rafmacalaba/datause-encoder-data huggingface dataset:
GEL 30,000 threshold. The results in Table 11a, however, suggest that firms with declared revenue just below the GEL 30,000 in 2009 reported between GEL 2,000 and 3,750 less on their 2010 tax return. These amounts correspond to 6 and 11 percent of average declared 2010 revenue just below the threshold (GEL 30,000). We thus find evidence that the reform led to less tax compliance by previously registered firms in 2010. However, this effect dissipated by 2011 and thus lasted for only one year.

**Table 11: Basic RDD to estimate the effect of the micro firm tax regime around GEL 30,000 on tax compliance by previously registered firms**

||(1)|(2)|(3)|
|---|---|---|---|
|Sample with revenue between|25,000 and 35,000|20,000 and 40,000|10,000 and 50,000|
|Panel A -|Dependent variable: D<br>|eclared revenue in 2010||
|2009 Revenue < 30,000 dummy|-3,750***|-1,289|-1,999**|
||(1,371)|(978)|(955)|
|2009 Revenue|0.268|0.771***|0.780|
||(0.242)|(0.082)|(1.027)|
|Constant|34,646***|17,843***|11,534*|
||(7,849)|(2,870)|(5,931)|
|R-squared|0.016|0.062|0.209|
|Observations(firms)|3,071|6,460|18,500|
|Panel B -<br>|Dependent variable: D|eclared revenue in 2011||
|2009 Revenue < 30,000 dummy|-634|505|-778|
||(2,012)|(1,401)|(1,362)|
|2009 Revenue|0.671*|0.856***|1.854|
||(0.350)|(0.118)|(1.461)|
|Constant|25,233**|18,460***|9,925|
||(11,337)|(4,129)|(8,450)|
|R-squared|0.007|0.033|0.111|
|Observations(firms)|2,619|5,477|13,290|
|Panel C -<br>|Dependent variable: D<br>|eclared revenue in 2012<br>||
|2009 Revenue < 30,000 dummy|-90|102|-1,281|
||(2,495)|(1,745)|(1,696)|
|2009 Revenue|0.800*|0.806***|2.991|
||(0.435)|(0.147)|(1.793)|
|Constant|22,182*|22,062***|6,751|
||(14,134)|(5,153)|(10,370)|
|R-squared|0.006|0.022|0.073|
|Observations(firms)|2,405|5,022|12,350|

Note: The regression in Column 3 additionally controls for a quadratic function of revenue (coefficients not reported to save space). Robust standard errors in parentheses. Statistical significance levels: *10 percent, **5 percent, *** 1 percent.

30
consumption and the need for infrastructure services is limited. When the production moves to manufacturing, economies of scale become larger, and producers will mostly produce for other people and no longer for themselves. As market range expands, good infrastructure will enable entrepreneurs to get their goods and services to market in a secure and timely manner and facilitate the movement of workers to the most suitable jobs (Lin 2011c). In addition, in the presence of global climate change and increasingly intense natural disasters, adequate infrastructure can support sustainable development, minimize vulnerability to natural disasters, and promote reliance on public transportation.

33. Improving infrastructure affects a country’s output through a variety of channels. In the short run, infrastructure investment can create jobs and growth in the local economy. In the longer run, it enhances productivity, increases private capital formation<sup>21</sup> (by raising expected returns on private investments as the marginal productivity of inputs increases or transaction costs decline) and facilitates the exploitation of agglomeration economies. Infrastructure may also have a significant effect on health and education outcomes (Agenor and Moreno-Dodson 2006). The construction of all-weather roads in Morocco increased school attendance by girls from 28 percent to 68 percent between 1985 and 1995, as the improvements in roads significantly freed women’s time.<sup>22</sup> They also improved health indicators as the number of visits to hospitals and health centers doubled during this time period (World Bank 1996).

34. Empirical cross-country studies confirm that infrastructure investment has a large effect on growth in developing countries. Calderón and Servén (2010a) estimate that as a result of infrastructure investments, annual growth among developing countries increased on average by 1.6 percent between the 1991-95 and 2001-05 periods. This effect was particularly large in South Asia, where it reached 2.7 percent per year. The authors shows that if Sub-Saharan African economies would cut the gap between their level of infrastructure and the average level of infrastructure in Pakistan or India by 50 percent, Central African low-income countries would

> 21 Using data from Uganda, Reinikka and Svensson (1999), for example, find that unreliable provision of electricity is a significant deterrent to investment.

> 22 Before road improvements, women had to spend an average of two hours per day collecting and carrying wood for fuel. Butane gas, used extensively in urban areas, did not reach the rural areas due to the high transport and distribution costs. Initially, a bottle of butane cost 20 Dh. Following improvement of the road, the price dropped considerably, to as low as 11 Dh, making it affordable for many families (World Bank 1996).

14
**Table 5. Impacts on Income per Capita (Total and by Components)**

||_Total_<br>_Income Per_<br>_Capita_|_Labor_<br>_Income Per_<br>_Capita_|_Labor_<br>_income from_<br>_salary work_|_Non-wage_<br>_income_|_Non-_<br>_Labor_<br>_Income_<br>_Per Capita_|_Private_<br>_Transfers_<br>_Per Capita_|_Public_<br>_Transfers_<br>_Per Capita_|_Other_<br>_non-labor_<br>_income_|
|---|---|---|---|---|---|---|---|---|
|**Measure of Shock**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||**Panel A:**|**Total**|||||
|t * (rainfall z-score> 2)|-44.4*|-52.4**|-33.6**|-18.8|12.4|2.9|0.6|-2.9|
||[26. 2]|[21.6]|[14.4]|[12.9]|[9.0]|[6.0]|[0.9]|[11.4]|
|Baseline Mean/Median|566.6|391.7|198.4|46.3|20.50|69.9|10.8|78.3|
||||**Panel B:**<br>|**Urban**<br>|||||
|t * (rainfall z-score> 2)|-57.4|-51.9*|-35.7**|-16.2|28.4**|7.2|1.1|-9.6|
||[40.7]|[30.4]|[18.1]|[23.1]|[12.6]|[9.5]|[1.0]|[23.6]|
|Baseline Mean/Median|781.7|556.3|326.2|35.4|15.740|70.8|7.2|142.7|
||||**Panel C:**|**Rural**|||||
|t * (rainfall z-score> 2)|-13.7|-22.1|-12.5|-9.6|6.8|2.3|-0.2|1.5|
||[30.0]|[23.4]|[16.0]|[14.7]|[10.4]|[7.6]|[1.3]|[6.9]|
|Baseline Mean/Median|438.6|289|126.5|50.1|23.4|69.2|13.4|31.8|

Observations: 26,163 Total; 10,905 Urban; 15,258 Rural.

_Notes_ : Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. All quantities are monthly p.c in Quetzales of 2006. All median baseline values are presented in all cases except for private transfers, public transfers and other non-labor income that the mean baseline value is presented. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy.***p<0.01,**p<0.05,*p<0.1. _Source:_ calculations by the authors based on data from Encovi (INE) and Insivumeh.

28
3

# TABLE **1**

# INDONESIA: MANUFACTURED EXPORTS CLASSIEFIED BY RESOURCE INTENSITY

|SITC||1985|1990|1994|Growth<br>**(%pa)**|Share<br> <br>_(%/)_<br>1985|Share<br>P/)<br>1990|Share<br>_(/V)_<br>1994|
|---|---|---|---|---|---|---|---|---|
|5-8<br> <br>|MANUFACTURING|2,044|<br>9,041|20,686|<br>29|100.0|<br>100.0|<br>100.0|
|(minus<br>68)|||||||||
|<br>|NATURAL<br>RESOURCE<br>INTENSIVE|993|3,311|<br>5,073|<br>20|_48.6_|_36.6_|<br>_24.5_|
|63|Wood and cork manuf.|952|3,065|<br>4,833|<br>20|46.6|33.9|<br>23.4|
|66-(664-<br>666)|Non metalik<br>manuf,<br>minerals|.27|147|137|<br>20|1.3|1.6|<br>0.7|
||UNSKILLED<br>LABOR<br>INTENSIVE|649|4,092|<br>9,291|<br>34|31.8|45.3|<br>44.9|
|65|Textiles,<br>yam, fabrc and prod.|240|1,241|<br>2,498|<br>30|11.7|<br>13.7|<br>12.1|
|84|<br>Garments|339|<br>1,646|<br> <br> <br>3,206|<br> <br>28|16.6|18.2|<br>15.5|
|85|Footwear|8|570|1,888|<br>84|0.4|6.3|9.1|
||PHYSICAL<br>CAPITAL<br>INTENSIVE|102|405|1.006|<br>29|5.0|4.5|4.9|
|51<br>|Organicchemicals<br>|18<br>|84|335|<br>38|0.9|0.9|<br>1.6|
|67|Iron and Steel|34|237|309|<br>28|1.7|2.6|<br>1.5|
||HUMAN<br>CAPITAL<br>INTENSIVE<br>|<br>99|666|2,454|<br>43|4.8|7.4|11.9|
|51|Organic<br>chemicals|18|84|135|<br>25|0.9|0.9|<br>0.7|
|64|<br>Paper,<br>paperboard<br>and manuf.<br>|21|154|594|<br>45|1.0|1.7|<br>2.9|
||thereof||||||||
|69|Manuf.<br>of metals,<br>nes|1|112|328|<br>95|0.0|1.2|<br>1.6|
|78|<br>Motorvehicles<br>and Parts|1|39|314|<br>92|0.0|0.4|1.5|
|781-783|Motorvehides|0|8|18|<br>58|0.0|0.1|<br>0.1|
|784|Motor vehicles<br>and components|<br>0|6|34|<br>79|0.0|0.1|<br>0.2|
|785|Two wheel vehicle|0|25|243|<br>103|0.0|0.3|<br>1.2|
||TECHNOLOGY<br>INTENSIVE|201|567|2,863|<br>34|9.9|6.3|<br>13.8|
|56|Fertilizers|15|18|<br>35|<br> <br>10|0.8|0.2|<br>0.2|
|752|Automatic<br>data proc mach and<br>|0|0|96|154|0.0|0.0|0.5|
||units||||||||
|759|Parts, nes of data proc. and<br>|0|0|136|<br>133|<br>0.0|0.0|<br>0.7|
||office||||||||
|76|Telecommunications,<br>Video and<br>|8|105|1,472|<br>79|0.4|1.2|<br>7.1|
||Audio||||||||
|761|Picture<br>Receiver<br>(include.<br>radio)|<br>0|6|183|<br>111|<br>0.0|0.1|<br>0.9|
|762|<br> <br>Sound<br>Receiver|<br>0|37|269|<br>136|<br> <br>0.0|<br>0.4|<br><br> <br>1.3|
|763|Sound<br>and picture<br>recorder|0|2|620|<br>-|0.0|0.0|<br>3.0|
|764|<br>Telecomm,<br>eq. and parts|7|60|400|<br>56|0.4|0.7|<br>1.9|
|77-775|<br> <br>Electrical<br>machine<br>and app<br> <br>|73|97|579|<br>26|3.6|1.1|<br>2.8|
||(minus<br>hhold app., elec. and non.||||||||
||<br>elec. eqpt)||||||||

_Source: Central Bureau of Statistics_
number of employees in the industry, to the extent that employees have sector-specific skills and if the median voter affects trade policy decisions (e.g., Mayer, 1984).

Additional industry characteristics may play a role in the use of antidumping once we also take into consideration the use of the policy across countries. For example, industries with historical experience of having their exports targeted by foreign antidumping may be more likely to pursue the policy themselves. One explanation is familiarity – the learning experience of having defended exports in foreign antidumping investigations may affect the likelihood of pursuing antidumping as an offensive weapon at home. A second explanation is that industries targeted by foreign antidumping may be more likely to use it as a means of retaliation (tit for tat) or to discourage future foreign use of antidumping.

The research literature on US and EU trade policy has examined whether a number of differentlyconstructed variables capturing these elements of the political-economic process help explain the pattern of antidumping use.<sup>10</sup> Given that both the industry-level data and data on the political process are of much higher quality for the US and EU than for the developing countries in our sample, we are not able to replicate with precision such measures in our empirical analysis. Nevertheless, we do take advantage of a reasonably disaggregated cross-country panel of industry-level data to construct measures that we use to assess whether many of the same political-economic considerations also affect the use of antidumping in developing countries. We describe this variable construction, data, and our formal econometric approach in the next section.

# **3 Empirical Approach and Data**

What are the determinants of industry pursuit and receipt of antidumping protection in the new user developing countries? Based on data requirements and limitations discussed in more detail below, our unit of observation for the econometric analysis is a 3-digit ISIC industry in one of nine developing countries (table 2). Figure 1 illustrates how we model the formal empirical analysis.

In the first stage of the model, each 3-digit ISIC industry decides whether to pursue an antidumping investigation. In the investigation, the industry would present evidence of dumping and injury to its national authority, and it would request that its government impose a trade restriction to shield it from import competition. In the second stage, the national government considers the evidence and decides not only whether the 3-digit ISIC industry has been injured from dumped imports, but it also determines how much protection to grant the industry. We measure the government’s second stage

> 10 A number of papers have examined different political-economic features of the government decision to grant antidumping protection. Examples for the US process include Hansen (1990) and Hansen and Prusa (1997). See Messerlin and Reed (1995) and Eymann and Schuknecht (1996) for research on the EU process, and see Francois

7
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# **Chart 5B: Exports to the U.S. of other private services as a share of source country GDP, 1990-2006**

<!-- Start of picture text --> 1990 1995 2000 2005<br>year<br>IND CHN BRA MYS KOR MEX<br>.006<br>.004<br>.002<br>0<br>Exports of other private services to the US (share of GDP)<br><!-- End of picture text --> _Source: U.S. Bureau of Economic Analysis._ See Note to Chart 5A above.

21
|**Table 15. Impact of WTO Accession on O**|**utput by Sect**<br>_change from_<br>National|**or and Fe**<br>_base year, (_|**deral Okr**<br>_%)_|**ug**|||||
|---|---|---|---|---|---|---|---|---|
|**Sector**|Average|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|Railway transportation|1.7|2.4|1.4|1.1|1.9|3.3|0.2|0.6|
|Truck transportation|7.6|11.4|8.0|7.2|11.4|1.5|5.4|11.6|
|Pipelines transportation|-3.7|-4.8|-4.2|-3.6|-3.7|-2.7|-5.1|-5.0|
|Maritime transportation|-5.5|-4.4|-5.0|-6.8|-3.6|-4.2|-11.6|-3.4|
|Air transportation|-2.0|-0.6|-2.8|-1.9|1.9|-3.4|-5.6|0.8|
|Other transportation|-7.6|-1.5|-7.1|-7.5|-1.6|-17.5|-11.0|-1.5|
|Telecommunications|9.7|17.4|9.7|9.2|17.2|-2.4|4.7|17.3|
|Science & science servicing|2.2|5.2|0.1|0.1|6.8|-0.8|1.5|4.7|
|Financial services|-6.3|-5.8|-6.3|-5.8|-6.1|-6.6|-6.6|-6.3|
|Ferrous metallurgy|27.2|27.5|36.1|46.0|23.7|23.6|12.6|22.2|
|Non-ferrous metallurgy|11.7|4.7|25.8|34.7|23.7|28.7|5.7|2.9|
|Chemical & oil-chemical industry|17.8|23.4|8.4|11.0|20.3|56.8|23.1|1.3|
|Mechanical engineering & metal-working|-2.8|-0.4|-2.3|0.1|-5.0|-3.4|0.1|-1.3|
|Timber & woodworking & pulp & paper industry|-3.5|-4.5|-11.5|-9.5|-0.3|0.1|-4.4|28.0|
|Construction materials industry|-7.8|-10.7|-8.7|-5.3|-5.6|-5.6|-11.6|-7.1|
|Food industry|-13.7|-16.4|-15.9|-10.9|-11.5|-8.0|-12.8|-11.6|
|Other industries|-2.9|-2.7|-1.4|-2.4|-6.1|-2.0|-6.5|-4.8|
|Public services, culture and arts|1.5|1.8|1.5|1.8|1.4|1.3|1.1|1.7|
|Agriculture & forestry|-3.6|-8.3|-3.5|-2.2|-4.2|1.5|-4.2|-5.4|
|Coalmining|4.7|6.6|6.6|6.5|6.9|6.2|4.1|6.4|
|Housing and communal services|1.8|2.2|1.5|2.3|1.7|2.4|1.0|2.4|
|Construction|0.5|-0.8|2.5|0.3|-0.9|-0.5|-1.6|-0.8|
|Electric industry|2.2|2.5|1.3|2.0|2.5|4.3|1.8|0.9|
|Gas|-1.6|-2.7||-1.6|-1.5|-1.6|-2.6|-1.9|
|Crude oil extraction|1.1|1.6||1.4|1.8|0.9|0.9|1.7|
|Oil refining and processing|0.9|-1.2|0.6|2.2|3.1|0.1|-3.6|2.2|
|Other goods-producing sectors|-9.6|-11.9|-4.3|-7.5|-11.5|-10.2|-12.7|-12.0|
|Post|1.3|1.6|1.7|1.9|1.5|-0.3|0.6|2.0|
|Wholesale and retail trade|3.6|5.5|4.4|4.1|5.0|0.0|4.0|4.8|
|Textiles and apparel|-3.0|-4.6|-2.6|-2.3|-3.0|-2.0|-5.1|-2.5|

58
multilateral (WTO) or bilateral (the Economic Partnership Agreements (EPA)) trade negotiations. This paper addresses the role of the trade facilitation in promoting intra-African trade. To do this, we extend the gravity model considered in the literature (see Wilson et al 2003a, 2003b, 2004; Clark et al 2004; and OCDE, 2007), with focus on the role of port efficiency, customs environment, regulatory environment, and services infrastructure.

Using a panel data that cover years 2003 and 2004 on a sample of 100 countries which includes 25 African countries, we find that port efficiency and services infrastructure have a positive impact on African trade but that customs and regulatory environments are the factors that lower intra-African trade.

The rest of the paper is organized as follows. Section 2 presents the review of literature. Section 3 describes the data and estimation framework. Section 4 discusses the results while section 5 concludes.

# **2. Previous studies**

Empirical studies on trade facilitation are very recent. Following Fox et al (2003) and OECD (2003), Denis (2006) uses the Global Trade Analysis Project (GTAP) Computable General Equilibrium model to investigate the impact of trade facilitation and regional integration in the Middle East North Africa (MENA) region. He shows that both intra-regional and integration with European Union (EU) have a favourable effect on welfare in the MENA region. With the addition of trade facilitation improvements to trade liberalization, the welfare gains from integrating with the EU increase from US $ 1.8 billion to US $ 7.2 billion (0.82 percent increase to base GDP) whereas the welfare gains from intra-regional integration increase from US $ 913 million to US $ 3 billion (0.1 percent increase to base GDP).

APEC (1999) uses the same GTAP model and finds that trade liberalization and trade facilitation could increase APEC countries’ income by US 75 billion (at 1997 prices, 0.4% of GDP). UNCTAD (2001) finds that a one percent reduction in the cost of maritime and air transport services in developing countries could increase global GDP by US 7 billion (1997 value).

Clark et al. (2004) find that improving port efficiency from the 25th to 75th percentiles reduces shipping costs by more than 12%. Wilson et al. (2003a, 2003b, 2004) show that port efficiency of both the importer and the exporter is positively associated with trade. Moreover,
- 34 -

Iable 9. Determinants of the T-Bill Rate in Philippines

# Variable Parameter Estimate T-Statistic

I. Philippines (1976-87) Quarterly Using Monetary Base (MB) DEP Variable r 'PHL*

|CONST|0.027|0.3|
|---|---|---|
|TSGP+|0.205|2.3|
|GDP/MB|-0.014|0.4|
|ff|0.421|2.8|
|'PHL-1|0.860|13.6|
|||R-Square-<br>0.846<br>Durbin-Watson-<br>2.1|

II. Philippines (1976-87) Quarterly Using Money Supply (M2)

|DEP Variable-<br>TPHL|||
|---|---|---|
|_%,>I ai.|.,-U.J|1.4|
|TSGP+|0.252|2.7|
|GDP/M2|0.091|1.3|
|**_if_**|0.428|2.9|
|TPHL-1|0.857|13.8|
|||R-Square-<br>0.851<br>Durbin-Watson-<br>2.1|
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strategies being used by farmers to cushion themselves against changing climatic conditions. The agricultural sector remains the key sector of the economy in Zimbabwe, but agricultural policy making has not yet given enough attention to the impacts of climate change and related issues. The main objective of this study is therefore to develop and apply empirical methods to assess the economic impacts of climate change on agriculture in Zimbabwe.

# **_1.1 Objectives of the study_**

This study followed the general objectives of the Global Environment Facility/World Bank (GEF/WB) Climate Change and African Agriculture Project and redefined them to focus specifically on the Zimbabwe country study. The objectives are:

1. To develop a Ricardian model and quantify the potential economic impacts of climate change on agriculture in Zimbabwe.

2. To predict potential future impacts of anticipated further changes in climatic conditions on Zimbabwe’s agricultural sector using the estimated Ricardian model under various simulation scenarios.

3. To identify alternative adaptation measures that Zimbabwean farmers are using to help mitigate potential impacts from anticipated changes in climatic conditions.

4. To inform policy using the empirical results from the study.

# **_1.2 Outline of the report_**

The rest of the report is structured as follows. Section 2 gives the background information about Zimbabwe, Section 3 is a brief literature review of empirical models used to assess economic impacts of climate change, Section 4 specifies the empirical Ricardian model for Zimbabwe, Section 5 presents the data needed for empirical model estimation and the sources, Section 6 discusses the empirical findings, and Sections 7 and 8 present the conclusions from the empirical analysis and discuss directions for future research.

# **2. Background information on the country of study – Zimbabwe**

Zimbabwe is a landlocked country in the southern African region, with an area of 390 760 square kilometers. It lies within the tropics between 15o 30' S and 22o 30' S and 25o E and 33oE. Three broad relief regions are generally recognized on the basis of elevation: the Lowveld (below 900m), the Middleveld (900–1200m), and the Highveld (1200–2000m). In addition, a narrow belt of mountains (2000–2400 m), the Eastern Highlands, runs north to south along the eastern border with Mozambique; and the deep cleft of the Zambezi River Valley forms the boundary with Zambia in the northwest. The climate is largely influenced by relief, as the rainfall increases with altitude. The mean annual rainfall varies from below 400mm in the extreme south of the Lowveld to above 2000mm on isolated mountain peaks in the Eastern Districts. Middleveld rainfall ranges from 500mm to 700 mm and that of the Highveld from 800mm to 1000 mm. Appendices A and B show the spatial distribution of rainfall and temperature in the country.

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# Table 4: Detailed school funding, infrastructure controls and school fixed effects

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Estimator|log(Payments<br>to teachers)<br>OLS|—log(Out-of-school<br>tutoring)<br>OLS|—log(Payments<br>to teachers)<br>FE|—_log(Out-of-school<br>tutoring)<br>FE|
|Migration|-0.449**|-0.062|-0.099|-0.074|
||(0.210)|(0.170)|(0.214)|(0.195)|
|Log(household income)|0.107|0.186***|0.142|0.123**|
||(0.087)|(0.055)|(0.086)|(0.052)|
|School infrastructure and budget|||||
|Schoolassetindex|-0.000|0.000|||
||(0.000)|(0.000)|||
|Wages (log)|On|-0.019|||
||(0.457)|(0.332)|||
|Teaching materials (log)|-0.062|-0.145|||
||(0.257)|(0.201)|||
|Utilities (log)|-0.244|-0.106|||
||(0.176)|(0.118)|||
|Transports (log)|0.138|0.041|||
||(0.101)|(0.056)|||
|Maintenance (log)|-0.034<br>(0.079)|-0.031<br>(0.067)|||
|Number ofschoolfixed effects|||148|148|
|Child characteristics|Yes|Yes|Yes|Yes|
|Household characteristics|Yes|Yes|Yes|Yes|
|N|873|880|875|882|
|R?/R?within|0.116|0.141|0.026|0.027|