rafmacalaba/datause-encoder-data huggingface dataset:
well, moreover, with other developing region trade blocs, such as Mercosur. For example, prior to the formal launch of Mercosur in 1991, its share of intra-regional trade stood at 14 percent.<sup>10</sup> Similarly, in ASEAN, a relatively high level of regional trade among member countries at the launching of their regional trading arrangements—16 percent—provided the necessary impetus for further regional integration.

If SAFTA is launched from low level of intra-regional trade, lower than any successful regional grouping, such a low base may be evidence that the South Asian countries are not, in fact, “natural trading partners.” The failure to meet this key conceptual criterion for beneficial integration may adversely affect the realization of a successful FTA in the region.

The above estimates of intra-regional trade are based on official trade data, which we know understates the extent of intra-regional trade. Could the above conclusion that the South Asian countries are not natural trading partners be altered if we take into account available “guesstimates” of informal trade in the region? For example, if the reported one billion dollars’ worth of informal trade between India and Pakistan cited by Taneja (1999) is incorporated, it would boost Pakistan’s share of intra-regional trade to around 14 percent of its total trade (referring to 1998 survey results), although it would still fall below the level of trade it undertakes with the United States (19 percent) and the European Union (22 percent) in the corresponding year. While informal exports to India from Bangladesh, Nepal, and Sri Lanka are relatively high, they comprise a sizable share of third-country goods that originate beyond regional sources. However, these national guesstimates of informal trade based on a sample survey of key locations may have high error margins and, hence, may not be fully reliable. Even with a less conservative

> 10 Mercosur’s initial members were Argentina, Brazil, Paraguay and Uruguay. Chile and Bolivia became members in 1996 in 1997, respectively. Immediately following the establishment of a common external tariff in Mercosur in 1995, there was a sharp rise in intra-Mercosur trade, to 23 percent in 1998, almost four times as much as the share of intra-regional trade in South Asia.

10
# **EXTERNAL AND INTERNAL BALANCE IN THE CFA ZONES**

<!-- Start of picture text --> CHART 2: AVERAGE INFLATION IN THE CFA CHART 3: REAL GDP GROWTH IN CFA<br>ZONES: 1998-2003 (%) ZONES: 1998-2003 (%)<br>5 6<br>5<br>4<br>4<br>3<br>3<br>2 2<br>1 1<br>0<br>0<br>1998 1999 2000 2001 2002 2003<br>1998 1999 2000 2001 2002 2003<br>CEMAC UEMOA<br>CEMAC UEMOA<br>CHART 4: FISCAL BALANCE (INCLUDING CHART 5: REAL GDP GROWTH IN CEMAC:<br>GRANTS): 1994-2002 (% OF GDP) 1998-2003 (%)<br>6 15.0<br>4 10.0<br>2<br>5.0<br>0<br>0.0<br>-2 1994 1995 1996 1997 1998 1999 2000 2 00 1 2 00 2<br>1998 1999 2000 2001 2002 2003<br>-5.0<br>-4<br>-6 -10.0<br>-8<br>Cameroon CAR Chad<br>CEMAC UEMOA Congo, Rep Gabon<br>CHART 6: TERMS OF TRADE MOVEMENTS IN CHART 7: CUURENT ACCOUNT DEFICIT<br>CFA ZONES: 1994-2002 (%) (EXCLUDING GRANTS) IN CFA ZONE 1998-<br>2003 (% OF GDP)<br>30<br>12<br>20<br>10<br>8<br>10<br>6<br>0 4<br>1994 1995 1996 1997 1998 1 99 9 2000 2001 2002 2<br>-10<br>0<br>-20 1998 1999 2000 2001 2002 2003<br>CEMAC UEMOA CEMAC UEMOA<br><!-- End of picture text --> Source: IMF, World Bank databases 11
;Economic Parameters of Deforestation 42 and k1 are constants. Several simulations were run for hypothetical parameter values and variations from the base case parameters that represent generic unanticpated shocks. The parameter values used in the simulations are listed in the Mathematica program in the appendix. The parameter values were chosen to represent a broad mix of relative profitability of logging unmanaged forests, managed forests and agriculture. Other parameter values were tried and yidded the same qualitative results as presented below. The chosen parameter values, however, yielded continuous land use patterns that make the main results easier to convey graphically. Note that if land classes include not only transportation costs but also other locational parameters, land classes adjacent in the diagrams need not be geographically adjacent.

The equilibrium timber price path and resulting profit maximizing land use in the base case scenario are shown in Figure 5. Note that in this and all other simulations, the resulting equilibrium price path for timber, shows the characteristic of declining rates of increase, as it was assumed for the analysis in section 2 (p, > 0, p, < 0). Intensity of logging and managing forests increases in the current timber price and decreases in site specific production costs. Hence, a scenario with a higher price path implies higher intensity in logging and managing forest at the same location, compared to the base case, and vice versa.

The following paragraphs describe the effect that various modifications in the economic parameters have on the land use patterns compared to the base case introduced above. For simplicity, all simulations are based on a modification of the initial model parameters. Altematively, an unanticipated shock could be imposed at a later time. The expected qualitative outcome would be the same. The focus of the following discussion is on the differences in land use patterns over time between the base case and the modified scenarios. These differences are the effects that changes in the economic paraneters at time zero have on land use, and are, thus, the focus of interest
2 This paper examines the regional labour market consequences of the rapid economic growth and the new economic strategy. It focuses on the period of the deregulation when there appears to have been a significant tightening of national labour markets, after real wages stagnated for much of the 1980s, (Manning, 1994; Rosner, 1995). The paper also examines how more recent developments differed from labour market developemnts during the oil boom and subsequent period of slower economic growth.

Two alternative hypotheses can be advanced regarding the relationship between interregional labour market trends, on the one hand, and patterns of economic growth associated with deregulation, on the other. The first, perhaps more plausible scenario is that rapid labour market transition has been primarily experienced on Java - especially in the major areas of non-oil export growth around Jakarta in the west and Surabaya in the east. We would expect the creation of new jobs in manufacturing and associated service sectors to be concentrated in these regions. Both wage growth and employment growth in non-agricultural sectors could be anticipated to have lagged in the Outer Island regions. Many of these - especially in Eastern Indonesia - have not played a major role in the acceleration in growth of manufacturing exports.

A second possibility is that the rapid growth of non-oil exports on Java and in selected Outer Island provinces has provided an impetus for labour market change throughout the archipelago. Inter-regional migration and remittances from workers in the centres of manufacturing growth might have been sufficiently large to stimulate employment growth in non-agricultural jobs and tightening labour markets in most regions outside Java as well. Combined with continued government support and expansion of the dominant agricultural sector, these impulses may have been sufficient to generate a broad-based shift in the balance of labour supply and demand in most regions in Indonesia.

_Data Sources._ The analysis of economic structure is based on national accounts and regional accounts data collected by the Central Bureau of Statistics in Jakarta and in the provinces. The regional data are less reliable in earlier years and even in later years only symptomatic of broad trends.<sup>4</sup> Employment and wage data are taken principally from the National Labour Force Surveys (SAKERNAS) conducted irregularly before 1986 and then on an annual basis since then. Comparisons of trends for the period 1971-1990 4 Central Bureau of Statistics staff suggest that there is considerable pressure for the regions to report data which are broadly consistent with national trends. The regional accounts data are likely to particularly unreliable for smaller sectors (personal communication, Jakarta, April 1996).
Table 5. Share of Transfers in Total Household Income

||F<br>|ull samp<br>|le<br>|(under<br>|Poor<br> US$1.2<br>|<br>5a day)<br>|(under<br>|Poor<br> US$2.50<br>|<br>a day)<br>|
|---|---|---|---|---|---|---|---|---|---|
||Initial<br>period|Final<br>period|Annual<br>change|Initial<br>period|Final<br>period|Annual<br>change|Initial<br>period|Final<br>period|Annual<br>change|
|Argentina,2000–10|4.7|6.5|3%|12.7|48.9|14%|8.7|32.9|14%|
|Brazil,2001–09|1.2|5.8|21%|4.9|47.0|33%|2.8|24.8|31%|
|Chile,2000–09||6.4|||42.3|||27.7||
|Colombia,2002–10|4.7|9.2|9%|9.2|30.8|16%|6.8|20.7|15%|
|Costa Rica,2000–08||4.5|||28.6|||19.8||
|Ecuador,2003–10|9.0|10.6|2%|24.8|35.3|5%|15.1|24.4|7%|
|Honduras,1999–2009|9.1|7.1|-3%|11.9|3.7|-11%|10.1|2.8|-12%|
|Panama,2001–09|10.1|12.3|2%|26.1|41.2|6%|21.8|32.3|5%|
|Paraguay,1999–2010|8.4|9.0|1%|8.3|16.9|7%|10.4|16.3|4%|
|Bangladesh,2000–10|2.7|3.5|2%|1.8|2.0|1%|2.5|3.1|2%|
|Ghana,1998–2005|5.1|5.1|0%|3.6|2.8|-3%|3.8|3.8|0%|
|Moldova,2001–10|4.5|24.0|20%|7.2|22.5|14%|6.2|19.0|13%|
|Nepal,1996–2003|3.0|4.9|7%|2.7|3.7|4%|2.9|4.7|7%|
|Peru,2004–10|7.6|5.4|-5%|4.2|10.4|16%|3.9|8.5|14%|
|Romania,2001–09|8.1|8.7|1%|29.7|||19.4|33.8|7%|
|Thailand,2000–09|9.9|10.7|1%|18.6|19.1|0%|11.7|14.3|2%|

_<mark>Source</mark>_ <mark>: Authors' calculations with data from SEDLAC (CEDLAS and the World Bank), RIGA, and National Household Surveys.</mark> 29
# WPS3532

**Remittances, Household Expenditure and Investment in Guatemala**

**Richard H. Adams, Jr.**

**Development Research Group (DECRG)**

**MSN MC3-303 World Bank**

**1818 H Street, NW Washington, DC  20433 Phone:  202-473-9037**

**Email:** **<u>radams@worldbank.org</u>**

**<u>Draft:</u> For comments only**

World Bank Policy Research Working Paper 3532, March 2005 _The Policy Research Working Paper Series disseminates the findings of work in progress to_
# **References**

- Acemoglu, Daron, Ufuk Akcigit, and William Kerr, “Networks and the Macroeconomy: An Empirical Exploration”, MIT Working Paper (2015).

- Ahsan, Ahmed, and Carmen Pages, “Are All Labor Regulations Equal? Assessing The Effects Of Job Security, Labor Dispute And Contract Labor Laws In India”, World Bank Policy Research Working Paper 4259 (2007).

- Alfaro, Laura, and Anusha Chari, “Deregulation, Misallocation, and Size: Evidence from India”, HBS Working Paper (2014).

-

- Amirapu, Amrit, and Arvind Subramanian, “Manufacturing Futures”, http://www.business <u>standard.com/article/opinion/amrit-amirapu-arvind-subramanian-manufacturing-futures114050901478_1.html (accessed 6/10/2014).</u>

- Anand, Rahul, Volodmyr Tulin, and Naresh Kumar, “India: Defining and Explaining Inclusive Growth and Poverty Reduction,” IMF Working Paper 14/63 (2014).

- Besley, Timothy, and Robin Burgess, “Can Labor Regulation Hinder Economic Performance? Evidence from India,” _Quarterly Journal of Economics_ (2004), 91-134.

- Bollard, Albert, Peter Klenow, and Gunjan Sharma, “India’s Mysterious Manufacturing Miracle”, _Review of Economic Dynamics_ 16:1 (2013), 59-85.

- De Loecker, Jan, “Product Differentiation, Multi-Product Firms and Estimating the Impact of Trade Liberalization on Productivity”, _Econometrica_ 79:5 (2011), 1407-1451.

- Deichmann, Uwe, Somik Lall, Stephen Redding, and Anthony Venables, “Industrial Location in Developing Countries,” _World Bank Research Observer_ 23:2 (2008), 219–246.

Duranton, Gilles, “Growth through Cities in Developing Countries,” Working Paper (2013).

- Duranton, Gilles, and Diego Puga, “Micro-Foundations of Urban Agglomeration Economies,” in _Handbook of Regional and Urban Economics,_ Volume 4, eds. J. Vernon Henderson and Jacques François Thisse (Amsterdam: North-Holland, 2004), 2063–2117.

Economist, “India’s Economy: A Himalayan Challenge,” October 11 2007.

Economist, “Small Business in Mexico: The Peter Pan Syndrome,” May 17 2014.

- Fernandes, Ana and Gunjam Sharma, “Together We Stand? Agglomeration in Indian Manufacturing,” World Bank Policy Research Working Paper 6062 (2012).

- Foster, Lucia, John Haltiwanger, and Chad Syverson, “Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?”, _American Economic Review_ 98:1 (2008), 394-425.

- Ghani, Ejaz, Arti Goswami, and William Kerr, “Is India's Manufacturing Sector Moving Away From Cities?” National Bureau of Economic Research Working Paper 17992 (2012).

- Ghani, Ejaz, Arti Goswami, and William Kerr, “Highway to Success: The Impact of the Golden Quadrilateral Project for the Location and Performance of Indian Manufacturing,” _Economic Journal_ (2014), forthcoming.

22
And while Bulgaria has made great stride in improving its business environment, it is still not at the OECD average. Particular areas in need of further attention are: • **Further reducing administrative burdens, overall and sector specific. In general,** implementation of the new business registry would further streamline and simplify business registration. At the same time, easing off the licensing and registration regimes for retail distribution of food and clothing would also help reduce burdens on businesses.

• **Improve communication of rules and procedures to the affected parties** by extending the program for optimization of licensing and permits system to municipalities and revisiting the effectiveness of the program at central government level.

Finally, it should be noted once more that the PMR methodology does not capture implementation or common practices associated with the rules and regulations it measures. Bulgaria has made impressive strides towards the enactment of rules and regulations that are less restrictive of competition. However, for this progress to be reflected in an improved business environment, government policy in years to come will need to be directed towards ensuring effective implementation and enforcement.

29
**Table 4: Cost-effectiveness of improving student attendance – Percentage of draws each program is in the bottom and top quartile of cost-effectiveness, and is as cost-effective as Girls’ merit scholarships, across 100,000 draws**

|**Program**|**Additional years of**<br>**student attendance**<br>**per $100**|**Standard**<br>**deviation**|**Percentage of**<br>**draws in bottom**<br>**quartile of cost-**<br>**effectiveness**|**Percentage of**<br>**draws in top**<br>**quartile of cost-**<br>**effectiveness**|**Percentage of draws as**<br>**cost-effective as**<br>**scholarships**|
|---|---|---|---|---|---|
|Providing earnings information, Madagascar|20.60|19.48|4%|93%|96%|
|Deworming, Kenya|12.50|7.40|0%|98%|100%|
|Iron & Vitamin A, India|2.73|2.63|5%|7%|95%|
|Village-based schools, Afghanistan|1.51|0.95|1%|1%|99%|
|Uniforms, Kenya|0.71|0.38|0%|0%|99%|
|Fellowship schools, Pakistan|0.34|0.15|0%|0%|98%|
|Information, Dominican Republic|0.24|0.16|7%|0%|79%|
|Girls' merit scholarships, Kenya|0.16|0.15|26%|0%|49%|
|Minimum conditional cash transfers, Malawi|0.09|0.07|70%|0%|5%|
|Average conditional cash transfers, Malawi|0.07|0.05|85%|0%|0%|
|Unconditional cash transfers, Malawi|0.02|0.02|100%|0%|0%|

Source: Data adapted from J-PAL (2014). Calculations by authors.

Notes: Using the mean and standard deviation of the impact estimate for each program and assuming a normal distribution, we randomly select a simulated point estimate for each program, re-doing this 100,000 times. We then calculate the proportion of times, out of the 100,00 draws, that each program falls in the top quartile, in the bottom quartile, and the proportion of times it is more cost-effective than Girls'merit scholarships (i.e., produces at least an additional 0.16 years of student attendance per $100), which are the figures <u>presented in this table.</u> 20
not threaten jobs for people like the respondent, limit personal freedoms, and hurt the economy is a strong predictor.” Similar to later work by Dunlap and McCright, the authors find that Democrats are more likely than Republicans to support government efforts to reduce emissions. The authors conclude that so called “cognitive” explanations of support for reducing greenhouse gas emissions are more powerful than economic or partisan heuristic ones. “People want to reduce emissions if they understand the causes of climate change, if they perceive substantial risks from climate change, if average surface temperatures increase, and if they think climate change mitigation policies will not cost them their jobs.” Although not inherently so, the assumptions of this article place it in the information deficit camp of why people fail to respond, which is criticized by Norgaard and Buckeley.

Sterman, John and Linda Booth Sweeney 2007 “Understanding public complacency about climate change: adults’ mental models of climate change violate conservation of matter” <u>Climatic Change</u> (2007) 80:213–238.

Sterman and Sweeney 2007 note the paradox that most Americans believe climate change poses serious risks but also believe inaccurately that emissions reductions can be deferred until there is greater certainty surrounding climate science. “Such wait-and-see policies erroneously presume climate change can be reversed quickly should harm become evident, underestimating substantial delays in the climate’s response to anthropogenic forcing.” The conducted experimental work with graduate students at MIT which found that even these highly educated adults had “widespread misunderstanding” of how climate change worked, misunderstandings that caused them to systematically underestimate the need for immediate action to stabilize the climate. In particular, research subjects conceived of the global climate as a system which was analagous to a bathtub filling over, once the input stops the tub level can be lowered right away. However research shows once in the atmosphere, climate gases will continue to rise even if emissions fall. The authors write that, “these beliefs – analogous to arguing a bathtub filled faster than it drains will never overflow – support waitand-see policies but violate conservation of matter. Low public support for mitigation policies may arise from misconceptions of climate dynamics rather than high discount rates or uncertainty about the impact of climate change.” Furthermore, the authors link this misunderstanding to the failure of response by U.S. policymakers.

# **Climate Skepticism**

Armitage, Kevin 2005. State of Denial: The United States and the Politics of Global Warming <u>Globalizations 2(3): 417–427.</u> Similar to work by McCright and Dunlap, as well as Boykoff, Armitage addresses the unique ways that climate science has been politicized in the United States, and 53
<!-- Start of picture text --> FDH FDH<br>0 .2 .4 .6 .8 1 .4 .6 .8 1<br>score score<br>Unorthogonalized Public Expenditure Orthogonalized Public Expenditure<br>8 15<br>6<br>10<br>4<br>Density Density<br>5<br>2<br>0 0<br><!-- End of picture text -->

**Figure 5. Density of Efficiency Scores – Gross Primary School Enrollment**

This paper uses nine indicators of education output and four indicators of health output.<sup>6</sup> The education indicators are: primary school enrollment (gross and net), secondary school enrollment (gross and net), literacy of youth, average years of school, first level complete, second level complete, and learning scores. Though the ideal educational output indicator are comparable learning scores, international assessments are based on samples mostly composed of developed nations, limiting the applicability to the present paper. However, Crouch and Fasih (2004) recently combined several international assessments to obtain a larger sample of comparable results.<sup>7</sup> Unfortunately they only do it for one period. The correlation between the learning scores and other output variables is high (.81 with net secondary school enrollment and .76 with average years of school), as shown in Figure 6<sup>8</sup> . The health output indicators are: life expectancy at birth, immunization (DPT<sup>9</sup> and measles), and the disability-adjusted life expectancy (DALE).

The cross-country comparisons with this set of indicators assume some form of data homogeneity, which might be problematic given the diversity of counties in the sample considered. Even for a more homogeneous group of countries, such as the OECD, there is call for caution when comparing expenditure levels in member countries (Jounard,

> 6 The data sources are: the World Bank World Development Indicators (WDI), Barro-Lee database, and Crouch and Fasih (2004)  the World Health Organization (Mathers _et al,_ 2000).A complete list of variables and data sources can be found in Table B.6 of Appendix B.

> 7 Crouch and Fasih (2004) consider several international tests of learning achievement in math, science and literacy applied at different levels of the school system.  The tests are the following: TIMSS (Third International Mathematics and Science Survey),  PIRLS (Progress in International Literacy Study), PISA (program for International Student assessment), Reading Literacy Study,  LLECE (Laboratorio  Since the tests have different samples, they converted all test scores through iterative comparisons to a single numeraire.Latinoamericano de Evaluaciond ela Calidad de la Educacion, SACMEQ (Southern Africa Consortium for Monitoring of Education Quality), MLA (Monitoring Learning Achievement)

> 8 The correlation coefficients and Figure 6 exclude developed nations for the Crouch and Fasih (2004) sample.

> 9 DPT is Diphtheria-Pertussis and Tetanus

10
|**Table 7**|**Cont**|**inued**||
|---|---|---|---|
|Weight -<br>foreign<br>index|Weight -<br>domestic<br>index|Score|Restriction|
||||**Lending funds by banks**|
|0.1425|0.1425|1.00<br>0.75<br>0.5<br>0.25|Banks are not permitted to lend to domestic clients<br>Banks are restricted to specified lending size or lending to<br>Government projects<br>Banks are restricted in providing certain lending services such as<br>leasing, credit cards and consumer finance<br>Banks are directed to lend to certain sectors such as housing and small<br>business|
|||0.00|Banks can lend to any source with only prudential restrictions|
|0.0950|0.0950|1.00<br>0.50|**Other business of banks - insurance and securities services**<br>Banks can only provide banking services<br>Banks can provide banking services plus one other line of business -<br>insurance or securities services|
|||0.00|Banks have no restrictions on conducting other lines of business|
|0.0475|0.0475|1.00<br>0.75|**Expanding operations - street branches, offices and ATMs**<br>One banking outlet with no new banking outlets permitted<br>Number of bank outlets is limited in number and location|
|||0.25|Expansion of banking outlets is subject to non-prudential regulatory<br>approval|
|||0.00|No restrictions on banks expanding operations|
|0.0095|0|1.00<br>0.75|**Movement of people - Temporary**<br>No temporary entry of executives, senior managers and/or specialists<br>Temporary entry of executives, senior managers and/or specialists up<br>to 30 days|
|||0.50|Temporary entry of executives, senior managers and/or specialists up<br>to 60 days|
|||0.25|Temporary entry of executives, senior managers and/or specialists up<br>to 90 days|
|||0.00|Temporary entry of executives, senior managers and/or specialists<br>over 90 days|
|0*|0|1.00|**Movement of people - Board of Directors**<br>Board cannot comprise of foreigners|
|||0.00|No restrictions on the composition of the board of directors<br>The score is inversely related to the percentage of the Board which<br>can comprise of foreigners. For example, if 20% of a BOD can<br>comprise of foreigners they would receive a score of 0.80.|
|0.9310|0.8075||Total|

* Weight was 0.019 in McGuire and Schuele (2000). _Source:_ Dee (2005), based on McGuire and Schuele (2000).

37