rafmacalaba/datause-encoder-data huggingface dataset:
type of school participating in this project received government funding depending upon the number of females enrolled in the school. This additional incentive induced many formally all-male madrasas to open their gates to female students.

The modernization scheme originally initiated in the early 1980s has succeeded in converting a large pool of orthodox, all-male madrasas which previously operated with own funds and eschewed teaching of modern subjects. Secondary madrasas therefore grew in numbers in Bangladesh in two distinct phases. The modernization scheme of 1980s saw a surge in numbers between 1980 and 1994. In post-1994 years, growth was driven mostly via enrollment of female students and allegedly by transforming formerly all-boy madrasas into centers for co-education. This feminization of modernised madrasas in Bangladesh dispels the common view that the rise of the religious education sector is explained away only by a lack of public provision<sup>7</sup> .

These features of the religious education system in Bangladesh provide important policy leverage in harmonizing schooling outcomes among students from diverse educational backgrounds. This scenario is at stark contrast with other countries in South Asia with large Muslim populations (and elsewhere), where most religious seminaries are of traditional orthodox types, predominantly single-sex (boys only), and still untouched by any significant changes in curriculum. Therefore, understanding the nature and process of female enrollment growth in madrasas is important to correctly inform the current debate over madrasa reform in other South Asian countries such as India and Pakistan.

Although the importance of the FSP scheme in feminizing the madrasa sector is wellacknowledged, relatively less is understood about the nature of growth of female enrollment in madrasas. Given that these secondary madrasas mostly rely on primary schools and primary madrasas for the supply of new students, it is of policy interest to examine whether female enrollee of madrasas come from secular education sector, in particular NGO schools which promote secular values, cater to children from poorer households and has an explicit agenda to empower rural women. Table 1 presents data on the type of primary school attended by grade 8 students in a sample of 221 secondary schools and 94 madrasas drawn from rural Bangladesh<sup>8</sup> . 9% of the female secondary students attended NGO schools for primary education which is consistent with the fact that BRAC schools predominantly educate girls. More importantly, madrasa and school girls are equally likely to have attended NGO schools for primary education.

> 7 The relevance of this hypothesis for Pakistan is also questioned by some researchers (e.g. see Andrabi et al., 2006).

> 8 The survey was conducted in the year 2005 in 60 rural unions. For a detailed description, see Asadullah et al. (2007).

6
**Figure 6. Parameter Stability: Recursive Coefficients — Production Function with Average Years of Schooling Specification, 1988-2002**

<!-- Start of picture text -->
-0.0 2 0.9 6 0.2 0<br>0.9 4<br>-0.0 4<br>0.9 2 0.1 5<br>-0.0 6 0.9 0<br>0.8 8 0.1 0<br>-0.0 8 0.8 6<br>0.8 4 0.0 5<br>-0.1 0<br>0.8 2<br>-0.1 2 0.8 0 0.0 0<br>88 90 92 94 96 98 00 02 88 90 92 94 96 98 00 02 88 90 92 94 96 98 00 02<br>Constant ± 2 S.E . Percent change of capital/worker ± 2 S.E . P. change of capital/worker [-1] ± 2 S.E .<br>-0.1 0 0.2 0 0.1 4<br>-0.1 5 0.1 2<br>0.1 6<br>-0.2 0 0.1 0<br>-0.2 5 0.1 2 0.0 8<br>-0.3 0 0.0 6<br>0.0 8<br>-0.3 5 0.0 4<br>-0.4 0 0.0 4 0.0 2<br>88 90 92 94 96 98 00 02 88 90 92 94 96 98 00 02 88 90 92 94 96 98 00 02<br>log GDP/worker [-1] ± 2 S.E . log capital/worker [-1] ± 2 S.E . log schooling [-1] ± 2 S.E .<br><!-- End of picture text -->

_Note:_ Based on the OLS estimate presented in Table 5, column 1.

-41-
away? evidence from indonesia. World Development, 31(6):10651084.

- Lockheed, M. E., Verspoor, A. M., et al. (1991). Improving primary education in developing countries. Oxford University Press for World Bank.

- Lou, J. and Li, J. (2022). Export expansion and intergenerational education mobility: Evidence from china. China Economic Review, 73:101797.

- Maasoumi, E., Wang, L., and Zhang, D. (2022). Generalized intergenerational mobility regressions. Working paper, Emory University.

- Maccini, S. and Yang, D. (2009). Under the weather: Health, schooling, and economic consequences of early-life rainfall. American Economic Review, 99(3):100626.

- Martinez-Bravo, M. (2017). The Local Political Economy Eects of School Construction in Indonesia. American Economic Journal: Applied Economics, 9(2):256289.

- Mazumder, B. (2005). Fortunate Sons: New Estimates of Intergenerational Mobility in the United States Using Social Security Earnings Data. The Review of Economics and Statistics, 87(2):235255.

- Mazumder, B., Rosales-Rueda, M., and Triyana, M. (2019). Intergenerational Human Capital Spillovers: Indonesia's School Construction and Its Eects on the Next Generation. AEA Papers and Proceedings, 109:243249.

- Mogstad, M. and Torsvik, G. (2021). Family Background, Neighborhoods and Intergenerational Mobility. NBER Working Papers 28874, National Bureau of Economic Research, Inc.

- Neidhofer, G., Serrano, J., and Gasparini, L. (2018). Educational inequality and intergenerational mobility in Latin America: A new database. Journal of Development Economics, 134(C):329349.

- Neilson, C. and Zimmerman, S. (2014). The eect of school construction on test scores, school enrollment, and home prices. Journal of Public Economics, 120:1831.

- Nicoletti, C. and Francesconi, M. (2006). Intergenerational mobility and sample selection in short panels. Journal of Applied Econometrics, 21(8):12651293.

- Orazem, P. F. and King, E. M. (2008). Schooling in Developing Countries: The Roles of Supply, Demand and Government Policy. In Schultz, T. P. and Strauss, J. A., editors, Handbook of Development Economics, volume 4, chapter 55, pages 34753559. Elsevier.

35
**-Cashew Nut Industy 'Decline and** Reovery **In** Mzinl

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We examine the impact of long-term climate impacts on agriculture productivity by relying on an empirical econometric specification similar to that adopted in the <u>Mendelsohn</u> _<u>et al.</u>_ <u>(1994).</u> That is, farmland value (H) reflects the present value of future net revenues, and depends on a host of exogenous determinants: where _PLE_ is the net revenue per hectare, _t_ is time, and φ is the discount rate. We assume that the net revenue per hectare, _PLE_ , depends upon _Pi_ , the market price of crop _i_ ; _Qi_ , the output of crop _i_ ; _R_ , a vector of input prices, and _X_ , a vector of purchased inputs (other than land). The crop output, _Qi_ , is in turn a function of the purchased inputs ( _X_ ); a vector of climate variables ( _F_ ), a vector of labor characteristics ( _M_ ), and a set of farm characteristics such as market access and access to infrastructure (including irrigation), ( _G_ ).

Given that we use a cross-sectional one period dataset of net revenue, H reflects net revenue in that particular year. We make the strong assumption that returns in the specific year of the study reflect long run profitability. Clearly, it would be preferable to gather multiple year data to get a better measure of long run profitability. In the absence of multiple year data, one must be careful to choose a year that is as close to normal as possible.

We assume farmers make a series of production choices aimed at maximizing profit given household preferences and endowments. In addition, the farm households are price takers in the model and hence individual farmers have no impact on market prices. Then the estimated specification of net revenues per hectare, _PLE_ , can be written as: where _βi_ are estimated coefficients, _ε_ is the unexplained portion, which is assumed to be independent and identically distributed. Implicit in this specification is that relative crop prices and input prices remain fixed despite the impact of climate or any other factor<sup>2</sup> .

The aggregate impact of climate change on farm profitability is estimated as the difference between the fitted values of net revenues per hectare evaluated with climate variables set to those before the climate change ( _P_<sup>ˆ</sup> _LEb_ ) with the fitted values of net revenues per hectare with climate variables set to those after the change ( _P_<sup>ˆ</sup> _LEa_ ). In other words, the value of a change in climate is equal to _P_ ˆ _LEa_ − _P_ ˆ _LEb_ .

We include households that make losses in the empirical analysis. It is our assertion that there is important information embedded in these losses. It is likely that some households will not only make losses in the short run today but also into the future.

> 2 One of the main criticisms of the Ricardian approach has been the omission of price (Cline 1996; Darwin <u>1999). Mendelsohn and Nordhaus (1999)</u> contend that the assumption of constant prices does bias the estimates, but that this bias is relatively limited if standard demand and supply elasticities apply.

13
_48_ variables.aZ/ We used population growth instead of the variable (n+g+6) because we do not know the depreciation rates and the rate of growth of technological progress that satisfy the SSA countries3s8. All regressions are corrected for hetroskedasticity by scaling all variables and the instruments by the estimated standard deviations of country specific residuals.3i -The results are reported in table 8.

_The_ first regression is the growth model in the unrestricted form. All variables are found to be statistically significant at the 5 percent level and bear the expected signs except for initial income (1971 income measured in U.S. dollar). This estimated coefficient is found to be statistically not different from zero at the _5_ percent level.40/ The second regression is the basic Mankiw, Romer and Weil model with the restrictions that b, +b2+b,=O imposed. We tested the restriction and found it to hold on the 10 percent level only.

The third regression is the unrestricted augmented growth model. Again, most variables are found to be statistically significantly different from zero at the _5_ percent level. Initial income is fbund to be insignificant at the _5_ percent level. The estimated coefficient on AIn[(SP)/P1<sup>.</sup> 1<sup>J is found to be positive but statistically insignificant. The term that includes the</sup> domestic inflation rates _[r._ In (SP/Pt.)J] is found to be negative as expected and statistically significant. This is interpreted as a negative effect of inflation on per capita real GDP growth in the transition to the steady-state (Fischer, 1993). We testtd the restriction b5+b6=O and found it to hold at the 5 percent level. Thus, when we control for effect of inflation, real depreciations have neutral effect on real per capita growth rates in the transitional period. Note that the fiscal

**37/** We measure human capital by the variable life expectancy at birth. This measurement is not the best measurement but we use it because data are readily available in World Tables. A better measurement can be life expectancy for population 16-55 years of age. This latter variable is not available. For example, Barro and Lee (1993) use enrollment ratiDs at the primary and secondary school levels to measure human capital. Life expectancy at birth is usually assumed to be an endogenous variable and a function of education and health. We cannot find complete time series for 1971-1991 for these two variables. The instruments used in the regressioas are simply lagged values.

38/ Tha term (n+g+6) is traditionally treated in the literature by assuming some values for g and B such that (n+g+5) is equal to In (n+.05).

39/ Two tests for homoskedasticity are used. The Breush-Pagan test and the White test.

40/ We do not test for convergence. We follow Friedman (1992) argument that testing for convergence in growth model is in fact a regression fallacy.
kilometer, there is limited room for area expansion among smallholders (Dorward 2006; RickerGilbert et al. 2014). Second, Malawi is also an important case study to estimate inorganic fertilizer profitability because it has been implementing a large-scale Farm Input Subsidy Program (FISP) since the 2005/2006 agricultural season. In terms of scope and coverage, FISP is perhaps the most well-known farm input subsidy program in Africa. Through FISP, the Government of Malawi currently provides approximately 50 percent of the country’s agricultural households with coupons that allow for inorganic fertilizer and improved maize seed purchases at up to a 95% discount for fertilizer and 100% discount for seed. Focusing on Malawi provides an opportunity to analyze how fertilizer profitability can inform the geographical targeting of large-scale farm input subsidy programs, and how the subsidy program affects the profitability of fertilizer use.

The present study adds to the existing literature by extending the work of Xu et al. (2009) and Sheahan et al. (2012). Xu et al. (2009) and Sheahan et al. (2012) use nationally representative panel data from Zambia and Kenya, respectively, to analyze the profitability of fertilizer use in maize production. We build upon and extend their work in three main ways. First, by virtue of the availability of a variable that identifies plots over time in the two-wave nationally representative panel data used in the analyses, this study is able to control for plot-level unobserved heterogeneity using plot-level fixed-effects (FE) estimation.<sup>2</sup> Controlling for such plot-level unobserved heterogeneity helps improve upon the validity of the NUE estimates in the study. To our knowledge, this is the first study on SSA to use plot-level FE with nationally representative, nonexperimental data when estimating fertilizer profitability.

Second, this study accounts for different spatial and temporal maize prices in Malawi, including i) farm-gate harvest-season maize price, ii) lean season retail-market maize price and iii)

> 2 A plot is defined as a continuous piece of land that is not split by a river or a path wide enough to fit an oxcart or vehicle, and can contain multiple sub-plots.

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<!-- Start of picture text --> 10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>-30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30<br>-2%<br>-4%<br>Relative Date to Event<br>Connected Titling Firms Non-Connected Titling Firms<br>Cumulative Abnormal Returns<br><!-- End of picture text -->

# **Figure 4. Stock market reactions of politically connected and non-connected titling firms in the event time of land titling program.**

The sample of SOE titling firms (grey bars) includes politically connected listed firms that receive land titles in the land titling program in Shenzhen. The sample of none SOEs (while bars) includes nonpolitically connected listed firms that receive land titles in the land titling program occurred in Shenzhen in 2009. The figure presents the cumulative abnormal returns of both types of titling firms in a period window between 30 days before and 30 days after the announcement day of the 2009 land titling program. **Resource:** CSMAR 45
An alternative hypothesis suggested by the theoretical results of (Diamond, 1984, 1991,1993) and the empirical results of Demirguc-Kunt and Maksimovic (1999) and Gianetti (2003), among others, suggests: _Hypothesis 1A: Poor legal quality implies that external capital will be concentrated in debt (via intermediaries) and in particular short-term debt thus there should exist:_

- _i. a positive relation between legal effectiveness and equity, and_

- _ii. a negative relation between legal effectiveness and total debt, and_

- _iii. a positive relation between legal effectiveness and debt maturity._

These hypotheses are predicated on the binding nature of the local legal system. If creditors and borrowers can avoid a poor local legal system by writing debt contracts that are enforced in countries with better legal systems, as we described earlier, then these predictions should only apply to local contracts. Since poor legal effectiveness is likely to be a binding constraint that results in less than optimal lending, we expect firms that can use foreign currency to loosen this constraint, will do so. Therefore, we suggest the following hypothesis.

_Hypothesis 2: When possible, firms will avoid the constraints of a poor legal system by contracting for debt outside their home country (i.e., by using foreign currency debt), thus there should exist:_

- _i. no relation between legal effectiveness and total debt or debt maturity for firms with sufficient access to foreign debt, and_

- _ii. a positive (negative) relation between legal effectiveness and the use of both shortterm and long-term local (foreign) currency debt._

In summary, observed empirical relations that appear contradictory can be explained by separately examining local and foreign currency debt.

# **III.   Data**

We rely on two primary data sources for our study. The first is firm-level accounting data from the WorldScope Database. We examine all non-financial firms with sufficient data for fiscal year 1996 in nine East Asian countries: China, Hong Kong, Indonesia, Malaysia, The Philippines, Singapore, South Korea, Taiwan, and Thailand. All together our sample includes 8
income) is allocated for consumption, transfers to other economic agents, and savings. The consumption/saving allocation is based on the marginal propensity of consumption, which is based on the data from the social accounting matrix (SAM) used for this study.

The total government revenues are generated through personal and corporate income taxes, excise taxes, import duties, and transfers from other economic agents (i.e., households, enterprises, and the rest of the world).<sup>10</sup> It is allocated for public expenditures, transfers to other agents, and public savings. Public expenditure accounts for government spending on wage and salary of government employees, expenditure and defense, healthcare and education. Since these are the essential services and required to be maintained by the government, the total government expenditure is fixed at the baseline level, following common tradition in CGE modeling. Moreover, the total government expenditure is allocated to various services (e.g., public administration) at the same proportion as in the base case. The remaining government income is saved after deducting the transfers. In CGE modeling, savings of the government is also referred to to public savings.

A government decision on the rates of any tax (income, excise, duty) reverberates throughout the economy through production, consumption, and trade channels. For example, if the government increases production tax rate, it will be passed to intermediate and final consumption as well as exports. It increases costs of production in all sectors and ultimately reduces household welfare. The reverse would happen if the government cuts production taxes. When a carbon tax is introduced and its revenue is used to cut production tax, both taxes affect throughout the economy through production, consumption and trade channels, the model calculates the net effect capturing all interactions between various the production sectors, between production and consumption sectors, between domestic production and imports, between domestic consumption and exports, and every variables in the model (e.g., wage rate, cost of capital, prices of goods and services).

In the case of international trade, demand for imports is modeled using a CES functional form that aggregates a domestically produced good with its imported components. An output from

> 10 Examples of household transfers to government are social security and other social contributions, fines penalties and forfeits. Dividends or repaying loans to the general government, rents to public properties, and social welfare contributions on behalf of their employees are examples of enterprise transfers to the government. Examples of the rest of the word transfers to government are grants received from foreign governments and international organizations.
(i.e., Brazil, Russia Federation, India, China, and South Africa). Other similar regional studies include Dogan and Seker [24] and Jebli et al. [25].

The added value of this paper is to complement the analysis using the LMDI approach with the econometric approach -- the generalized method of moments so that results derived from the former technique can be validated or strengthened with the latter thereby enhancing the robustness of the findings. Unlike many existing studies that determine the drivers of CO2 growth at the national or regional level, this study considers a global analysis using data from 100 plus countries.<sup>3</sup> The data span (19802015) considered in this study is much longer as compared to other existing literature.

The remainder of this paper is structured as follows. Section 2 outlines the methodology and data used. Section 3 discusses the results from the decomposition analysis. Section 4 discusses the results from the econometric analysis. Section 5 conducts a comparison of the results between the two approaches: statistical vs. numerical. Section 6 offers policy discussion and concluding remarks.

# **2. Methodology and data**

Let us define global CO2 emissions from energy consumption in a country for a given year with the following identity: where _C_ denotes the global CO2 emissions; _C ij_ represents the amount of CO2 emissions of energy type _j_ in country _i_ ; _E ij_ stands for the energy consumption by energy type _j_ consumed in country _i_ ;<sup>_E_</sup> _i_<sup>,</sup> _GDPi_ , and<sup>_P_</sup> _i_<sup>refer to the total primary</sup>

> 3 Two other global studies include Wang et al. [26] and Bacon et al. [27]. However, their period of analysis is much shorter than the one used in this paper.

> [26] Wang S, Li G, Fang C. Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels. Renew Sust Energ Rev. 2017.

> [27] Bacon RW, Bhattacharya S, Damania R, Kojima M, Lvovsky K. Growth and CO2 emissions: how do different countries fare. Environment Department Papers. 2007;113.

4