rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.9:** Robustness Checks on Nepotism (Table 4)

||**Village head survey**|**Bureau**|**crat survey**|
|---|---|---|---|
||Employs relative|Parent was leader|Parent served ingovt|
||(1)|(2)|(3)|
|1. Baseline|-0.385<sup>_∗∗∗_</sup><br>(0.178)|-0.066<br>(0.051)|-0.168<sup>_∗∗_</sup><br>(0.082)|
|2. No controls|-0.191<br>(0.179)|-0.040<br>(0.046)|-0.165<sup>_∗∗_</sup><br>(0.084)|
|3. Region fxed effects|-0.208<sup>_∗_</sup><br>(0.161)|-0.051<br>(0.046)|-0.164<sup>_∗∗_</sup><br>(0.084)|
|4. 3rd-Degree polynomial|-0.516<sup>_∗∗_</sup><br>(0.237)|-0.096<br>(0.065)|-0.209<sup>_∗_</sup><br>(0.120)|
|5. 1/2 MSE-opt. bandwidth|-0.456<br>(0.401)|-0.080<br>(0.073)|-0.188<br>(0.131)|
|6. 3/4 MSE-opt. bandwidth|-0.442<br>(0.326)|-0.076<br>(0.064)|-0.200<sup>_∗_</sup><br>(0.109)|
|7. 2x MSE-opt. bandwidth|-0.067<sup>_∗∗_</sup><br>(0.165)|-0.027<br>(0.046)|-0.134<sup>_∗∗_</sup><br>(0.073)|
|8. Excl. Suharto villages|-0.379<sup>_∗∗_</sup><br>(0.182)|-0.078<sup>_∗_</sup><br>(0.049)|-0.172<sup>_∗∗_</sup><br>(0.085)|
|9. Fuzzy RD|-0.237<sup>_∗∗_</sup><br>(0.178)|-0.086<sup>_∗_</sup><br>(0.062)|-0.396<sup>_∗∗∗_</sup><br>(0.129)|
|10. Excl. lame-duck|-0.419<sup>_∗∗∗_</sup><br>(0.179)|-0.079<sup>_∗_</sup><br>(0.050)|-0.150<sup>_∗∗_</sup><br>(0.077)|
|Observations|442|1067|1067|
|Control mean|0.36|0.054|0.27|
|Bandwidth size (%)|12.7|17.5|22.5|

_Notes_ : This table reports robustness checks on Table 4. The first row reports our baseline estimates. In the second row, we remove election year dummies and our control for the survey experiment treatment, which are included in our baseline estimation. In the third row, we include region fixed effects and dummies for pairs of election years (2015-2016, 2017-2018, etc.). The main regions in our sample are Java, Sulawesi, Sumatra, Kalimantan, and NTB-Bali. In the fourth row, we use a 3rd-degree polynomial to construct the point estimator. In the fifth, sixth, and seventh rows, we use a RD bandwidth equal to 1/2, 3/4, and twice the MSE-optimal bandwidth from Calonico et al. (2014), respectively. In the eighth row, we exclude villages with bureaucrats appointed during the Suharto era. In the ninth row, we report fuzzy RD estimates of _γ_ in equation (1). We use 1 (margin _jt >_ 0) from equation (1) to instrument for a dummy equal to 1 if the current village head in our survey sample is a different individual from the incumbent who competed in the last election. In the tenth row, we exclude villages where the current village head is serving in their third term. The dependent variables are identical to those in Table 4. The bottom panel reports the bandwidth size for the baseline estimation. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-correct standard errors in parentheses. In columns 2–3, standard errors are additionally clustered by village.

49
second-year survival rates of entrants); and unit prices (e.g., per exporter of a given product). The - - - Database is publicly available at <u>http://data.worldbank.org/data catalog/exporter dynamics database. Details on how the countries’ customs data were cleaned and harmonized and how the</u> Database was constructed are provided in Appendix 1. Further details on all the measures in the Database for each type of aggregation level are provided in Cebeci, Fernandes, Freund, and Pierola (2012).<sup>6</sup> Table 1 presents a summary of the measures used in this paper, reporting averages per country for the period 2006-2008, which is the period most commonly covered across countries and captures trade performance before the global financial crisis that started at the end of 2008.<sup>7</sup> Some interesting cross-country patterns emerge from Table 1. First, there is tremendous variation across countries in the number of exporters and the average exports per firm. Among developing countries, the largest numbers of exporters are found in Turkey and Mexico whereas the smallest pools of exporters are found in Niger and Mali. This pattern seems to mirror the countries’ size and level of development, and these links are studied further in Section 4. The number of exporters per 1000 inhabitants is also the largest in the more developed countries in the sample. Average exports per firm are the highest for Belgium, followed by Brazil, Chile, and Mexico and the lowest in Albania, Lebanon, Macedonia, Yemen, and Kenya. The tremendous difference between mean and median exports per firm reflects the highly skewed exporter size distributions in all countries with some very large exporters driving total exports.<sup>8</sup> For most

> 6 Due to confidentiality concerns, the Database does not provide information on the exporter size measures for cells at countryyear-HS 2-digit, country-year-destination, or country-year-HS 2-digit-destination level that include a single exporter.

> 7 A sharp drop in world trade as a result of the global financial crisis occurs in 2009. For countries for which data is available for only 1 or 2 years within the period 2006-2008 we compute the average based on those years. For Kuwait and Portugal the data coverage does not include that period. Hence we use averages for the 2009-2010 period for Kuwait and averages for the 2003-2005 period for Portugal in Table 1.

> 8 In related work, Freund and Pierola (2015) use the cross-country exporter-level data and show that exports are dominated by a small group of very large exporters (so-called ‘export superstars’) considered as the top exporter or the top 5 exporters, who account for on average 15 and 30 percent of exports, respectively. Their focus is on how this group of large firms defines the sectoral pattern of exports. Focusing on data at the country-industry or product level, Easterly and Reshef (2014) show that both African and non-

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<!-- Start of picture text --> Containership Fleet Composition<br>ee<br>80%<br>70%<br>60%<br>Size Category<br>@ 50% + <3000 TEU<br>oo<br>a 40% -* 3000-8000 TEU<br>40% —s >8000 TEU<br>20% See aaa.<br>100 % bet<br>1995 2000 2005 2010 2015 20201995 2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->
Table 8: Impact of Heat Exposure on Standardized Scores

||St|andardized|Score||
|---|---|---|---|---|
|||Quartiles|||
||(1)|(2)|(3)|(4)|
|T_>_33C|||_−_0_._014|_−_0_._003|
||||(0_._048)|(0_._006)|
|T 30-33C||_−_0_._017|_−_0_._005<sup>_∗_</sup>|_−_0_._003|
|||(0_._015)|(0_._003)|(0_._006)|
|T 27-30C|_−_0_._010<sup>_∗∗∗_</sup>|_−_0_._001|_−_0_._001|_−_0_._001|
||(0_._003)|(0_._002)|(0_._002)|(0_._006)|
|T 24-27C|_−_0_._003<sup>_∗∗_</sup>|_−_0_._002<sup>_∗_</sup>|_−_0_._001|_−_0_._007|
||(0_._001)|(0_._001)|(0_._002)|(0_._006)|
|T 21-24C|_−_0_._003<sup>_∗∗_</sup>|_−_0_._003<sup>_∗∗∗_</sup>|_−_0_._002|_−_0_._007|
||(0_._001)|(0_._001)|(0_._002)|(0_._007)|
|T_<_18C|_−_0_._001|_−_0_._001|0_._001|_−_0_._017|
||(0_._002)|(0_._001)|(0_._008)|(0_._029)|
|Outcome Mean:|0.028|0.102|-0.009|-0.133|
|No of Schools:|300|300|300|299|
|Mean Days T_>_30C:|0|0.152|10.689|107.428|
|Female Control|X|X|X|X|
|**Fixed Effects**|||||
|Year|X|X|X|X|
|School|X|X|X|X|
|Stream|X|X|X|X|
|Observations|574,115|588,150|650,821|319,549|
|R<sup>2</sup>|0.236|0.293|0.234|0.219|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variable is the standardized test scores of students taking the 12th grade exams. We divide the the schools into quartiles based on the mean number of days that each school experienced temperatures above 30<sup>_◦_</sup> C; the sub-headers represent the subsample of quartile 1-3 and quartile 4. All regressors include the number of days in the temperature bin. All regressions control for the number of days in the precipitation bins. Errors are clustered at the school level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

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We use the following specification to measure the impact of unilateral facilitation on jobsearch and migration: where _Yi_ is the outcome variable for respondent _i,_ measured in the 2012 endline survey. j Di is a binary indicator equal to one if respondent _i_ is assigned to combination _j_ of application information [T1], financial information [T2], passport information [T3], website assistance [T4], or passport assistance [T5].

Vector _B_ includes the barangay/client-status set of stratification cell fixed effects, along with an indicator for whether the respondent was randomly selected to be in the passport sample. The coefficient on this indicator would be non-zero if simply being interviewed in the passport sample affected our endline outcomes. (In practice, this coefficient is consistently close to zero and not statistically significant.) To increase the precision of our estimates, we also include a vector of pre-specified controls, _X_ , for the following baseline characteristics: female (indicator); age (continuous); high school completion (indicator); some college or vocational training (indicator); college completion (indicator); interested in working abroad (indicator); willingness to take risks (0-10 scale); household income (in thousands of pesos); household savings (in thousands of pesos); whether the household has ever taken out a loan (indicator); asset ownership (normalized index of durable asset holdings); whether the respondent has extended family overseas (indicator); and whether the respondent has immediate family overseas (indicator). Missing covariate values are coded as zeros, and we include a set of missing value indicator variables.

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14 obstructive pulmonary disease and lung cancer from coal smoke<sup>16</sup> and emerging evidence suggests that IAP also increases the risk of other child and adult health problems, including low birth-weight, perinatal mortality, asthma, ear infections, tuberculosis, and other (WHO 2007).

The high risks combined with the large population of solid fuel users make IAP a major global public health issue. In 2000, indoor air pollution, mostly from stoves burning solid fuels, was responsible for more than 1.5 million deaths and 2.7% of the global burden of disease according to the World Health Organization (2007). A meta-analysis of China implicated IAP from solid fuel use in China as responsible for approximately 420,000 premature deaths annually, more than the approximately 300,000 attributed to urban outdoor air pollution in the country (Zhang and Smith, 2007). Although robust documentation of health effects is not the purpose of the research reported here, the data do suggest some adverse impacts of solid fuels on respiratory health, especially among the persons responsible for cooking.

Splitting the sample at the median of dirty fuel use (by time), we find that among those 50% of households that use dirty fuels the most, 42 percent reported coughing over the past year, compared to 34 percent of low users of dirty fuel in cooking (Table 4, numbers refer to the main person responsible for cooking in the household). Bringing up phlegm from the lungs, another indicator of possible respiratory illness, is also slightly more prevalent among high than low users of solid fuels (21 compared to 16 percent) and the same is found for wheezing or whistling in the chest (16 against 11 percent). Consistently, high users of solid fuels are more likely to have seen a doctor for one or several of these respiratory symptoms and report higher average medical expenses for this purpose. Similarly, children below the age of 14 were also more likely to have shown symptoms of cough last month (23 percent in high dirty fuel using households compared with 18 percent in low-dirty-fuel households). These results also appear to carry over to biogas per se, with only 20 percent of children in households having biogas

> 16 Smith et al, (2000), Ezzati and Kammen (2001), WHO (2002), Duflo, Greenstone, and Hanna (2008), Pitt, Rosenzweig and Hassan (2010), Rehfuess, Bruce and Smith (2011).
# _How fast is the developing world progressing toward the health MDGs?_

Figure 1a shows the average annual growth rates for all countries (ranked in ascending order of their growth rate) on each of our MDG indicators; see also columns 1 and 2 of Table 5.<sup>7</sup> The growth curves in Figure 1a also allow us to see at a glance the median annual growth rate for each indicator, _and_ the fraction of countries experiencing negative and positive growth. For the health status (intervention) indicators, progress entails a country having a negative (positive) value of average annual growth in Table 5 and being below (above) the zero line on the y-axis in Figure 1a.

On all indicators except contraception, the majority of countries have made progress. For example, reading across the zero line on the y-axis in the stunting chart, we see that around 65 percent of countries have reduced the prevalence of stunting; for underweight, the figure is 80 percent. All but 22 per cent of countries have seen increases in ANC coverage. Reading up the median line on the x-axis in the IMR chart, we see that the median annual rate of reduction of IMR is around 0.03 or three percent; the median rate annual rate of increase of measles immunization is around two percent.

Several results shown in Figure 1a are worth noting. First, despite progress for the developing world as a whole, not all countries have made progress toward the MDGs; and, for some MDGs, the fraction not making progress is very high. Over half of countries have seen no progress in contraceptive prevalence, one quarter of countries have not recorded increases in immunization coverage, and one fifth of countries have not made progress in

> 7 Growth rates are computed using information from all available survey years. We assume a compound growth process, and estimate the average annual percentage change by regressing – for each country and each indicator, separately for the population and the poorest 40 percent – the natural logarithm of the indicator on the date of the survey. The average annual rate of growth is equal to the exponential of the (regression) - coefficient on the year variable minus one (see e.g. <u>http://data.worldbank.org/about/data overview/methodologies).</u>

7
Figure A6. Impulse Responses to a Positive Interest Rate Shock <!-- Start of picture text --> CPI Inflation Output<br>0 0<br>-0.1<br>-0.5<br>-0.2<br>-1<br>-0.3<br>-0.4 -1.5<br>5 10 15 20 5 10 15 20<br>Interest Rate Oil Prices<br>0.15 0<br>-0.1<br>0.1<br>-0.2<br>0.05<br>-0.3<br>0 -0.4<br>5 10 15 20 5 10 15 20<br><!-- End of picture text --> Notes: The impulse responses are based on the economic model introduced in the Appendix A. The impulse responses follow a one-standard deviation of a positive interest rate shock.

49
**Figure 9: Wholesale prices of maize in Nairobi, Kampala and International commodity markets**

_Source: Nairobi and Kampala wholesale from FAO, International price given by the U.S. No.2 Yellow, FOB Gulf of Mexico, U.S. price, as reported by the IMF. Note: All prices in Kenyan shillings per kg._

# **During times of tight regional supply, Kenyan prices have risen substantially relative to**

**international prices.** Figure 9 shows Kenyan and Ugandan wholesale prices, as well as international prices<sup>78</sup> .  While, they mostly move together, there are some clear instances where they diverge – for example in 2009, and 2011/2012. During times of short regional supply (or when disruptions such as violence or export bans affect trade), when importers need to look beyond the region, the tariff (50 percent) has a large and significant effect, preventing imports from bringing prices down<sup>79</sup> .  Maize grain imports have averaged about 10 percent of consumption over the last decade<sup>80</sup> , but annual flows have shown considerable volatility – in 2009 for example, imports (mostly from South Africa) spiked to 38 percent of local supply<sup>81</sup> .

**The use of ad hoc changes in the import tariff policy during times of tight supply creates further problems.** During times of tight supply, large millers lobby the government for a remission of the duty of grain<sup>82</sup> , which sometimes results in a sudden removal or reduction of the duty (prior to joining the EAC this could be done unilaterally, but can still be done now via an agreed remission of duty). Due to the scale economies inherent in importing grain, it is typically the large millers (directly or via the NCPB) that are best positioned to access lowtariff or tariff-free grain during these periods of duty remission.  This also has a side effect on competition from _posho_ meal, as these small-scale millers are often unable to access sufficient maize grain in these times<sup>83</sup> .  In addition, the associated reduction in flour costs tends to lag the change in tariff because of bottlenecks in transport infrastructure and trader withdrawal

> 78 International price given by the U.S. No.2 Yellow, FOB Gulf of Mexico, U.S. price, as reported by the IMF.  We are not able to adjust the maize prices appropriately for costs incurred through importing, due to lack of data, most particularly on tariffs that have varied substantially over time.

> 79 Ariga et al (2010)

> 80 FAOSTAT data 2000 – 2009.  Reports of unrecorded informal and illegal imports across borders with Uganda and Tanzania suggest that actual imports may have been much higher.

> 81 FAOSTAT data 2000 - 2009.

> 82 Kirimi et al (2011)

> 83 Kirimi et al (2011)

21
output.<sup>24</sup> Next, we look at the Mexican post-1990 data as a case study to evaluate the predictions of our model.

# 4.3 Mexico 1990-2007<sup>25</sup>

As in many other developing economies,<sup>26</sup> Mexican output is more volatile than that of the US. Table 1 reports the standard deviation of GDP per working age person<sup>27</sup> at the business cycle (or high) frequency (...rst column) and over the medium term cycle (third column) for the US (row 10) and Mexico (row 1).<sup>28</sup> Mexico’s output ‡uctuations at high and medium term frequencies are approximately twice more volatile than those of the US.

Our model predicts that an important factor contributing to the higher volatility of the Mexican economy is that Mexican GDP responds signi...cantly to US shocks while the US does not respond much to Mexican shocks. To explore these predictions Figure 7A plots US and Mexican GDP ...ltered using an HP ...lter to capture the high frequency (i.e. business cycle) ‡uctuations. The correlation coe¢ cient between the two is 0.43 and, despite the short length of the series, it is signi...cant at the 10% level. Beyond this statistic, the internet-driven expansion in the US during the second half of the 90s was accompanied by a similar expansion in Mexico. The 2001 US recession driven by the burst of the dot-com bubble also coincided with a decline in Mexican GDP that quickly recovered following the US expansion between 2002 and 2007. Finally, the US ...nancial crisis of 2008 also had a strong impact in Mexican GDP though this

> 24This is also the case in the single-country model of Comin and Gertler (2006) where the volatility of consumption and output match the US data.

> 25We restrict our attention to the post 1990 period for two reasons. First, the volume of US-Mexico trade and FDI increased very signi...cantly during this period making the mechanisms emphasized by our model much more relevant than before. Second, after 1990 FDI became the most signi...cant source of capital ‡ows from developed to developing economies making the model’s assumptions about the capital markets most appropriate for this period.

> 26See, for example, Neumeyer and Perri (2005).

> 27In what follows we scale all variables by working age population, i.e. population aged between 16 and 64.

> 28To be precise, we follow the de...nitions of medium term used by Comin and Gertler (2006). In particular, the sort run or high frequency will be measured using an HP ...lter which roughly isolates frequencies associates with cycles of amplitude smaller than 8 years. The medium term refers to frequencies associated with cycles of amplitude between 8 and 50 years. The medium term cycle is the “sum”of the high frequency and the medium term. That is, it captures cycles with amplitude smaller than 50 years. Comin and Gertler (2006) show that despite the relatively short time series, medium term cycles can be identi...ed in the data quite precisely.

23
The paper is organized as follows. Section two examines the patterns of seasonality in income, consumption and poverty in Bangladesh using the household income and expenditure surveys (HIES) of 2000 and 2005. Section three discusses the model of consumption smoothing and its estimation strategy. Section four presents the results on whether income seasonality affects consumption. Section five presents the results of the impacts of income seasonality on poverty. Section six assesses the impact of policies and programs on mitigating seasonality in consumption and poverty. And lastly, section seven summarizes the paper with policy implications.

# **2. Household welfare and seasonality in Bangladesh**

Given that Bangladesh is an agrarian economy, the important policy question is: how much seasonality in agriculture matters to consumption and income? If _monga_ is a seasonal poverty and only pronounced in greater Rangpur, does it mean that seasonality in agriculture is less pronounced in other regions of Bangladesh? The extent of seasonality in income and consumption can be shown through a disaggregate analysis of income and consumption data by season. In HIES surveys, households were interviewed at various times of the year across the country. Consumption and income data were collected, among other information, from all households. For example, income data was collected on crops, non-crop and non-farm sources over a year preceding the date of interviews. Thus, although seasonality in income of all types (especially non-farm) could not be traced for alternative seasons, the crop income can be sorted out by season because we know which crop is grown during which months of the year. On the other hand, for consumption, it is possible to categorize it by season based on the information collected for the week and the month preceding the date of 6
Rapid and even accelerating urbanization will require very high rates of urban infrastructure investment to accommodate cities’ population growth while at the same time improving coverage and service standards.

By some measures, China’s rate of urban infrastructure investment to date has been less robust than it may first seem. The World Bank’s World Development Report of 1994 found that infrastructure investment in developing countries tended to average about 50% of total government investment, 20% of total national investment, and about 4% of GDP. “Urban” infrastructure investment—i.e. infrastructure investment serving the municipal level--averaged about 3 to 4% of urbanized GDP. Table 7 shows that urban infrastructure investment levels in China have been rising rapidly over time, but remained a relatively modest share of total national investment as of 2000 (note that the urban infrastructure investment ratios in the table are calculated relative to total national investment and total national GDP). The most successful cities have made much greater commitments to urban infrastructure investment. Over the Ninth Five-Year Plan period (ending in 2000), Shanghai devoted 12.5% of total investment to urban infrastructure and 5.9% of regional GDP. In Beijing, 13.7% of total investment went to urban infrastructure, which corresponded to 6.3% of regional GDP, more than triple the share from earlier years.

34