently named in the petition. The initial relative supply shock appears similar to those identified elsewhere in the literature, but the later, narrow import demand shock has not been identified empirically as an important trigger of AD.

We are able to observe this second shock because we exploit the granularity of U.S. import data. In particular, we use these data to observe changes over shorter times and narrower product classes than other authors in the literature. A limitation of exploiting these data is that they do not have an analogue in domestic production, so we cannot make statements about changes in domestic supply. Nonetheless we allow ourselves to speculate. The second shock affects imports from all sources, and therefore might well have a domestic origin. The narrowness of the shock in product space may suggest that a positive domestic supply shock is more viable than a negative domestic demand shock as a source of the import demand shock. One possible explanation of this pattern, similar to that in Feinberg (1989), is that the initial shock unsettles domestic collusive arrangements, and that the breakdown hits a narrow set of products, with the AD case employed as an attempt to restore the collusive order. This is one potential mechanism, others are also possible. We leave these questions for future research.

24
# **The Forestry Sector in the Russian Economy**

2.44 In 1999, the forest sector accounted for 1.5% of GDP, 4.8% of industrial output, 4.5% of export earnings, 1.5% of consolidated budget revenues<sup>16</sup> , 3.3% of foreign direct investment, 8% of industrial employment, and 2% of total employment (Goskomstat; Gostamozhkomitet; Miklashevskaya 2000; NIPIElLesprom).

2.45 The sector has been affected by the economic downturn surrounding the transition to a market economy. Timber harvesting levels declined drastically as domestic demand for wood products fell and transportation costs rose, from over 300 million cubic meters in 1990 to 80 million cubic meters in 1997 (see Annex). The officially harvested volume nation-wide amounted to 157 million cubic meters in 1999, including thinnings and salvage. Illegal logging and exports are widespread (BROC _et al._ 2000; OECD 1999; World Bank 2000a).

2.46 Output from the downstream industry also plummeted, especially in the pulp-andpaper branch. However, production volumes recovered in 1998 and 1999, in the wake of the August 1998 real depreciation of the ruble, which boosted exports and import substitution. The recovery is continuing in 2000, with a 17% increase in output in the first 6 months of the year compared to the first 6 months of 1999 (World Bank 2000b). Table 14 gives the dynamics in reported profitability of the Russian forest sector. The logging branch is reported to have been in dire financial straits until 1998, while wood processing, in particular plywood manufacturing, has fared relatively well. 1998 and especially 1999 have seen sharp gains in profitability across the board.

Table 14. Profitability of the Russian Forest Industry

||||(inp|ercenta|ge)|||||
|---|---|---|---|---|---|---|---|---|---|
|Branch|1990|1992|1993|1994|1995|1996|1997|1998|1999*|
|Overall|17.4|36.0|24.0|10.1|_15.5_|-7.4|-7.7|-4.2|25.5|
|Logging|11.2|35.0|15.0|5.4|-6.2|-14.1|-20.9|-11.1|9.4|
|Sawngoods|24.1|39.6|23.0|6.7|2.2|-12.9|-14.6|1.8|11.2|
|Plywood|16.2|46.3|45.1|18.7|17.2|4.7|8.7|28.4|50.6|
|Furniture|32.0|30.0|33.2|23.0|10.5|1.2|4.1|7.1|17.6|
|Pulpand|14.9|32.0|27.0|22.6|35.2|-5.9|-2.3|12.9|37.9|
|paper||||||||||

9 months.

Source: Burdin _et al._ (2000) 2.47 The fact that logging was reported unprofitable for four continuous years between 1995 and 1998 is hard to believe, even allowing for accumulation of debt arrears. One must remember that revenue concealment in one form or another is rampant in the Russian 16 Including an estimated 6.7% of export duties, 2.8% of CPT, 1.1% of VAT, and 3% of natural resource user fees.

23
large number of countries is enrollment in preschool. This section therefore begins with an analysis of preschool enrollment rates in Latin America.

Table 1 presents statistics on the gross preprimary enrollment rate for individual countries in Latin America and the Caribbean, as well as averages for the region. Data are from databases of the World Bank (World Bank 2005b); they include 144 countries, and refer to the year 2000. For each country or region, the table presents three figures. The first column gives the gross primary enrollment rate. The second and third columns are based on regressions of gross preprimary enrollment on a polynomial in per capita GDP, including GDP level, its square, cube, and quartic, as well as a dummy variable for a given country or region. (In the second column, each country observation is weighted by its population, while the third column provides the results from unweighted regressions.) The table reports the coefficient on a country or region dummy, and notes when the coefficient is significant. These regressions therefore benchmark performance in a country or region by comparing it with other countries of similar income levels.

The results from these regressions are informative. Taken as a whole, Latin America and the Caribbean does not appear to have a deficit in preschool enrollment: The (weighted) average of the gross enrollment rate across countries in the region is 61.1 percent. This is above what would be expected for the income level of the region—the coefficient on the dummy for Latin America and the Caribbean is positive, although it is not significant. However, there is a great deal of variation across countries. A handful of countries are “over-performers” in terms of preprimary enrollment rates, including Bolivia, Costa Rica, Ecuador, Guatemala, Jamaica, and Peru. By contrast, a number of other countries are “under-performers”, including Argentina, the Dominican Republic, Honduras, Panama, and Venezuela. Put differently, preprimary enrollment is essentially the same in Argentina and Peru, although per capita GDP in Argentina is roughly four times that in Peru; per capita GDP is higher in Venezuela than in Costa Rica, but preprimary enrollment is almost 40 percentage points higher in Costa Rica.

Simple comparisons of countries like those presented in Table 1 have obvious limitations. The analysis of over- or under-performers may be misleading if enrollment in the average country is “too low” or “too high”. For example, if there truly are high returns to preprimary enrollment, and if most countries invest little in preschool, even some of the apparently over-performing countries may benefit from expansions in coverage of preschool. Another problem with these comparisons is that they obviously abstract from quality differences across countries; relatedly, coverage of the preschool system need not be a good indication of the development of a given set of skills or competencies. Nonetheless, enrollment in preschool is often thought to have important benefits for participants, and the evidence for at least one country in Latin America, Argentina, 12
_Table 12: Description of robustness checks_

|Robustness check|Software<br>package|Options|
|---|---|---|
|0. Baseline<br>estimates|Modified R<br>EMDI|Simulation stage weights, no heteroscedasticity correction. MLE<br>fitting method, welfare transformed using ordered quantile<br>normalization, point and MSE estimates benchmarked to survey<br>super-areas, lasso model selection. Poverty rate of 4% in Sri Lanka<br>and 20% in Tanzania.|
|1. Stata<br>parametric<br>bootstrap|Stata SAE<br>version 3|Same as (0) but with sample weights, heteroscedasticity<br>correction and H3 fitting method|
|2. Stata traditional<br>bootstrap|Stata SAE<br>version 2|Same as (1) with traditional non-parametric bootstrap instead of<br>parametric bootstrapduringsimulation stage|
|3. No Empirical<br>Best|Stata SAE<br>version 2|Same as (2) with empirical best and bootstrap options disabled|
|4. Stata<br>parametric<br>bootstrap with no<br>heteroscedasticity<br>correction|Stata SAE<br>version 3|Same as (1) with no heteroscedasticity correction|
|5. No weights|R EMDI|Same as(0)with no simulation stage weights|
|6. Box-Cox<br>transformation|Modified R<br>EMDI|Same as (0) but transform welfare using Box-Cox transformation<br>with lambda selected to minimize skewness instead of ordered<br>quantile normalization.|
|7. Stepwise model<br>selection|Modified R<br>EMDI|Same as (0) but select prediction model and heteroscedasticity<br>model(for Stata SAE)usingstepwise selection atp=0.01|
|8. Swapped<br>povertyrates|Modified R<br>EMDI|Same as (0) but with poverty rate of 20% in Sri Lanka and 4% in<br>Tanzania|

Table 13 displays the results of these eight robustness checks. For the purposes of brevity, we report the mean poverty, MSE, correlation, and coverage rate.<sup>31</sup> We begin by comparing the results produced by the baseline EMDI estimates with version 2 of the Stata SAE package, which implements the traditional bootstrap method, with and without the Empirical Best option. For all three estimates, the unweighted average of the predictions prior to benchmarking are close to the actual poverty rate in the census. The Stata package with the traditional bootstrap, however, gives significantly less precise estimates in both countries, as seen by the higher mean squared errors and CV. Purely synthetic predictions obtained without the empirical best option are substantially less precise than the empirical best predictions using the traditional clustered bootstrap in Sri Lanka, and moderately less precise in Tanzania. In both countries, however, the synthetic predictions are far less precise than estimates obtained using a parametric bootstrap approach, demonstrating the high efficiency cost of failing to incorporate sample

> 31 Results for all areas are available upon request.

30
of the domestic currency vis-a-vis the dollar, which is used to aggregate country debt data. This indicates the change in the localcurrency value of the stock of external dollar-denominated debt. The other is the revaluation (in dollars) of external debt denominated in non-dollar currencies. It is captured by subtracting net dollar disbursements from the change in dollar DOD (adjusted for any rescheduling) from the beginning to the end of the year.<sup>2 5</sup> Finally, the government revenue from financial repression is calculated by computing the differential between foreign borrowing costs and the domestic borrowing cost, times the average annual stock of domestic debt. In this calculation, an important adjustment has been made to central government domestic debt, namely, the elimination of debt held by the monetary authorities. Even though the central bank receives interest on its holdings, the acquisition of government debt is financed by increases in the money supply, and not borrowing within a resource-constrained environment. Thus it has little to do with the existence or absence of financial repression and is different from, rather than a substitute for, foreign borrowing. Moreover, the interest rate paid on government debt held by the central bank is not really a cost, since interest receipts contribute to central bank profits which are typically returned to the budget.

Aside from restricting the government sector to central government, there are three other reasons why the estimates of the revenue from financial repression we report may be understated. First, we disregard the second-order effect of changes in the exchange rate on foreign interest payments. Second, arrears on foreign debt may be understated. And third, the stock of non-interest-bearing domestic government debt may be underreported: its inclusion would lower the estimate of the effective interest rate, thus raising the implicit tax on domestic lenders. It would also increase the estimate of the financial-repression tax base, thus raising the revenue from financial repression.

> **25 These items can be positive or negative but have typically added to the foreign cost of borrowing. Both of these non-interest elements represent accrued cost, of which only part is amortized annually.**

13
## ANNEXES

# Prices and Costs in the Oil Extracting Industry

(US$/ton)

||1999|||200|0|
|---|---|---|---|---|---|
||Notransfer<br>pricing|Transfer <br>pricing|N|otransfer<br>prcing|Transfer<br>pricing|
|Exportshare(ofoilextracted)|37.5%|37.5%||39.0%|39.0%|
|Salesprice|110.5|110.5||177.9|177.9|
|Exportduty|4.8|4.8||20.9|20.9|
|Customsfee|0.1|0.1||0.2|0.2|
|Transportationtoexportgate|13.7|13.7||14.2|14.2|
|Borderpricea|91.9|91.9||142.6|142.6|
|ShareofdomesticandCIS sales(of||||||
|oilextracted)|62.5%|62.5%||61.0%|61.0%|
|Domesticsalespriceb|44.5|27.1||67.1|37.4|
|Averagepricefortaxestimation|62.2|27.1||96.5|37.4|
|Oilexcise|2.2|2.2||2.0|2.0|
|Averagepricefortaxestimationd|60.0|24.9||94.6|35.4|
|Subsoiluserfee(royalty)|5.2|2.1||8.1|3.0|
|Mineralreplacementfee|6.0|2.5||9.5|3.5|
|Roadtax|1.6|0.6||2.5|0.9|
|Labor(includingauxiliarypersonnel)|3.0|3.0||3.2|3.2|
|Depreciation|2.5|2.5||2.4|2.4|
|Productioncostse|6.2|6.2||6.8|6.8|
|Payroll taxes toextra-budgetary||||||
|funds|1.2|1.2||1.2|1.2|
|Housing tax|1.0|0.6||1.5|0.9|
|Othertaxes|_0.5_|_0.5_||0.7|_0.5_|
|Grossprofit|32.9|5.6||58.6|12.9|
|Profittax|8.3|_5.0_||14.3|8.2|
|Capitalinvestments(excl.mineral<br>replacementfee)|7.6|7.6||13.5|13.5|

a) Delivered at frontier, i.e. export gate.

b) Delivered at the junction between local producer's and main pipelines, before VAT.

- c) Excluding VAT, tariffs and transportation.

d) Excluding VAT, tariffs, excises and transportation.

- e) Except labor costs, taxes and depreciation.

Source: IFEI (2000).

50
10 lower at the end of the period without the increase in inequality. Alternatively, without growth, the headcount would have been eight and twenty points higher in respectively rural and urban areas. Had there been no growth in urban areas, and no increase in inequality in rural areas, the headcounts would have converged in the two sectors to 35 (rural) and 37 (urban) percentage points using the lower poverty lines. The same applies with the upper lines, but with headcounts twenty points higher. What can be concluded from the above results? As can be seen from Table 4 (and Figure 1), the growth only and inequality only scenarios move in opposite directions. This indicates that positive (negative) growth tends to be associated with rising (decreasing) inequality. But at this stage, this remains an impressionistic result without a firm analytical grasp as to the elasticity of inequality to growth. Moreover, a cursory look at Table 4 would indicate that the relationship between growth and inequality is similar in urban and rural areas. If this were indeed the case, combining this finding with the results based on Kakwani's formulaes would suggest that poverty is likely to be reduced more through urban than through rural growth (compare the two MPRS). In fact, it is exactly the reverse which is true: rural growth appears to be more poverty reducing than urban growth. This is because of the correlation between growth and inequality is much lower in the rural than in the urban sector, as we shall now see.

**IV Regional panel estimates**

_IV 1 The relationship between growth and inequality_ The techniques illustrated in the previous sections do not provide us with a clear picture of the long term relationships between growth, poverty, and inequality. The key missing piece is an estimate of the correlation between growth and inequality which cannot be readily estimated with Kakwani's formulae. But it can be found using our regional panel by estimating the following regression: Log Gkt = a + , Log Wkt + ak + skt (9)
<!-- Start of picture text --> +.11 II II 1I II<br>i} I | 1 |<br>I1III<br>11 I |I i}I |I<br>- i}I II I| Ii} I|<br>i} I | i} |<br>1 I I I<br>i} | i} |<br>1 I I<br>2 i} i} |<br>@ i} i} |<br>SN I I |<br>a) | |<br>i} |<br>i} |<br>i} |<br>i} |<br><.||<br>oO<br>-6 -4 -2 0 2 4<br>Annualized percentage change in Gini (%)<br><!-- End of picture text -->
**Box 2. Theory of change: Science for the Knowledge Economy Project in Chile**

<mark>The 2003 Science for the Knowledge Economy Project in Chile supports the country’s transition to a knowledge-based economy by investing in the innovation system and the stock of human capital. To determine where to monitor progress, a causality tree was drawn to display how activities and goals interlink. For each arrow in the model an indicator was selected.</mark> <!-- Start of picture text --> Transition to a<br>knowledge- based<br>economy<br>Improved human Strengthened Chile’s national<br>capital innovation system<br>Continual Conducive innova- Awareness and high State of the art Enhanced public- Improved private<br>monitoring tive policies and quality S&T scientific private linkages sector research<br>and evaluation instruments education community and innovation capacity<br>Development of Teacher S&T Funds for Acquisition Grants for Team Cooperative Insertion of<br>strategy for innovation upgrading institutions of S&T international grants for research researchers<br>and human capital pilots of excellence equipment cooperation research consortia into industry<br>objective Program<br>objective Project<br>development<br>outcome component Direct<br>activity<br>Sub-component<br><!-- End of picture text --> <mark>The pool of indicators provides the project management timely management information on the project’s theory of change. Identified weak links permit evidence-based improvements during implementation.</mark> _Source: World Bank (2003b)_ Indicators come in four types: **Inputs** involve the human, financial and physical resources received to support programs, activities and services, such as funding, enrollments and staffing. The performance criterion corresponding to inputs is _economy_ , i.e., the timely acquisition of quality inputs at the lowest possible cost. **Processes** are the manner in which inputs are procured and the means by which results are delivered. Processes are internal to an institution and include teaching methods, the use of technology and procurement. Process indicators can be useful proxies for performance when output or outcomes cannot be defined with clarity.

**Outputs** reflect the quantity of products produced such as the number of graduates or the number of research articles published. The performance criterion corresponding to outputs is _efficiency_ , i.e., maximizing the quantity of outputs in relation to a given total amount of inputs. **Outcomes** cover the quality and societal impact of the products produced. Outcome indicators include learning results, job placements and user satisfaction. The performance criterion corresponding to outcomes is _effectiveness_ , i.e., maximizing outcomes in relation to the outputs produced (Schiavo-Compo 1999).

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