rafmacalaba/datause-encoder-data huggingface dataset:
WPS3857

# **Trade liberalization, factor market flexibility, and growth:**

# **The case of Morocco and Tunisia**

# Allen Dennis<sup>1</sup>

_In recent years there has been an increasing recognition of the importance of complementary policies in enhancing the benefits of a more open trade regime. This study focuses on the importance of factor market flexibility to trade reforms. Using the GTAP model and database, the results show that the welfare impact of trade reform is contingent on the flexibility of factor markets, with higher welfare gains occurring where factor markets are more flexible, and vice-versa. Defining two extreme factor market scenarios over Morocco and Tunisia, we find that the welfare gains of trade reforms under conditions of flexible factor markets can be as much as six times the gains compared to a rigid factor market scenario. This is so because whereas trade reforms may improve the incentive structure for resource re-allocation, the extent to which resources move from less efficient to more efficient sectors of an economy is dependent on the degree of flexibility of factor markets._

# World Bank Policy Research Working Paper 3857, March 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Comments received from Andrew Stone (MNSIF) and Zoubida Allaoua (MNSIF) have been helpful in the revision of this paper. Ernesto Valenzuela (DECRG) and Maros Ivanic (DECRG) have also been helpful on the modelling aspects of the study.

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18 the distribution of these issues in the dataset. However, it is also apparent that more treaties address multiple issues in recent years (since 1951).

The distribution of the issues agreed in the treaties by year also provides useful information. As noted in Table 6, more pollution control and flood protection issues are addressed in treaties in the past 25 years while more water allocation and hydropower production issues were addressed in treaties in the first 50 years covered by our dataset.

# **Results of the Econometric Analyses**

The descriptive statistics of the various variables is presented in the Appendix. In general, cooperation, measured both by the number of treaties signed among the two riparian states, and by the principal component variable that integrates the shares of issues in the treaties is well explained by water scarcity, trade and governance regimes of the riparian states. Our estimates of likelihood of treaty formation ( _Treaty/no-treaty_ ) did not yield compelling results and therefore are not presented.

The geography variables, _Through-border_ and _Border-creator_ , were not always significant, so that they cannot robustly support the hypothesis that river geographies lead to different levels of cooperation. This is in line with our claim that the effects of geography on treaty cooperation may be ambiguous. However, as explained in Dinar S. (2006b, 2007), geography may not explain the level of cooperation but rather it is essential to understanding cooperation patterns and, most importantly, the allocation of costs and benefits among the riparian states.

The statistical results are robust in the sense that various estimation procedures applied to different datasets suggest similar value ranges, significance levels, and signs for the coefficients. The results are stable for all three subsets of the basins (all basins, basins with treaties only, and basins with water allocation treaties only). They are especially similar in terms of values, signs and significance for the governance variables and the trade variables. The results are also quite alike for trade variables derived from the IMF and UN datasets, with slightly better performance for those based on the IMF dataset. In addition, the results are comparable for the two scarcity variables, both in terms of signs and level of significance of the estimated coefficients in the various estimated equations. Finally, the results are quite analogous in terms of significance and signs for the various functional forms used. Our estimates explain 13-40 percent of the variation in cooperation levels and all have significant fit or pseudo fit test values.
# List of templates

|Template 1|Jurisdictional responsibilities in infrastructure service delivery|12|
|---|---|---|
|Template 2|Special funds financing infrastructure service delivery|15|
|Template 3|Private participation in infrastructure|16|
|Template 4|ODA in infrastructure service delivery|18|
|Template 5|Basic budgetary institutions, national level|21|
|Template 6|Budgetary cycle diagram, national level|23|
|Template 7|Governance of public operators|26|
|Template 8|Macroeconomic parameters for normalization|30|
|Template 9|Macroeconomic parameters for budgetary context of infrastructure spending|35|
|Template 10|Functional and economic classification of government expenses (central and local)|40|
|Template 11|Public operators’ financial data|42|

# List of tables

|Overview table 1 Data collection matrix|3|
|---|---|
|Table 1.1 Rwanda 2006, jurisdictional responsibilities in infrastructure service delivery|14|
|Table 1.2 Uganda 2006, special funds in infrastructure|16|
|Table 1.3 Kenya 2006, private participation in infrastructure|17|
|Table 1.4 Sierra Leone 2007, ODA in Infrastructure|18|
|Table 1.5 Indicative checklist of documents and sources|19|
|Table 3.1 Indicative checklist of data sources and documents|29|
|Table 3.2 COFOG codes capturing infrastructure cost elements|32|
|Table 3.3 Economic classification of government expenses—current expenditures|34|
|Table 3.4 Economic classification of government expenses—capital expenses|35|
|Table 3.5 Kenya functional code mapping|37|
|Table 3.6 Rwanda economic code mapping|38|

# List of figures

Figure 2.1 Turkey 2004, budgetary cycle diagram 24

# List of boxes

Box 1.1 On-budget versus off-budget entities 11 Box 3.1 Why use the GFSM 2001’s economic and functional classifications? 33 v
Ravallion, M. 1999. "The Mystery of the Vanishing Benefits: Ms Speedy Analyst's Introduction

- to Evaluation." _Handbook on Evaluating the Poverty Impact of Projects._ The World Bank.

- Sellin, B. 1999. _European Trends in the i)evelopment of Occupations and Qualifications._ Luxembourg. CDEFOP.

- Smith, P. 1999. "The Internationalization of Vocational Education and Training." _Review of Research._ Adelaide. NCVER.

World Bank. 1997. "Mexico: Training Assessment Study." White Cover Draft.

- -----. 1998. "Enhancing Total Factor Productivity Growth." Report No. 17392-ME (Gray Cover).

- ---------. 1999a. "Export Dynamics and Productivity: Analysis of Mexican Manufacturing in the 1990s." Report No. 19864-ME (Green Cover).

- ---. 1999b. "Mexican Labor Markets; New Views on Integration and Flexibility." Volume Two: Technical papers. Poverty Reduction and Economic Management Unit. Mexico Department.

- ---. 2000. "Earnings Inequality after Mexico's Economic and Educational Reforms." Report

- No. 19945-ME (Gray Cover). December. Mexico Department.

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<!-- Start of picture text --> Figure 8. Wheat Stocks, Actual &<br>Simulated<br>250<br>200<br>150<br>100<br>50<br>0<br>2001 2002 2003 2004 2005 2006 2007<br>Actual Simulated<br>Source: DECPG<br>Million Tons<br><!-- End of picture text --> <!-- Start of picture text --> Figure 9. Wheat Prices vs. Stocks<br>(Index 2000=100)<br>250<br>Prices<br>World Stocks<br>200<br>150<br>100<br>50<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007<br>Source: DECPG<br><!-- End of picture text --> 12
importing countries are statistically different from each other.<sup>5</sup> The results suggest that the Internet is more beneficial to the exporters as it reduces the fixed costs of trade: the Internet provides a channel to market their products and allows sellers to communicate with foreign customers. The impact of the Internet that we find here is very similar to Freund and Weinhold (2004), where a 10 percent increase in the growth of Internet increases export growth by 0.2 percent.

The decomposition of export flows into the intensive and extensive margins provides further information about whether the Internet increases the intensity of trade or encourages new products to be exported. The regression results using the trade margins with country-pair fixed effects are presented in Table 3, and the results of total exports are also presented for completeness. As OLS is a linear operator, the additive properties ensures that the coefficients in the intensive and extensive margin regressions (column 2 and 3) add up to the coefficient for bilateral export flows (column 1).

**Table 2: Bilateral Export Flows and Internet Adoption**

|VARIABLES|(1)<br>O|(2)<br>LS|(3)<br>PPML|
|---|---|---|---|
|Internet adoption exporter (log)|0.140***|0.191***|0.145***|
||(0.0127)|(0.0108)|(0.0327)|
|Internet adoption importer (log)|0.104***|0.0586***|0.128***|
||(0.00905)|(0.00874)|(0.0202)|
|GDP exporter (log)|0.144***|0.359***|0.459***|
||(0.0379)|(0.0156)|(0.0454)|
|GDP importer (log)|0.0891***|0.0697***|0.433***|
||(0.0132)|(0.0139)|(0.0639)|
|Distance (log)|-1.573***||-0.869***|
||(0.0190)||(0.0299)|
|Contiguity|0.867***||0.522***|
||(0.100)||(0.0790)|
|Common language|0.583***||0.0686|
||(0.0411)||(0.0733)|
|Colony|1.188***||0.281|
||(0.135)||(0.238)|
|Common legal origin|0.349***||0.188***|
||(0.0269)||(0.0517)|
|Observations|215,796|215,796|407,352|
|R-squared|0.773|0.862|0.899|
|Year FE|Yes|Yes|Yes|
|Importer and exporter FE|Yes|No|Yes|
|Country-pair FE|No|Yes|No|

Standard errors are clustered at the country-pair. *** p<0.01, ** p<0.05, * p<0.1

> 5 The p-value of the F-statistic for equality of coefficients is equal to 0.

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household sector, as a net holder of money-denominated assets, tends to be lower in inflation-adjuste **d** ata. The data analyzed in this paper are based on money and not on inflation-adjusted accounting.

A third major difficulty derives from the existence of discrepancies between the sectoral income and outlay accounts and the financial data sources on which the flow of funds tables are based. These discrepancies have already been noted in the case of Korea, but they exist in all countries, and are often very large, by comparison with the sector surpluses. The treatment of these discrepancies can strongly influence the data.

The fourth problem, which is pervasive, is comprehensiveness. For the most part, the balance sheets of financial institutions and the records of organized securities markets provide the basis for much of the capital finance accounts of the flow of funds. The equity injections by owners of closely-held or quasi-corporate enterprises may often fail to be captured in the accounts, as may much of trade credit. Intra-sectoral claims are frequently netted out or omitted, and omissions are not evident because they do not violate adding-up constraints.

In the present paper, no attempt has been made to refine the flow of funds estimates prepared by other authors. Reliance is thus being implicitly placed on ar. assumption that the discrepancies and errors in the data are sufficiently random and non-systematic to allow the true systematic features to emerge from the statistical analysis.

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Figure 1 plots the probability that a municipality falls into a particular bin against the margin of victory at the midpoint of the bin, and provides evidence that incumbent mayors were more likely to win elections decided by a margin of between 0 and 1.5 percentage points. – Table 1 reports McCrary test results i.e., estimates of equation (1), based on 30 bins, 15 on each side of the threshold. The constant term represents the height at which the pdf intercepts the threshold of zero from below, which is an estimate of how likely incumbents were to lose tightly contested elections. The coefficient of the incumbent victory indicator reflects the discontinuous change in the pdf at the zero threshold. For the entire sample, the analysis estimates a constant of 0.028 and a discontinuous increase of 0.016, which is statistically significant at the 5 percent level. Thus, incumbents were approximately 57 percent (0.016/0.028) more likely to win tightly contested elections than to lose them. This constitutes evidence that incumbents were able to manipulate tightly contested elections in order to secure victory. Below, the paper shows that this pattern of close incumbent electoral wins closely corresponds to the pattern of violence observed after the election.

The results presented in Table 1 are based on a bandwidth of 5 percentage points. In general, estimates with smaller bandwidths should be closer to the true value since they are more strongly informed by observations closer to the threshold (McCrary, 2008). However, reducing the bandwidth also decreases the effective sample size and therefore leads to less precision. The results in Appendix Table A.1 provide evidence that the estimates are robust to limiting the bandwidth to 3 or 4 percentage points around the zero threshold.

Previous evidence suggests that poorer regions tend to be more corrupt and are more likely to have weak institutions (Olken and Pande, 2012; Ferraz and Finan, 2008; Besley and Burgess, 2002). The paper explores whether incumbents are more likely to win in poorer versus richer municipalities. The classification of municipalities as rich or poor is based on the 2006 municipal-level poverty estimates provided by the Philippine Statistics Authority.<sup>15</sup>

> 15These data are available at `https://data.humdata.org/dataset/`

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Figure 1: Scatter plot and kernel regression: claim payments against yields, for all weather indexed products sold in one state in one year <!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0% 50% 100% 150% 200%<br>Subdistrict average yield, as percentage of average historical yield<br>1999-2007<br>WBCIS claim payment, as percentage of sum insured<br><!-- End of picture text --> Note: The lines show the point estimate and 95% confidence intervals for an Epanechnikov kernel with a bandwidth of 0.8.

On this basis we estimate that conditional on total crop loss, that is an area average crop yield of zero, there is a 1-in-3 chance of receiving no claim payment from the WBCIS (Figure 2). This may arise because of perils not captured by the weather station data, such as localized weather, pestilence or disease, or weather events not adequately captured by the index used in the WBCIS product. Conversely, there is a high risk of farmers receiving a positive WBCIS claim payment, even in years with good yields. For example, again reading off from the kernel regression point estimate in , conditional on the yield being twice the historical average the WBCIS products included in this analysis paid claims with probability of 45% (Figure 2).

Figure 2: Kernel regression: probability of positive claim payment against yields, for all weather indexed products sold in one state in one year <!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>0% 50% 100% 150% 200%<br>Subdistrict average yield, as percentage of average historical yield...<br>positive<br>claim payment is<br>Probility that WBCIS<br><!-- End of picture text --> Note: The lines show the point estimate and 95% confidence intervals for an Epanechnikov kernel with a bandwidth of 0.8.

However, the interpretation of this analysis requires care due to three data limitations. First, the yield data are based on crop cutting experiments which may not be wholly reliable. In past years the results from crop cutting experiments determined the claim payment due to farmers under the NAIS, and there are concerns both about measurement error and that some yield figures may have been underreported to trigger claim payments (Mahul et al. 2011). However, there is limited 10
based on data from the late 1960s, and the picture could have changed considerably since then. Some studies did find a positive relationship between size and productivity (Byerlee, 1984, 1986; Akhter, 1986), but this can be explained by slower diffusion of green revolution technologies to small farmers.

Table 6: Distribution of Land.

|Size of Farm (ha)|As % of number|As %ofArea|
|---|---|---|
|less than_0.5_|13|1|
|0.5 to 1.0|14|3|
|1.Oto2.0|21|8|
|2.0 to 3.0|16|10|
|3.0to 5.0|17|17|
|Total Small Farms|81|39|
|_5.0_to 10.0 (Medium)|12|21|
|10.0 to 20.0|_5_|16|
|20.0 to 60.0|2|14|
|largerthan 60.0|0.3|<br>101|
|Total Large Farms|7|40|

Source: Census of Agriculture, 1990.

At the farm level a number of reasons for an inverse link between size and productivity (and perhaps sustainability) can be suggested. It is generally accepted that there is an inverse relationship between size of farm and _intensity of cultivation_ This relationship holds for Pakistan (Table 7). There is a clear fall-off in cultivation as size rises. This may not necessarily represent an efficiency loss, since large farms are more likely to contain uncultivable waste-land. In addition, some marginal farms are forced to use land very intensively to meet subsistence needs - something which may not be efficient from a long-run perspective. However, it is undoubtedly true that some of the largest farms represent absentee landlords and underused lands and therefore give rise to a loss of efficiency. Incentives to own land arise from the use of land as a tax-shelter, or simply as a source of status. And since land until recently has not been taxed, and irrigation charges have been low, there is little need to cultivate land to meet associated expenses. This is why large tracts of land can remain idle for long periods of time.

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ship-to-shore radio after 1900, aircraft observation after 1940, and satellite observation after 1960. Measurement error has undoubtedly fallen over the years, but we cannot judge whether earlier technologies imparted any systematic bias to the recording of cyclone occurrences, track locations, wind speeds and storm radii. If historical observations have been unbiased estimates of storm characteristics, then the aggregative estimates in this paper should be also be regarded as unbiased, but with significantly-decreasing variance over the 140-year period.

The remainder of the paper is organized as follows. Section 2 provides a technical discussion of database construction. Section 3 employs the database to investigate trends in the incidence and power of Indian Ocean cyclonic storms from 1877 to 2016. Section 4 focuses on trends in the spatial distribution of cyclone landfalls in Bangladesh, West Bengal and Odisha, while Section 5 broadens the analysis to impact zones around cyclone landfalls. Section 6 summarizes and concludes the paper.

# **2.  Georeferenced Database Construction**

We combine data from BMD, IMD and IBTrACS to document all recorded cyclonic storms in the Indian Ocean region from 1877 to 2016. We construct a panel database; the component for each storm includes a unique identification number; year and month of occurrence<sup>1</sup> ; maximum wind speed (kt); radial distance (km) from storm center to zone of maximum wind speed; IMD storm classification derived from wind speed; time indices for successive observation points<sup>2</sup> ; and latitude/longitude for each point.

We use ArcGIS to construct individual storm-track polylines by interpolation from timeindexed latitude/longitude observations for each storm. Then we determine maximum-impact

> 1 The first month for storms that occur in two months.

> 2 Incommensurate day/time tracking in the three databases (BMD, IMD, IBTrACS) could be addressed in a more complete exercise.  For our purposes, identifying successive observations by cardinal units [1,2,3, ...] is sufficient for constructing GIS polylines that trace individual storm tracks.

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Although education was, in principle, supposed to be devolved to local councils in 2005, the actual devolution of this sectoral function has been delayed due to significant resistance from the Ministry of Education. The first direct transfers to local council accounts for education were not released until 2007 (Casey, 2009). Given this delay, we expect the association between the quality of education and local councillors’ trustworthiness to be weaker than correlation between the quality of health care and councillors’ trustworthiness. We also expect the effect of education on councillors’ trustworthiness to be stronger in 2008 than in 2007. The community survey asked respondents whether there was a primary school in their community. If there was a primary school, respondents were asked who runs the school: the government; an NGO or mission; the community; or a private group. They were then asked a series of questions about the quality of the school’s infrastructure including the type of roof over the school and whether there is a latrine at the school. Respondents were also asked several questions about access to education and perceptions of its quality. If respondents have a child that goes to primary school, they were asked whether the school provides textbooks for free. The survey probed respondents about their level of satisfaction with the school: In general, are you satisfied with 1) the school building and materials; 2) the teacher; and, 3) what your children are learning in school? These questions were coded either “yes” or “no”. We summed the number of items to which the the respondent answered “yes” and created an index that captures satisfaction with schools. We also include a measure of how long it takes for respondents’ children to travel to school.

# **Local Councils’ Honesty, Competence and Capacity**

Respondents were asked the following hypothetical question that taps their perceptions of the honesty of local government officials: a) If the local council “was given 500 million Leones to complete a project in this area, do you believe they would spend all the money doing a good job on the project or would they cut some of the money?” The 2006 and 2008 Comprehensive Local Government Performance Assessment System (CLoGPAS), which was carried out across the 19 Local Districts, includes measures of councils’ administrative capacity (Decentralization Secretariat, 2007). CloGPAS includes a range of questions measuring local government capacity to meet seven ‘minimum conditions’ (MCs) capturing major functional and thematic areas. More precisely, the MCs examine functional capacities of the local councils in terms of their preparedness to take over devolved functions and deliver services at the acceptable standards and assess/review the compliance of local councils with existing laws and regulations that guide the decentralization process. In this paper, we use the CloGPAS aggregate score for MCs. As additional measure of government (fiscal) capacity, we also include the total amount of central government sectoral transfers to each local council for 2006 and 2007.

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