rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6. Loans**

<!-- Start of picture text --> United Kingdom<br>United States<br>New Zealand<br>Sweden<br>Australia<br>Colombia<br>Japan<br>Canada<br>High Income Average<br>Average<br>Finland<br>Netherlands<br>LAC Average<br>Germany<br>Mexico<br>Peru<br>Brazil<br>Italy<br>Ireland<br>France<br>Belgium (French)<br>Belgium (Flemish)<br>Austria<br>0% 2% 4% 6% 8% 10% 12% 14% 16% 18%<br>Loan/GDP per capita (%)<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries Note: Brazil, Mexico, and Peru have loan programs in operation. However, the loan amount offered to the average student is less than 1 percent of GDP per capita.

Availability of student loans in the selected four Latin America countries is limited, with the exception of Colombia. The four studied Latin American countries all have loan programs. However, for Brazil, Mexico and Peru, the average offered amounts to less than 1 percent of GDP per capita. In Colombia, the average student receives a loan equivalent to eight percent of GDP per capita. The average student in the four countries receives a loan equivalent to two percent of GDP per capita. This compares to five percent for the average student in a high-income countries. In particular, Anglo-Saxon countries have invested in large student loan programs to finance tertiary education. Student loans reduce the financial outlays by 11 percent. Loan programs in Northern European countries equally cover a substantial part of the costs (equivalent to seven percent of GDP per capita).

<u>Out-of-Pocket Costs</u> refer to the total education costs (education and living costs) net of student assistance (grants and student loans).<sup>20</sup> The out-of-pocket costs represent what the family has to pay during the studies (Figure 7).

> 20 Further, it is net of any tax breaks offered by high-income countries.

18
two scenarios reduces trade diversion under the EPA for products where the MFN tariff that the EU’s competitors face is also being lowered.

# **Box 3.  Sequential EPA Tariff Phase-out**

The comparative static experiment conducted in this study compares the Nigerian economy in two different states of the world: one (the baseline) in which an EPA is not in place, and another in which it is. The exercise does not provide information about the speed at which changes occur, or about what happens to various dimensions of the economy during the transition. In reality, an EPA would be implemented during quite a lengthy phasing out of tariff liberalization (around 15 years). The impact of such an EPA will then be spread out over time, so that the actual shock to the Nigerian economy at any given point in time would be much smaller, especially relative to changes that would have happened to the Nigerian trade flows anyway.

To illustrate how a phased out EPA would impact Nigerian imports overtime, a baseline projection of Nigeria’s import growth without an EPA is first constructed (based on the average import growth rate for products in this HS 1 digit category over the last five years and extrapolated for the 2006-2025 period). Sequential liberalization with the EU is then assumed to begin with the lowest tariff bands starting in 2010 and to be completed in 2025 with full free trade with the EU (i.e., removing tariffs <5% in 2010, removing tariffs <10% in 2015, removing tariffs <20% in 2020, and the remaining in 2025). As shown in the graph, the impact of the phased out EPA is indeed very small at each given point of time, especially compared the baseline import growth.

<!-- Start of picture text --> Projected imports with and without EPA 2006-<br>2025<br>Total imports (baseline) Import from EU (baseline)<br>Total imports (w /EPA) Import from EU (w /EPA)<br>80,000<br>70,000<br>60,000<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>2006 2010 2015 2020 2025<br>mln. US$<br><!-- End of picture text --> Source: Computed by the authors from TRIST.

19
# **7 Conclusions**

This paper highlights the importance of complementary public infrastructure assets in the spatial distribution of economic activity, focusing specifically on the quality of the public roads and electrical grid networks. In doing so, it develops a multi-sector spatial equilibrium model with endogenous government investment in these two types of infrastructure and calibrates it to the Brazilian economy in 2010. A novel feature of the model is its ability to characterize spillovers from population and public infrastructure on location-specific productivities in the agriculture, industry, and services sectors. We have used the full structure of the model to estimate these spillovers by the simulated method of moments.

The paper’s key insight is the evidence of significant distortions in investment in both infrastructure types and substantial gains from reallocating these investments. Owing to the existence of economic complementarities, the government can substantially increase aggregate welfare by jointly planning investment in both types of public goods. On the other hand, welfare gains diminish if fiscal restrictions or political preferences affect the infrastructure choices. All in all, our results underscore the significance of coordinated technical planning of welfare-enhancing infrastructure investments. These efforts can, however, be obscured by complex political economy reasons.

There are many interesting, related issues that we have not addressed. They include the analysis of other sources of public capital misallocation, like the regional equalization policies, fiscal decentralization, the artificial creation of Brasilia, and deforestation prevention policies. All of them deserve further investigation. Another important problem is understanding the impact of optimal infrastructure investment on spatial inequality. The incorporation of inputoutput linkages into our model could be another interesting avenue. We leave them to future research.

# **References**

- Abbasi, M., M. S. M. Lebrand, A. B. Mongoue, R. Pongou, and F. Zhang (2022): “Roads, Electricity, and Jobs: Evidence of Infrastructure Complementarity in Sub-Saharan Africa,” Policy Research Working Paper 9976, The World Bank.

- Alder, S. (2023): “Chinese Roads in India: The Effect of Transport Infrastructure on Economic Development,” Working Paper.

- Allcott, H., A. Collard-Wexler, and S. D. O’Connell (2016): “How Do Electricity Shortages Affect Industry? Evidence from India,” _American Economic Review_ , 106, 587–624.

- Allen, T. and C. Arkolakis (2014): “Trade and the Topography of the Spatial Economy,” _The Quarterly Journal of Economics_ , 129, 1085–1140.

38
# **5. Conclusion and outlook**

Use of data from an actual land policy experiment leads to insights on the equity and productivity effects of land rights arrangements that are of interest in terms of policy and point towards a number of areas for future research.

Even though the results obtained here suggest that the adoption of the "to no" policy was, _ex post,_ not associated with a negative impact on households' investment in response to exogenous shocks, there are three areas where further research would be of great interest. First, uncovering the short-term fluctuations caused by exogenous shocks could provide a great deal of information on the mechanisms underlying this phenomenon that might be of broader policy relevance. Even though aggregate investment in the long term may remain stable in response to a shock, whether or not consumption has to be cut back, the duration of such cut-backs, and the extent to which they are related to household characteristics, will be relevant for policies aiming to reduce household vulnerability in more general terms. Second, the literature is very clear that, even if there is no change in investment _ex post,_ policy changes may lead to _ex ante_ adjustments of asset portfolios which will have a systematic impact on the returns households will be able to receive from such assets. Information on short term changes in households' asset portfolios can provide insights into this issue. Finally, the importance of transfer rights for investment suggests that it would be of great interest to explore the relationshlp between the adoption of the "two nos" policy and the extent to which, by transferring land to its most productive use, land markets can improve equity and efficiency at the same time.

A key concern of policy-makers in the past has been that, even though it may be associated with economic benefits, a modified property rights regime would be opposed by the majority of the rural population. This notion was based largely on studies that asked hypothetical questions rather than on exploration of households' actual reaction to a policy change and did not account for the complexity of the situation. Our results suggest that such concerns are unfounded, for two reasons. First, we find a strong learning effect whereby households in Guizhou, where more secure such rights were introduced exogenously, are much more in favor of a policy of "two nos" than those in the remainder of the provinces. Second, we find that with overall economic development, household support for this policy increases considerably. Better access to other mechanisms to insure and smooth consumption, as well as increased levels of non-land wealth, are all associated with higher levels of support for the policy of "two nos". Exploring how this would translate into other provinces is an important task for future research. Concerning agricultural investment, our findings point towards a positive impact of the policy of "two nos" but at the same time highlight that, in quantitative terms, allowing transferability of land rights is likely to have a bigger investment-increasing impact. This suggests that, to reap the 23
# 4.4. Who are the women and men who migrated internally?

**This section provides descriptive statistics of women and men who migrated internally and those who did not migrate.** Descriptive statistics are used to create a sociodemographic profile of movers and understand how they differ from those who stayed in the same state between 2015 and 2020. The profiles are constructed using 2020 Census data, meaning they refer to characteristics of women and men five years after the migration decision.

**Women who migrated internally are younger than those who stayed in the same state of residence.** On average, women who migrated are 30 years old, while women who did not migrate are five years older. Moreover, almost 50 percent of migrant women are concentrated in the age group 15-34 years against 34.1 percent of those who did not migrate (Figure 12). This pattern is similar among men (Figure 13).

Figure 12. Distribution of migrant and non-migrant women by age group Figure 13. Distribution of migrant and nonmigrant men by age group <!-- Start of picture text --> Women who migrated Men who migrated<br>Women who did not migrate Men who did not migrate<br>30.0 26.4 30.0 26.2<br>23.2<br>25.0 25.0 21.6<br>20.0 16.8 15.6 20.0 17.2 16.8<br>15.0 15.0<br>8.9 9.7<br>10.0 4.8 4.4 10.0 5.1<br>3.5<br>5.0 5.0<br>0.0 0.0<br>Percentage<br>Percentage<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

Source: Own estimates based on Population and Housing Census 2020.

**There are no significant differences across migration status in the percentage of women that are household heads.** Indeed, approximately 19 percent of both women who migrate and women who did not migrate are heads of household. On the other hand, migrant men are more likely to be household heads than those who did not migrate (45 versus 42 percent).

**Marital status significantly differs among women who migrated compared to those that stayed in the same state.** Almost 35 percent of women who did not migrate are married against 26.1 percent of women who migrated internally. Around 29.7 percent of migrant women are cohabiting,<sup>7</sup> while this percentage is only 17.5 percent among those women who did not migrate (Figure 14). A similar pattern is observed among men (Figure 15).

**Women who migrated have, on average, higher levels of educational attainment than those who did not migrate.** Around 22.7 percent of them have upper secondary and 23.6 percent have tertiary education versus 19.5 and 16.6 percent of those who did not migrate, respectively (Figure 16). Comparing across

> 7 Cohabitating refers to individuals who live with their partners in a free relationship without a legal bond.

14
have also registered large increases in their CO2 emissions during the 1980-2004 period. Australia and New Zealand’s combined emission has increased by 94 percent, Mexico has exhibited a 67 percent increase.

**Figure 1: CO2 Emissions by Countries and Regions for years 1980 and 2004 (Million Metric Tons of CO2)**

<!-- Start of picture text --> Canada<br>China<br><!-- End of picture text --> <!-- Start of picture text --> U.S. Canada Mexico OECD EU<br>45000<br>Japan South Korea ANZ Russia<br>40000<br>OECA China India Other Asia<br>Middle East Africa Brazil OLA<br>35000<br>30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030<br>Million Metric Tons<br><!-- End of picture text --> Source: EIA (2007a) for historical emissions and IEA (2007b) for projected emissions.

-11-
**Figure 5: Female labor share, global firms vs. non‐global firms, by sector**

<!-- Start of picture text --> GVC participants vs. non‐participants<br>60%<br>50% 18<br>30<br>40%<br>16<br>30% 15<br>36 2422 17 33 19 32<br>20% 2531<br>26 20 21<br>2829 2334<br>10% 35<br>27<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70%<br>Female labor share (%), GVC participant<br>FDI vs. non‐FDI firms<br>60%<br>18<br>50%<br>30<br>40% 16<br>15<br>30% 33 17 3219<br>22 24<br>20% 35 23 21 25 31 36<br>26 20<br>34<br>29 28<br>10%<br>27<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70%<br>Female labor share (%), FDI<br>Female labor share (%), non‐participant<br>Female labor share (%), non‐FDI<br><!-- End of picture text --> Source: The female labor shares are averages using firm employment as weights. Sector numbers relate to ISIC Rev. 3 sectors. For the sectoral distribution of firms, see Appendix 2.

# **3.3 Differences across Countries**

This section shifts the focus to the average female labor share by country. The number of firms surveyed varies drastically across the country sample, ranging from over 6,000 firms in India, to over 1,000 firms in Bangladesh, China, the Arab Republic of Egypt, Indonesia, and the Russian Federation to around 100 in other countries. The findings of the country analysis therefore need to be treated with caution. They nevertheless allow to get a first idea about the female labor participation across different countries. The main observation of Figure 6 is that the female labor share ranges from almost two‐thirds in Myanmar (MMR, 64.7%) to less than 10% in Pakistan (PAK, 8.4%) and Iraq (IRQ, 5%).

13
Table 5: Heterogeneous effects of SPS provisions across sectors Panel A: SPS <u>provisions</u>

||Export v|alue of agricu|ltural products|
|---|---|---|---|
|Sector|Animal|Vegetable|Foodstufs|
|1<sup>_st_ </sup>tercile _×_ Prov|0.520<sup>_∗∗_</sup>|0.361<sup>_∗∗∗_</sup>|0.424<sup>_∗∗∗_</sup>|
||(0.250)|(0.101)|(0.0921)|
|2<sup>_nd_ </sup>tercile _×_ Prov|-0.424<sup>_∗∗_</sup><br>(0.167)|-0.0295<br>(0.0921)|0.0450<br>(0.0785)|
|3<sup>_rd_ </sup>tercile _×_ Prov|-0.168|-0.0335|0.177|
||(0.293)|(0.234)|(0.132)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|
|cluster|ij|ij|ij|
|N|239414|795806|496914|

# Panel B: TBT <u>provisions</u>

|||||||E|xport val|ue of any|product||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Sectors:|Animal|Vegetable|Foodstufs|Chemicals|Plastics|Raw Hides|Wood|Textile|Footwear|Stone|Metals|Machinery|Transportation|Miscellaneous|
|1<sup>_st_ </sup>tercile _×_ Prov|0.203|0.278<sup>_∗∗∗_</sup>|0.557<sup>_∗∗∗_</sup>|0.763<sup>_∗∗_</sup>|0.212|0.0506|0.723<sup>_∗∗_</sup>|-0.756<sup>_∗_</sup>|-0.272|0.179|1.117<sup>_∗∗_</sup>|1.016<sup>_∗∗∗_</sup>|2.094|0.962<sup>_∗∗∗_</sup>|
||(0.352)|(0.104)|(0.130)|(0.317)|(0.481)|(0.623)|(0.366)|(0.389)|(0.603)|(0.721)|(0.483)|(0.331)|(1.789)|(0.340)|
|2<sup>_nd_ </sup>tercile _×_ Prov|-0.709<sup>_∗∗∗_</sup>|-0.0841|0.0658|-0.464<sup>_∗_</sup>|0.0599|0.914<sup>_∗∗∗_</sup>|0.00289|-0.668<sup>_∗∗∗_</sup>|0.866|0.229|0.824<sup>_∗_</sup>|0.461<sup>_∗_</sup>|1.625<sup>_∗∗_</sup>|-0.157|
||(0.227)|(0.107)|(0.125)|(0.238)|(0.275)|(0.322)|(0.272)|(0.228)|(0.599)|(0.614)|(0.433)|(0.259)|(0.780)|(0.251)|
|3<sup>_rd_ </sup>tercile _×_ Prov|-0.203|-0.0377|0.0435|-0.400<sup>_∗_</sup>|-0.364|0.212|0.202|-0.0167|0.828<sup>_∗∗_</sup>|1.143<sup>_∗∗∗_</sup>|0.126|0.326|1.836|-0.115|
||(0.308)|(0.295)|(0.142)|(0.238)|(0.332)|(0.477)|(0.249)|(0.257)|(0.373)|(0.428)|(0.333)|(0.282)|(1.162)|(0.256)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|
|cluster|ij|ij|ij|ij|ij|ij|ij|ij|ij|ij|ij|ij|ij|ij|
|N|239414|795806|496914|511177|423791|141723|477309|1292868|89365|210349|447294|700472|70289|386647|

_Notes_ : PPML estimation is used. Standard errors clustered by origin-destination presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Provision measures are normalized between 0 and 1. The specifications control for tariffs, GDP of the destination, and PTA depth.

35
**increasing government expenditure.** That **is,** according to the<sup>standard</sup> national accounts equation, increasing government expenditure has the<sup>same</sup> effect on the government deficit as reducing income taxes. Indeed,<sup>the</sup> results in Tables 5 through 8 verify that, aside from a few minor cases,<sup>the</sup> qualitative effects are exactly the opposite. The same intuitive<sup>explanation</sup> follows accordingly. Basically, the effects of an increase<sup>in the tax rate</sup> are a reduction in consumer disposable income and inflation, an<sup>increase in</sup> the value of the dollar, and a resulting decline in feed<sup>grain prices and</sup> exports.

An Increase in the Federal Deficit. Intuitively, the effect<sup>of the</sup> federal deficit would seem to be the same as government expenditures<sup>and</sup> opposite of the effect of income taxes. Indeed, the results<sup>for increasing</sup> the deficit in Tables 5 and 6 are qualitatively almost identical to the<sup>case</sup> of increasing expenditures and almost opposite to the case of<sup>increasing the</sup> tax rate. However, exogenous control of the deficit in the FAIRMODEL<sup>causes</sup> some substantive differences from the case where expenditures or taxes<sup>are</sup> controlled exogenouslI These differences are illustrated by Tables 7 and 8 where the effects of the deficit are qualitatively similar<sup>to the effects of</sup> the tax rate and almost the opposite of the effects of<sup>expenditures.</sup> The contrast of results between the two time periods turns<sup>on the</sup> exchange rate effects. The reason for the different effects is that<sup>an</sup> increase in the deficit has offsetting effects on the exchange<sup>rate; which</sup> effect iominates depends on current circumstances.<sup>On one hand, an increase</sup> in the deficit (say, by an increase in expenditure) has a<sup>positive effect on</sup> inflation which tends to reduce the real interest rate<sup>and the real cumulated</sup> deficit. On the other hand, an increase in the deficit causes<sup>government to</sup> increase the demand for capital to finance the deficit<sup>which tends to bid up</sup> 19
# **Community Managed Forest Groups and Preferences for REDD+ Contract Attributes: A Choice Experiment Survey of Communities in Nepal**<sup>**1**</sup>

## **1. Introduction**

Over the past 150 years, deforestation has contributed an estimated 30% of the atmospheric build-up of CO2 (WRI and IUCN, 1998). Curbing deforestation is a highly costeffective way of sequestering carbon (Stavins and Richards, 2005) in that it also has potential implications for climate change adaptation (Stern, 2006). Many research findings demonstrate the potential for using forests as an efficient method for reducing atmospheric concentrations of carbon dioxide (McKenny et al., 2004; van Kooten et al., 1992; Cannel, 1999). Given the significant amount of forest currently under community management (18% of global forest and 25% of developing countries’ forest) and its rapid increase<sup>2</sup> (World Bank 2009, Agrawal _et al._ 2011) it can be argued that the success of programs designed to reduce deforestation are closely linked to how community managed forests are incorporated into the program.

REDD+ (Reducing Emission from Deforestation and Forest Degradation) is a payment for ecosystem services (PES) program created under the United Nation’s Framework Convention on Climate Change (UNFCCC) that tries to reduce deforestation and degradation in countries not subject to requirements under the convention (non-Annex 1 countries) and, therefore, release less

> 1 Author Information: Sahan T. M. Dissanayake, Assistant Professor of Economics, Colby College, and Assistant Professor of Economics (by courtesy), Portland State University; Prakash Jha, Ph.D. student, University of Venice Ca Foscari, Italy, Bhim Adhikari, Senior Program Specialist, IDRC; Rajesh Bista, Research Officer, ForestAction; Randall Bluffstone, Professor of Economics, Portland State University; Harisharan Luintel, Portland State University and ForestAction; Peter Martinsson, Professor of Economics, University of Gothenburg; Naya Sharma Paudel, Environmental and Governance Specialist, ForestAction; E. Somanathan, Professor, Planning Unit, Indian Statistical Institute; Michael Toman, Lead Economist, Development Research Group, World Bank.  Financial support for this research was provided by the World Bank through the Knowledge for Change Program.  We would like to thank seminar participants at REDD workshops in Dhulikhel and Kathmandu, Economics Department Seminar Series at the University of New Hampshire Reed College for valuable feedback and suggestions on this work.  The contents of the paper is the responsibility of the authors alone and should not be attributed to their institutions, the World Bank, or its member countries.

> 2 The forest decentralization is rapidly increasing over time and therefore the area of community forests roughly doubled to 250 million hectares during the period 1997–2008 (World Bank 2009) .

2