rafmacalaba/datause-encoder-data huggingface dataset:
|IL<br>**MANRAC**SPEICFICATON<br>**OF IANA iwLnT_O**<br>**AVMTLAPL**SECTOR<br>**_W_**<br>......................<br>**1.3**|
|---|
|*<br>Set Dofhnitions ................................<br>1.83|
|*<br>Variable List<br>..................................<br>1.84|
|*<br>Equation List<br>..<br>1.85|
|*<br>Paramoter List<br>..<br>1.87|

|PAT TWO. COCOA TAX AND R|EVEME ALTERNATVES<br>.2.1||
|---|---|---|
|I.<br>|hntroducton .2.1||
|II.<br>|The Analysis of Taxes and Tax Reform .2.5<br>||
|III.<br>|Salient Features of Ghanaian Cocoa Prichg .2.8<br>||
|IV.|Brie History of Cocoa Production Prichg and<br>||
||Taxation hI Ghana .2.11||
|V.|The Consequences of the Dclino In Cocoa Production|2.28|
|VI.<br>|Other C0nsequences of Exchange Rate Liberalization .<br>|2.35|
|VIl.<br>|Issuesin Setthg the Producer Price of Cocoa....|2.37|
|Vill.<br>|Alternatives to the Cocoa Export Tax .2.38||
|IX.<br>|COCOBOD'S<br>Costs .2.40<br>||
|X.<br>|The Optknal Export Tax .2.41<br>||
|Xl.<br>|Standard Arguments for Output Taxes .2.43<br>||
|XiI.<br>|Quallfications .2.54||
|XiII.|The Risks hnoived In Setting the<br>||
||Producer Price Too Low .2.56||
|XIV.|kIpilcatlonsof Taxing Cocoa||
||<br>for Input for Input Subsidies .2.58<br>||
|XV.<br>|Spatial Variations In Cocoa Pricing ............<br>|2.59|
|XVI.<br>|Price Stabilization .2.60<br>||
|XVII.<br>|knplicatlonsfor Taxes on Consumer Goods .2.62<br>||
|XVIII.|onclusions .2.84||
||References....................<br>......|2.65|
|APPENDIX<br>A:|A Note on Data .|1-6|
|PART THEE: GLOBAL PERSP|ECTIVES<br>ON COCOA SUPPLY<br>AND DBAAND ...<br>.........|3.1|
|I.|Introduction .3.1||
|II.|World Cocoa Market Environment,1988-2000.3.3||
||A. Cocoa Hectarage, Major Producnrs,1970-87...|3.3|
||B. World Cocoa Production & Prospects,1975-2000|3.6|
||C. World Cocoa Production Summary,1988-2000|3.21|
||D. World Cocoa Consumptlon,1975-85.3.23||
||E. World Supply/Demand and Price||
||Forecasts,1988-2000.3.24||

- 2-
Using actual and predicted values for growth, we compute the residuals<sup>εˆ</sup> _it_<sup>and its variance</sup> ˆ 2 _V_ ˆ( ε _it_ ) =σ _i_ .<sup>27</sup> The 60% confidence interval around the forecasted growth rate is then given by ± .084 σ ˆ _i_ 2 , while the formula for the 90% confidence interval is ± .164 σ ˆ _i_ 2 .<sup>28</sup> We compute two types of regional averages. The first one reflects weights which represent the share of per capita income in the simple average, regional per capita income. The second one uses weights which reflect economic size and are computed as the share of income in the average regional income. The two measures may differ substantially if the region is dominated by a large country such as China in the case of East Asia and Pacific (Table 3A and Table 4).

For the developing world as a whole we forecast average, annual real per capita growth of 2.2%. We arrive at this growth rate if we use weights which represent the share of per capita income in the simple average, regional per capita income. If we use weights which reflect the size of the economies and are computed as the share of income in the average regional income, the growth rate for the developing world as a whole is 3.5%. This forecast is almost identical to the one published in _Global Economic Prospects_ (2004)<sup>29</sup> and shown in Table 4. Even though in two instances (Europe and Central Asia and Sub-Saharan Africa, Table 4) the GEP’s regional forecasts are close to ours, in all the other cases they are higher than our estimates implying a growth forecast for the developing world higher than 3.4%.

The forecasts, obtained using the pure cross section version of the forecasting model, are displayed in Table 5. The model excludes the initial output gap and all stabilization policy variables except inflation. Again, we assume that transitional convergence is the only factor

> 27 For countries having information on actual and predicted values for growth for no more than one period, σ ˆ _i_ 2 is estimated using the variance of<sup>εˆ</sup> _it_<sup>for all countries.</sup>

> _T_ − 2

> 28 For the average growth rate between _t+sT_ and _t+s(T-1)_ the formula is given by ± .164 σ ˆ _i_ 21( + ( _s_ α )2 ∑ 1( + _s_ α )2 _k_ . _k_ = 0

> 29 See Table 1.5, pp.43 in _Global Economic Prospects_ 2004.

21
|**Ta**<br>**_(Ro_**|**ble 1: What**<br>**_bust std. err_**<br>(1)|**Explains V**<br>**_ors inparen_**<br>(2)|**ariation in**<br>**_theses; _**<sup>***</sup>**_p<_**<br>(3)|**Reported Vo**<br>**_0.01_**,<sup>**</sup>**_p<0.0_**<br>(4)|**te buying?**<br>**_5, _**<sup>*</sup>**_p<0.10)_**<br>(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Mayor’s family reported<br>as major employer or<br>landowner|-0.184<sup>***</sup><br>(0.055)|-0.107<sup>**</sup><br>(0.049)|-0.124<sup>**</sup><br>(0.06)|-0.114<sup>**</sup><br>(0.05)|-0.08<br>(0.055)|-0.09<br>(0.06)|-0.09<br>(0.06)|
|Indicator variable = 1 if no<br>clan dominates municipal<br>politics||-0.092<sup>*</sup><br>(0.05)|-0.114<sup>**</sup><br>(0.05)|-0.007<br>(0.165)|-0.004<br>(0.219)|0.02<br>(0.22)|0.02<br>(0.23)|
|Average margin of victory<br>(in 2007, as proportion of|||-0.271<sup>***</sup><br>(0104)|||||
|total votes)<br>Congressional District effect|s (omitted 4<sup>t</sup>|<sup>h</sup>district char<br>|.<br>acterized by<br>|most intense<br>|inter-clan ri<br>|valry):<br>||
|District 1||-0.02<br>(0.09)<br>|0.013<br>(0.11)<br>|-0.037<br>(0.101)<br>|-0.047<br>(0.101)<br>|-0.08<br>(0.10)<br>|-0.08<br>(0.10)<br>|
|District 2||-0.172<sup>**</sup><br>(0.08)|-0.184<sup>*</sup><br>(0.104)|-0.153<sup>*</sup><br>(0.089)|-0.179<sup>~~**~~</sup><br>(0.09)|-0.19<sup>**</sup><br>(0.09)|-0.19<sup>**</sup><br>(0.10)|
|District 3||-0.287<sup>~~***~~</sup><br>(0.075)|-0.214<sup>~~**~~</sup><br>(0.104)|-0.279<sup>~~***~~</sup><br>(0.08)|-0.304<sup>~~***~~</sup><br>(0.109)|-0.334<sup>~~***~~</sup><br>(0.115)|-0.334<sup>~~***~~</sup><br>(0.116)|
|Poverty estimate (at<br>village level)||||0.002<br>(0.003)|0.003<br>(0.003)|0.004<br>(0.003)|0.004<br>(0.004)|
|Poverty * Indicator for no<br>clan domination||||-0.003<br>(0.005)|-0.003<br>(0.005)|-0.004<br>(0.005)|-0.004<br>(0.006)|
|“Social Capital” (% of<br>respondents reporting<br>_bayanihan_activities)|||||0.206<br>(0.213)|0.191<br>(0.216)|0.191<br>(0.226)|
|Number of political<br>affiliations of mayor|||||0.014<br>(0.042)|0.015<br>(0.041)|0.015<br>(0.044)|
|Population (log)|||||-0.018<br>(0.06)|-0.024<br>(0.063)|-0.023<br>(0.08)|
|Location (travel time to<br>_centro_)|||||-0.001<br>(0.001)|-0.001<br>(0.001)|-0.001<br>(0.001)|
|Indicator for village roads<br>needing repair|||||-0.022<br>(0.087)|-0.028<br>(0.089)|-0.028<br>(0.09)|
|3<sup>rd</sup>Term Incumbent||||||0.045<br>(0.068)|0.044<br>(0.068)|
|Municipal dependency on<br>national revenue share|||||||0.025<br>(0.55)|
|N, R-sq|60, 0.13|60. 0.334|50, 0.36|58, 0.345|58, 0.378|58, 0.383|58, 0.383|

30
costly. A similar reform could be considered for the income protection system. The idea there is to unify the unemployment insurance system and FGTS. The core of the unemployment benefits would come from FGTS (again minimizing distortions), while the UI component (and part of the dismissal tax) would be used to finance explicit redistribution within the system (i.e., to top up the benefits of low income individuals).

There are, of course, limitations to the analysis. First, the model remains a simplified representation of reality. While it can reproduce the distribution of a given cohort across states, there is no guarantee that it is a fair representation of how individuals react to _change_ (even if their preferences remain the same). For instance, as Prospect Theory tells us, individuals might react differently to gains than to losses. We have addressed this by looking at a broad range of possible behavioral responses, but still the results should be interpreted with caution.

The second limitation is that we work in a partial-equilibrium framework. Several of the reforms discussed here are likely to affect the demand for labor and equilibrium wages and this would influence the steady-state impact of the proposed reforms.

Third, given the considerable demands on computing time, we have not been able to look at the pensions and unemployment insurance systems in their totality, and have focused instead on a single age-gender cohort and two income levels. The net effect of the reforms and their costs would of course depend on the distribution of individuals by income groups. Moreover, other individual characteristics (e.g., education) are likely to be relevant in determining individual preferences and therefore behavioral change. We also know that our estimates of the posterior joint distribution of model parameters could be improved if working with individual records instead of a pseudo panel. We expect that this will be possible in the future.

# **References**

Anderson, P. M., A. L. Gustman, And T. L. Steinmeier, 1999. Trends in Male Labor Force Participation and Retirement: Some Evidence on the Role of Pensions and Social Security in the 1970s and 1980s. Journal of Labor Economics, 17, 757-783.

Anderson, P. M., Meyer, B. D., 1993. Unemployment Insurance in the United States: Layoff Incentives and Cross Subsidies. Journal of Labor Economics, 11, S70-95.

Barros, R. P. de, Corseuil, C. H., Bahia, M., 1999. Labor Market Regulations and The duration of employment in Brazil. IPEA, Texto para discussão No. 676.

Blundell, R., Meghir, C., Smith, S., 2002. Pension Incentives and the Pattern of Early Retirement. Economic Journal, 112, C153-70.

38
In the case of this Vietnam rural roads project we ask whether there is evidence to support the standard economic argument on fungibility, or whether the evidence supports the alternative assumption made by aid donors, that donors get what they paid for.

An initial casual look at our project evaluation data suggests partial fungibility. We find about half the expected difference between project and non-project comparison communes in the length of roads undergoing rehabilitation during the project period. Taking a simple mean difference between participating and non-participating communes shows a 2.5 kilometers cumulative average difference in favor of the project. Yet, independent administrative data reports that an average of 4.6 kilometers per commune was rehabilitated under the project. Project supervision and other evidence confirm that the project was implemented as planned.

This appears to indicate partial fungibility, representing about half of the resources received. But on closer inspection, the data further suggest that the project communes built significantly more kilometers of new roads than did the non-project communes. This suggests the possibility of a sectoral flypaper effect of the aid within the road sector. Yes, displacement occurred in that less rehabilitation occurred than the aid had (ostensibly) financed. However, the aid stuck to the sector with the donor getting more built roads.

As often argued in the literature, empirical evidence for the flypaper effect may be due to model misspecification and entirely spurious (Hines and Thaler, 1995, review the arguments). An alternative explanation for our observed difference in rural road length is that we are not using an appropriate counterfactual. In judging the impact of the project we may need to deal with the possibility of selection bias. What looks like a flypaper effect of project aid may in fact be the outcome of biases in the empirical methods.

While rural roads have been extensively championed as poverty alleviation instruments by aid donors, rigorous impact assessments have been rare. Such assessments require appropriate controls, and must allow for the factors that influence both program placement and outcomes. We use a panel data set (collected for the purpose of conducting such an impact evaluation) of communes and households within project and non-project areas. The dataset comprises a baseline and post-project follow-up data. The data allow us to more rigorously test our hypothesis of full fungibility and no flypaper effect of the project aid on balancing the observable covariates of project and comparison areas.

2
counterfactual distribution for agricultural and non-agricultural households separately with per capita incomes of the respective household types growing with the BaU rates, i.e. by 1.3 percent annually for non-agricultural and 2.4 percent annually for the agricultural households (column II).

_<u>Table 6: Poverty and inequality in a distributionally neutral scenario</u>_

||2001 level <br>All|<sup>% of BaU</sup><br>change I<br>household|% of BaU<br>change II <br>s|<br> 2001 level<br>Non-agri|% of BaU<br>change I<br>cultural hou|% of BaU<br>change II <br>seholds|<br> <sup>2001 level </sup><br>Agricu|<sup>% of BaU</sup><br>change I<br>ltural house|% of BaU<br>change II<br>holds|
|---|---|---|---|---|---|---|---|---|---|
|PC income|314.9|100.0|100.0|351.9|117.7|98.6|148.3|65.7|102.9|
|P0|23.6|91.7|102.4|18.6|139.8|133.3|45.9|56.5|90.5|
|P1|9.6|90.9|97.7|7.1|132.5|119.7|20.8|61.9|93.2|
|P2|5.3|86.8|97.9|3.7|125.6|114.3|12.1|62.6|93.4|

Source: Authors’ calculations.

The comparison of the counterfactual simulations of the “completely” distributionally neutral (column I) and the “separately” neutral (column II) scenarios shows that the growth bias in favor of agricultural households is poverty reducing. Yet, the difference between the BaU and the completely neutral scenario does not seem too pronounced. This is due to the fact that poverty among non-agricultural households is reduced much more than in the BaU, where the income distribution among these households worsens. This “slight” worsening of the income distribution hence hampers quite strongly the potential of growth to reduce poverty among non-agricultural households. In addition, the differences between the two neutral scenarios for non-agricultural households illustrate that a 0.2 percentage point difference in annual growth rates for 14 years can make a difference in terms of poverty reduction. The last two columns of Table 6 show the importance of growth for reducing poverty among agricultural households as well. A 0.9 point percentage point difference in annual income growth rates for 14 years implies a reduction of about 5 percentage points less in the headcount over this time period. In contrast to what we see for non-agricultural households, the impact of the pro-poor distributional shift for agricultural households observed in the BaU is relatively small. In other words, had the income distribution among agricultural households not improved, growth would have reduced poverty by only little less.

The poverty reductions recorded in the BaU scenario are due the change in skill endowments, the increase in real factor prices, and inter-sectoral movements. A main advantage of micro-simulation techniques is their ability to decompose the total effect in different partial effects that can be attributed to single causes. A slight complication arises because different causes interact. The interaction arises because factor incomes 33
Figure 3 shows the average disclosure rate by company size for the 15 sectors with the highest disclosure rates. In the full sample, consumer durables show the highest disclosure rate of 65.4%. Consumer durables also show the highest disclosure rate for MNEs (62.4%) and large domestic companies (70.1%), but are not among the top 15 sectors for SMEs. The second highest disclosure rate in the full sample can be found in water utilities (64.2%) which is entirely driven by large domestic companies (65.4%), while this sector is not among the top 15 for MNEs and SMEs.

**Figure 3: Average disclosure rate, by sector and company size**

<!-- Start of picture text --> All MNEs<br>Consumer Durables 65.4% Consumer Durables 62.4%<br>Water Utilities 64.2% Metals Products 59.7%<br>Technology Hardware 55.0% Automotive 58.8%<br>Energy Utilities 55.0% Retailers 56.6%<br>Textiles and Apparel 55.0% Technology Hardware 56.1%<br>Household and Personal... 54.1% Computers 54.2%<br>Computers 54.0% Health Care Products 52.8%<br>Health Care Products 53.5% Chemicals 52.4%<br>Metals Products 52.7% Textiles and Apparel 51.6%<br>Chemicals 52.3% Energy Utilities 49.5%<br>Construction Materials 51.8% Mining 49.1%<br>Energy 51.6% Household and Personal... 49.0%<br>Retailers 50.6% Energy 49.0%<br>Automotive 50.3% Construction Materials 47.8%<br>Mining 46.9% Telecommunications 46.1%<br>Large SMEs<br>Consumer Durables 70.1% Water Utilities 59.3%<br>Textiles and Apparel 66.3% Aviation 53.7%<br>Water Utilities 65.4% Mining 51.9%<br>Household and Personal... 59.7% Energy 50.7%<br>Media 59.1% Construction Materials 47.5%<br>Automotive 57.7% Universities 47.3%<br>Health Care Products 56.5% Household and Personal... 46.3%<br>Energy Utilities 56.4% Healthcare Services 43.3%<br>Construction Materials 55.8% Logistics 39.6%<br>Technology Hardware 54.7% Waste Management 38.5%<br>Chemicals 54.0% Chemicals 37.8%<br>Computers 52.9% Retailers 37.0%<br>Forest and Paper Products 52.5% Other 33.9%<br>Energy 52.3% Metals Products 32.1%<br>Metals Products 51.8% Food and Beverage Products 31.5%<br><!-- End of picture text --> Note: See Appendix 2 for the full sector coverage.

Technology hardware, energy utilities and textiles and apparel show the third largest disclosure rate in the full sample (55.0% each). These are sectors with high disclosure rates for both MNEs and large domestic companies, but are not among the top 15 sectors for SMEs. While for MNEs disclosure rates are higher in technology hardware (56.1%) compared to textiles and apparel (51.6%) and energy utilities 12
et al. 2021, Amin and Soh 2021, and Amin 2023). For instance, Distinguin et al. (2016) use the WBES data to analyze how informal competition affects formal firms’ access to finance. They instrument informal competition faced by a firm by its country-level average.

Applied to our study, the strategy of using cell averages assumes that formal manufacturing SMEs within a cell, a country-year pair in our case, face a similar level of informal competition. The assumption holds in our case.<sup>5</sup> Regarding endogeneity concerns, it is highly unlikely that a single firm’s R&D activity will have any effect on the chances of formal firms in the entire countryyear competing against informal firms (the cell average). Therefore, when using cell averages, reverse causality is unlikely to be a problem. Similarly, a single formal firm’s own characteristics that may affect its R&D activity are unlikely to be correlated with the cell average or the chances of all the formal firms in the country-year facing informal competition (the omitted variable bias problem). For instance, Ozler (2000) estimates the impact of the exports-to-sales ratio on a plant’s share of female workers in Türkiye. It uses the average value of the exports-to-sales ratio across all plants within an industry (cell average) instead of a plant’s own exports-to-sales ratio. Regarding the endogeneity problem, Ozler (2000, page 1241) notes that: “It would be difficult to argue that one plant would be causing the export share of output of an industry to increase unless the industry had only a couple exporting plants.” Cell averages also help to control for potential measurement errors if some formal firms choose not to respond or misreport informal competition (see Paunov 2016).

- 3.2 _Testing for theoretical predictions_

> 5 The correlation between informal competition at the firm level and its cell average at the country-year level (defined below) is high, equaling 0.38.

12
# **1. Introduction**

Over the past 20 years, primary school enrollment has increased dramatically in Sub-Saharan Africa—from a gross enrollment rate of 81.8 percent at the turn of the century to 97.3 percent in 2017. However, the quality of learning has not kept pace, despite the rapidly expanding enrollment levels.<sup>2</sup> Compared with low- and middle-income countries in other regions, the measured level of learning in Sub-Saharan Africa is quite low. In particular, students’ performance in mathematics and science is well below that of students in comparator countries in all internationally comparable assessments (Bashir et al. 2018; Evans, Santos, and Arias 2019; Bold et al. 2017). The situation of education is a microcosm of this continent-wide phenomenon. Specifically, although the countries registered impressive improvements in enrollments, learning outcomes have not progressed satisfactorily.

To improve mathematics and science education in secondary schools, The Gambia has implemented a new pedagogical innovation called the Progressive Science Initiative® (PSI®) and Progressive Mathematics Initiative® (PMI®) since 2012. The program has been piloted in 24 schools, one of which is a basic cycle school (grades 1 to 9), seven are upper basic schools (middle schools), nine are senior secondary schools (high schools), and seven are combined upper basic and senior secondary schools.

PSI-PMI incorporates technology in the teaching of mathematics and science subjects (physics, chemistry, and biology) in schools. The PSI-PMI model creates a student-centered environment with interactive teaching and learning methods. Central to this model is the use of technologies such as interactive white board (IWB) software and a student polling device (responders), as well as emphasizing group discussions. Rather than being a remedial tool like other computer-assisted learning (CAL) programs, the PSI-PMI model is directly integrated into the teaching and learning curriculum, replacing the way science and mathematics subjects are traditionally taught in the country. The Gambia was the first country to adopt this program in

> 2 One World in Data: https://ourworldindata.org/search?q=gross+enrollemt+ration+primary+education+Africa. One World in Data: https://ourworldindata.org/search?q=gross+enrollment+ratio+secondary+education+africa. One World in Data: https://ourworldindata.org/tertiary-education. All accessed August 13, 2019.

2
<!-- Start of picture text --> 3,000 7.00<br>R? = 0.963<br>_2,000 nceyea5,21 se5.00<br>4,500 al 5<br>pe ° © Mortalidad; por 3.00<br>4,000 zz el VIH (SIDA).1<br>°°<br>500 ff e ———TasaLogaritmicapor 100.000 Hab.(Tasa por 100.000 Hab.) 2.001.00<br>Tasa<br>- 0.00<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text -->
Vulnerability of Bangladesh to Cyclones in a Changing Climate: Potential Damages and Adaptation Cost Susmita Dasgupta<sup>1</sup> Mainul Huq<sup>2</sup> Zahirul Huq Khan<sup>3</sup> Manjur Murshed Zahid Ahmed<sup>4</sup> Nandan Mukherjee<sup>5</sup> Malik Fida Khan<sup>6</sup> Kiran Pandey<sup>7</sup>

**Acknowledgements** Financial support for this study was provided by the Economics of Adaptation to Climate Change study administered by the Environment Department of the World Bank. Funding for the Economics of Adaptation to Climate Change study has been provided by the governments of the United Kingdom, the Netherlands, and Switzerland.

We would like to extend our special thanks to Ms. Maria Sarraf, Dr. Ainun Nishat and Mr. Khawaja Minnatullah for their valuable guidance. We are thankful to Dr. Robert Mendelsohn and participants of the Economics of Adaptation to Climate Change country consultations for their useful comments and suggestions. We are also grateful to Ms. Roufa Khanam for her valuable help with the GIS and Mr. Brian Blankespoor for editorial help.

> 1 Lead Environmental Economist, Development Research Group, The World Bank, Washington DC, USA.

> 2 Development Policy Group, Dhaka, Bangladesh.

> 3 Director, Institute of Water Modeling, Dhaka, Bangladesh.

> 4 Senior Specialist, Institute of Water Modeling, Dhaka, Bangladesh.

> 5 Adaptation Specialist, Climate Change Division, Center for Environmental and Geographic Information  Services, Dhaka, Bangladesh.

> 6 Director, Climate Change Division, Center for Environmental and Geographic Information  Services, Dhaka, Bangladesh.

> 7 Senior Economist, Environment Division, The World Bank, Washington, USA.