rafmacalaba/datause-encoder-data huggingface dataset:
tobacco growers, there are only 86 household heads that are unmarried females and only 28 of these are younger than 45 years old. Clearly, the F-tests estimated in the first stage of our models rule out these concerns.<sup>17</sup> We now turn to the outcomes. We begin with yields and use this to deploy a series of robustness and sensitivity tests of our results. The impacts of burley clubs on yields are reported in panel B of Table 6. Results reinforce the notion that club membership increases tobacco yields: output per acre is between 71.6 and 122.2 percent higher due to burley membership. In model 4, which includes the most comprehensive set of controls, yields are in fact 73.9 percent higher due to club participation. These impacts are even higher than the matching estimates of Table 5. Notice that some of the variables included in the set of controls B and C capture part of the services provided by the tobacco clubs that may generate those higher yields. The set of controls B includes variables such as the number of visits of extension workers, which is one of the benefits the clubs offer through better institutional access. Similarly, the set of controls C includes the use of each type of fertilizer, which, in the case of club members, is determined by collective acquisition through loans. Hence, it is reasonable to find that the causal effects of clubs decrease as these controls are included in the regressions (compare Models 1 and 4).

Although the estimated impacts of burley club membership are quite large, they are reasonable. To see this, recall that, as we reported in Table 2, club membership provides a number of services that can significantly affect yields. According to both the Integrated Household Survey and the Tobacco Clubs Surveys, most clubs offer access to credit, inputs and knowledge, all services that should boost yields. In addition, burley clubs offer two powerful channels through which tobacco growers are induced to increase their effort and thus outperform non-members. On the one hand, the clubs increase the expected profit margins of the members by raising the likelihood of fetching higher prices at the auction floor in comparison with the alternative of selling the tobacco to intermediate buyers and

> 17Another issue is that life expectancy at birth in 2007 was 42.61 years old. This begs the question of whether the “under 45” constraint is actually binding. In fact it is. The reduction in life expectancy in Malawi is a somewhat recent event associated with AIDS. In the relevant sample from the IHS, 6.4 percent of the population is female and older than 45 years old. Further, 2.5 percent of household heads are women, unmarried and over 45 years old.

27
Hence, no macro-wage curve can be revealed using unemployment as pressure of demand variable as the level of unemployment plays no significant role in the longer term in explaining nominal wages.

**Table 4** ECM **for total wages (including GDP)**

|Variable|Coefficient<br>Std. Error|t-value|t-prob|Part RP|Instab|
|---|---|---|---|---|---|
|W_1|-0.317<br>0.105|-3.008|0.007|0.301|0.05|
|Constant|2.471<br>1.183|2.09|0.049|0.172|0.05|
|CPI_1|0.089<br>0.053|1.664|0.111|0.117|0.05|
|-CPI|-0.023<br>0.131|-0.172|_0.865_|0.001|0.05|
|GDP_1|0.099<br>0.038|2.649|0.015|0.250|0.05|
|-GDP|0.083<br>0.036|2.312|0.031|0.203|0.04|
|Trend|0.022<br>0.009|2.534|0.019|0.234|0.05|
|i1984|-0.114<br>0.034|-3.364|0.003|_0.350_|0.05|
|i80-84|0.086<br>0.019|4.613|0.000|0.503|0.03|
||**Definitions of**|**Variables**||||
||**W_I**is log total wa<br>**U_1**is log total unemploy<br>-U is the first order<br>CPI_1 is the log of the consum<br>-CPI is the diffe<br>**GDP_I**is the log of<br>-GDP is the first order<br>iXXXX is a dummy f|ges lagged o<br>ment rate la<br>difference o<br>er price inde<br>rence of CPI<br>GDP lagged<br>difference o<br>or the(se) ye|nce<br>gged once<br>f U<br>x lagged o<br> <br>once<br>f GDP<br>ar(s)|nce||
||**Test Stat**|**istics**||||
||R= 0.788 F(8, 21) =9.7442 [0<br>RSS = 0.018 for 9 variabl<br>Variance instability test: 0.439<br>Information Criteria: SC = -6.396<br>AR 1- 2F( 2, 19)=<br>ARCH I F( 1, 19) <br>Normality Chi<sup>2 </sup>(2)=<br>Xi<sup>2 </sup>F(16, 4)_=_0<br>RESET F( 1, 20)|.000]a=0.<br>es and 30 ob<br>Joint instab<br>HQ = -6.682<br> 0.869 [0.43<br>=0.035 [0.8<br>6.940 [0.03<br>.416 [0.907]<br>= 0.165 [0.6|029 DW=<br>servations<br>ility test: 1.<br>FPE = 0.0<br>5]<br>53]<br>1]<br> <br>891|2.33<br>797<br>0111629||

22
3 from the agreement such as political cooperation, enhanced negotiating power and better credibility for the members' general economic reform programs, the possibility of achieving otherwise unattainable economies of scale in production, or dynamic trade gains. These are legitimate objectives which, if achieved, could well more than offset trade distortionary effects. In the present context, however, nothing can be said about them. Second, although there are other possible standards, the counterfactual comparison used in this study is an equivalent degree of liberalization on a nondiscriminatory basis.

# II. Mercosur: Recent Trade Trends

Available data indicate that member countries' trade patterns have experienced major changes since the formation of Mercosur.I For example, Table I provides summary statistics on Mercosur's intra-trade and on exports to destinations such as all OECD countries, NAFTA, or OECD Europe. These data are shown for selected periods from 1979-81 to 1994 in order to help determine when trend changes occurred.2 For the earlier intervals three year periods were used to tabulate data in order to reduce the influence of any annual irregular variations in the trade statistics -- such as those that might accompany significant fluctuations in commodity prices. However, statistics for 1993 and 1994 are shown separately in order to more accurately reflect the recent influence of Mercosur on trade flows.<sup>3</sup> 'This analysis has been restricted by data availability to exploring trade patterns only up to and including 1994. This is sufficient to identify the effects of preferential trade policies, however, for some preferences were included in the Argentina-Brazilian sectoral agreements at the end of the 1980s and widespread preferences were introduced in the transition period for Mercosur starting in June 1991. However, our exercise clearly sheds no light on events since 1994, such as the further progress towards a Common External Tariff or any progress in the non-trade policy aspects of the agreement. These remain on the agenda for further research 2Globally there has been a long-term decline in the relative importance of the Mercosur countries. According to UN COMTRADE data, in 1950 member countries accounted for 4.6 percent of world exports, yet this figure fell to 1.4 percent in 1992. Since most of the countries ran substantial trade surpluses since the mid-1980s Mercosur's share of world imports is even smaller, i.e., 1.0 percent in 1992.

3A more detailed analysis of the annual trade data used in the construction of Table 1 strongly suggests that 1991 was the year that intra-Mercosur trade began its significant increase in relative importance. In June of 1991 Mercosur began to implement discriminatory tariff preferences on intra-Mercosur trade.
shows that there is enough ‘noise’ in trade data to more than overwhelm whatever economic content the SSM formulas might intend.<sup>56</sup>

# **8. SSM AS PART OF THE MARKET ACCESS ISSUE**

Though Director-General Pascal Lamy singled out the SSM as the issue on which the July 2008 WTO meetings foundered, it is difficult to separate the SSM from the other parts of the agricultural negotiations. Tentative agreement on modalities for tariff reduction formulas and exceptions may have been more a matter of shifting problems to the SSM discussions than of resolving them.

## <u>Tariff rates</u>

Not only do agricultural tariff rates differ markedly from one WTO Member to another, bound rates for a number of Members are many percentage points above applied rates. Differences are as pronounced among developing Members or among developed as between the groups. Among developed Members, the spread between bound and applied rates is near zero for several, above 60 percentage points for others. Among developing Members, the spread is near zero for China and the three Chinese customs territories treated as separate WTO Members (Hong Kong, China; Macau, China and Chinese Taipei); 80 percentage points for India, over 100 percentage points for a number of others. (Annex 4 enumerates tariff rates and bound-applied spreads.) The large bound-applied spreads mean that reductions of bound rates (the coin of GATT/WTO negotiations) will have to be large relative to the level of cuts achieved in previous rounds if market access is to be enhanced – applied rates reduced. Because the spread between bound rates and applied rates varies considerably among Members at comparable levels of development, arithmetically equal reductions of bound rates will have a greater bite for some than for comparable Members.

Differences among Members in the present spread between bound and applied rates also mean that pre-Doha Agreement bound rates as limits on SSM application will mean more stringent limits on some Members than on others.

## <u>Tariff formula and flexibilities</u>

The tentatively modalities provide tariff reduction formulas are complex and ‘flexibilities’ that allow each Member considerable latitude to exclude products from the formula reductions – as special or sensitive products. Martin and Mattoo’s analysis indicates that the flexibilities have the potential to reduce the

> 56 A recent analysis by de Gorter, Kliauga and Nassar (2009) applied the price formula of the July 2008 modalities to the imports of India, China, Korea and Indonesia over the period 1998-2003. They report (Table 3) that the formula would have authorized SSM duties on 6 percent of these countries’ agricultural imports from developed countries, as compared with 25 percent of imports from least developed countries and 10 percent of imports from developing countries.

36
sample became landless, their asset and income levels in 1999 were not significantly different from those in the autarky group with their per capita income significantly above the latter. Even though some sales may have been undertaken out of distress, those who sold land did, on average, not become worse off and may even have done slightly better than the rest, e.g. because they took up non-farm activities.

Turning to participants in non-market transactions, we note that, in the parent generation, those inheriting out had significantly higher levels of land endowments, assets, incomes, and unmarried sons or daughters than the rest. However, different from what had been observed for market transfers, households who received land through inheritance did not manage to accumulate non-land assets or increase their consumption over time faster than those who did not benefit from such inflows. Although their per capita land endowment was, with 0.97 as compared to 0.72 ha still significantly above that for the rest, levels of assets and per capita consumption both were insignificantly different from those for autarky households. Although they have a slightly higher level of income than those who did not inherit, the difference is smaller than for those who purchased land. This suggests not only that markets are an important avenue to access land for those born into a landless dynasty but also that the latter are better able to make beneficial use of such land than those who inherited it.

# **4. Econometric Results**

We report results from the Cobb-Douglas production frontier function and the ordered probit regression for sale’s market participation as well as the probit regression results for non-market participants. The coefficients for most of the inputs in production function are as expected with the most contribution from land. Results from the ordered probit equation are also in line with expectations, indicating among others that households with better agricultural ability, more sons between 5-25 in 1981, and landless dynasty households, are more likely to purchase land. The results also point towards imperfection of credit markets having had an impact on land markets via distress sales.

# **4.1 Participation in land sales markets**

To obtain a measure of dynasties’ agricultural ability, a production function, coefficients for which are reported in appendix table 1, was estimated. The high value (0.77) of the _R_<sup>_2_</sup> for the OLS regression points towards a very good fit. Coefficient estimates from maximum likelihood estimation of the Cobb-Douglas stochastic production frontier are similar to those obtained by OLS. Land is estimated to be by far the most important input to crop production; doubling cultivated land area alone would lead to an almost 50% increase in total crop production. This is followed by labor with an estimated elasticity between 18.8% and 20.8.%. Compared to these two variables, returns to other factors are estimated to be more moderate with elasticities of about 5%, 4%, 2.5%, and 2.3% for fertilizer, assets, other, and pesticide expenditures.

13
2010; Robles _et al_ 2013). In 2008, according to Ribaldo (2010), “90% of all cocaine entering the United States did it through Mexico or its territorial waters”.

In 2004 the U.S. Federal Assault Weapons Ban (AWB) expired and the U.S. government did not renew it. This multiplied the supply of guns available to Mexican criminals, increasing the groups’ lethal power. This was particularly true in municipalities located in border states where the volume of drugs being smuggled was on the rise with a likely contribution in homicides.<sup>4</sup> For instance, from 2006 to 2010 the share of homicides by fire guns increased from 54 percent to 71 percent. Dube et al. (2013) found that as of 2006, 90 percent of the guns seized in Mexico were traced back to the United States, and out of these, 49 percent could be traced back to either Texas or Arizona. Although it is difficult to quantify the total number of weapons supplied to Mexican DTOs from the United States, between 60,000 and 85,000 firearms recovered by Mexican authorities have been traced back to the United States.<sup>5</sup> As early as 2005-2006, municipalities with entry ports into Texas, Arizona, and New Mexico saw homicides rise by 60 percent as compared to municipalities 100 miles away from the border, according to Dube et al. (2013). Furthermore, they estimate that 21 percent of all homicides in bordering municipalities stemmed from the expiration of the AWB. Chicoine (2011) found that the expiration of the AWB explains at least 16.4 percent of the increase in the homicide rate nationally between 2004 and 2008.

These external factors were compounded by conflicts among Mexican DTOs and a more confrontational approach by the Mexican Government. By 2006, DTOs had initiated what amounted to open war against each other and authorities in Ciudad Juárez, Nuevo Laredo, and Michoacán as they sought to control strategic areas for the traffic and production of drugs for the U.S. market. In December 2006, President Calderón received a request from the governor of Michoacán for intervention by federal forces to stop the bloodshed. Governors of other states soon made similar requests. The President’s response to these calls for help was to deploy thousands of army troops and marines to fight the cartels. The institutions normally in charge of providing citizen security—the Prosecutor’s Office and the Federal Police—were soon displaced to a minor role. The Mexican public came to view the

> 4 In California, a state-level ban on assault weapons remained in place. 5 Grimaldi, James and Sari Horwitz, (2010); Hiatt, Fred, (2011).

7
Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

Table A.2. Adjusted Wald test of means by displacement status and gender, for age groups

|Country|Children Pop<br>Female<br>IDPs v non-I|ulation (0-17)<br>DPs||IDPs<br>Females v M|ales||Non-IDPs<br>Females v M|ales||
|---|---|---|---|---|---|---|---|---|---|
||MPI|H|A|MPI|H|A|MPI|H|A|
|Ethiopia|56.06***|56.27***|1.19|0.78|0.26|4.56**|0.44|0.40|1.48|
|N.E. Nigeria|1.00|1.53|3.16*|3.30*|3.14*|0.52|0.04|0.06|3.25*|
|Somalia|5.29**|6.01**|0.94|0.09|0.14|0.08|0.81|0.80|0.01|
|South Sudan|2.74|2.90*|2.70|0.37|0.19|2.53|5.33**|5.63**|0.03|
|Sudan|163.21***|162.68***|6.37**|0.39|0.25|1.03|4.88**|5.16**|0.41|
||Adult Popula<br>|tion (18+)||||||||
|Country|Female<br>IDPs v non-I<br>|DPs<br>||IDPs<br>Females v M<br>|ales<br>||Non-IDPs<br>Females v M<br>|ales<br>||
||MPI|H|A|MPI|H|A|MPI|H|A|
|Ethiopia|66.42***|65.46***|8.98***|29.13***|30.91***|5.55**|2.60|2.92*|4.08**|
|N.E. Nigeria|3.63*|4.53**|6.49**|33.72***|28.83***|0.50|0.94|1.09|2.27|
|Somalia|9.36***|9.94***|1.33|3.32*|2.87*|0.82|2.57|2.28|0.55|
|South Sudan|1.90|1.80|0.58|4.38**|4.16**|5.34**|15.73***|14.90***|3.15*|
|Sudan|120.59***|119.95***|2.68|2.34|1.62|4.72**|0.27|0.45|3.72*|

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

37
19 well does not seem to be of primary concern. It confirms in a sense what was found in hedonic analysis of property prices measuring the value of a water connection in Central America by Nauges et al. (2005). These authors show that a private well on one’s property is valued as much as a connection to the piped network, by non-connected households.

# **4.3. Estimation of water demand of non-piped households**

# _a. Estimation of aggregated non-piped water demand_

We consider the whole sample of non-piped households (1,004 households). We estimate a single equation model where the dependent variable is the log of non-piped water consumption per capita (measured in litres per day). If some non-piped household combines water from different sources, we aggregate consumption from these sources. We assume that the quality of water is the same across sources. We do control to which source each household has access to by introducing four dummy variables equal to 1 if the household has access to water provided by neighbors, a private well, a public well, or a public tap, respectively.<sup>16</sup> Specification tests have been made in order to determine the “best” set of regressors. Estimation results are shown in Table 6.

The value of capital or total value of the investment (as measured by the sum of the monthly equivalent of capital cost and monthly operating cost) made by the household in order to collect water is highly significant in this model. The higher the value of the equipment purchased to source non-piped water, the more convenient to collect non-piped water, the higher per capita non-piped water consumption is. Time cost has the expected negative sign and is also significant at the 1 percent level: the more time needed to go to and to wait at the source, the lower per capita consumption. It is interesting here to note that time spent to collect water, which may seem low on average, has a very significant effect on consumption. Income elasticity is rather small (0.07) but significant at the 10 percent level. In this model, household size is found highly significant and the negative sign of its coefficient illustrates the so-called scale effects (the larger the family, the lower per capita consumption). A bigger house and the possibility to store water (although the latter is not significant) are found to increase per capita water consumption. The variables measuring concern about taste have an

> 16 We do not include any dummy variable to control for access to other sources such as vendors, surface water, and rain water, as these sources are used by very few households in our sample.
23 <!-- Start of picture text --> APPAREL GROUP NO. 9 (KNIT SHIRTS)<br>Price Per Unit in US Dollnrs<br>19"1-116<br>.3 4 s 7<br>'1* .<br>o<br>N<br>C<br>4/ NY=<br>S<br>K ~ _ * /S __KS<br>3 II00a<br>3 4 & 6 7<br>UNITED STATES<br>Figure 3(ix)<br><!-- End of picture text --> <!-- Start of picture text --> APPAREL GROUP NO. 10 (UNDERWEAR)<br>Price Per Unit in U S Dollare<br>9<br>13 o T 03 Ol § 8 4 1 3<br>:!<br>0<br>N<br>0<br>HAUNB<br>~~~~~~~~~~~~~~~0 F 3<br>00 03 OS @* I 0 33 II6<br>UNlTM SYA1U<br>Figure 3(x)<br><!-- End of picture text --> <!-- Start of picture text --> APPAREL GROUP NOS. 1-10 (ALL)<br>Price Per Unit in U S Dollars<br>3M31 - 3968<br>30<br>25<br>80~~~~ * . ~<br>20<br>H a<br>C K o OF , C<br>G 3<br>/'{Z~~~'<br>10<br>0 S 10 IS 20 25 30<br>UNITED S?A1L<br>iu" - x x Am.<br>Figure 3 ( xi)<br><!-- End of picture text -->
|Age|35.3|35.89|0.399|0.528|
|---|---|---|---|---|
|Chinese|0.16|0.17|0.078|0.781|
|Indian|0.08|0.04|2.928|0.088|
|Bumiputera|0.73|0.78|2.293|0.131|
|EDU: up to secondary|0.18|0.18|0.001|0.975|
|EDU: Post-secondary|0.26|0.24|0.161|0.689|
|EDU: Bachelor’s|0.39|0.44|1.38|0.241|
|EDU: Professional|0.05|0.06|0.022|0.882|
|EDU: Masters+|0.11|0.08|2.382|0.123|
|Low Income|0.32|0.32|0.014|0.905|
|Med Income|0.34|0.36|0.363|0.547|
|High Income|0.34|0.32|0.242|0.623|
|In Standard Employment|4.11|3.94|0.252|0.616|
|Time Preference Index|0.49|0.57|3.011*|0.083|
|Risk Appetite (0-10)|6.71|6.59|0.329|0.567|
|N|262|266|||

# c. Econometric Analysis

We sequentially examine the results for unemployment insurance coverage, retirement savings, and insurance for workplace injuries.

## Unemployment Insurance

Table 2 below shows the coefficients of the LPM with dependent variable is the dummy variable for the choice of Job A (= 1 if the respondent chooses job A, 0 if she chooses job B). In the case of unemployment insurance, Job A includes no unemployment insurance and earnings are set at the same level as the respondents’ current income.

Table 2: Likelihood of Choosing Job A with alternate unemployment insurance coverage offers/ earnings, total and by Gender, Age Group _(Dep Var = 1 if Job A preferred to Job B, else 0. Job A has no unemployment insurance and earnings equal to respondents’ current income)_

||Ge|nder||Age||All|
|---|---|---|---|---|---|---|
|UI Coverage,Earnings features in Job B|Male|Female|Age < 30|Age 30 - 39|Age >= 40|respondents|
|Payment of RM 800 per month for 6<br>months, 0.5% lower income|0.204***<br>[0.0328]|0.178***<br>[0.0371]|0.195***<br>[0.0428]|0.192***<br>[0.0449]|0.192***<br>[0.0449]|0.193***<br>[0.0246]|
|Payment of RM 800 per month for 6<br>months, 0.2% lower income|0.150***<br>[0.0303]|0.185***<br>[0.0351]|0.129***<br>[0.0350]|0.183***<br>[0.0430]|0.183***<br>[0.0430]|0.167***<br>[0.0230]|
|Payment of RM 1,200 per month for 6<br>months, 0.5% lower income|0.167***<br>[0.0318]|0.120***<br>[0.0301]|0.143***<br>[0.0401]|0.191***<br>[0.0408]|0.191***<br>[0.0408]|0.145***<br>[0.0221]|
||0.196***|0.156***|0.230***|0.179***|0.179***|0.177***|

10