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Ogawa. H and M. Fujita. 1980b. Multicentric urban configuration: equilibrium and optimum, 36
BOXES AND FIGURES

**Figure 1. Cotton in total merchandise for selected SSA countries (1961-2007)**

<!-- Start of picture text --> 70.00<br>60.00<br>50.00<br>40.00<br>30.00<br>20.00<br>10.00<br>0.00<br>Years<br>Benin Burkina Faso Chad<br>Mali Sudan Togo<br>Uganda Central African Republic Zimbabwe<br>Source: FAOSTAT (January, 12, 2011). The data has been smoothed with a lowess factor of<br>0.8 for better readability.<br>Figure 2. Top cotton exporters in 2009<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Top exporters<br>Share of total export value<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007<br>Global export share (%<br>United States India WCA Brazil Australia C-4 Greece Uzbekistan Burkina Faso Pakistan Turkey Benin<br><!-- End of picture text --> Source: FAOSTAT (January, 12, 2011). The data has been smoothed with a lowess factor of 0.8 for better readability.

Source: UN Comtrade (January, 12, 2011) 19
# CHAPTER III: LESSONS OF MIDENO

3.01 The previous chapter focused exclusively on MIDENO itself: its organization; its history; its interventions in the agriculture sector, particularly extension; and its record of success in reaching women farmers. The purpose of this chapter is to distill from the particular experience of MIDENO a set of more general lessons. These lessons are presented in two sections. The first section provides lessons on delivering agricultural services to women farmers arising from the way MIDENO implemented its activities in the agricultural sector. The second, and concluding, section reflects the extent to which MIDENO's innovative programming can be sustainec and replicated in areas outside the North West Province, either elsewhere in Cameroon or elsewhere in Africa.

# LESSONS FROM IMPLEMENTING INNOVATIVE PROGRAMS FOR WOMEN IN AGRICULTURE

3.02 A review of the growing number of studies and monographs examining ways to improve the capacity of agricultural extension services to reach women farmers, particularly in Africa, reveals remarkable consistency in recommended actions.' The recommendations fall into three categories or themes:

1. Focus agricultural extension on the small farmer rather than the progressive or more advantaged farmer.

2. Redress the male orientation or bias in extension, particularly in terms of which gender is perceived as the target of extension efforts.

3. Recognize the roles women play in rural society that may have an effect on the way women respond to particular extension strategies or to packages of recommendations.

3.03 The additional research conducted for this case study not only strongly confirms previous findings (Walker, 1987) that much of MIDENO's success with extension could be attributed to adherence to these three themes, but also explains MIDENO's success in its other agricultural interventions. Thus MIDENO's experience from these three themes deserve to be made explicit guidelines for designing and implementing agricultural development activities.

# Three Themes: A Strategy for Reaching Women Farmers

3.04 Each of the three themes is discussed below and summarized in Table 11. It should be noted that while these themes are discussed separately, they are really closely interrelated.

**1 These studies *nd .onographh aclude thace** of Ashby **(1961),** the **PAO** (1983b)i Berger, Delancey **and Mellenc-ap** (1984)1 **Weldemann** (1987)1 **and** Due, hollel **_and_** Malone (1987).
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29
We combine Laffont and Tirole with Buchanan and Tullock to arrive at a double cost model where resource costs are represented by the unit expenditures in construction, maintenance and administration and preference costs are measured by a proxy of user vehicle operating costs which is the percentage roads in bad condition. Our main departure from the Laffont and Tirole model is that we assume both cost and quality to be observable and verifiable. However, we maintan their assumption that effort is an increasing function of the provision type (in our case the degree of decentralization), but we propose measures of revealed level of effort, as we are concerned with the level of productivity under different degrees of decentralization. To estimate the optimal level of decentralization, we use results from proofs provided by Olsen and Torsvik (1995) to develop the model specification. We provide justification for these assumptions in the folowing section.

# **_Outputs from a RoadAgency_**

An agencyj, which may be centralized or decentralized, having responsibilities for roads within a well-defined jurisdiction, produces road services through undertaking production and/or management of some or all of the following three activities: (a) major construction works on new road links to expand access to the network or improve the connectivity of the network as well as rehabilitation and reconstruction of existing road links to improve their transitability; (b) maintaining existing road links in passable conditions by perforning necessary repair works as well as carrying out preventive maintenance activities to prolong the life of existing assets; and (c) administering day to day operations on the network such as managing contracts for roadworks in the case where such works are contracted out to another agent, dealing with emergencies such as landslides and road accidents, and undertaking any planning, coordination and regulatory responsibilities that relate to the provision of roads.

The agents (central government, local governments) producing road services generate a vector of outputs q measured in terms of the quality profile of road services provided across the jurisdictions they are responsible for. Quality is measurable in scalar terms by the percentage of roads in good or bad condition, and verifiable by the degree of satisfaction road users have for a provided quality of road.

At each time period t, we can observe the expenditures on roads by the agent, classified according to the three key activities above, which we denote by a cost C and the quality of produced road links which we denote by q. The costs are expressed in $ per km in order to remove effects of scale<sup>7</sup> and are allowed to vary by the type of function i (construction, maintenance, administration), the type of agentj (centralized or decentralized), and the time period t. Quality is measured as the percentage roads in bad 7We do not control for different road sizes as measured by number of lanes and width of lanes. Neither do we control for the type of materials, and presence or absence of roadside furniture.

5
# **4.3. Verifying Randomization**

We first test whether the PAC ballot correctly randomizes “stayer” households into a treatment and a control group by examining whether the stayer group within the households containing ballot losers are statistically different than the stayer group in households containing ballot winners (both the migrant families and the non-compliers). The results in Table 1 show that most ex-ante pre-migration characteristics are the same for ballot winners and losers (at 95 percent confidence level). The only exceptions are that stayer adults in successful ballot households have higher education levels and that there are more children amongst the stayer group in successful ballot households. We present all regression results with and without controls for the characteristics of these stayer members to examine the robustness of our findings to small sample differences in the treatment and control group.

# **4.4. Calculating Experimental Estimates**

Throughout the remainder of the paper, when we present experimental estimates of the impact on households and individuals of having household members move to New Zealand under the PAC, we do not directly compare means of the treatment and control groups due to concerns about dropout bias from non-compliers. Instead, instrumental variables regression (IV) models, where ballot success is used as an instrument for having coresidents emigrate, are used to estimate the treatment effect on the treated.<sup>15</sup> The PAC ballot outcome can be used as an excluded instrument because randomization ensures that success in the ballot is uncorrelated with unobserved individual attributes which might also affect outcomes among the stayer household members and success in the ballot is strongly correlated with migration.

# **4.5. Looking for Evidence of Selection**

In addition to obtaining consistent estimates of the impacts of migration, one of the other goals of this paper is to examine how these estimates might change if we were unable to correctly control for the double-selectivity. Table 2 and Table 3 examine the evidence for selection in terms of the household and individual outcomes of interest, respectively. As we

> 15 While an IV regression usually estimates the local average treatment effect (LATE), Angrist (2004) demonstrates that in situations where no individuals assigned to the control group receive the treatment (i.e., there is no substitution) the IV-LATE is the same as the average treatment effect on the treated. We focus on the average treatment effect since this is the parameter we can cleanly identify and which gives the overall impact of migration. With a larger sample, we could examine the average treatment effect for subgroups of households, such as those from poorer backgrounds, or single vs married applicants, or those with different household compositions. We see such analysis of the heterogeneity of migrations’ impacts as a fertile area for future studies should other migration lottery data be able to be collected.

14
<!-- Start of picture text --> (a) Absolute transport emissions (b) Change in transport emissions<br>3500<br>300<br>3000 =<br>2500 =8 250<br>a©Q 2000 83"200<br>= 15001000 2s3 150<br>500 fp 100<br>————E—E—EE<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>(c) Absolute transport emissions per capita (d) Change in transport emissions per capita<br>80 oe ee ld is ae 200180<br>25 s<br>= 160<br>fe}w 2.0 gu 140<br>Qis 2<br>= % 120<br>051.0 rn a ee 3= 108 0<br>0.0 —$—$—_—__—_——_—_—————————————— 60<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>4e-5 (e) Absolute transport emissions per GDP (f) Change in transport emissions per GDP<br>9<br>8 = 100<br>S<br>a? "<br>=65 x3=o 80<br>4<br>70<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>— Highincome —— Lowincome —— Lower middle income | —— Upper middle income<br><!-- End of picture text -->
Table 5: Post-Sanction Collaboration among Sanctioned Firms

||(1)|(2)<br>**Aver**|(3)<br>**age Collabor**<br>|(4)<br>**ator per P**|(5)<br>**atent**|(6)<br>|
|---|---|---|---|---|---|---|
||U.S.|Euro+|Adv. Asia|U.S.|Euro+|Adv. Asia|
|Treatment_×_Post|-0.115***|-0.041***|0.012|0.257**|-0.070**|0.305***|
||(0.038)|(0.009)|(0.052)|(0.091)|(0.023)|(0.124)|
|Treatment_×_Post_×_pre-US||||-0.446***|0.035*|-0.351**|
|||||(0.134)|(0.019)|(0.158)|
|No. Obs.|5,442|5,442|5,442|5,442|5,442|5,442|
|R-squared|0.367|0.174|0.398|0.379|0.198|0.399|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS on a set of firms that had positive patenting. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

Table 6: Prior U.S. Collaboration and Post-Sanction Innovation Performance

||(1)<br>**Pate**<br>|(2)<br>**nt Applic**<br>|(3)<br>**ation**<br>|(4)<br>**Pat**<br>**(Int**<br>|(5)<br>**ent Applic**<br>**ensive Ma**<br>|(6)<br>**ation**<br>**rgin)**<br>|
|---|---|---|---|---|---|---|
||Total|Intl.|High-Tech|Total|Intl.|High-Tech|
|Treat_×_Post|0.444***|-0.025|0.553***|0.096|0.134|0.073|
||(0.126)|(0.036)|(0.137)|(0.102)|(0.102)|(0.081)|
|Treat_×_Post_×_pre-US|-0.650***|-0.173***|-0.698***|-0.267**|-0.432***|-0.296***|
||(0.153)|(0.052)|(0.145)|(0.104)|(0.104)|(0.075)|
|No. Obs|6,334|6,334|6,334|5,442|2,040|3,690|
|R-squared|0.631|0.611|0.647|0.653|0.516|0.610|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS. columns (4) to (6) are on a set of firms with positive patenting * at _t_ . Standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

39
_natural resources_ as s̅� �1 �s̅�. Applying expression (15), the ratio of the growth rates of TFP with and excluding natural capital reduces to, In discrete time, the TFP growth rate with natural resources exceeds the growth rate calculated excluding natural resources if fixed capital is growing faster than natural resources. The difference in the growth rates increases with the share of natural resources in GDP. This result is qualitatively exactly the same as the continuous time case. The generalization of this equation for two or more natural resources is given in Annex II.

In presenting the empirical results of these calculations in the next section, it is important to note that the calculation of the average rate of TFP growth for a given country is calculated as the average annual rate of change (AARC) of TFP. To measure the AARC of TFP growth over n years we must calculate the geometric mean of g��t��1 over all years, then subtract 1. Here g��t� is the growth rate of TFP in year t: Because this is not a simple average of growth rates, we calculate the measure of variation in the TFP growth rate as the root-mean-squared (RMS) of the differences between the annual measures of TFP growth and the AARC. This notation signals that what is presented is not a classic standard deviation.

For calculating aggregates of TFP growth, such as for low-income countries as a group, or developing countries in South Asia, we measure a simple weighted average of the country figures, with country GDP in constant US dollars providing the weights.

# _Data sources_

Table 1 presents the data sources for the TFP growth calculations. For detailed information on the measurement of the natural resources appearing in the _Wealth of Nations_ database, the reader is referred to Appendix A of _The Changing Wealth of Nations 2018,_ or the detailed methodology documentation which can be found at <u>https://datacatalog.worldbank.org/dataset/wealthaccounting.</u>

# **Table 1. Data sources**

|**Indicator**|**Source**|
|---|---|
|**Resource rents**: oil, natural gas, coal,<br>minerals, cropland, pastureland, timber|_The Changing Wealth of Nations 2018_|
|**Production**: oil, natural gas, coal, minerals,<br>timber|_The Changing Wealth of Nations 2018_|
|Agriculture Gross Production Index|Food and Agriculture Organization of the<br>United Nations|
|Employed labor compensation|Penn World Table 9.0 (Feenstra et al 2015)|

8
30 57. According to the 1981 census, In individual ejidos, 50 percent of jildatarlos employ casual wage labour at some tkne In the crop cycle. However, these data shed no light on the number ot days per year durkn which labour is hired In (hiring-in Is usually restricted to seasonal peaks, particularly harvesting). The same source Indicates that only 6 percent of ajidatarlos are Involved In cooperative forms of production with other households (Table 15). This Is undoubtedly a gross underestimate and may reflect a tendency for ojkdataros to conceal practices whose legality Is In doubt. The data almost certainly fall to reflect the frequency of sharecropping arrangements among ejldatarlos, a practice that case-study evidence Indicates to be widespread (Flnkler,1978; Gregory,1986; Mummort,1 987).

58. Finally, the available labour use data are of Iklited use because they refer only to labour employed on the farm: they provide no way of assessing the role of off-farm employment In the total labour profile of the ejido household. 59. In all ejldos, the employment pattern Is highly diversified. Diversity Is a response to three circumstances. First, there Is considerable wealth differentiation within the ejldo, Initially a consequence of the differences In parcel size and land quality: latecomers to the ejido are generally worst off In terms of resource endowment.

60. Second, the majority of ejldatarlos In Mexico control too little land to depend entirely on this as a source of Income: they are obliged to look for offfarm work, either as casual agricultural labourers or In the urban sector (particularly In construction). In one study, conducted In the 1960s, It was calculated that 84 percent of all ejido plots were too small to provide full employment and an adequate Income for a peasant family; the proportion was roughly the same (85 percent) among private farms (Stavenhagen,1975:146).
**Figure 1: Restrictiveness of Services Trade Policies by GDP per capita, 2007 (102 countries)**

<!-- Start of picture text --> ETH<br>ZWE IND IRN<br>PHLIDN<br>DRC EGYCHN THA PANMYS OMN BHR<br>MWIMOZRWAMDNPLUGAMLIBGDTZAGHAZMBLSOKENCIVSENKHMNGAKCMRYEMUZBPAKNICVNM G MNGZHNDMARLKABOLPRYGTMGEOJORNAMARMDOMUKRALBTUNDZAPERCOLBRAZAFLBNBLRCRIKAZURYBGRMUSROMVENTURARGBWACHLMEXRUSPOLLTUHUN PRTSAUCZEKORNZLGRCESPITA FRAJPNAUSDEUFINBELGBRSWEDNKAUTCANNLD IRL USA<br>ECU TTO<br>0 10000 20000 30000 40000<br>GDP per capita, 2007<br>Note: GDP per capita, PPP (constant 2005 internat US$)<br>Kuwait and Qatar excluded<br>Source: Borchert, Gootiiz and Mattoo (2010).<br>Figure 2: Restrictiveness of Services Trade Policies by Sector (102 countries)<br>SAR MENA EAP World LAC AFR ECA OECD<br> Financial Transportation<br> Telecom Professional<br> Retailing<br>100<br>80<br>60<br>40<br>20<br>Restrictiveness of services trade policy<br>0<br>80<br>60<br>40<br>20<br>Restrictiveness of services trade policy<br>0<br><!-- End of picture text --> Source: Borchert, Gootiiz and Mattoo (2010).

**Figure 2: Restrictiveness of Services Trade Policies by Sector (102 countries)**

Source: Borchert, Gootiiz and Mattoo (2010).

In 2000, a new round of WTO negotiations on services was launched, as called for by the Uruguay Round agreement. These negotiations were folded into the Doha round when it was launched in 2001. The talks aimed to increase the sectoral coverage of commitments. As offers made by countries are confidential it is difficult to accurately assess what additional market access has been offered. Borchert, Gootiiz and Mattoo (2010) use available public information on the offers by 62 WTO members that had been put on the table in the Doha Round to assess how much these would increase the coverage of the commitments of WTO members. Doha offers improve on GATS commitments, but at this stage the gap between offers and actual policy is still large: the best offers submitted so far improve on current GATS commitments by about 10 percent, but remain on 6