rafmacalaba/datause-encoder-data huggingface dataset:
**Ghana's** Cocoa and **Foreign Exchange Polcis** ......

**IIL**

3.42

**IV.** Conchlslons and Sumuary ................

3.44

# FO NMTEATION OF POLICY WPLICATINS **FROM IDIVmUAL** S1UDES AND **RE DAT** ............................

............................

4.1

# **ANNEXES:**

# **1.** Multiporlod Agricultural Sector Model and Numerkcal Shnulatlon Esthuates

# II. Optimal Trade Taxes on Agriculture In Developing Countries

Ill. Ghana's Cocoa Environemnt

# TMALES: PART ONE

|1.I.1:|Distribution of Survey Households by Reglon .<br>.|. 1.7|
|---|---|---|
|I.B.2:<br>|<br>Age Distribution of Household Members by Region..|1.8|
|1.8.3:<br>|Average Landholding per 4ousehold by Region ..<br>|1.9<br>|
|1.8.4:|AverageSize ofPlot by Region|1.10|
|I.B.5<br>|Number of Farmers Growhg Cocoa and Oil Pam..<br>|1.12|
|I.B.6:<br>|Age Distribution of Tradional Cocoa Trees by Region<br>|1.12<br>|
|1.I.7:|Mean Age Distribution of Hybrid Cocoa Trees by Region|<br>1.12|
|1.8.8:<br>|Mean Age Distribution of Oil Pakn Trees by Region..<br>|1.13|
|1.8.9:|Labor Use on Farms by Region|1.14|
|I.B. 1 0|Mean Labor Requirement for Farm Operatlons.<br>.|1.16|
|1.I.1 1:|Mean Labor Requirement for Harvesting One Acre of<br>||
||Various Annual Crops (Mandays).<br>.|1.19|
|I.8.12:|Mean Labor Requirement for Threshing Produce from<br>||
||One Acre of Various Annual Crops.<br>.|1.20|
|l.B.13:|Llvestock Numbers and Holders ..............<br>..|<br>1.21|
|I.C.1:<br>|Ghana Macroeconomic Projection|1.31|
|I.C.2:|Dlsaggregation of Macroeconomic Projections, 1984-85|1.32|
|I.C.3:|Annual Decomposition of Multiperiod Model||
||Validatlon Period|1.34|
|I.C.4:|Comparison of Slnulated and Actual Cocoa Production|1.36|
|I.C.S:|Calculatlon of Farm Income (Concept 3) from the||
||National Accounts|1.37|
|I.C.6|Arc Elasticlties of Cocoa Supply|1.41|
|I.C.7|Sbnulated Annual Pattern of Labor Use In 2015-19|1.49|
|I.C.8<br>|Food Grainknports|1.55|
|I.C.9|Annual Decomposition of Multiperiod Model Solutions|1.76|
|I.C.1 0|Cocoa Supply Esthiates|1.82|

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Finally we ask, to what extent is the variation in sales of male and female-owned businesses attributable to between industry gaps v. within industry gaps. To address this question we use _one-way analysis of variance methods_ . The results, shown in Table 9, indicate that while the variation in sales revenue of male entrepreneurs is highly concentrated within industries (91% of total variation comes from within industry); for female entrepreneurs this variation comes both from within industry gaps (74%) and also from between industry gaps (26%). This suggests that choice of industry has a relatively bigger role in explaining firm size/sales for female entrepreneurs than their male counterparts.

The findings that women i) fare worse in more capital intensive industries, ii) are concentrated in industries with lower average firm sales and iii) are operating at a suboptimal scale lead us to ask whether female-owned firms are being constrained to be small by lack of access to capital.

# **_5.2 Are Women’s Firms Constrained to Be Small?_**

Although economically the increasing returns found in Table 4a and 4b are not large, they suggest that over time we should observe the scale of firms increasing in the economies of Central Asia and Eastern Europe. Moreover, if women’s firms are currently more constrained to be inefficiently small than firms owned by men, removing these constraints will result in greater growth of female-owned firms than male-owned firms. In this section we ask to what extent male and female business owners are constrained from reaching their optimal scale by lack of access to capital. We first ask whether or not women are more constrained than men in access to capital and we then test for the relative gender effects of capital constraints on their scale of operation and productive efficiency.

Since another paper answers the first question with the same BEEPS data, we rely on the results of this study. Muravyev, Schafer and Talavera (2007) have shown that women are (5.4%) less likely than men to get a loan, when correcting for selection on whether the firm needs a loan, and controlling for the profit of the firm, capacity utilization, age, and competition faced by the firm, as well as industry and country fixed effects. They note the importance of controlling for capacity utilization and age; if women are operating firms that are younger and at lower capacity utilization than men 18
# **6. REFERENCES:**

- Aron, Janine (2000), "Growth and Institutions: A review of the evidence." The World Bank. Washington, DC.

- Brown Ashley C., J. Stern, B. Tenenbaum, and D. Gencer. 2006. “Handbook for Evaluating Infrastructure Regulatory Systems”. The World Bank, Washington DC.

- Correa, Paulo, C. Pereira, B. Mueller, and M. Melo. 2006. “Regulatory Governance in Infrastructure Industries: Assessment and Measurement of Brazilian Regulators” _._ The World Bank/Public-Private Infrastructure Advisory Facility, Washington DC.

- Cubbin, John and J. Stern (2006), "The Impact of Regulatory Governance and Privatization on Electricity Industry Generation Capacity in Developing Countries." The World Bank Economic Review Advance Access published. Washington.

- Gilardi, Fabrizio. 2002. “Policy credibility and delegation to independent regulatory agencies: a comparative empirical analysis” _._ Journal of European Public Policy.

- Gilardi, Fabrizio. 2005a. “Evaluating Independent Regulators” in “Designing Independent and Accountable Regulatory Authorities for High Quality Regulation”. Organization for Economic Cooperation and Development, Paris.

- Guach, Jose Luis and P. Spiller. 1999. “Managing the Regulatory Process: Design, Concepts, Issues and the Latin America and Caribbean Story”. World Bank and Caribbean Studies, Washington DC.

- Gutierrez, Luis H. 2003. “Regulatory Governance in the Latin American Telecommunications Sector”. Utilities Policies. Obtained at www.sciencedirect.com.

- International Energy Agency. 2001. “Regulatory Institutions in Liberalized Electricity Markets”. Organization for Economic Cooperation and Development. Paris.

- Jacobzone, Stephane. 2005. “Strategy and Structure the Political Economy of Agency Independence and Accountability” in Designing Independent and Accountable Regulatory Authorities for High Quality Regulation”. Organization for Economic Cooperation and Development, Paris.

- Johannsen, Katja Sander. 2003. “Regulatory Independence in Theory and Practice –a Survey of Independent Energy Regulators in Eight European Countries” _._ AKF, Denmark.

- Kaufmann, Daniel, A. Kraay A., and M. Mastruzzi. 2005. **_“_** Governance Matters IV: Governance Indicators for 1996-2004”. The World Bank, Washington DC.

- Levi-Faur, David and J. Jordana. 2004. “The Rise of the Regulatory State in Latin America: A Study of the Diffusion of Regulatory Reforms Across Countries and Sectors”. Centre on Regulation and Competition. Institute for Development Policy and Management. University of Manchester.

- 54 -
of refugees or host communities. A few studies on poverty among FDPs appeared before 2010, including a few studies on the well-being of Palestinian refugees (Hejoj 2007), and occasional studies on refugees in upper income countries (Bollinger and Hagstrom, 2004, Kriechbaum 2006), but these studies were extremely rare and relegated to specialized journals, or specialized agencies’ reports. This changed after 2010 with tens of studies now published on refugee poverty and household well-being of host populations in top economics journals (Verme and Schuettler, 2021).

The surge in general interest on the topic, the media exposure of the Syrian and European crises and the growing preoccupation of donor countries with the political and economic implications of refugees led development organizations and poverty specialists to start considering FDPs more closely and collaborate with humanitarian organizations. The UNHCR and the World Bank, for example, started to collaborate very closely around 2014 with the first joint study on the welfare of Syrian refugees in Jordan and Lebanon (Verme et al, 2016). This study used UNHCR’s administrative and survey data to measure poverty among refugees, contributing to improving the targeting system of the UNHRC’s cash assistance program. It found that - using the international extreme poverty line of 1.9 USD/day in purchasing power parity (PPP) – 7 in 10 refugees were poor, a figure that rose to 9 in 10 refugees if the national poverty lines of the host countries were used.

This study led the World Bank to reconsider its role in working with refugee populations and encouraged this organization to strengthen cooperation with the UNHCR, leading to the establishment of a joint research program (“Building the Evidence on Forced Displacement”), country-level cooperation on data collection in several countries, a first round of analytical studies on refugees’ well-being (Pape et al., 2018, Pape et al., 2019a, Cuevas et al., 2019, Beltramo et al., 2021), the establishment of a Joint Data Centre between the two organizations, and the implementation of joint rapid poverty assessments for FDPs in the course of the COVID-19 pandemic (see World Bank, 2021 for example). This collaboration was instrumental in equipping the World Bank with improved knowledge on refugee populations and the UNHCR with improved knowledge on data collection and poverty measurement among FDPs.

Better and more data and knowledge on FDPs, in turn, attracted significant investment in FD research on the part of donors, generating a real boom in research on the topic which quickly became mainstream, even in disciplines that traditionally disregarded FD such as economics. By 2021, most of the top economics journals had published articles on FD, including the Quarterly Journal of Economics, the Journal of Political Economy, and the Review of Economics and Statistics, with special issues being published by development journals such as the Journal of Development Economics. This literature, in turn, is providing hard evidence on highly debated topics such as the impact of FD on host communities. A review of this specific literature 5
16 we did further research and made changes where appropriate.<sup>10</sup> Then we extend the measure in accordance with Brender and Drazen’s coding rule in order to match the extended coverage of spending.

Our main controls include two economic variables and two political ones. The economic variables are standard in the literature (e.g. Brender and Drazen 2005, Shi and Svensson 2006, Alt and Lasen 2006b, Hanusch 2012 b). To capture business cycle fluctuations, we use Brender and Drazen’s variable of the output gap (computed using the Hodrick-Prescott filter) when replicating their findings. For our extended expenditure measure and the general government final consumption measure we simply use real economic growth, as in Shi and Svensson, Alt and Lassen, and Hanusch, taken from the World Bank’s _World Development Indicators_ (WDI). The second standard measure is the log of real GDP per capita to capture a country’s degree of economic development, also taken from the WDI.

Because we are concerned with the effects of parties on budget cycles, we control for political factors capturing institutional arrangements that might influence both political incentives to engage in pre-electoral transfers to voters and incentives to organize political parties. Saporiti and Streb (2008) argue that political budget cycles are less likely to occur if government is divided and a second chamber can veto the budget, especially before elections.<sup>11</sup> A control, _unified government_ (named ‘allhouse’ in the DPI), takes this effect into account. Prior research also argues that the size of the ruling coalition influences political incentives to increase spending around elections. Hanusch (2012a) demonstrates formally that parties have different incentives to engage in electioneering, depending on their size and ideology, while Chang (2008) shows empirically that more parties in a coalition dampen the magnitude of political budget cycles since each additional coalition member adds another veto player. Our

> 10 These adjustments are documented in appendix 1.

> 11 Whether election rules are proportional or plurality-based can also influence the effects of parties on candidate behavior, but no theory links these rules to political budget cycles.  In fact, controlling for whether legislative seats are elected using rules of proportional representation has no effect on our coefficient of interest, the interaction of the party variables with the election year.  Electoral rules are themselves rarely significant and never in the preferred System-GMM specification.
Table 10 Job category

|Office clerk|Sales Receptionist BPO Driver|
|---|---|
|Treatment 0.0550*|0.0756**0.1290*0.0483 0.0006|
|(1.92)|(2.07) (1.91) (0.91) (0.02)|
|# applications 0.0487**|0.0439**0.0828**0.0442 0.0066|
|(2.62)|(2.62) (2.05) (1.45) (1.21)|
|Treatment * # applications -0.0207|-0.0356*-0.0590 -0.0268 0.0010|
|(0.90)|(1.89) (1.34) (0.81) (0.11)|
|R squared 0.1722|0.1471 0.3007 0.2038 0.0326|
|N obs 367|175 143 225 133|

*** 1%, ** 5%, * 10% significance. Numbers in parentheses are absolute t values using robust standard errors. The sample consists of job seekers who were in the system more than 30 days at the time of taking the test and applied for less than 30 positions during the tracking period.

23
31

# V : ICONONBTRIC ANALYSIS Of MM PLANTING

The new planting equation is derived In a manner similar to that for replanting, but the specifilcation of the costs of adjustment is different1 4 New planting decisions are based on expected profit calculations. In estimating expected gross revenue the operating costs and average yield at maturity should be those which apply to the variety being considered. It is likely that the average yield at maturity of newly planted areas may be substantially different from the yield on older areas due to differences in soil fertility, chosen varieties and the average age of planted area. Despite this the first alternative considered for proxying expected revenue of new planting is AREVPROD. Again two lagged values of AREVPROD are used to proxy GREVE. The major justification for this Is that, given the relative unimportance of new planting before 1966, and the rather low level of new planting until 1975, reliable estimates of the revenue of new planting based on the yield of newly planted area cannot be formed except for a rather small part of our sample. Estimation based on such a small sample will not yield useful results. On the other hand, it is at least plausible that the movements in the revenue of newly planted areas should closely mirror the movements in revenue of the older areas since both depend to some extent on PRICOCO.

Yet another possibility is to specify expected revenue to be a linear function of two lagged values of PRICOCO. Such a specification ignores an important component of revenue, viz, average yield, but could involve a relatively smaller error than using AREVPROD variable if the past yield on 14 It may be argued also thet the relevant measure of expected revenue also should be different.
<!-- Start of picture text --> Figure 1. Gross Portfolio Flows to Latin America<br>Selected Countries<br>US$ millions<br>25<br>20 --. ----------- - ------ 7- - - - - - - -_ --_<br>10 l_<br>-5 -- --------<br>01989 1990 1991 1992 1993<br>Note: Latin America includes Argantina, Brazil, Chile and Mexico<br>Figure 2. Gross Portfolio Flows to Asia<br>Selected Countries<br>US$ millions<br>25<br>2 0-<br>15-<br>10 --------------------------------- ----<br>-<br>5 --- ---------------------------------<br>.0 #tt ;$~r4¢wwtttM ti Equity Flows -- " 4f~w;| | 1 f.,..<br>1989 1990 1991 1992 1993<br>Note: Asia includes India, Indonesia, Korea and Thailand<br>Souirce: The World Bank World Debt Tables 1993-94<br><!-- End of picture text -->
There is a substantial variation in the total share of informal workers across countries and sectors. According to the World Bank Group (2021), the scale of the informal economy varies substantially among emerging markets, developing economies, and their regions. For example, in 2018, the share contributed by the informal sector ranged from around 10 percent to 68 percent of GDP, while informal employment measured in terms of self-employment ranged from near-zero to 96 percent of total employment. In particular, the highest share of informality is observed in some countries in the Sub-Saharan Africa region, in which informal employment constitutes more than 90 percent of total employment. There is also a considerable variation across industries: among the nine industries in this study, agriculture, trade, and construction have on average the highest levels of informality accounting respectively for 68, 50 and 34 percent of total employment. The lowest informality rates on average are observed in utilities (12 percent), finance (16 percent), and other services (20 percent). Also, due to its capital-intensive production the mining sector employs very few workers and accounts for less than 5 percent of total employment in many countries. Hence, the number of informal workers in this industry is insignificant in the broader picture of total informal employment.

Other variables used in the regressions are the capital-labor ratio, share of sectoral value added in total GDP, share of imported intermediate goods in total intermediate goods, and emissions from intermediate usage of imports and domestic products in a metric of carbon dioxide equivalent. The latter is a dependent variable in the regressions which is divided by the value added to take into account the size of the sectors. All these indicators are obtained from the GTAP data set which provides data for 65 sectors and 141 countries and world regions for the year 2014. Country mapping between the I2D2 and GTAP data shows that only 70 developing countries are represented in both databases and thus are included in the sample for estimations. Among the GTAP indicators, the capital-labor ratio is computed by dividing the payment to capital by the payments to labor and emissions include the carbon dioxide (CO2) (mega metric). This CO2 emission data comprises carbon emissions from fossil fuel combustion by sectors.<sup>12</sup> Finally, the GTAP data are aggregated into nine sectors to combine with the I2D2 data set, omitting the public administration and defense sector which is assumed to have no informal employment.

> 12 Detailed description of CO2 emission data calculations can be found in Lee, H.L. (2008), “The Combustion-based CO2 Emissions Data from GTAP Version 7 Data Base,” Center for Global Trade Analysis, Purdue University: West Lafayette.

11
<!-- Start of picture text --> Figure 2c. Private Portfollio, net<br>(lBillions of USD)<br>100.0<br>50.0<br>0.0<br>‐50.0<br>‐100.0<br>‐150.0<br>‐200.0<br>‐250.0<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>1. Emerging and developing eco 67.0 17.2 ‐76.9 ‐86.4 ‐3.8 10.0 ‐5.6 ‐100.7 39.5 ‐155.2 ‐234.5 ‐195.3<br>2. Africa 9.1 ‐2.1 ‐7.9 ‐1.6 ‐0.4 5.6 4.2 17.6 9.9 ‐15.8 0.9 4.1<br>3. Central and eastern Europe 6.7 4.7 0.2 3.1 9.7 25.3 25.9 9.4 ‐7.4 ‐13.2 ‐6.1 4.6<br>4. Commonwealth of Ind. States & Mongolia ‐0.9 ‐10.0 ‐1.2 0.4 ‐0.4 4.3 ‐4.9 12.9 14.5 ‐36.8 1.6 3.4<br>5. Developing Asia 54.1 19.7 ‐50.7 ‐60.2 10.3 10.2 ‐5.3 ‐107.2 11.2 ‐65.9 ‐192.1 ‐204.5<br>6. Middle East ‐9.2 2.1 ‐11.8 ‐16.1 ‐18.0 ‐21.7 ‐36.2 ‐25.7 ‐31.0 ‐12.3 ‐14.4 ‐6.4<br>7. Western Hemisphere 7.3 2.8 ‐5.6 ‐12.0 ‐5.0 ‐13.6 10.7 ‐7.7 42.3 ‐11.2 ‐24.4 3.6<br>Source: Authors' computations using WEO, July 2009 data<br>Notes: (1) The regional classifications are based on World Economic Outlook Database, july 2009 and IMF classifications;<br>Usd billions<br><!-- End of picture text -->

- (2) _Western Hemisphere_ is composed of 32 countries, including _LAC_ and other countries;

<!-- Start of picture text --> figure 2d. Official Capital flows,net<br>(in billions of dollars)<br>100<br>50<br>0<br>‐50<br>‐100<br>‐150<br>‐200<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>1. Emerging and developing eco 21.163 ‐35.502 2.275 14.768 ‐43.321 ‐64.948 ‐98.522 ‐154.139 ‐100.536 ‐59.989 57.568 ‐28.094<br>2. Africa 4.115 6.938 6.457 8.794 6.203 4.215 0.516 ‐9.993 5.043 11.132 15.128 12.799<br>3. Central and eastern Europe ‐3.988 0.802 5.186 4.47 ‐2.368 ‐4.127 ‐0.042 ‐7.93 ‐5.959 7.271 26.846 9.609<br>4. Commonwealth of Ind. States & Mongolia ‐1.914 ‐5.83 ‐5.11 ‐10.764 ‐9.408 ‐7.59 ‐19.619 ‐29.768 ‐5.913 ‐0.725 25.059 6.232<br>5. Developing Asia 4.162 ‐11.481 ‐13.139 2.622 ‐18.408 ‐13.403 ‐21.717 ‐21.668 ‐36.592 ‐13.065 ‐11.314 ‐40<br>6. Middle East 8.69 ‐23.378 ‐12.809 ‐8.185 ‐24.392 ‐33.911 ‐27.28 ‐67.036 ‐58.884 ‐75.576 ‐9.416 ‐22.117<br>7. Western Hemisphere 10.099 ‐2.554 21.69 17.831 5.052 ‐10.132 ‐30.381 ‐17.743 1.77 10.974 11.264 5.383<br><!-- End of picture text --> Source: Authors' computations using WEO, July 2009 data

- Notes: (1) The regional division is based on  World Economic Outlook Database, july  2009 and IMF classifications;

- (2) _Western Hemisphere_ is composed of 32 countries, including _LAC_ and other countries.

- (3) Emerging and developing economies consist of 149 countries, including _BRIC.._

42
the next child; if the next pregnancy<sup>terminates in a non-live-birth</sup> outcome, the reported gestational length is<sup>used in the calculation. For those children</sup> who die before their mothers become<sup>pregnant again, age at next pregnancy is the</sup> age that the child would have attained<sup>if he had survived.)</sup> Because the EFS histories are collected<sup>retrospectively</sup> and only from women aged 15-49 at the time of the survey,<sup>the sample for this analysis is</sup> restricted to last and next-to-last births occurring within five years (60 months) of the survey. The five-year constraint<sup>is imposed for three reasons.</sup> First, it mitigates selection problems that<sup>result from using retrospective</sup> reports from a sample of women with an<sup>upper age restriction (49 in this</sup> instance). As the time period from which<sup>births are drawn is extended</sup> backwards, the data represent a smaller and<sup>smaller fraction of women in the</sup> childbearing ages and thus the sample becomes<sup>selective of certain maternal</sup> characteristics. In the five years preceding<sup>the survey, only children born to</sup> women aged 50-54 at the time of the survey<sup>are omitted, a negligible loss. The</sup> second reason for the time period restriction<sup>is to maximize reliability. We</sup> assume that information about events<sup>closer to the survey is of higher quality</sup> (more complete coverage, more accurate<sup>dating). Thirdly, .he EFS contains</sup> breastfeeding information for the last two<sup>children only, hence the restriction</sup> of the sample to last and next-to-last births. The further back that the<sup>time</sup> period of observation is extended, the more<sup>selective last and next-to-last</sup> births become of all births in the period.<sup>Over three-fourths (78%) of the</sup> births occurring in the five years<sup>immediately</sup> prior to the survey are either last or next-to-last births. Clearly an observation period<sup>briefer than five</sup> years would further alleviate each of these<sup>three problems. But a shorter time</sup> period would lead to less precise estimates,<sup>due to a smaller number of</sup> 14