rafmacalaba/datause-encoder-data huggingface dataset:
# **<<B>>** _Disaggregated commodities_

The results reported in table 2 are based on an aggregated measure of protection. However, various sectors are taxed and subsidized differently for a number of reasons, including differences in demand and supply conditions and because these sectors are characterized by different market structures (e.g., small vs. large farms), which influences rent-seeking behavior. The heterogeneous nature of agricultural protection may also cause an aggregation bias in measures such as the _NRA_ (see Aksoy 2005). To investigate potential heterogeneity in the political reform effects across different groups of commodities, table 3 reports regression results by separating importing and exporting sectors (columns 1 and 2) and four commodity groups (columns 3–7).<sup>23</sup> TABLE 3. Effect of Democratic Reforms on Agricultural Protection at the Sector Level

|Estimation<br>|||D-in-D regr<br>|essions<br>|||
|---|---|---|---|---|---|---|
|Products/sectors|Import<br>sectors|Export<br>sectors|Grains<br>and tubers|Livestock<br>products|Oilseeds|Tropical<br>crops|
||(1)|(3)|(4)|(5)|(6)|(7)|
|Dependent variable|_NRA_|_NRA_|_NRA_|_NRA_|_NRA_|_NRA_|
|_Democracy_|17.4|6.8|17.4|5.1|31.0|7.1|
||_(.007)_|_(.026)_|_(.000)_|_(.632)_|_(.050)_|_(.367)_|
|Treatment|All|All|All|All|All|All|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Time fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Continental trends<br>|Yes|Yes|Yes|Yes|Yes|Yes|
|_R_<sup>_2_</sup>(within)|0.239|0.146|0.228|0.279|0.391|0.424|
|Country-sectors|519|440|269|238|80|112|
|Observations|13,278|9,558|8,932|6,920|2,510|3,869|

_Source:_ Own calculations based on the data described in the text.

_Notes_ : _p_ values based on robust standard errors clustered at country-sectors in parentheses. Additional controls include the _log of per capita GDP_ , the _log of population_ , _agricultural employment share_ , _land per capita_ , and _product value shares_ . All regressions include time and country fixed effects and interactions between years and continental dummies (for African, Asian, and Latin American countries).

21
# Figure 7. External vulnerabilities in EMDEs

## A. External debt

<!-- Start of picture text --> Percent of GDP<br>60 2018 2010<br>55<br>50<br>45<br>40<br>35<br>30<br>EMDEs Commodity Commodity<br>exporters importers<br>C. Current account balance<br>Percent of GDP<br>2018 2010<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>-6<br>EMDEs Commodity Commodity<br>exporters importers<br>E. Foreign reserves adequacy<br>ARA metric<br>4 Interquartile range Median Mean<br>3<br>2<br>1<br>0<br>2010 2019<br><!-- End of picture text -->

## B. External debt

<!-- Start of picture text --> Number of economies<br>30 2018<br>2010<br>25<br>20<br>15<br>10<br>5<br>0<br>0-25 26-50 51-75 76-100 100+<br>Percent of GDP<br>D. Current account balance<br>Number of economies<br>80<br>2018 2010<br>60<br>40<br>20<br>0<br> < -5 -2.5 to -5 0 to -2.5 > 0<br>Percent of GDP<br>F. Non-resident holdings of local-currency debt<br>Percent of total debt<br>30 Interquartile range Mean Median<br>25<br>20<br>15<br>10<br>5<br>0<br>2010Q4 2019Q2<br><!-- End of picture text -->

## F. Non-resident holdings of local-currency debt

Source: Ha, Kose, and Ohnsorge (2019); International Monetary Fund; World Bank.

A. Unweighted average of total external debt-to-GDP ratios for 31 EMDE commodity exporters and 30 EMDE commodity importers.

B. Based on data for 61 EMDEs.

C. Unweighted average of current account balance-to-GDP ratios for 88 EMDE commodity exporters and 56 EMDE commodity importers.

D. Based on data for 144 EMDEs.

E. Based on data for 48 EMDEs. Dark blue bars show minimum and maximum values. Assessing reserve adequacy (ARA) metric is based on IMF (2011) which determines the appropriate reserve cover on a risk-weighted basis covering short-term debt, medium and long-term debt, and equity liabilities. Broad model and export earnings. Risk weights are based on observed outflows during periods of exchange rate pressure. Values above 1 suggest that countries are fully able to meet balance of payments needs using reserves.

F. Based on data for 22 EMDEs.

27
**Table 7 Summary statistics**

||**Observations**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|_Governance_||||||
|Trust|2176|0.8|0.4|0|1|
|Corruption|2103|0.8|0.4|0|1|
|Accountability|1167|0.6|0.5|0|1|
|_Local Capacity_||||||
|Road|1148|0.3|0.5|0|1|
|Market*|615|0.3|0.5|0|1|
|Health*|547|0.4|0.5|0|1|
|Cleaning|1146|0.4|0.5|0|1|
|Revenue*|511|0.7|0.4|0|1|
|Spending*|446|0.5|0.5|0|1|
|_Education Services_||||||
|Expensive|2201|0.3|0.5|0|1|
|Manuals|2207|0.4|0.5|0|1|
|Learning|2194|0.3|0.5|0|1|
|Absenteeism|2204|0.3|0.5|0|1|
|Crowded|2200|0.4|0.5|0|1|
|Equipment|2186|0.3|0.5|0|1|
|Corruption*|573|0.2|0.4|0|1|
|_Health Services_||||||
|Expensive|2207|0.4|0.5|0|1|
|Pills|2207|0.4|0.5|0|1|
|Care|2209|0.2|0.4|0|1|
|Absenteeism|2207|0.3|0.4|0|1|
|Long line|2205|0.4|0.5|0|1|
|Clean|2199|0.3|0.5|0|1|
|Corruption*|567|0.2|0.4|0|1|

_Note:_ *Variable not available in all three rounds.

29
Table 13: Uncertainty statistics across simulations, all features and interactions

|||Covera<br>|ge rate<br>|||Width<br>|of CI<br>||
|---|---|---|---|---|---|---|---|---|
||EBP|Cubist|XGB|BRF|EBP|Cubist|XGB|BRF|
|**Panel A: Assets**|||||||||
|Burkina Faso|0.913|0.806|0.929|0.902|2.275|1.492|1.793|2.818|
|Madagascar|0.626|0.940|0.962|0.991|0.722|1.298|1.442|1.984|
|Malawi|0.809|0.769|0.723|0.738|1.633|1.080|0.919|1.278|
|Mozambique|0.641|0.953|0.913|0.971|0.687|1.060|0.887|1.348|
|Sri Lanka|0.909|0.930|0.930|0.919|0.607|0.718|0.686|1.001|
|Tanzania|0.344|0.713|0.721|0.771|0.627|0.940|0.951|1.181|
|Vietnam|0.980|0.994|0.978|0.988|1.281|1.572|1.201|1.723|
|Average|0.746|0.872|0.880|0.897|1.119|1.166|1.126|1.619|
|**Panel B: Poverty**|||||||||
|Malawi|0.744|0.763|0.872|0.831|0.464|0.452|0.495|0.575|
|Tanzania|0.871|0.865|0.884|0.943|0.297|0.297|0.295|0.515|
|Average|0.808|0.814|0.878|0.887|0.380|0.374|0.395|0.545|

Note: The first four columns present the coverage rate across simulations for each country. The coverage rate is defined as the proportion of confidence intervals that contain the true value, derived from the census. The last four columns present the average width of the confidence interval across simulations for each country. The results are based on 100 simulations for each country and method. Candidate features include all variables used in the main specifications, along with mosaiks and interactions between features and urbanity.
**Table 7: Income composition across poverty categories**

|Variable name|_Income&_<br>_assetpoor_|_Income rich_<br>_&assetpoor_|_Income poor_<br>_&asset rich_|_Income&_<br>_asset rich_|Test statistics<sup>a</sup>|
|---|---|---|---|---|---|
|Absolute income (PPP USD<br>AEU)|559|1 661|756|2 966|F=308.86***|
|_Income shares_||||||
|**Total**|**1**|**1**|**1**|**1**|**-**|
|Cash|0.54|0.62|0.53|0.63|F=88.60***|
|Subsistence|0.46|0.38|0.47|0.37|F=88.60***|
|**Crop**|**0.30**|**0.31**|**0.31**|**0.31**|**NS**|
|Cash|0.10|0.13|0.10|0.14|F=36.03***|
|Subsistence|0.21|0.18|0.21|0.17|F=29.13***|
|**Livestock**|**0.11**|**0.16**|**0.13**|**0.13**|**F=15.90*****|
|Cash|0.06|0.11|0.07|0.08|F=25.11***|
|Subsistence|0.05|0.05|0.06|0.05|F=2.62**|
|**Forest env. inc.**|**0.21**|**0.21**|**0.19**|**0.19**|**F=6.16*****|
|Cash|0.07|0.10|0.08|0.09|F=10.12***|
|Subsistence|0.13|0.10|0.11|0.09|F=50.37***|
|**Non-forest env. inc.**|**0.08**|**0.06**|**0.09**|**0.07**|**F=20.25*****|
|Cash|0.01|0.02|0.01|0.02|F=3.18**|
|Subsistence|0.07|0.04|0.08|0.05|F=48.96***|
|**Plantation**|**0.01**|**0.01**|**0.01**|**0.01**|**F=9.42*****|
|Cash|0.00|0.00|0.00|0.00|F=7.46***|
|Subsistence|0.01|0.01|0.01|0.01|F=4.55***|
|Household business (cash)|0.05|0.07|0.08|0.10|F=50.39***|
|Wage (cash)|0.17|0.12|0.12|0.12|F=24.35***|
|Other(cash)|0.07|0.07|0.07|0.07|NS|
|_Diversity_||||||
|Herfindahlindex|0.40|0.46|0.40|0.45|F=57.73***|
|Share of hh with index>0.6|0.10|0.20|0.09|0.17|F=35.66***|
|N|2863|801|801|2864||

*, **, *** Significantly different at 0.1, 0.05 and 0.01 level. NS=Not significant.<sup>a</sup> One-way ANOVA.<sup>b</sup> Mean shares are calculated by taking the mean of household shares.

Splitting total and sectoral incomes between subsistence and cash income also gives a more distinct pattern between the income categories, cf. Table 7. The average cash income share in the full sample is 58%.<sup>21</sup> There is a marked difference between the two income poor groups; the income rich have cash shares of 62-63% compared with 53-54% for the two income poor groups. Thus there is a significant positive correlation between income level and the cash income share (r=0.22<sup>22</sup> ). This pattern is due to higher cash shares for key income sources: crop, livestock and forest income, rather than higher shares of pure-cash incomes such as business and wage.

> 21 Note that this is the average share across households, and not the share of village economy that is cash (which is higher, as richer households tend to have a higher cash share).

> 22 r is the simple (Pearson) correlation coefficient.

21
16

|Ar9ent|na**0.7**|**8**U.07|0.34|0.8|80.50|0.24|0.48|0.66|0.51|0.54|<br>1,09|0.65|0.49|0.30|<br>0.59|<br>0 77|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Brailt**|0,.2|0.42|<br>2.31|<br>0.58|0.5|70.24|<br>0.56|<br>0.90|0.4|7 0.24|0.;|0.9|30.44|0.38|0.68|0,90|
|Chile|0.7|5 0.23|<br>0.90|<br>0.57|0.4|00.13|0.22|0,84|0.5|6-0.41|<br>0.92|0,5|60.47|0.51|<br>0.96|<br>0.55|
|**Colob**|0.7|50.00|<br>00|1.0|00.52|0.33|0.70|||0.21|0.0<br>,|0.4|0.3|50.10|<br>0.16|<br>091|
|Ecuad|or"||||0.7|90.11|<br>0.49|0.87|||||0.41|0.26|0.45|<br>0,83|
|tMexic|o 0.6|70.04|0.11|0.|06|02|0.05|<br>0.9|0g.5|1 G.A|<br>0.85|0.62|0|0.30|0.50|0.76|
|**iPeru**|t0.9|0- 0.27|<br>2.78|0.29|0.42|0.13.|<br>0.22|0.60|0.77|.0.52|<br>2.29|0025|0.52|0.54|<br>1.13|0.31|
|**Verez**|**uela**<br>0.5|<br> <br>20|00.205|70.|0..0.|04|;|0 .95' 0|.5<br>033|:0.|01|0a.8|0:4|2 02|10.2<br>0.4|3<br>0.39|

2 Differenced Arima residual model in the case ofBrazil, Die and Peru. In the case ofEcuador the comparison is made between the Arima Residual Model and the simple model, both of them without equity prices.

__ __

|**Argen**|**tina**<br>0.7|<br>8 0.07|0.34|0.88|0.7|0 0.14|<br>0.47|<br>0.86|0.5|9 0.53|1.27|0.67 0.5|2 0.30 0.63|t.31|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Brazil|0.8|2 0.42|2.31|0.58|0.7|2 0.33|<br>_1.1_|0.84|0.3|5 0.76|U1.1|0,840.5|6 042<br>1.02|(.84|
|**Chile**.|0.75.|0.23.|090|0.57|0.51|0.02|<br>0.04|<br>0.97|.0.65|<br>0|1.70|0.430.64|0.50 1.38|**0.48**|
|**Colmb**|**ia 0.75**|**0.00**|<br>0.00|1.00|0.54<br>- -|<br>0.0|<br>0.10|_O_.95|0O0|0.47|<br>1.17|0.75 .4|0.120.23|(.C9|
|Ecuad|or":||||0.8|9 0.11|<br>0.95|<br>0.43|0.5|2 0.61|1.27|0.44 .0.5|7 0.39 0.93|056|
|**Mexic**|**o**0.6|79004|0.11|<br>0.9|3 0 .63|0.05|<br>0,14|<br>0.91|0.5|6 041|M 0.94|0.66 0.4|4 0.41 2.73|F0'0|
|Peru|0.9|0 0.27|2.78|<br>0.2|9 0.2|9 0.13|<br>0.18|0.69|0.8|0 0.52|2.56|0.18 0. 19|.0.43<br>3.53|_040_|
|**Venez**|**uela**<br>0.8|<br>5 0.20|0.57|0.86|0O|R.52<br>0.09<br>.|<br>0.20|<br>0.91|053|.0.-|0.80|0.82 0.3|2 0.10 3.14|0 95|

± Venezuela industrial production index was avaiable from 1989. Ecuador IP was available only from 1990.
Overall, the reform produced a significantly lower return to schooling for men. This is consistent with a previous study using the same reform, but a different identification study based on an earlier survey (Aydemir and Kirdar 2017). They find that the return from an extra year of schooling is about 8 percent for women and no more than 2.5 percent for men. Torun (2018) also uses the 1997 reform as an instrument and finds small effects on the earnings of men but large positive effects on earnings of women. But lower returns using an IV are not typical in the literature for developing countries (Duflo 2001; Patrinos and Sakellariou 2005), but it is not unheard in developed countries (Pischke and von Wachter 2008 find zero returns to additional schooling in Germany; see also Devereux and Hart 2010; Stephens and Yang 2014). However, Pischke and von Wachter’s (2008) results for Germany have been challenged by Cygan-Rehm (2018) who finds that a 1960s compulsory schooling reform had positive effects on earnings in Germany. The low level of the estimates for men is explained by low attainment overall, while the much higher returns for women are because women were more likely to complete high school because of the reform (Aydemir and Kirdar 2017) and move into higher skill and formal sector jobs (Torun 2018).

It is interesting to note that other instruments have been used for Turkey. Ozturk and Tumen (2018) refer to the student protests of the 1970s and the subsequent military coup and the decline in university graduation which compressed wages and use the unexpected decline in educational attainment as an instrument to estimate returns to schooling. They find that the returns to an additional year of schooling range between 11 and 12 percent.

Overall, the results show a decent return to schooling. Women receive considerably higher returns to schooling regardless of the method used to calculate the returns. Table 3 shows the returns, ranging from 8.8 percent overall based on OLS to a high of 13.4 percent for women when corrected for selection. The IV results are a low 6.9 percent for men and a high 10.0 percent for women.

**<u>Table 3: Summary of Returns to Schooling, Turkey 2017 (percent)</u>**

|All|Males|Males|Females|Females|Females|
|---|---|---|---|---|---|
|OLS|OLS|IV|OLS|Heckman|IV|
|8.8|8.3|6.9|10.3|13.4|10.0|

Source: 2017 HLFS; see Annex Tables A-1, A-2, A-3, A-4 8
Figure 7: Incidence of Fatalities by Local Government Areas, 2011-2016 <!-- Start of picture text --> 2011 2012 2013<br>200 - 1000<br>100 - 200<br>50 - 100<br>10 - 50<br>1 - 10<br>0 - 1<br>2014 2015 2016<br>No Fatalities<br><!-- End of picture text --> Source: Authors’ elaboration based on ACLED Data (http://www.acleddata.com/data/) 24
Heymann **D.,** and **A.** Leijonhufvud, **_1995._** High Inflation _Oxford University Press._ Jaramillo, F., and F. Schiantarelli, **1996.** "Access to Long-Term Debt and Effects on Firms' Performance: Lessons from Ecuador." Manuscript. The World Bank.

Jensen, M. and W. Meckling, **1977.** "Theory of the Firm: Managerial Behavior, Agency Costs, and Ownership Structure." _Journal of Financiai Economics._ Kaminsky, **G.,** and **C.** Reinhart, **1999.** "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems." _American Economic Review._ Kaminsky, **G.,** and **S.** Schmukler, **1999.** "On Financial Booms and Crashes: Regional Patterns, Time Patterns, and Financial Liberalization." Manuscript. The World Bank.

Krugman, **P.,** 1994. "The Myth of Asia's Miracle." Foreign Affairs. November/December.

Krugman, P., **1999.** "Balance Sheets, the Transfer Problem, and Financial Crises." Manuscript. Massachusetts Institute of Technology.

McKinnon, R., and H. Pill, **1997.** "Credible Economic Liberalizations and Overborrowing" _American Economic Review._ Modigliani, F. and M. Miller, **_1958._** "The Cost of Capital, Corporation Finance, and the Theory of Investment." _American Economic Review._ Morris, **J., 1976.** "On Corporate Debt Maturity StrategiLes." _Journal ofFinance._ Myers, **S., 1977,** "Determinants of Corporate Borrowing." _Journal of Financial Economics._ Myers, **S.,** 1984. "The Capital Structure Puzzle." _Journial ofFinance._ Myers, **S.** and **N.** Majluf, 1984. "Corporate Financing and Investment Decisions When Firms Have Information that Investors Do Not Have." _Journal ofFinance._ Neumeyer, P., **1998.** "Inflation-Stabilization Risk in Economies with Incomplete Markets." _Journal of Economic Dynamics and Control._ Samuel, **C., 1996.** "The Stock Market as a Source of Finance: **A** Comparison of **U.S.** and Indian Firms." Policy Research Working Paper **_1592._** The World Bank.

Schiantarelli, F., and V. Srivastava, **1996.** "Debt Maturity and Firm Performance: **A** Panel Study of Indian Public Limited Companies." Manuscript.<sup>TheWorld Bank.</sup>

**33**
Policy Research Working Paper 9898

# **Abstract**

This study employs a synthetic panel approach based on nationally representative micro-level data to track poverty and income mobility in Malaysia in 2004–16. On aggregate, there were large reductions in chronic poverty and increases in persistent economic security, but those who remained poor in 2016 were increasingly likely to be poor in a structural sense. Further, the poverty and income dynamics differ notably across geographic dimensions. Such disparities are most striking when comparing affluent urban Peninsular Malaysia with poorer rural East Malaysia. Although there are important differences in welfare levels between the main ethnic groups in Malaysia, the mobility trends generally point in the same direction. While the findings show that there is still scope for poverty reduction through the reduction of interethnic inequalities, the study underscores the importance of taking regional inequalities into account to ensure a fairer distribution of socioeconomic opportunities for poor and vulnerable Malaysians. Hence, addressing chronic poverty is likely to require additional attention to less developed geographic areas, as a complement to the current policies that are largely ethnicity-based.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at g.rongen@aighd.org and zaliahmad@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
sources. In 2002, the average NE benefit was 2389 Lek, roughly 50 percent of the poverty line, and 25 percent of the monthly minimum wage.<sup>7</sup> Finally, the social administrator submits the list of eligible families along with the calculated social assistance allocations to the elected commune council. The latter subsquently determines the _final actual_ allocation for each household. The council has the authority to (i) condition social assistance on participation in community projects, and (ii) revise the list and payment per household, according to the availability of funds from the center. The social assistance receipts may thus change throughout the year, based on (i) semi-annual evaluations by the social administrator and (ii) bi-monthly fund allocations from the center to the commune.

As Table 1 indicates, in 2005, the total expenditure on the NE program was 3.3 billion Lek (1$ = 103.3 Lek, 12/2005 Average) or 6.0 (0.4) percent of total government expenditure on social protection (GDP) (World Bank, 2006c). Over the period of 1993-2004, the program budget decreased from 1.4 to 0.47 percent of GDP, and from 3.4 to 1.9 percent of government spending (Kolpeja, 2006).

# **2.2 Old-Age Pensions**<sup>8</sup>

In addition to the NE program, the estimation of the impact of household old-age pension receipts on household consumption and work-eligible individual labor supply is at the heart of this analysis. According to Table 1, the government allocated the greatest share (68.0 percent) of social protection expenditure in 2005 to old-age pensions by spending 40.2 billion Lek. The eligibility for old-age pensions is based on the number of years of contributions and the retirement age, which was 60/55 for men and women, respectively, in 2001. Since 2002, it has

> 7 The monthly minimum wage was 9400, 10080, 11800 Lek in 2002, 2004, and 2005, respectively. (World Bank, 2006b)

> 8The background information on old-age pensions is obtained from World Bank (2006a).

9
some of the treatment GPs could have seen them withdrawn due to a shortage of funds. The attrition of control GPs was thought to be more likely than the attrition of treatment GPs. Accordingly, it was decided to sample 60 GPs from each of the treatment arms and 80 GPs from the control arm. So, the final sample had 260 GPs of 24 households each, for a total number of 6,240.

# 4.2 Sampling Strategy for GPs

Baseline SBMG data on GP-level toilet coverage were used to select the GPs in the sample. Of the 12,786 GPs from Punjab listed in the SBMG data set, those excluded (i) had already received interventions under the SBMG (Tranche 1 and 2); (ii) were going to benefit from improvements in water service delivery; (iii) had water quality problems that were going to be addressed in the near future under the project or the National Rural Water Development Program; (iv) were GPs where political interests might make it difficult to withhold interventions, as identified by the officials of the DWSS; and (v) lacked data on toilet coverage. Once we excluded these GPs, the total number of GPs in our target population was reduced to 7,764.

Further, to ensure that a full-fledged BCC campaign would be undertaken in the sample GPs, a construction requirement of at least 25 toilets was set as a minimum threshold for being selected. Similarly, to ensure that the project interventions would be completed within six months, GPs where the number of households without individual latrines exceeded 300 were excluded from the sample. After imposing these conditions, the remaining 4,868 GPs were identified as our baseline population from which the samples were drawn. A computer program was used to draw random samples stratified at the district level and maintain proportional representation of GPs in districts in each of the treatment and control arms similar to the baseline population.

# 4.3 Sampling Strategy for Households

Up to 250 households were listed in each of the selected GPs. Basic information on these was then collated: the total number of household members, number of children below 5 years of age, number of children aged 6 to 14, and toilet ownership. All the households were listed in GPs of 250 or fewer. For larger GPs, households were listed by ward until the maximum of 250 was reached.

13