rafmacalaba/datause-encoder-data huggingface dataset:
5 REVIEW AND FUTURE DIRECTIONS

# **5.3 Monitor debt levels from China**

Kenya still has a heavy debt burden and China’s loans can bring debt to unsustainable levels. Some of China’s loans are nonconcessional, which can raise debt to GDP levels quickly. As of 30 June 2015, Kenya’s gross public debt to GDP ratio was 49.7 percent, and external loans contributed to the KSH 420.9 billion (US $4.3 billion) increase in public debt from June 2014 (National Treasury 2015). China is already Kenya’s top source of external financing. Figure 34 shows Kenya’s top external creditors as of June 2015. China holds the largest amount of debt at US $2.6 billion, 57 percent of Kenya’s total debt of US $4.51 billion. China’s large proportion of public debt may be part of the trend of partnerships between developing countries.

## **5.3.1 Debt to China is growing quickly**

China’s loans for energy and infrastructure investments such as SGR and geothermal power are accumulating quickly. Figure 33 shows Kenya’s external debt to China from 2010 to 2014<sup>13</sup> . Debt grew at an annual rate of 54 percent between 2010 and 2014, reaching US $821 million in 2014 from just US $146 million in 2010. Figure 35 shows the growth of debt to China from the third quarter of 2014 to the second quarter of 2015. China’s stock of Kenyan debt rate grew at 16.5 percent per quarter from US $ 753.3 million to US $2.56 billion; Kenya’s other top sources of debt stagnated or even declined. Kenya’s debt to Japan fell 2.98 percent per quarter and France declined 0.58 percent per quarter. Traditional donors must coordinate efforts with China to avoid undermining governance and debt sustainability programs.

<!-- Start of picture text --> 800<br>600<br>400<br>200<br>2010 2011 2012 2013 2014<br>Year<br>Debt (USD Millions)<br><!-- End of picture text --> Figure 33: Kenya’s debt to China is growing quickly (2010-2014) Source: Kenya National Bureau of Statistics Economic Survey 2015 13Here 1 USD = 98.48 KSH. We take this from the UN Operational Rates of Exchange Effective date 30 Jun 2015 37
|P043310|1996|1999||12 Closed|India|
|---|---|---|---|---|---|
|P043310|1996|2000|4/21/2000|15 Closed|India|
|P043310|1996|2001|6/29/2001|19 Closed|India|
|P043310|1996|2002|3/12/2002|21 Closed|India|
|P043311|2003|2003|6/30/2003|1 Closed|Nepal|
|P043311|2003|2004|5/12/2004|3 Closed|Nepal|
|P043311|2003|2005|5/27/2005|5 Closed|Nepal|
|P043311|2003|2006|1/3/2006|6 Closed|Nepal|
|P043311|2003|2007|7/18/2006|7 Closed|Nepal|
|P043311|2003|2007|1/31/2007|8 Closed|Nepal|
|P043311|2003|2008|8/27/2007|9 Closed|Nepal|
|P043311|2003|2008|3/7/2008|10 Closed|Nepal|
|P043311|2003|2009|9/29/2008|11 Closed|Nepal|
|P043311|2003|2009|5/23/2009|12 Closed|Nepal|
|P043311|2003|2010|11/29/2009|13 Closed|Nepal|
|P043311|2003|2010|5/28/2010|14 Closed|Nepal|
|P043311|2003|2011|12/12/2010|15 Closed|Nepal|
|P043311|2003|2011|6/26/2011|16 Closed|Nepal|
|P043311|2003|2012|1/3/2012|17 Closed|Nepal|
|P043311|2003|2013|11/6/2012|18 Closed|Nepal|
|P043311|2003|2013|6/19/2013|19 Closed|Nepal|
|P043311|2003|2014|12/28/2013|20 Closed|Nepal|
|P043311|2003|2014|1/19/2014|21 Closed|Nepal|
|P043311|2003|2020|5/8/2020|22 Closed|Nepal|
|P043338|1997|2000|6/29/2000|6 Closed|West Bank and Gaza|
|P043338|1997|2001|6/27/2001|8 Closed|West Bank and Gaza|
|P043338|1997|2002|5/23/2002|10 Closed|West Bank and Gaza|
|P043338|1997|2003|4/15/2003|12 Closed|West Bank and Gaza|
|P043338|1997|2004|5/25/2004|15 Closed|West Bank and Gaza|
|P043338|1997|2005|3/31/2005|17 Closed|West Bank and Gaza|
|P043339|1996|2000|6/29/2000<br>|10 Closed|West Bank and Gaza|
|P043339|1996|2001|6/25/2001|12 Closed|West Bank and Gaza|
|P043339|1996|2002|5/8/2002|14 Closed|West Bank and Gaza|
|P043339|1996|2003|4/23/2003|17 Closed|West Bank and Gaza|
|P043367|1997|1997||3 Closed|Yemen,Republic of|
|P043367|1997|1998||5 Closed|Yemen,Republic of|
|P043367|1997|1999||7 Closed|Yemen,Republic of|
|P043367|1997|2000|4/8/2000|9 Closed|Yemen,Republic of|
|P043367|1997|2001|5/17/2001|12 Closed|Yemen,Republic of|
|P043367|1997|2002|11/30/2001|13 Closed|Yemen,Republic of|
|P043412|2005|2005|4/29/2005|1 Closed|Morocco|
|P043412|2005|2006|11/29/2005|2 Closed|Morocco|
|P043412|2005|2006|6/28/2006|3 Closed|Morocco|
|P043412|2005|2007|11/26/2006|4 Closed|Morocco|
|P043412|2005|2007|6/19/2007|5 Closed|Morocco|
|P043412|2005|2008|12/27/2007|6 Closed|Morocco|
|P043412|2005|2008|5/21/2008|7 Closed|Morocco|
<!-- Start of picture text -->
- OmML<br>E N OTJK<br>& OzMS OGHA ©coe<br>= omw? KGz OARM °<br>| °<br><!-- End of picture text -->

<!-- Start of picture text --> So OG HEMNG OSeEN oD, ONER opo<br>ss OYEM<br>_ ORWA OBDI<br>N<br>OUGA<br>| 0 1 2<br>Index of Successful reforms<br><!-- End of picture text -->
Table 1: Summary Statistics and Experimental Balance

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Mala|wi<sup>a</sup>|Liber|ia<sup>b</sup>|
||FTFI|SI|FTFI|SI|
||Mean [SD]|- FTFI|Mean [SD]|- FTFI|
|**Panel A. Demographics**|||||
|=1 if currently married or has partner|0.97|-0.01|0.97|-0.00|
|||(0.01)||(0.01)|
|Age|37.97|-0.94|37.13|0.67|
||[12.88]|(0.60)|[10.96]|(0.61)|
|Number of household members|5.03|-0.02|5.59|-0.09|
||[1.78]|(0.09)|[2.27]|(0.13)|
|**Panel B. Education and mobile phone owner**|**ship**||||
|Years of education|5.22|0.01|2.44|-0.01|
||[3.50]|(0.17)|[3.43]|(0.19)|
|=1 if able to write/read|0.66|-0.01|0.30|0.01|
|||(0.02)||(0.03)|
|=1 if has access to mobile phone|0.45|0.03|0.42|-0.01|
|||(0.02)||(0.03)|
|**Panel C. Household wealth**|||||
|Food security index (z-score)|0.00|-0.09*|0.00|0.05|
||[1.00]|(0.05)|[1.00]|(0.06)|
|Total expenditure (monthly)|26.03|-2.13*|65.71|-0.52|
||[24.46]|(1.17)|[47.08]|(2.59)|
|Net value of durables, livestock, and fnancial asset|162.55|4.24|416.43|33.88|
||[235.93]|(11.45)|[823.80]|(51.15)|
|Non-agricultural income (monthly)|10.27|0.96|7.84|0.85|
||[16.73]|(0.81)|[20.52]|(1.13)|
|**Panel D. Empowerment-related outcomes**|||||
|=1 if has her own income source|0.44|0.01|0.31|-0.03|
|||(0.02)||(0.03)|
|Age diference from spouse|2.94|-0.16|4.09|0.72|
||[10.78]|(0.51)|[12.59]|(0.72)|
|Observations|1,73|7|1,26|1|

Note: Sample is restricted to women with an intimate partner over the 12 months prior to the survey, and those who do not report any vision or hearing impairments. Columns 1 and 3 present the mean for the FTFI groups, and Columns 2-4 show the difference between the ACASI and FTFI groups. Standard deviation is in square brackets in Columns 1 and 3 and standard error in parentheses in Columns 2 and 4. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

19
Table 3: Impacts on Socio-Emotional Skills

||(1)|(2)|(3)|
|---|---|---|---|
||Aggregate soft-skills<br>index|Intrapersonal<br>skills index|Interpersonal<br>skills index|
|Offered Training|0.03|0.04|0.03|
||(0.03)|(0.03)|(0.04)|
|Baseline soft-skills index|0.51<sup>_∗∗∗_</sup><br>(0.05)|||
|Baseline intrapersonal index||0.44<sup>_∗∗∗_</sup><br>(0.04)||
|Baseline interpersonal index|||0.50<sup>_∗∗∗_</sup><br>(0.05)|
|Control Mean|-0.02|-0.02|-0.02|
|Observations|849|849|849|

Notes: OLS regressions controlling for strata dummies used in the randomization and baseline value of the outcome. Soft skills were only measured at baseline and endline. Robust standard errors shown in parenthesis. Intrapersonal skills include emotional awareness, self awareness, emotional regulation, self control, perseverance, personal initiative, and problem solving. Interpersonal skills include listening, empathy, expressiveness, relatedness, influence, negotiation, and collaboration.

Table 4: Impacts on Search

||(1)<br>Searche<br>in Last|(2)<br>d For Job<br>Month|(3)<br>Confdenc<br>Empl|(4)<br>e Approaching<br>oyers (0-3)|(5)<br>CV<br>Digital|(6)<br>in<br>Format|(7)<br>People<br>Jobs|(8)<br>Discusses<br>with|
|---|---|---|---|---|---|---|---|---|
|Offered Training|0.01|-0.03|0.06<sup>_∗_</sup>|0.08<sup>_∗_</sup>|0.03<sup>_∗∗_</sup>|0.04<sup>_∗∗_</sup>|0.91<sup>_∗∗∗_</sup>|1.07<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.03)|(0.05)|(0.01)|(0.02)|(0.18)|(0.23)|
|Offered * Round 2||0.00||-0.01||-0.02||-0.25|
|||(.)||(0.06)||(0.02)||(0.25)|
|Offered * Round 3||0.08<sup>_∗∗_</sup>||-0.06||-0.01||-0.22|
|||(0.04)||(0.06)||(0.02)||(0.27)|
|Control Mean|0.64||2.52||0.90||5.42||
|Control Mean R1||.||2.52||0.89||5.51|
|Control Mean R2||0.66||2.50||0.89||5.50|
|Control Mean R3||0.62||2.53||0.92||5.26|
|Observations|1,728|1,728|2,597|2,597|2,597|2,597|2,597|2,597|
|p-value Round 2||0.40||0.13||0.38||0.00|
|p-value Round 3||0.08||0.64||0.09||0.00|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, dummies for stratification and survey wave dummies. Standard errors are clustered at the respondent level.

20
To sum up, the crop income equations show that (i) land, owned and rented in, significantly contributes to crop income and (ii) the contribution of machines to crop income depends on land types and sizes, and in particular, machine services seem to augment the value of rent in land, but not own land, indicating that land is rented in to enhance the efficiency of machine use. The results are more clearly interpretable among relatively large farms. That is, rent in land and machine services are complementary for large farmers. This finding implies that the possibility of renting in land to expand the scale of farm operation as well as the availability of machine services that substitute for labor are particularly important among relatively large farms. In other words, the advantage of large-scale farming is realized by the increasing incidence of land renting and the increasing availability of machine services.

# **6. Conclusions**

Using farm panel data from China, collected in six provinces, i.e., Hebei, Hubei, Liaoning, Shaanxi, Sichuan and Zhejiang, in 2000 and 2008, we examined the dynamics of land transactions, machine investments and the demand for machine services. China’s agriculture in general experienced an expansion of machine rentals and machine services provided by specialized agents in the past decade, which contributed to mechanization in agricultural production. In particular, we investigated the effects of nonagricultural and agricultural wage growth and changes in the proportion of nonagricultural income and the migration rate, all of which are estimated at the village level, on changes in self-cultivated land, rent-in land, machine investments and machine service used. Our results show that an increase in nonagricultural wage leads to the expansion of self-cultivated land size, and the effect tends to be larger on larger farms. A rise in the proportion of nonagricultural income or the migration rate also increases the size of self-cultivated land significantly among relatively large farms.

Interestingly, relatively educated farm households respond to the above changes in an opposite way, i.e., decreasing the size of self-cultivated land, which suggests that land is rented out from relatively
# **5. DISAGGREGATED MORTALITY**

# **5.1 By age groups**

This section examines mortality by age, gender and types of diseases.

Disaggregated by age, we observe that mortality for 25-34 years old is significantly and negatively affected by unemployment, that is, unemployment actually reduces deaths among this age group. This is probably because procyclical accidents, suicides and homicides are accounted mainly for by individuals younger than 45, similar to Ruhm (2000).

Table 5.1a: Age, Unemployment and Mortality (IV with controls)

|VARIABLES|<1|1-4|5-14|15-24|25-34|35-44|
|---|---|---|---|---|---|---|
|Instr. Job Losses|-0.0264|-1.184|0.232|0.194|-0.700***|0.0596|
||(0.295)|(0.855)|(0.640)|(0.434)|(0.258)|(0.210)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|-0.0898|-0.0531|1.246*|0.166|0.0395|-0.156|
||(0.272)|(0.742)|(0.678)|(0.259)|(0.206)|(0.132)|
|Observations|1,107|245|310|1,409|1,599|2,035|
|R-squared|0.019|0.147|0.150|0.043|0.059|0.097|

Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 19
for every RTA that enters into force during the period of observation, while controlling for tariffs, thereby allowing every RTA to have a distinct NTB effect.

Our analysis uses the newly available International Trade and Production Database for Estimation (ITPDE, Release 2) put together by Borchert et al. (2020). Those data include domestic trade flows—this is crucial to identify trade regulations that do not vary across trade partners (Heid et al. 2021); usefully, the data also include services trade. The database has almost universal country coverage and a longer time span than alternative sources such as the WIOD provided by Timmer et al. (2015). To measure the depth of RTAs—and hence their ambition regarding NTBs—we build on two sources that are standard in the literature: first, the World Bank’s Deep Trade Agreement Dataset (DTA data) put together by Hofmann et al. (2019), second, the DESTA database provided by Dür et al. (2014).

We estimate a state-of-the-art structural gravity model following all recommendations by Yotov (2022). In essence, the strategy consists in running a Poisson Pseudo Maximum Likelihood (PPML) model on panel data of sectoral bilateral trade flows (including domestic transactions) with the largest possible array of fixed effects to control for other determinants of trade.

In contrast to the existing literature, we cannot confirm a robust trade-creating role for non-tariff provisions in trade agreements (TAs) using gravity, while tariffs—once properly measured—are robustly shown to be potent inhibitors of trade. We present some evidence that, in deep TAs, non-tariff provisions create trade in a non-discriminatory fashion. This is welcome from a global welfare-theoretic point of view. However, if policy makers want to use trade agreements to grant preferences between geopolitically aligned countries, tariffs are the better choice. The result is robust to alternative empirical specifications and is not driven by treatment heterogeneity. Our finding does not necessarily mean that RTAs do not reduce NTBs, instead, the lack of a positive estimate of the NTB effect could be simply the result of yet another measurement error. The standard measure of depth assumes the more provisions an RTA has, the deeper and hence, more trade liberalizing it is. However, more provisions might also mean more regulations that in turn can be harmful to trade. Moreover, the finding does not imply that including NTB-provisions in trade agreements is useless as they may yield more efficient allocations albeit with reduced trade flows.

In the second part, we find widely differing effects across the 258 RTAs covered in our sample. Of the estimated NTB effects, 157 are positive with large differences across RTAs, and 101 turn out to be negative. However, most of these effects are insignificant: only 23% of all RTAs seem to lower NTBs, while 9% appear to even reduce trade, possibly a more balanced regulation evens out comparative advantages; the remaining RTA-specific NTB effects turn out to be insignificant. With these RTA-specific results at hand, we check if the content of trade agreements can 3
||||||**Payments or**<br>|**Payments or**<br>**Receipts**<br>|
|---|---|---|---|---|---|---|
|**Economy**|**Methane**<br>**(mil tons)**|**Population**|**Methane**<br>**perperson**|**Deviation from**<br>**Global Average**|**Receipts USD Billions**<br>**($7.5per ton)**|**USD Billions**<br>**($10per ton)**|
|Palau|0.02|0.02|1.12|-0.02|0.00|0.00|
|Tuvalu|0.01|0.01|0.88|-0.26|0.00|0.00|
|Cook Islands|0.01|0.02|0.57|-0.57|0.00|0.00|
|Tonga|0.10|0.10|0.98|-0.16|0.00|0.00|
|Seychelles|0.09|0.10|0.93|-0.20|0.00|0.00|
|Liechtenstein|0.02|0.04|0.53|-0.61|0.00|0.00|
|Albania|3.26|2.88|1.13|-0.01|0.00|0.00|
|Marshall Islands|0.03|0.06|0.52|-0.62|0.00|0.00|
|Dominica|0.05|0.07|0.63|-0.51|0.00|0.00|
|Andorra|0.05|0.08|0.65|-0.49|0.00|0.00|
|Saint Vincent and the<br>Grenadines|0.07|0.11|0.64|-0.50|0.00|0.00|
|Kiribati|0.02|0.11|0.18|-0.96|0.00|0.00|
|Bahamas|0.32|0.38|0.83|-0.31|0.00|0.00|
|Luxembourg|0.55|0.59|0.93|-0.21|0.00|0.00|
|Lesotho|2.25|2.09|1.08|-0.06|0.00|0.00|
|Portugal|11.58|10.29|1.13|-0.01|0.00|0.00|
|São Tomé and Príncipe|0.03|0.21|0.14|-0.99|0.00|0.00|
|Eritrea|3.66|3.42|1.07|-0.07|0.00|0.00|
|Latvia|1.99|1.95|1.02|-0.12|0.00|0.00|
|Malta|0.22|0.44|0.49|-0.65|0.00|0.00|
|Trinidad and Tobago|1.26|1.38|0.91|-0.23|0.00|0.00|
|Solomon Islands|0.40|0.64|0.62|-0.52|0.00|0.00|
|Bosnia and Herzegovina|3.49|3.36|1.04|-0.10|0.00|0.00|
|Slovenia|1.99|2.08|0.96|-0.18|0.00|0.00|
|Estonia|1.11|1.32|0.84|-0.30|0.00|0.00|
|Djibouti|0.68|0.94|0.72|-0.42|0.00|0.00|
|Maldives|0.12|0.49|0.24|-0.90|0.00|0.00|
|Netherlands|18.92|17.02|1.11|-0.03|0.00|0.00|
|Cabo Verde|0.13|0.54|0.24|-0.90|0.00|0.00|
|Guinea-Bissau|1.46|1.83|0.80|-0.34|0.00|-0.01|
|Cyprus|0.71|1.18|0.60|-0.54|0.00|-0.01|
|Comoros|0.28|0.81|0.34|-0.79|0.00|-0.01|
|Costa Rica|4.88|4.95|0.99|-0.15|-0.01|-0.01|
|Armenia|2.57|2.94|0.87|-0.27|-0.01|-0.01|
|Bulgaria|7.25|7.10|1.02|-0.12|-0.01|-0.01|
|Gambia, The|1.65|2.22|0.74|-0.39|-0.01|-0.01|
|Gabon|1.46|2.06|0.71|-0.43|-0.01|-0.01|
|Norway|5.03|5.29|0.95|-0.19|-0.01|-0.01|
|Croatia|3.74|4.18|0.89|-0.25|-0.01|-0.01|
|Moldova|3.39|4.06|0.84|-0.30|-0.01|-0.01|
|Finland|4.55|5.51|0.83|-0.31|-0.01|-0.02|
|Honduras|8.90|9.43|0.94|-0.19|-0.01|-0.02|
|Slovak Republic|4.35|5.45|0.80|-0.34|-0.01|-0.02|
|Kyrgyzstan|4.76|6.19|0.77|-0.37|-0.02|-0.02|

33
An adult individual can get SS through his or her formal job, through his or her spouse‘s job or through parents if he or she is a dependant)<sup>9</sup> . The decision of spouses to move sectors based on SS status is likely to be taken at the household level. If it is a joint decision, ignoring the spouse‘s decision is an omitted variable; but including it introduces endogeneity.

We address this potential concern by estimating the model at the household level. This abstracts from whether one or more members in the household switch between formal and informal jobs to look at the status of the household as a whole. It limits the effects to those switches that affect the household‘s source of health insurance.

An important concern is that the rollout of _SP_ could be correlated with the informality trends in municipalities. This would affect our estimates because the error term would be correlated with our estimate of interest. The results presented in Table 2 however indicate there is little correlation between the share of formality in a given period and municipality and the probability that municipality receives _SP_ in the following period. The probability of _SP_ being rolled out is measured with a dummy variable that takes a value of 1 when a municipality shows at least one affiliate in the _SP_ registry, and zero otherwise. This is true whether the share of formality is lagged one or two years (Columns (1) – (4)). The rate of change of formality (measured as a percentage of households or of individuals in SS) in a municipality is likewise uncorrelated with the timing of _SP_ ‘s rollout (Columns (5) and (6)). These results limit concerns of the possible endogeneity of the deployment of _SP_ across municipalities.<sup>10</sup> SS and _SP_ are not perfect substitutes. Contributing to SS also gives right to pension and job security benefits. _SP_ only provides for health benefits, which in principle are lower than those granted by SS. However, as previously mentioned, and unlike SS, _SP_ provides benefits for all members of the household. Importantly, _SP_ is also free. Therefore, the household‘s decision to choose between _SP_ and SS could also be a function of wealth (richer people may opt for private health care even if SS or _SP_ is available), the expectations of wages in each sector (formal and

> 9 In Mexico, ss through a formal job covers spouse, parents living in household, and children up to 15, or up to 26 if student or disabled and living in the same  house.

> 10  There was some discussion among policymakers of targeting the rollout to more populous states first.  There is some evidence of this, but controlling for various state and municipality characteristics rather than including municipality fixed effects, the correlation with the size of the formal sector remains insignificant.

17
Limited observations of unit values might inflate the SEs of budget shares and median prices of 2005 substantially, or even worse, we might not able to calculate inflation rates for some strata because no unit value is available for some food groups in some rounds of the simulation. If this happened, we excluded the groups, and reweighted the expenditure shares of the rest of food groups so that the revised weights totaled 1.

Table A3.1 shows the numbers of simulations without any unit value for each food group and each stratum. We can see unit values for cooking oil and soft drinks were missing for several rounds of simulation, but the impact would be negligible since the budget expenditure shares for these items are very small (table A3.2). On the other hand, it is worrisome that unit values of food grains were frequently missing for Rajshahi (SMA) and Sylhet (Municipality) because the budget shares of food grains are large.

Table 9 shows the effects of the limited observations of unit values on the inflation rate and poverty line estimates. The first column shows the inflation rates constructed using the full 2000 and 2005 HIES samples, while the second column shows the inflation rates constructed using the full sample of 2000 and a subsample from 2005. The differences are very limited. For most strata, the differences in inflation rates between the first and the second columns are minimal. As expected, Rajshahi (SMA) and Sylhet (Municipality) exhibit relatively large differences, but they are still below 10 percentage points. For rural and urban areas, the differences are less than 1 percentage point.

|**Table 9: Estimates of Price In**|**dex and 2005**<br>Price<br>Full|**Poverty Lin**<br>index based<br>Subsampl<br>HHs, 1<br>repetiti|**es from su**<br>on:<br>e (640<br>00<br>ons)|**bsamples**<br>2005 pove<br>Full|rty line base<br>Subsampl<br>HHs, 1<br>repetitio|d on:<br>e (640<br>00<br>ns)|
|---|---|---|---|---|---|---|
|Stratum|sample|Estimate|SE|sample|Estimate|SE|
|Barisal (Rural)|1.283|1.277|0.014|915.6|911.2|9.8|
|Barisal (Municipality)|1.229|1.224|0.046|938.5|935|35.4|
|Chittagong (Rural)|1.206|1.208|0.018|884|885.3|13|
|Chittagong (Municipality)|1.153|1.154|0.021|954.8|955.2|17.3|
|Chittagong (SMA)|1.183|1.177|0.019|1,157.9|1151.5|18.6|
|Dhaka (Rural)|1.278|1.28|0.011|831.7|832.9|7|
|Dhaka (Municipality)|1.183|1.183|0.013|877.6|877.5|9.8|
|Dhaka (SMA)|1.173|1.17|0.023|1,003.9|1001.2|19.8|
|Khulna (Rural)|1.263|1.263|0.011|735.3|735.4|6.4|
|Khulna (Municipality)|1.177|1.172|0.016|812.8|809.5|11.1|
|Khulna (SMA)|1.193|1.197|0.029|922.2|925|22.7|
|Rajshahi (Rural)|1.270|1.273|0.018|758.8|760.8|10.7|
|Rajshahi (Municipality)|1.194|1.198|0.019|844.5|847.5|13.4|
|Rajshahi (SMA)|1.235|1.138|0.05|843|776.7|34.3|
|Sylhet (Rural)|1.237|1.213|0.04|817|801|26.5|
|Sylhet (Municipality)|1.201|1.182|0.046|1,012.3|996.4|39|
|Rural (population weighted)|1.257|1.257|0.016|815.2|814.8|10.7|
|Urban (population weighted)|1.182|1.178|0.023|965.2|961.6|18.9|

_Source:_ Authors’ estimations using 2000 and 2005 HIES data. _Note:_ HH = household.

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