Day 3: : Land: Characteristics, Use and Investment

1. Day 3: : Land: Characteristics, Use and Investment Department of Economics Trinity College Dublin, Ireland

2. Today’s Commands _Variables Return a list of saved statistics Distributions and lorenz curves Finding, downloading and using STATA commands recode variable (value=value) recodes values of a categorical variable

3. Exercise 1 Open the data file individual2.dta in the folder 'Day 3‘ Using the bysort and egen command generate a variable for the size of the household (‘hhsize’) [Hint: use the variable 'hhmemid‘] Create a household level dataset by collapsing the variables ‘malehead’, ‘married’ and ‘hhsize’ by household Label the household size variable Sort data and save as temp1.dta Open Day3.dta and merge with temp1.dta Tabulate then drop ‘_merge’ to check the number of observations Erase temp1.dta from PC Count the number of observations Use the describe command to review the data in memory Sort by household and save changes to Day3_new.dta

4. _Variables STATA has a number of built in variables that are created after certain commands are executed. For example, when you merge files STATA stores information in about the merger in the variable ’_merge’ _n acts as a running counter within a group when used with the bysort and gen commands For example, if you want a running counter of the number of observations in the province: bys tinh_2008: gen provcount=_n This assigns a unique identifier to each observation in the province

5. Exercise 2 A certain number of indicator variables need to be constructed to ensure that you are using the correct sample of plots in constructing the variables and also that you only count each household once when constructing tables of summary statistics for households Generate a variable that assigns a unique identifier to each plot within households and tabulate Run the next set of commands that generate an indicator for household that have land use rights to some plots (either owned or rented in) and try and understand what each line of command is doing Since we are only interested in agricultural land generate an indicator for agricultural plots at plot (use the variable p6q7_ - see do-file for hints)

6. Exercise 2 Consider Table 3.1 in the 2006 Statistical Report and run the set of commands for generating the same table for columns 1 and 2 using the 2008 data Create a variable for the land area used for annual crops at the plot (anplotarea) and household level (totplotarea) Write the commands necessary to create column 3 of Table 3.1 Generate a variable ‘nrplot’ for the number of plots used by the household for agricultural purposes Label the variable Run the commands that generate the average number of plots by groups as in column 4 of Table 3.1 Write the commands necessary to create column 5 of Table 3.1

7. Statistics saved by STATA After running commands for descriptive statistics STATA stores statistics as scalars that can be used in the next set of commands By using the command return list STATA will display all statistics stored For example, after using the command summarize STATA stores the following: r(N) number of observations r(mean) mean r(min) minimum r(max) maximum r(sum_w) sum of the weights r(Var) variance r(sum) sum of variable r(sd) standard dev. You can use these in subsequent commands. For example if you want to only include observations that are below the mean in a table you would append the command if var<r(mean)

8. Distributions We are often interested in knowing how a variable is distributed across observations A histogram can be used to show the frequency at which observations occur across a range of values histogram varname: Gives a graphical display of the frequencies for varname Many different options can be appended (see STATA help for a complete list). One option we will use is to specify whether to use densities, frequencies or fractions in constructing the histogram. To alternate simply append ,frequency or , fraction (the default is density)

9. Distributions We are often interested in knowing how equal a distribution is (for example is land equally distributed across households? Does this distribution vary across provinces?) A convenient way of illustrating inequality is to use a Lorenz Curve This is constructed by first ranking observations from lowest to highest (i.e. Household with smallest area of land at bottom and household with highest area of land at top) The share of total land that each household owns is then plotted starting with lowest This produces a Lorenz Curve

10. Cumulative % Land Area Distribution of Land Cumulative % Households

11. Installing new Commands • Finding, downloading and using STATA commands • Before you start creating a commandname/program to apply in STATA a method which is likely to be known and used by others, it might be useful to look into the additional programmes/commands created and tested by other people. • E.g. commands related to poverty calculations can be found by typing: • findit poverty • Stata returns a series of possible commands to calculate poverty indices, to calculate inequality, produce lorenz curves etc… • If you find any of the returned suggestions useful, you can download it. It will be stored and you can use it as a normal STATA command thereafter.

12. Exercise 3 Consider Figure 3.1 in the 2006 report. In this figure we eliminate households in the top 5% of the land area distribution We call these outliers and their removal will prevent an overly skewed distribution We cannot apply weights as we have reduced the size of the sample in a non-random way First we must find the cut off point for the 95th percentile of the distribution. To find this use the summarize command for the variable ‘totplotarea’ appending , detail [Remember to only use one observation per household!] What is the threshold land area? Use the return list command to see what scalars are stored. Generate a variable measuring the 95% cutoff point Draw a histogram of the distribution of the area of agricultural land for lower 95% of sample

13. Exercise 3 Generate an indicator variable for "North" vs. "South" regions Draw a histogram of the distribution of the area of agricultural land for lower 95% of sample by region In panel c. and panel d. of Table 3.1 we wish to construct lorenz curves, however, there are is no lorenz curve command built into Stata. Use the findit lorenz command to see if others have created commands like this. Follow the links to install the glcurve command and browse the help file to try and understand the next command in the dofile. Construct a similar curve for plot area of annual crops below 30000 ha Compare to the findings of the 2006 report

14. Exercise 5a Consider Table 3.7 in the 2006 report. In this table we look at the current status of land investment and for the purpose of this exercise are specifically interested in irrigation (Columns 1 to 3) Generate indicator variable for plot is irrigated (plotirrig) and the number of plots irrigated in household (nrirrig) [Hint: variable p7q12_]. Consider all agricultural plots used. Generate a variable for the proportion of plots used by household that are irrigated [i.e. Nrirrig as a proportion of nrplot from Exercise 2] Run the command that tabulates the proportion of plots that are irrigated across province and construct the same statistics across gender of head of household and food quintile. Run the commands to construct the statistics for Column 2 of Table 3.7 [The proportion of owned plots without a red book that are irrigated] Write the commands that construct the relevant statistics for Column 3 of Table 3.7 [The proportion of owned plots with a red book that are irrigated] Compare to the findings of the 2006 report

15. Exercise 5b Consider Figure 3.7 which looks at the dependence on public/cooperative infrastructure and perceptions of the quality of the irrigation system Run the commands that generate a variable indicating that the household is dependent on irrigation (p20q2_ - note discrepancy in survey and name of data file!) Generate a variable (genirr) for households who are dissatisified with irrigation (p20q3_ ) Run the commands that create a bar chart measuring dependence on public/cooperative irrigation and perceptions of irrigation across provinces. Write the commands to construct a similar bar chart across the gender of the household head, food quintile and for the total Compare to the findings of the 2006 report

16. Exercise 5c Consider Table 3.8 which looks at investment on land. Here we will focus on irrigation investments Generate a variable (invwater) indicating that the household invested in irrigation/water/soil conservation on agricultural plots that they use [Hint: use variable p13q1_] Generate a variable (nrinvwater) indicating the total number of plots that the household invested in irrigation/water/soil conservation Generate a variable (propinvwater) for the proportion of plots household invested in irrigation/water/soil conservation [i.e. nrinvwater as a proportion of nrplot from Exercise 2] Tabulate proportion of plots household invested in irrigation/water/soil conservation across province, gender of household head and food quintile Generate a variable (valinvwater) indicating that total cash investment the household made in irrigation/water/soil conservation (p13q3) Create a table summarizing investment across province, gender of household head and food quintile Compare to the findings of the 2006 report