rafmacalaba/datause-encoder-data huggingface dataset:
# **5. Multivariate Analysis**

# **5.1 Returned (Y/N)**

We start our multivariate analysis by showing the characteristics of the respondents who had already decided to return to their place of origin before the first wave of the survey (August 2014). In this first analysis, we aggregate IDPs and refugees into one comparison group (forcibly displaced). Column 1 Table 1 shows the estimated probit marginal effects using the whole sample, while in Column 2 only respondents who were the household head or spouse are considered.<sup>9</sup> For the sake of completeness Column 3 presents results for IDPs only.

It is interesting to note that asset-rich households were much more likely to return (up to 46 percentage points). The same can be said about households belonging to the Songhai ethnic group. We find an even larger increase in the likelihood of return among respondents who were originally from Kidal. We should also mention the positive coefficient associated to the regressor “Police issues”: individuals who experienced some difficulties with the national security forces or with strangers during the displacement were more likely to return to Northern Mali. When asked to give additional details about these issues, most of the respondents (53%) complained that there were too many controls, while 15% of them stressed the lack of respect during these controls.

The decision to return seems to reflect the conditions during displacement as well as perceptions about the situation at home. Better conditions during displacement favor remaining displaced, while the prospect to be reunited with one’s family incentivizes return. Age or gender do not affect the decision to return. Thus, having received assistance is associated with a lower probability of returning home. Larger households are less likely to go back to Northern Mali (perhaps because returning is more costly). Households whose respondent worked during displacement are also less likely to return, as are households in which the respondent had completed secondary education, or higher. One may wonder whether this last result is due to these individuals being better informed about the situation in northern Mali, or whether their educational level gave them more flexibility and a greater ability to adapt and integrate in the new environment, which led them to not wanting to go back. The former hypothesis can be sustained by considering that, among these highly educated individuals, almost all of them received some news from their place of origin, while 15% of the lower educated respondents did not receive any information. Furthermore, highly educated individuals received this news mainly

> 9  Following (Joshua D Angrist, 2001) and (J. D. Angrist & Pischke, 2009), we have also estimated the same models using OLS. Results are qualitatively similar. When not reported, tables are available upon request. A detailed description of the variables used in this section is available in the Appendix. We should also stress that we are not making any causality claim in this section, but we rather want to describe respondents’ characteristics and their migration decisions.

11
agreement with the country, and the sixth indicates the change in ALI that we are considering.<sup>30</sup> The final four columns indicate the predicted change in travel along existing routes, which is 161-184 percent. Growth in flights along city-to-city routes for which direct flights exist is much more modest, 28 to 34 percent depending on the specification. Recall that only one of these estimates relies on a statistically significant regression coefficient, so even this much growth is uncertain.

As the earlier counterfactual estimates suggested, the link between changing ALI and more traffic appears to operate more through the extensive margin (more city pairs with direct flights) than through the intensive margin (more traffic for city pairs in which a flight exists). In Table 6 we illustrate this with estimates of the impact on city-to-city routes associated with the liberalization of commitments that would arise were Turkey and the ACAC to negotiate an agreement with ALI = 50. The second column of Table 6 indicates the number of city pairs that are possible between Turkey, and each ACAC country, if every location that handled international flights in the ACAC country were to share a flight with every location in Turkey that operates an international flight.<sup>31</sup> The third and fourth columns indicate the benchmark ALI score as well as the change in ALI that we use in our calculations. Columns 5 and 6 show the change in the odds ratio for flights between Turkey and each respective country, using estimates from Table 4, columns 2 and 3. These estimates suggest that the odds of a flight for any given city pair rise by a factor of roughly 1.7 to 3 depending on the estimates.

To make these estimates more tangible we use the final four columns of Table 6 to report our estimates of the number of city pairs in which the probability of flight (as predicted by our model) goes from below 0.5 to above 0.5 when we consider the policy change. In Table 6, we see that new city pairs emerge in many countries. As many as six new flights (to or from Lebanon) are predicted by the estimates in column 2 of Table 4. The estimates from Column 3 are roughly the same magnitude, but

> 30 The ALI governing traffic between Turkey and some ACAC members is not reported in the WTO database, because these countries did not have an existing bilateral arrangement with Turkey at the time these data were collected.  We impute these, using the passenger-weighted average of each country’s ALI with partners from the rest of the world. The weighted ALI average values are calculated and reported by the WTO.

> 31 There were 13 such cities in Turkey in 2010.

22
**Table 17. Financial flows to Uganda’s infrastructure, 2001-2009**

<u>US$ millions per year</u>

||O&M<br>Public sector|Public<br>sector|Ca<br>ODA|pital expenditure<br>Non-OECD<br>financiers|<br>PPI|Total<br>CAPEX|Total<br>spending|
|---|---|---|---|---|---|---|---|
|ICT|0.3|0.3|2|0|128|130|130|
|Irrigation|0.1|0.1|0|0|0|0|0.2|
|Power|226|71|88|0|65|224|451|
|Transport|81|81|75|2|22|180|261|
|WSS|37|18|47|3|41|109|147|
|Total|345|170|213|5|256|644|989|

_Source:_ Derived from Foster and Briceño-Garmendia (2009).

_Note:_ O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development; WSS = water supply and sanitation; ICT = information and communication technology.

_Existing infrastructure spending in Uganda amounts to around 11.3 percent of GDP (figure 26)._ Uganda devotes a relatively high share of its GDP to infrastructure compared to many other LICs in Africa. Relative to its peers, Uganda invests twice as much in power infrastructure and comparable or slightly smaller amounts on ICT, transport, and water infrastructure (figure 27). All infrastructure sectors receive at least as much or more private finance than other peer low-income African countries.

**Figure 26. Uganda’s existing infrastructure spending is quite high, 2001-2009**

<!-- Start of picture text --> SSA<br>LIC‐NoFragile<br>MIC<br>LIC‐Fragile<br>Resource‐Rich<br>Zimbabwe<br>Ethiopia<br>Uganda<br>Kenya<br>Central African Republic<br>Botswana Capex O&M<br>South Africa<br>Congo, Dem. Rep.<br>% of GDP 0 5 10 15 20<br><!-- End of picture text --> _Source:_ Derived from Foster and Briceño-Garmendia (2009).

_Note:_ LIC = low-income country; MIC = middle-income country; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure 44
when the boat owner went fishing at least three days during that week. This latter sample restriction further excludes periods of absence from the labor market due to sickness or some other special temporary condition that interferes with boat owners normal labor market participation. Our third sample is even more restrictive and focuses on weeks when boat owners were active at least for four days. All these samples exclude extended absences from fishing due to temporary migration or long periods of inactivity. Note that the samples are unbalanced because individuals become boat owners at different points in time. In addition, the samples exclude boat-owner-weeks with missing participation data, so that all boat-owner-weeks have participation data for all 6 working days.<sup>20</sup> Perhaps due to their access to credit, average savings (in bank deposits, cash at home, jewelry and other forms of savings) appear low at INR 2,840 which is less than the average value of catches for two days.

Table 2 shows descriptive statistics for the variables that affect boat owners’ value of catches. During 2001-2007, the average price of frozen Indian calamari at the Spanish market stays stable around EUR 3 per kilogram, and the Real Exchange Rate INR-EUR is 4 on average.<sup>21</sup> Finally, the real price at which a gallon of kerosene is exchanged in the Port of – – Singapore the main center of fuel exports to the rest of Asia is approximately INR 63 (USD 1.2) per gallon, and unlike the price of calamari, it fluctuates considerably, with a variance of 21.7.

# **3 Conceptual Framework and Empirical Strategy**

## **3.1 A Model of Boat Owner Labor Supply**

Every day, boat owners decide whether to go fishing or not, considering their expected catches, their level of fatigue, and, possibly, the cumulative catches they have obtained during that week. A full description of their optimal decision requires solving a stochastic dynamic programming problem. Instead, we follow the approach of Farber (2005) and discuss a static approximation of the optimal decision problem.

> excludes the period between December 26th of 2004 and February 17th of 2005 which coincides with the aftermath of the 2004 Indian Ocean tsunami.

> 20For the median boat owner, we drop 8 percent of the weeks due to first sample restriction (going fishing at least one day of that week). When restrict the sample to weeks when boat owners are active for 3 and 4 weeks, we eliminate 20 and 30 percent of the original sample, respectively.

> 21The Real Exchange Rate (RER) is RER=er/e*(Pe/Pi), where Pe and Pi are the Current Price Indexes (CPI) in Europe and India respectively. RER and kerosene prices use 2007 as base year. Sources: International Monetary Fund (CPI), European Central Bank (ER) and US Energy Information Administration (Kerosene prices).

9
# Does it matter who you sign with? Comparing the impacts of North-South and South-South trade agreements on bilateral trade

_Alberto Behar_<sup>a</sup> _, Laia Cirera i Crivillé_<sup>b</sup>

> a Development Economics Research Group, _The World Bank; financial support from the governments of Finland, Norway, Sweden and the United Kingdom through the Multidonor Trust Fund for Trade and Development is gratefully acknowledged; part of this work was completed while at St Anne’s College, University of Oxford, and funding from the Biegun-Warburg Junior Research Fellowship is gratefully acknowledged (abehar@worldbank.org). b Department of Economics, Universitat Pompeu Fabra (laia.cirera@upf.edu)._ The authors would like to thank Chris Adam, Carlos Caceres and Adrian Wood for helpful comments.
34

# **Figure 6: Credit-deposit ratio across transition economies over time**

<!-- Start of picture text --> 160<br>140<br>120<br>100<br>80 Credit-Deposits<br>60 Benchmark<br>40<br>20<br>0<br>year 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: Global Financial Development Indicators, own calculations

**Figure 7: Household credit across transition economies over time**

<!-- Start of picture text --> Figure 1: Total Household Credit to Households, 1995-2007<br>60<br>40<br>20<br>0<br>1995 1997 1999 2001 2003 2005 2007<br>Bulgaria Croatia Czech Republic Estonia<br>Hungary Latvia Lithuania Poland<br>Romania Slovak Republic Slovenia Turkey<br>EU-15 and Cyprus<br>(% of GDP)<br><!-- End of picture text --> Source: Allen et al. (2011)

**Figure 8: Mortgage credit across transition economies over time**

<!-- Start of picture text --> Figure 2: Mortgage Credit to Households,1995-2007<br>80<br>60<br>40<br>20<br>0<br>1995 1997 1999 2001 2003 2005 2007<br>Bulgaria Croatia Czech Republic Estonia<br>Hungary Latvia Lithuania Poland<br>Slovak Republic Slovenia Turkey EU-15 and Cyprus<br>(% of Total Household Credit)<br><!-- End of picture text --> Source: Allen et al. (2011) 34
13 significantly different from zero, no long term relation between credit and money can be reliably postulated. All variables are measured in seasonally adjusted real terms<sup>2 4</sup> .

_(a)_ MhM aieslts. For three of the four countries the expectation that domestic credit will have a transitory effect only is confirmed. Table 3 shows illustrative regression equations. In the franc zone the results for Cote d'Ivoire give the best results in this respect. Rather more than one-half of domestic credit "leaks out" in the same quarter with no 25 significant long-term effect . The two rand zone countries display no strong link between credit and money either in the short run (CZ is small or insignificant) or in the long run (e is insignificant).

Cameroon once again provides the exception. Despite zxperiments with time trends, which proved insignificant, the lagged credit '.a." persists as significant. About one-third of credit expansion seea.E to augment the money stock permanently (indeed the point estimate of - is L.57). We can only retain the hypothesis on the assumption that credit here provides a better proxy for the omitted variables determining money demand. It may also be that credit expansion in Cameroon was more conservative and in line with general economic expansion.

# 4 Interest rates

In all of the countries that we are looking at some interest rates are controlled administratively. In a closed economy, the existence of administrative controls could spill over and affect uncontrolled interest rates. Thus, for example, compulsory non-remunerated reserve requirements would be expected to affect deposit and lending rates of the commercial banks. The openness implied by the fixed exchange rate and freedom of capital movements in the currency unions suggests that uncontrolled rates, at least for large deposits and for loans to first-class risks may be determined by international arbitrage rather than by domestic considerations. This prompts us to examine available data to see to what extent domestic interest rate controls influence the uncontrolled rates.

24 The franc zone data is deflated by the French consumer price index.

25 Actually, a clear short-run impact of credit on money was obtained for all of the UMOA countries. And, with the possible exception of Burkina Faso, the coefficient v was insignificant, implying no long-term effect.
# CHAPTER II: hIDENO AND ITS INNOVATIONS IN GIQRC2LJVU4L PROGRAHMING

2.01 This chapter presents a description of MIDENO and the development activities it has supported in three sections of increasing detail. The third, and longest, section in this chapter is devoted to an examination of MIDENO's efforts to revitalize the extension service of the North West province and the impact of those efforts on wor.men. However, this discussion can only be fully appreciated in the corntext of the other activities that were part of MIDENO's integrated intervention into the province's agricultu-al system. Thus this chapter begins with a section that provides a general overview of MIDENO, from its inception to August 1988. The second section then provides brief descriptions of the four project components, other than extension, that focused on the agricultural sector. The experience of the activities in input supply, animal traction, credit, and adaptive research provide a basis for a richer and more complete understanding of MIDENO's innovations in extension.

# AN OVERVIEW OF MIDENO

# Rationale and Design

2.02 The rationale for an agricultural development project in the North West Province is obvious when the significant agricultural potential of the region described in Chapter I is compared with investments in the province and the incomes of its inhabitants. As can be seen from Table 4, in 1980, when the MIDENO project was designed, the North West Province was contributing about oneseventh of Cameroon's production, but was receiving only one percent of government investment. As a result, the province, despite its potential, remained one of the poorest areas in the country.

Table 4: ComDariSOn pf Agricultural Production and Investment. 1980

||Contribution<br>to<br>Total Agricultural<br>Production|Governmental<br>Investment|Per Capit<br> <br>(F CF<br>---------|a Income<br>A)<br>---------|
|---|---|---|---|---|
|Province*|(percent)|(percent)|1961|1971|
|Center-South|19.6|40.0|40000|61000|
|East|5.6|11.0|20000|33700|
|Littoral|6.0|22.0|70000|92250|
|North|28.0|11.0|15000|18200|
|North West**|14.2|1.0|25000|25700|
|South West**|9.1|14.0|25000|25700|
|West|17.5|1.0|40000|45400|
|Total|100.0|100.0|||

Source: CRC (1980) cited in Ntangsi (1987): table 3.2 Notes: * There were only seven provinces in 1980.

** Disaggregated income figures for North West and South West provinces are unavailable.
**Template 10 Functional and economic classification of government expenditures**

|**GFS**|**Government function**<br>**(local currency)**|f employees|**Us**<br>**good**<br>**serv**|**e of**<br>**s and**<br>**ices**|fixed capital|**Subs**<br>ions|**idies**<br>ises|ers (current)|xpenditure|es,<br>pment|**Capi**<br>_n_|**tal expe**<br>ts|**nditur**<br>itures|**e**<br>tal<br>wer<br>ents||**... ex**<br>**fun**<br>t|**ternal**<br>**ding**<br>ects|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||o|e||f|at|pr|sf|e|ur<br>ui|_o_|se|nd|pi<br>lo<br>m|al|or|oj|
|**Code**|**Name**|Compensation|Maintenanc|Other|Consumption o|To public corpor|To private enter|Grants and tran|Other current|Buildings, struct<br>machinery, and eq|_... rehabilitati_|Other fixed as|Other capital expe|Transfers of ca<br>expenditures to<br>levels of govern|Tot|Budget supp|Earmarked for pr|
|**704**|**Economic affairs**<br>|||||||||||||||||
|**7042**|**Agriculture, forestry,**<br>**fishing, and hunting**<br>**(irrigation only)**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|
||Irrigation (amount<br>specific to irrigation<br>systems from 70421)||||||||||||||0|||
|**7047**|**Other industries**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|
|70474|Multipurpose<br>development projects||||||||||||||0|||
|**7043**|**Fuel and energy**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|
|70434|Other fuels||||||||||||||0|||
|70435|Electricity||||||||||||||0|||
|70436|Nonelectric energy||||||||||||||0|||
|**7045**|**Transport**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|
|70451|Road transport||||||||||||||0|||
|70452|Water transport||||||||||||||0|||
|70453|Railway transport||||||||||||||0|||
|70454|Air transport||||||||||||||0|||
|**7046**|**Communication**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|
|70460|Communication||||||||||||||0|||
|**705**|**Environmental**<br>**protection**|||||||||||||||||
|**7052**|**Wastewater**<br>**management**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|
|70520|Wastewater<br>management||||||||||||||0|||
|**706**|**Housing and**<br>**community amenities**|||||||||||||||||
|**7063**|**Water supply**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|
|70630|Water supply||||||||||||||0|||
|**Totalpro**|**ductive infrastructure**|**0**|**0**||**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|||

# **_Local governments_**

Public spending flows also need to be collected for local governments so as to get a full picture of overall public spending flows. But depending on the degree of decentralization and the level of central monitoring of local government expenditures, it might be necessary to provide information for a few—not all—local authorities.

47
for production workers on province- and year dummies, minimum wages, the gender composition of the production workforce and their interaction. The third specification, presented in columns 5 and 6, includes additional controls for ownership structure, firm age, size, the occupational composition of the firm, whether or not a firm-exports, productivity proxied by the log of value added per worker, sector- and year dummies, and interactions between variables measuring the composition of the production workforce and minimum wages. The fourth specification, presented in columns 7 and 8, adds controls for the educational composition of the production workforce and which are only available for 1994-1997 and 2006. All specifications are estimated by both OLS and fixed effects, with the latter being our preferred estimation methodology.

Table 2 makes three points clearly. The first is the extent of gender wage gaps in Indonesia. When controlling for time and location only (specification 1), a one percentage point increase in the share of female production workers is associated with a 0.56 percentage point decrease in wages in the OLS specification and a 0.21 percentage point decrease in overall wages when controlling for firm fixed effects.<sup>xii</sup> These effects are large, but not out of line with earlier firm-level studies of the gender pay gap. For instance, Hellerstein and Neumark (1999) document a gender pay gap of around 24% in Israel, while Bayard et al. (2003) document gaps between 33% and 38% in the United States, which drop to roughly 19% when industry and occupation controls are added. The gender pay gap remains statistically significant as additional firm controls are included (specifications 2-4), but shrinks somewhat. That controlling for firm characteristics and firm fixed effects diminishes gender pay gaps attests to sorting being part of the explanation for women’s lower earnings (note that such sorting may itself be driven by discrimination if, for example, better paying firm are less likely to hire women). Second, minimum wages are consistently associated with significantly higher wages. Third and of focal 16