rafmacalaba/datause-encoder-data huggingface dataset:
producers. All else being equal, greater access to irrigation would therefore be expected to magnify the positive relationship between rainfall and agricultural import tari¤s. To examine this prediction, we use country-level data on the proportion of total agricultural land that is irrigated. As mentioned above, these data are available for 32 of the 70 countries initially considered. Hence our estimation sample is reduced accordingly. Table 11 reports the corresponding results. In line with the predictions of our theoretical model, the estimates in columns (2) and (4) suggest that greater access to irrigation contributes to magnify the positive relationship between rainfall and agricultural import tari¤s.

## [Table 11 about here]

# 8 Concluding remarks

We have examined theoretically and empirically the e¤ects of rainfall shocks on tari¤ setting in the agricultural sector. In a model of strategic trade policy, we have shown that the impact of a negative rainfall shock on optimal tari¤s is generally not clear-cut, depending crucially on the weight placed by the domestic policy maker on each of the various policy objectives, and on the shape of the food demand function. We have then estimated the impact of rainfall shocks on applied tari¤s. Using detailed panel data on applied tari¤s and rainfall for 70 nations, we have found robust evidence that rainfall shortages generally lead to lower tari¤s on agricultural imports. When interpreted in conjunction with our theory, this result suggests that policy makers place a considerable weight on domestic producer surplus when setting import tari¤s.

# Appendix

[Tables A.1-A.3 about here]

# References

- [1] Andreyeva, T., Long, M.W. and Brownell, K.D. (2010). "The Impact of Food Prices on Consumption: A Systematic Review of Research on the Price Elasticity of Demand for Food." American Journal of Public Health, 100: 216-222.

- [2] Banerjee, L. (2007). "E¤ects of ‡ood on agricultural wage formation in Bangladesh: An Empirical Analysis." World Development, 35: 1989-2009.

- [3] Bagwell, K. and Staiger, R. (2001). "Strategic trade, competitive industries and agricultural trade disputes." Economics and Politics, 13: 113-128.

21
# **1. Measures Which Tend to Tax or Regulate Exports**

Table 9 shows the frequency of developing-country use of various kinds of policy measures to tax or regulate their exports. The table is constructed much like those on imports and shows snapshots of the frequency of use of different measures by various countries in different time periods. The frequencies are defined in terms of the proportion of total product groups at the HS 2 level (and some at HS 6) which may contain products that are taxed or regulated by different measures in different countries. The measures include export taxes or similar levies, minimum export prices, non-automatic export licensing (or approval), export prohibitions (total or partial) and export quotas. The last column shows whether the country also uses export subsidies.

Just as with imports, export taxes are the most commonly used measure, although licensing used to be very important in earlier periods. The variation among countries, especially during the earlier periods is rather large, with some countries, such as Brazil, Indonesia, Malaysia controlling more than 20 percent of export product groups through licensing or other approval procedures, while many others limited such licensing to the implementation of health and safety standards and environmental obligations.<sup>15</sup> There is no obvious pattern with respect to export taxes: their use has increased in some cases (Brazil, Sri Lanka) but decreased in others (India, Kenya).

The TPRs suggest that often a combination of measures - sometimes a quantitative measure, such as a license in combination with a price linked measure, such as an export levy -- would be used. A rather crude adding up of the number of countries and measures that are imposed by product group showed that the most frequently regulated/taxed product groups of exports by developing countries in 1990-1998, at the HS 2-digit level, were live animals, coffee, cocoa and tea, fuels, hides and skins, wood products, and cotton (Michalopoulos,2001 p.70). Much the same products appeared to be regulated in the period since 2000.

29
**[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C17a_maternity1 Would your employer allow you to take maternity leave from this MAIN job if you need it?

1. Yes 2. No à **Go to C18** 888. Don’t know à **Go to C18** 999. Refused à **Go to C18**

**[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C17b_maternitylength1 How many weeks of maternity leave (paid or unpaid) would your employer allow you to take if you needed it? **[If as needed then put “As needed”] [If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

**[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C17c_maternitypaid1 For how many of those weeks would your employer pay you? **[If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

C18_overtime1 Does your employer give you extra pay for overtime work in this MAIN job?

1. Yes 2. No 3. Do not work overtime 888. Don’t know 999. Refused

C19_bonus1 Does your employer offer any bonus payments in this MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused C20a_providentfund1 Does your employer offer a Provident Fund in this MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused

C20b_gratuity1 Does your employer offer gratuity (lump sum) in this MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused

7
# Successful Nutritioni Programs in Africa What Makes Them Work?

# by Eileen Kennedy

# Table of Contents

# Introduction

Introduction 1 Case Studies 5 Macina Child Health Project, Segou Region. Mall 5 Infant F-eding Project. Togo 5 Imo State Child Survival Project, Nigeria 6 Applted Nutrition Program, Ghana 6 Mali Institutional Development Enterprise and Nutrition 6 Program. Mali Nutrition Project, Kinshasa. Zaire 7 Factors Associated with Successful Nutrition Programs 7 Community Participation: Multi-Level Perspective 8 Program Flexibility 14 Ins.itutional Structure: Government versus Private Institutions 17 Recurrent Cost Recovery 19 Multi-Faceted Program Activities 21 Training and Staff Qualifications 22 Infrastructure 24 Discussion and Conclusions 27 Appendix: Case Study Descriptions 30 References 42
labor, input expenditures and income. Similarly, a variable reflecting the reported number of crimes in the community is also included in the regressions. As reported in other studies on Albania (Castaldo et al., 2005; McCarthy et al., 2006), criminality is likely to deter involvement in production activities, and especially agriculture. Lastly, a distance index from public facilities is created through principle component analysis to capture accessibility of public facilities, which also serves as an indicator of household remoteness and, thus, access to markets and services. The index accounts for (i) distance from primary schools; (ii) distance from ambulatory facilities; and (iii) distance from the closest bus stop. Consequently, the higher the distance from these services, the higher the household isolation, thus reducing its choices of engaging in other activities except agriculture, and increasing the amount of labor devoted to agriculture.

# **VI.  Estimation Results**

# **_a) IV testing_**

There are no readily available tests for the validity of the instruments for censored variables as there are for continuous uncensored dependent variables. We thus run the relevant overidentification test using the _ivreg_ command in Stata on the uncensored specification. To account for heteroscedastic errors, we generate Hansen’s J statistics<sup>6</sup> to test for the joint hypothesis that the model is correctly specified, and the orthogonality condition is satisfied. A rejection of the null hypothesis indicates that either the instrumental variables are wrongly excluded from the regression, or the orthogonality condition is not satisfied. Estimated J-statistics for each regression are reported at the bottom of table 2-4 and show that the null hypothesis cannot be rejected and our instruments are valid.

The consequence of using instruments with little explanatory power in predicting our endogenous variable is larger biases in the estimated IV coefficients (Hahn and Hausman, 2002), thus potentially rendering the use of instrumented models less beneficial _vis a vis_ non-instrumented estimators. For this purpose, we also run a joint significance test of the instruments in the first stage regression based on the Cragg-

> 6 We use Hansen’s J statistics instead of the Sargan test because of the assumption of heteroscedastic errors.  The two tests are equivalent in the case of homoscedasticity.

14
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Table 5. Averages for explanatory variables, by <u>gender</u>

||Men<br>(1)|Women<br>(2)|
|---|---|---|
|Respondent characteristics|||
|HH head|0.64***|0.15***|
|Age: 18-24|0.23|0.22|
|Age: 25-34|0.15*|0.13*|
|Age: 35-44|0.26**|0.24**|
|Age: 45-54|0.14|0.15|
|Age: 55+|0.21***|0.26***|
|Years of schooling|6.54***|4.76***|
|Worked/ran NFE in last 7 days|0.21|0.22|
|Worked for wage in last 7 days|0.44***|0.26***|
|Worked in agr. in last 7 days|0.46*|0.43*|
|Worked in off-farm (NFE or wage work)*share of|***|***|
|household annual off-farm earnings|0.37|0.25|
|Married/non-formal union|0.78***|0.69***|
|Separated/divorced|0.01***|0.04***|
|Widowed|0.03***|0.15***|
|Months member is away|0.72***|0.38***|
|Respondent’s children and composition of other HH|||
|members|||
|# Boys aged 0-5|0.18***|0.16***|
|# Boys aged 6-15|0.28***|0.27***|
|# Boys aged 16+|0.20***|0.23***|
|# Girls aged 0-5|0.17***|0.16***|
|# Girls aged 6-15|0.30***|0.29***|
|# Girls aged 16+|0.24***|0.30***|
|# other HH members: 65+|0.22***|0.25***|
|# other HH members: men, 31-64|0.30***|0.78***|
|# other HH members: women, 31-64|0.92***|0.36***|
|HH characteristics|||
|Rural area (Y=1 N=0)|0.73|0.73|
|Log of HH size|1.49***|1.45***|
|HH has electricity|0.85|0.86|
|HH has piped water|0.26|0.27|
|Concrete or brick construction|0.26|0.26|
|Respondent’s asset ownership|||
|Mobile phone (exclusively owned)|0.47***|0.40***|
|Mobile phone (jointly owned)|0.44***|0.37***|
|Financial account|0.14**|0.12**|
|Motorized vehicle|068***|053***|
|Livestock (exclusively owned)|.<br>0.16***|.<br>0.21***|
|<br>Livestock (jointly owned)|<br>0.23|<br>0.23|
|Residential land (reported, exclusively)|0.19**|0.22**|
|Residential land (reported, joint)|0.55|0.55|
|Non-residential land (reported, exclusively)|0.17|0.18|
|Non-residential land (reported, joint)|0.39|0.38|
|Observations|1,845|2,093|

Notes:(1) Estimates weighted by household sampling weight. Significant gender differences are indicated by asterisks; ***p<0.01 **p<0.05 *p<0.10 . (2) Additional fixed effects for district, interview date, and enumerator were included (specification (A)).

21
|**Table 1: Baseline Characteristics of the Sample by Treat**|**ment Group**<br>**Curr**|<br>**ent Enterpri**|<br>**ses**|**Pot**|<br>**ential Enterp**|<br>**rises**|
|---|---|---|---|---|---|---|
||Control|Training<br>only|Training +<br>Cash|Control|Training<br>only|Training +<br>Cash|
|_Variables stratified on_|||||||
|Total Monthly Profits (Rs.)|3987|3981|4001||||
|Have no children or have someone to look after them|0.55|0.54|0.55||||
|Colombo district|0.20|0.20|0.20|0.19|0.20|0.21|
|Kandy district|0.21|0.20|0.20|0.20|0.20|0.20|
|Has taken concrete steps to opening business||||0.51|0.50|0.51|
|Has never worked before||||0.18|0.17|0.19|
|_Variables not stratified on_|||||||
|Age|35.94|37.71|36.58|34.38|34.05|33.72|
|Married|0.89|0.86|0.80|0.84|0.91|0.89|
|Number of children under 18|1.55|1.47|1.40|1.40|1.47|1.59|
|Years of Education|10.16|10.34|10.51|10.51|10.56|10.53|
|Risk-seeking score|6.81|6.87|6.53|6.73|6.82|6.75|
|Digitspan Recall|6.00|6.04|6.01|6.03|5.93|6.06|
|Raven test score|2.58|2.75|2.68|2.76|2.59|2.81|
|Total household income from all sources|17192|18245|17595|16422|16690|16393|
|Wealth index (principal component)|0.01|0.05|0.28|-0.09|-0.12|-0.11|
|Household has a fridge|0.45|0.53|0.51|0.39|0.41|0.43|
|Household has a sewing machine|0.56|0.60|0.60|0.51|0.54|0.55|
|Household has an oven|0.08|0.08|0.12|0.09|0.05|0.08|
|Household has a gas cooker|0.25|0.23|0.30|0.28|0.24|0.24|
|Age of Firm (years)|6.47|6.88|6.35||||
|Ever had a loan from financial institution|0.23|0.18|0.20||||
|Total Monthly Sales (Rs.)|12523|12485|12640||||
|Capital Stock excluding land and buildings (Rs.)|28649|27418|35187||||
|Truncated Capital Stock (Rs.)|28649|27418|34997||||
|Business Practices Score|4.59|4.99|4.98||||
|**Number of Firms**|224|200|200|228|200|200|

34
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expensive outcome variable of interest is most notable when a larger share of the total error is due to the sampling error (rather than the model error).

It is conceivable that larger gains can be obtained under certain conditions, for example, when the cost of collecting the outcome variable of interest is extraordinarily high, when the cheaply available predictors exhibit an exceptionally high correlation with the outcome variable of interest, and when the data exhibits very little spatial correlation. We conjecture, however, that these circumstances represent the exception rather than the rule. Moreover, we have currently abstracted away from model misspecification error. Ignoring this component of error obviously favors the prediction estimator. Accounting for misspecification error is not obvious; it is hard to quantify since the true model is inherently unknown and any given estimate of the model can be misspecified in infinitely many ways.

Given these observations, when new data is to be collected, we recommend that the outcome variable of interest should be included so that one does not have to rely on predicted data. This does not mean that there is no role for prediction estimators. Under the right circumstances we believe they could be of great value. For one, prediction estimators provide the means of leveraging already existing data (think of non-nested double sampling estimators, see for example Kim and Rao (2012) and Douidich et al. (2015)). Furthermore, if no previous data exists and the budget is particularly constrained such that one may be left with the choice between predicted data or no data, then the former may be preferred over the latter. In such a data-poor environment, which is not unheard of in developing countries, double sampling estimators may continue to provide a valuable option.

# **References**

- Ahmed, F., C. Dorji, S. Takamatsu, and N. Yoshida (2014) ‘Hybrid survey to improve the reliability of poverty statistics in a cost-effective manner.’ World Bank Policy Research Working Paper 6909, The World Bank

- Aliaga, A., and R. Ren (2006) ‘Optimal sample sizes for two-stage cluster sampling in demographic and health surveys.’ DHS Working Papers 2006 No.30, ORC Macro

31
One of the things that stood out from the FGDs was the lack of adequate transportation, the need to spend one’s wages on transportation, and, in some cases, one’s wages not being high enough to justify one’s transportation costs. According to the NHBPS 2014/15, the average transportation cost per person per year is SDG 71 and increases with household income. For example, a female youth from Khartoum who was working said, “The working time is related to the transportation, as if I finished my work late, I will not find any transportation to get back home.” A woman from Khartoum who did not have a job said, “Yes, exactly, because of the transportation. The transportation is hard and costly.” A working woman from Khartoum said, “Yes, maybe, you can finish your working hours at 5 p.m. and you can’t find a transportation or you can find a means of transport but it will cost you a fortune and at the end of the month you wouldn’t have any salary left.” 51
**Table 3. Lagged correlates of sending a work migrant, household level**

|VARIABLES|(1)|(2)|
|---|---|---|
|De facto female-headed household|0.06***|0.03|
||(0.01)|(0.02)|
|De jure female-headed household|0.03***|0.03|
||(0.01)|(0.03)|
|Age of household head|0.00***|0.00|
||(0.00)|(0.00)|
|Primary incomplete (head)|0.03***|-0.04*|
||(0.01)|(0.02)|
|Primary complete (head)|0.05***|-0.02|
||(0.01)|(0.03)|
|Secondary incomplete (head)|0.07***|-0.02|
||(0.01)|(0.03)|
|Secondary complete (head)|0.08***|-0.02|
||(0.02)|(0.05)|
|Post-secondary technical (head)|0.12***|0.00|
||(0.02)|(0.05)|
|University and higher (head)|0.07*|-0.17|
||(0.04)|(0.13)|
|# of adult males (15-59) in HH|0.04***|0.02|
||(0.01)|(0.01)|
|# of adult females (15-59) in HH|0.04***|0.01|
||(0.01)|(0.01)|
|# of adults aged 60+ in HH|0.03**|-0.01|
||(0.01)|(0.02)|
|Urban household|-0.02|-0.04|
||(0.01)|(0.03)|
|Central (excl. Kampala)|0.03*||
||(0.02)||
|Eastern|-0.03*||
||(0.02)||
|Northern|-0.05***||
||(0.02)||
|Western|0.01||
||(0.02)||
|Constant|-0.17***|0.01|
||(0.02)|(0.05)|
|Household fixed effects?|No|Yes|
|Observations|8,345|8,345|
|R-squared|0.09|0.59|
|F|17.50|8.094|

Source: UNPS. Also includes year fixed effects. Robust standard errors, clustered at the enumeration area level, in parentheses. Significance levels are reported as follows: * p<0.10, ** p<0.05, *** p<0.01.

9
longitudinal survey of immigrants had randomly selected a large pool of the ballot winners as potential individuals to interview in their survey, and to avoid respondent burden, their details were not released to us at the time of these initial surveys. In addition, there were individuals we could not locate, or who had moved to parts of New Zealand where it was too costly to conduct our in-person surveys.<sup>6</sup> In late 2012 we received permission from the immigration authorities to view the names of all PAC ballot winners from 2002-05, including those earlier reserved for the official government survey. In order to have a larger sample to examine long-term impacts, this paper attempted to track all migrants from this full list. However, the only address details for these migrants were from about eight years earlier, when they had submitted their residence applications, and in many cases these addresses were for relatives in either New Zealand or Tonga. In order to locate these migrants so as to survey them, we needed fieldwork in both New Zealand and Tonga since people in their home village were often the best source of updated contact information. It is notable that only one-third of the migrants surveyed used fixed line telephones, with the remainder relying on mobiles and even those with fixed lines were typically not listed in any type of directory. Moreover, a large fraction of the migrants had moved out of the main destination city of Auckland, into regional centers, which made it harder to track them through churches and other local social networks. These tracking and surveying operations were costly and unlikely to be possible in many other contexts when the countries and populations involved are larger.

Our main survey approach was an extensive face-to-face interview, coupled with health data measurements such as anthropometrics, blood pressure, and peak lung flow, and lab-inthe-field games whose results will be used in a companion paper. This long survey was completed for 133 of the winning principal applicants in New Zealand between late 2013 and

> 6 McKenzie et al.(2010) report a tracking rate of just over 70% for the sample frame they were given.

8
financial development gap, but the evidence on Equity Bank is encouraging, since it points to the potential for financial institutions with novel strategies to foster financial inclusion and help promote convergence of the African financial development model with the rest of the developing world.

One component of Equity Bank’s expansion strategy has been to target the segment of the population speaking local languages. There are two ways of doing this. The first one is penetrating districts characterized by a high proportion of the people speaking minority languages. The second way is to use minority language in the branches. We start by exploring whether Equity Bank has moved more than other banks to districts in which English or Swahili is not the predominant language.

By interacting the district-level proportion of people speaking Swahili or minority languages with the 2009 dummy variable, we get a better sense of the types of districts that Equity Bank was entering between 2006 and 2009. The interactions between the 2009 dummy variable and the language variables are positive and significant (Model 8). While the negative coefficients for minority dialects and Swahili speakers are larger (in absolute value) than the coefficients on the interactions, the positive coefficients on the interactions do indicate that Equity Bank moved toward serving nonEnglish speakers from 2006 to 2009. A similar pattern holds for non-Equity Bank branches (Models 5 to 7), but: (a) the coefficients on the interactions between minority language and the 2009 dummy are not significant, (b) the coefficients on the interactions between Swahili and the 2009 dummy are much smaller than for Equity Bank, and (c) the ratio of the (absolute value of the) coefficient for the language dummies to the coefficient for the interactions is much larger for the non-Equity Bank sample. Therefore, it appears that Equity Bank moved _more_ toward minority-language speakers than other banks between 2006 and 2009.

Thus, the overall evidence from Table 4 suggests that Equity Bank’s branching strategies are distinct from those of other banks; such branching patterns suggest that Equity Bank was promoting financial inclusion during the period of 2006-2009.

# **IV.2 Bank Penetration and Financial Access: A Closer Look at Equity Bank**

Our previous results suggest that Equity Bank has pursued different branching strategies from other commercial banks. Our next step is to explore whether its expansion strategy has led to different 14