rafmacalaba/datause-encoder-data huggingface dataset:
- Gabaix, Xavier and Yannis M. Ioannides. 2004. Evolution of city size distributions. In J. Vernon Henderson and Jacque-Francois Thisse. _Handbook of Urban and Regional Economics_ , vol. 4. Elsevier. Amsterdam.

- Hiremath, R.B., Bimlesh Kumar, P. Balachandra, N.H. Ravindranath, and B.N. Raghunandan. 2009. Decentralised renewable energy: Scope, relevance and applications in the Indian context, _Energy for Sustainable Development_ , 13, 4–10

- Hongwei, Dai, Yu Yixin, Huang Chunhua, et al. 1996. Optimal planning of distribution locations and sizes – model and algorithm. _Electrical Power & Energy Systems_ , 18(6) 353-357.

- IPCC IV (Intergovernmental Panel on Climate Change). 2007.  Fourth Assessment Report. Available online at <u>http://www.ipcc.ch.</u>

- Khandker, Shahidur R., Douglas F. Barnes and Hussain A. Samad. 2009. Welfare Impacts of Rural Electrification. A Case Study from Bangladesh. Policy Research Working Paper No. 4859. World Bank, Washington, D.C.

- Klose, Andreas and Andreas Drexl. 2005. Facility location models for distribution system design. _European Journal of Operations Research_ , 162, 4-29.

- KMOE (Kenya Ministry of Energy). 2008. Updating of the Rural Electrification Master Plan, Volume 1, prepared by DECON and Re-Engineering Africa Consortium for the Ministry of Energy of Kenya and Ministry of Foreign Affairs of Finland, Nairobi.

- Neij, Lena. 2008. Cost development of future technologies for power generation — A study based on experience curves and complementary bottom-up assessments. _Energy Policy_ , 36 (6), 2200-2211.

- Nordhaus, William. 2007a. Critical Assumptions in the Stern Review on Climate Change. _Science_ , 317, 201-202.

- Nordhaus, William. 2007b. The Challenge of Global Warming: Economic Models and Environmental Policy. Dept. of Economics, Yale University, July 24.

- ORNL. 2008. Landscan Population Database. Oak Ridge National Laboratory, <u>http://www.ornl.gov/sci/landscan/</u> .

- Parshall, Lily, Dana Pillai, Shashank Mohan, Aly Sanoh and Vijay Modi, 2009, National electricity planning in settings with low pre-existing grid coverage: Development of a spatial model and case study of Kenya, _Energy Policy_ , 37, 2395–2410.

Ramachandran, Vijaya, Alan Gelb, and Manju Kedia Shah. 2009. Africa's Private Sector: What's Wrong with the Business Environment and What to Do About It. Center for Global Development, Washington: DC.

- Stern, Nicholas, et al. 2006.  Stern Review on the economics of climate change.  Available online at <u>http://www.hmtreasury.gov.uk/independent_reviews/stern_review_economics_climate_change/sternrevi ew_index.cfm</u>

- SWERA (Solar and Wind Energy Resource Assessment Project). 2001. Description available online at: http://swera.unep.net/

- SWERA. 2004. High Resolution Solar Radiation Assessment for Kenya, Final country report prepared by DLR. Available online at: http://swera.unep.net/

- Tomomatsu, Y. and B. Swallow. 2007. Jatropha curcas biodiesel production in Africa: economics and potential value chain development for smallholder farmers.

25
finished in 2012. It has in total 12 lines covering 226 kilometers. The whole system has 195 transit stations with 24 transfer stations. The BRT system (Metrobus) has six lines covering 125 kilometers. The Metrobus is newer than the Metro with its first line opened to the public in 2005 and the last one in January 2016. Mexico City also has other public transportation systems working at the local level or connecting to the Metro or the Metrobus:

- The Mexibus in the state of Mexico;

- The electric Trolebus (zero emissions) with 15 lines;

- The RTP buses connecting different Metro stations to other areas of the city;

- The LRT in the south of the city with 16 stations and two terminals; and

- Microbuses or small buses (capacity of 24 passengers) that use the main roads of the city and stop every time a passenger requires a stop.

Table 3 shows the information for each line opened in the period between 2002 and 2013, as well as the dates in which they were inaugurated and the dates in which the construction was announced (the announcement date is important to consider anticipation effects in the analysis).

**Table 3: Announcement and inauguration dates of different transportation infrastructures during the** **<u>2002-2013 period.</u>**

||**Distance**|**Number of stations**|**Inauguration date**|**Announcement date**|
|---|---|---|---|---|
|Metro line 12|24.5km|20|October 2012|August 2007|
|Metrobus line 1|30km|47|First<br>part:<br>June<br>2005<br>Second part: March<br>2008|First part: 2003<br>Second part: 2007|
|Metrobus line 2|20km|36|December 2009|January 2007|
|Metrobus line 3|17km|33|February 2011|November 2009|
|Metrobus line 4|28km|35|April 2012|November 2010|
|Metrobus line 5|10km|18|November 2013|February 2013|
|Mexibus|52km|95||October 2010|

Some transportation investments are not included in Table 5 because they were announced, inaugurated and constructed in a period different than 2002-2013.

# _2.3.Housing and Population Censuses at the AGEB (census tract) level_

Finally, in order to capture heterogeneous effects between households belonging to different socioeconomic levels or between neighborhoods with different proportion of residents employed in the informal sector, we use information from the Housing and Population Censuses of 2000, 2005 and 2010, disaggregated at the AGEB level.

14
**Figure  16:  Outward foreign investment of Korea’s textiles, clothing and footwear industries**

<!-- Start of picture text --> Thousand US$,<br>constant 2005 US$<br>600,000<br>500,000<br>400,000<br>300,000<br>200,000<br>100,000<br>0<br>80 82 84 86 88 90 92 94 96 98 00 02 04 06 08<br>asia_textiles m_america_textiles others_textiles<br>asia_clothing m_america_clothing others_clothing<br>asia_footwear m_america_footwear others_footwear<br><!-- End of picture text --> Note: Data for 1980 is a cumulative figure from 1968 to 1980. Data is recalculated on the basis of constant 2005 US$. Source: Korea Eximbank

# **III. The Opportunities and Challenges of China’s Rise**

40. Industrial upgrading to higher value-added commodities is particularly important in view of the opportunities and challenges to Latin America‘s economies due to the rise of China. The first decade of the twenty-first century will be considered a strategic inflection point in the global economic landscape. For the first time in almost two hundred years emerging economies will catch up with and race ahead of the developed ones, in terms of gross domestic product—a trend that has received added impetus from the financial crisis. China‘s meteoric rise is the hallmark of this transformation.<sup>16</sup> 41. Over the next few decades, the world will be subject to very significant demographic changes. While Latin America will grow robustly in terms of population, it will be outpaced by other countries in the developing world. Between the years 2010 and 2030, the population of Latin America is expected to increase by approximately 100 million people, but over the same period the population of India is expected to increase by almost three times that amount. China‘s population growth is expected to decelerate and China‘s population is likely to plateau around 2030. Table 1 illustrates the population forecasts for these regions.

> 16 Gupta and Wang (2009).

20
often require that products be wholly obtained in the member states (Mo ̈ıs ́e, 2002). To reflect this, we impose a somewhat _ad hoc_ requirement that the ROO threshold in agriculture must be 1.5 times the threshold in manufactured goods (which remains equal to the overall ROO threshold in earlier scenarios). When evaluated at the aggregate level, the results are dominated by manufactured goods, which make up for more than 90% of all tariff lines and 94% of the trade in our sample. Therefore, the differences between the set of scenarios where the ROO costs differ by sector and the set where they do not are negligible when total trade is considered. To emphasize the effects of allowing ROO costs to vary by sector, Table 4 also shows preferential trade shares for agriculture and manufacturing.

Table 4: Preferential imports as a share of world total under varying ROO thresholds, 2000-02

|Tarif Threshold (MFN duty level)|% of all trade|% of agr. trade|% of mnf. trade|
|---|---|---|---|
|0.0% in all sectors|21.5|23.5|21.4|
|3.0% in all sectors|15.4|20.0|15.1|
|4.0% in all sectors|14.3|19.5|14.0|
|5.0% in all sectors|12.8|18.8|12.3|
|6.0% in all sectors|11.4|18.1|11.0|
|3.0% in mnf, 4.5% in agr|15.4|19.4|15.1|
|4.0% in mnf, 6.0% in agr|14.2|18.1|14.0|
|5.0% in mnf, 7.5% in agr|12.7|17.3|12.3|
|6.0% in mnf, 9.0% in agr|11.3|15.9|11.0|

_Source:_ Author’s estimates.

Note that the estimates of this section report the liberalization _potential_ rather than the extent of effective liberalization by region and income level—that is, the share of trade that _could_ be considered preferential if no products were excluded and all relevant agreement provisions were immediately and effectively implemented. However, virtually all PTAs currently in force contain such exclusions and limitations, and, while a true measure of the significance of preferential flows in world trade would take account of them, they are extremely difficult to quantify. We will return to the problem of accounting for exclusions and sensitive lists as well as phase-in schedules at a later point in the paper. For now, we simply emphasize that the estimates in Table 4 represent the upper limit of the magnitude of preferential flows, while the true numbers are likely to be smaller.

27
India is the second-largest producer of sugarcane in the world (following Brazil) and accounted for approximately 19 percent of the world cane production in 2012 (FAOSTAT 2014). The sugar industry is crucial to the Indian agro economy. It serves approximately 7.5 percent of the rural population through the production, transportation and processing of sweeteners. It involves approximately 45 million cane farmers and employs a larger number of agricultural laborers (Government of India 2014). Sugar manufacturing is the second-largest agro-processing industry in India, accounting for nearly three percent of the total value of industrial production in India (FAO 2014).

The Indian government has instituted a variety of pricing, marketing, and distribution policies for sugarcane, sugar, and allied products. The primary objectives of government intervention have been to provide a support price for cane farmers, to ensure the availability of cane to sugar factories at that support price, to promote the production of sugar, and to ensure the availability of sugar to consumers at a reasonable price. The complex structure of the policies, however, raises important questions about how these regulations interact to impact the welfare of participants in the cane and sugar marketing chain. This article explores a unique cane procurement mechanism adopted by processors in one of India’s major producing states. The article shows that this mechanism has emerged as a direct consequence of these government policies and functions as a device to transfer rents from cane farmers to processors.

In India’s tropical state of Andhra Pradesh (AP), private sugar processors issue ‘permits’ to selected cane growers a few weeks before harvest. These permits allow growers to deliver a specified amount of cane during a specified period of time. Why do sugar processors in AP create uncertainty among farmers by using ex post permits instead of offering ex ante production contracts? The theoretical model demonstrates that ex post permits are more profitable than 2
Emmanuel Saez. Using elasticities to derive optimal income tax rates. _The Review of Economic Studies_ , 68(1):205–229, 2001.

# **A An example to illustrate the conditions under which Assumption 1 holds**

**Example 1.** Consider the following simple example in which _ai,_ 0 = 0 for all _i_ and there are only two permanent states, _si,t ∈{_ 0 _,_ 1 _}_ (where _si,t_ = _si,_ 0 for all _t_ ). The utility function takes the form:

In this example, only agents with an _si,t_ = 1 shock are able to supply labor. Agents with _si,t_ = 0 are unable to supply any labor, because any positive amount of labor supply results in utility of _−∞_ . In addition, given that shocks are permanent and initial wealth is set to 0, they cannot borrow or consume out of savings. Thus, an optimal policy would allow them to choose _li,t_ = 0 and provide them with some government transfers to consume.

Assume that _β_ (1 + _rt_ ) = 1 for all _t_ . Under this assumption, a time invariant tax and transfer policy is optimal and the equilibrium can be solved in a static framework. Households with _si,_ 0 = 1 choose their permanent labor supply, _li,t_ = _l_ , subject to a tax rate _τ_ , and households with _si,t_ = 0 consume the government transfer. Assuming a production function with no capital inputs ( _F_ ( _K, L_ ) = _L_ ), the transfer is given by _b_ = _μτl_ .

19
- 22 -

46. In sum, it can be concluded that large cities in the Caucasus and Central Asia region exhibit congestion diseconomies. In general, firms dislike to be located in populated areas. Despite the congestion effect, however, it is still important to improve local market accessibility in order to bring in more new firms.

**<u>Table 6. Separated GMM estimation by population density</u>**

|_POPD_ >1|2.67|_POPD_ <12.67|
|---|---|---|
|Coef.|Std.Err.|Coef.<br>Std.Err.|
|ln_N_t-1<br>0.683|<br>(0.087) ***|0.655<br>(0.059) ***|
|ln_N_t-2<br>0.177|<br>(0.038) ***|0.176<br>(0.060) ***|
|ln_N_t-3<br>0.122|<br>(0.070) *|0.152<br>(0.050) ***|
|ln_HRREGION_<br>-0.009|<br>(0.019)|0.035<br>(0.027)|
|ln_HRLOCAL_<br>-0.062|<br>(0.012) ***|-0.021<br>(0.027)|
|ln_POPD_<br>-0.051|<br>(0.020) **|-0.071<br>(0.053)|
|_t_=2014<br>-0.007|<br>(0.032)|0.083<br>(0.036) **|
|_t_=2015<br>-0.057|<br>(0.035) *|-0.061<br>(0.039)|
|_t_=2016<br>-0.043|<br>(0.030)|-0.058<br>(0.033) *|
|_t_=2017<br>0.080|<br>(0.034) **|0.073<br>(0.032) **|
|_t_=2018<br>0.148|<br>(0.040) ***|0.494<br>(0.045) ***|
|_t_=2019<br>0.118|<br>(0.053) **|0.138<br>(0.079) *|
|_t_=2020<br>0.100|<br>(0.044) **|0.195<br>(0.102) *|
|constant<br>0.400|<br>(0.093)***|0.208<br>(0.088)**|
|Obs.<br>1,374|||
|Wald stat<br>.|||
|No. of groups<br>255|||
|No. of instruments<br>212|||
|Arellano-Bond test:|||
|AR(1)<br>-5.70|***||
|AR(2)<br>0.23|||
|<br>Hansen overidentifying restriction<br> <br>|test:<br>||
|Chi2<br>163.36|||
|p-value<br>0.861|||
|Structural test (Ho: All coef. are t|he same):||
|Chi2<br>134.06|***||

Note: The dependent variable is ln _N_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

47. Another interesting structural test may be carried out to investigate potential heterogeneity across countries. Recall that our data comprises five countries. Obviously, there may be systematic differences in agglomeration process among the countries. From the growth point of
_Connecting Lagging and Leading Regions: The role of labor mobility_

# **1.  Motivation and Main Findings**

_How can policies improve the welfare of people in economically lagging regions of countries?_ The answer to this question is not straightforward, and policymakers in developed as well as developing countries struggle in making choices between the market solution of promoting out-migration and the intervention option of promoting economic growth in specific regions. In most countries policy discussions of improving welfare in lagging regions often focus on targeted interventions or “incentives” for moving production to these places. While these efforts are likely to be politically attractive, there is considerable evidence highlighting the limited effectiveness of targeted incentives. And when incentives go against the grain of market forces, they can subtract from rather than add to national economic growth.

At the same time, policymakers have often viewed internal migration – or labor mobility from lagging to leading regions, or rural to urban areas – as a consequence of failed place based policies. And in many countries, policies raise barriers to the movement of labor. Consider the household registration system (the _hukou_ system) in China, which has been a barrier to rural-urban migration. Not having a _hukou_ in urban areas means that migrants do not qualify for public education or health benefits. This can produce large interregional wage differences. Recent research indicates that removing such mobility restrictions would reallocate labor across areas, reduce wage differences, and lower income inequality (Whalley and Zhang 2007).

The World Bank’s _World Development Report 2009_ (WDR 2009) – _Reshaping Economic Geography_ (World Bank 2008) provides a new framework for territorial development, arguing that policies should focus on integrating lagging and leading regions – and not be exclusively concerned with stimulating growth in lagging regions. The WDR 2009 highlights that enabling geographic mobility of labor and improving economic connectivity between lagging and leading regions are key ingredients for countries to gain from the geographic concentration of economic activities along with convergence in living standards. Which policies can help? Policies that are “ _spatially blind_ ” in design can have the spatially sharpest effects. These include progressive income tax policies, 2
**Figure 1. GTAP 8 Database and modified protection rates in the Levant countries**

<!-- Start of picture text --> Importing country: Iraq Importing country: Jordan<br>Newly industrialized countries Sub-Saharan Africa<br>Iran, Islamic Rep. Iran, Islamic Rep. of<br>China Rest of OECD<br>Latin America Turkey<br>Japan China<br>Rest of Asia Latin America<br>Turkey Rest of Asia<br>Sub-Saharan Africa Japan<br>India India<br>European Union Newly industrialized countries<br>Russian Federation United States of America<br>United States of America Rest of Europe and FSU<br>Rest of Europe and FSU European Union<br>Rest of OECD Russian Federation<br>West Bank and Gaza Lebanon<br>Gulf Cooperation Council Yemen<br>Yemen Egypt, Arab Republic of<br>Egypt, Arab Republic of Morocco<br>Morocco Algeria<br>Jordan West Bank and Gaza<br>Syrian Arab Republic New rate Syrian Arab Republic<br>Tunisia Tunisia<br>Algeria GTAP rate Iraq New rate<br>Lebanon Gulf Cooperation Council GTAP rate<br>Libya Libya<br>0% 10% 20% 30% 40% 0% 20% 40% 60%<br>Importing country: Lebanon Importing country: Syrian Arab Republic<br>China Japan<br>Rest of Asia Newly industrialized countries<br>Japan Iran, Islamic Rep. of<br>Newly industrialized countries China<br>Iran, Islamic Rep. European Union<br>Turkey Rest of Asia<br>European Union Latin America<br>United States of America Russian Federation<br>Rest of Europe and FSU India<br>India Sub-Saharan Africa<br>Latin America Rest of OECD<br>Sub-Saharan Africa Turkey<br>Rest of OECD United States of America<br>Russian Federation Rest of Europe and FSU<br>Iraq West Bank and Gaza<br>Morocco Lebanon<br>Tunisia Jordan<br>Egypt, Arab Republic of Libya<br>Gulf Cooperation Council Morocco<br>Jordan Gulf Cooperation Council<br>Yemen Tunisia<br>New rate<br>Algeria Algeria<br>Libya GTAP rate Egypt, Arab Republic of New rate<br>West Bank and Gaza Yemen GTAP rate<br>Syrian Arab Republic Iraq<br>0% 10% 20% 30% 0% 10% 20% 30%<br>Importing country: Egypt Arab Republic Importing country: Turkey<br>Sub-Saharan Africa Yemen<br>Japan Latin America<br>China Egypt, Arab Republic of<br>Newly industrialized countries Rest of Europe and FSU<br>Rest of Asia United States of America<br>India Rest of Asia<br>Rest of OECD Japan<br>Latin America Newly industrialized countries<br>Iran, Islamic Rep. of Syrian Arab Republic<br>United States of America India<br>Russian Federation Russian Federation<br>Rest of Europe and FSU Sub-Saharan Africa<br>European Union China<br>Turkey Morocco<br>Yemen Lebanon<br>Gulf Cooperation Council Jordan<br>Lebanon Gulf Cooperation Council<br>West Bank and Gaza Rest of OECD<br>Jordan Tunisia<br>Libya Iran, Islamic Rep. of<br>Iraq European Union<br>Tunisia Iraq New rate<br>Morocco New rate Libya GTAP rate<br>Syrian Arab Republic GTAP rate Algeria<br>Algeria West Bank and Gaza<br>0% 5% 10% 15% 20% 25% 0% 20% 40% 60%<br><!-- End of picture text --> 10
# **I. Introduction**

With a per capita gross domestic product (GDP) of US$449 (2014), Madagascar is one of the five poorest countries in the world. To finance the country’s development, there must be a significant increase in the state budget. With this end in view, the tax revenue ratio, which is one of the lowest in Africa (around 10% of GDP), should increase.

In order to increase domestic revenue mobilization, Madagascar revenue collection authorities (i.e., tax and customs administrations) should notably combat fraud more effectively. The fight against tax evasion involves that revenue collection authorities should detect more to deter more. To this end, the analysis of discrepancies in international statistics may be very helpful. Zucman (2013) uses the differences between international assets and liabilities to evaluate the degree of tax evasion by households. Based on Bhagwati (1964), Raballand et al. (2012) use discrepancies in trade statistics to identify and assess customs frauds. In a perfect scenario,<sup>2</sup> any statistical anomaly—that is, any difference between the declared export value (price, volume, weight) and the declared import value for the same trade flow, is suspect. When exports are not taxed, the exporting company has no incentive to make a non-compliant declaration. In such a situation, assuming that the gap is due to the importer is therefore a reasonable assumption.

Using original customs data, this paper aims to identify, in Madagascar, some high-risk products and high-risk operators (importers and brokers). The proposed method is complementary to risk analysis methods based on compliance (see e.g. Geourjon and Laporte (2012)). A two-step procedure is adopted. Based on discrepancies in trade statistics (mirror statistics), the paper presents products or sectors in which customs fraud is deemed to be significant. A quantification exercise of customs losses is provided. Then, through the use of highly disaggregated customs data, high-risk operators (importers and brokers) are identified.

Despite the fact the methodology is straightforward, to our knowledge there is hardly any paper using this approach (at the importer/broker level) due to the fact that it is usually difficult to get access to such information. The paper demonstrates how useful such detailed customs data can be and should convince the Head of Customs and/or Ministers of Finance to give access to them to researchers, since they can be used for an operational use. For researchers, it enables to identify some fraud techniques and collusive practices.

The use of export data provided by exporting countries allows us to compute for each sector/product the mirror gap. The mirror gap is, for each product/sector, defined as the difference between export _X_ to Madagascar reported by the exporting country and import _M_ from the exporting country as reported by Madagascar customs. Export data are downloaded

> 2 This means that the same nomenclature is used between the exporting and the importing country, both declarations are made in the same currency, and the same incoterm is used.

2