rafmacalaba/datause-encoder-data huggingface dataset:
# Table 2. Summary Statistics

||Incumbent victory|Incumbent loss|RD balance test|
|---|---|---|---|
||(1)|(2)|(3)|
|Casualties in 12 months before election|0.70|0.74|0.29|
||[1.92]|[1.79]|(0.69)|
|Violent incidents in 12 months before election|0.33|0.40|0.12|
||[0.68]<br>|[0.84]<br>|(0.27)<br>|
|At least 1 casualty in 12 months before election|0.12|0.24|0.12|
||[0.32]<br>|[0.43]<br>|(0.20)<br>|
|At least 3 casualties in 12 months before election|0.072<br>|0.098<br>|-0.093<br>|
||[0.26]|[0.30]|(0.14)|
|At least 5 casualties in 12 months before election|0.026|0.039|0.050|
||[0.16]|[0.19]|(0.042)|
|Casualties in 12 months after election|0.61<br>|0.35<br>|1.61<br>|
||[2.28]|[1.34]|(0.98)|
|Violent incidents in 12 months after election|0.20|0.18|0.44*|
||[0.62]|[0.62]|(0.25)|
|At least 1 casualty in 12 months after election|0.13|0.1|0.20**|
||[0.34]|[0.30]|(0.10)|
|At least 3 casualties in 12 months after election|0.084|0.057|0.21**|
||[0.28]|[0.23]|(0.09)|
|At least 5 casualties in 12 months after election|0.024<br>|0.029<br>|0.08<br>|
||[0.15]<br>|[0.17]|(0.06)<br>|
|Population|40594|32265|-1742|
||[62379]|[20504]|(8165)|
|Area (km<sup>2</sup>)|210<br>|220<br>|77<br>|
||[267]|[260]|(74)|
|Poverty rate|32.8|35.0|3.0|
||[16.0]|[16.7]|(5.1)|
|Incumbent vote share in 2004 election|0.536|0.534|-0.025|
||[0.146]|[0.133]|(0.049)|
|Afliated with national governing coalition|0.90|0.77|0.18|
||[0.30]|[0.42]|(0.14)|
|Insurgent presence|0.265<br>|0.271<br>|0.056<br>|
||[0.44]|[0.45]|(0.18)|
|Share of households with electricity|0.57|0.55|-0.18|
||[0.26]|[0.25]|(0.081)**|
|Share of households with piped water|0.44|0.36|0.012|
||[0.27]|[0.27]|(0.085)|
|Share of households with indoor plumbing|0.66|0.61|-0.095|
||[0.21]|[0.24]|(0.072)|
|Share of buildings with walls made of “strong” materials|0.69|0.67|-0.069|
||[0.19]|[0.17]|(0.062)|
|Share of buildings with roofs made of “strong” materials|0.64|0.59|-0.18|
||[0.24]|[0.24]|(0.072)**|
|Located in Luzon|0.52<br>|0.64<br>|-0.045<br>|
||[0.50]|[0.48]|(0.18)|
|Located in Visayas|0.25|0.15|0.095|
||[0.44]|[0.37]|(0.14)|
|Located in Mindanao|0.23|0.20|-0.049|
||[0.42]|[0.40]|(0.15)|
|Located in ARMM|0.036|0.029|0.028|
||[0.19]|[0.17]|(0.022)|
|Municipalities|83|70|153|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ Summary statistics for the sample of 153 municipalities in which incumbents won or lost the 2007 election by a margin of 5 percentage points or less. Following the classification of the 2000 Census of the Philippines, “strong” building materials for walls are defined as concrete, brick, stone, wood, galvanized iron or aluminum, asbestos and glass. “Strong” building materials for roofs are defined as concrete, galvanized iron or aluminum, clay tiles and asbestos. Standard deviations are reported in brackets. Column 3 reports results of RD regressions using the municipality characteristics as outcomes. Standard errors of these regression estimates are reported in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels.

17
the main findings are robust to using alternative transformations of these variables. In particular, I applied an inverse hyperbolic sine (IHS) transformation to household consumption expenditure per capita and a hybrid hyperbolic sine and its inverse transformation of the wealth score, in the latter case following Ravallion (2017). In Table A5, I report the results based on these transformations of the main continuous outcome variables and using the logarithmic or IHS transformation of refugee intensity. Moreover, I present the results for the levels of the main binary outcome variables and using an IHS transformation of refugee intensity in the same table. All the main findings remain robust.

# **7.4. Falsification Test**

I discussed that potential reverse causality between refugee intensity and the outcome variable(s) is a threat to causally identifying the impact of hosting refugees. I use time-lagged refugee intensity in the main analyses to assuage this threat. Nonetheless, such a technique may not circumvent all the potential sources of the threat. For instance, refugees may anticipate the living standards across host areas and adjust their decisions, such as where or when to cross the border, to influence their subsequent settlement site. Such anticipation effects, if present, cause reverse causality and will not be addressed by the strategy I followed. As refugees arguably follow adaptive expectations, their anticipation about the living standards of hosts in the near future is highly dependent on the current level of living standards. This implies that a significant correlation between the current living standard (welfare) of hosts and future refugee intensity is an indication of the existence of such anticipation effects, causing reverse causality. I formally test whether this is the case by including future refugee intensity, constructed from the numbers of refugees hosted in the refugee settlement sites in the two years immediately after the year outcome variables are measured, as an additional explanatory variable in the main specification, which controls for household- and roundspecific fixed effects, lagged refugee intensity, and 12 other time-varying covariates.<sup>30</sup> Table A6 presents the results of this test for the main outcome variables capturing household welfare and the status of poverty. The results show that future refugee intensity is not significantly related to any of the four outcome variables, suggesting nonexistence of such anticipation effects.

> 30Data on refugees in the period 2011-2017 is used to construct future refugee intensity.

28
take-up rate. This effect is several times larger than cutting the price of the product by half. Notably, this treatment effect is magnified amongst poor households, who are likely to have less access to the financial system. In addition to this experimental evidence, in surveys, sixty-four percent of non-participating farmers in the Andhra Pradesh sample cite “insufficient funds to buy” as their most important reason for not purchasing insurance. Finally, wealthier households (a proxy for access to finance) are more likely to purchase insurance.

Beyond liquidity and price, households do not fully trust the insurance product. To measure the importance of trust, we vary whether the insurance educator receives an endorsement by a trusted local agent during the household visit or not. Demand is 36 percent higher when the insurance is offered from a source the household trusts (i.e. the insurance educator is endorsed by a known local agent). In a subset of the Gujarat experiments, demand increases if an insurance flyer includes symbols of the household’s own religion, also consistent with trust effects.

Trust may be particularly important because many households have only limited numeracy skills and financial literacy, likely reducing their ability to independently evaluate the insurance. For example, households are only able to correctly answer simple addition and multiplication questions 60% of the time. In cross-sectional regressions, demand is higher in villages which previously experienced a payout, and amongst households with previous experience with insurance, higher measured financial literacy and greater familiarity with probability concepts.

Finally, while average take-up is 28 percent among treated households in Andhra Pradesh and 24-29 percent in Gujarat, it is close to zero amongst the general population in the same villages that did not receive any treatments. Thus, being assigned a visit from an insurance educator, or a flyer treatment, increases take-up significantly, even for households not receiving the high cash reward or other beneficial treatments. This result is suggestive of the importance of limited attention or salience for demand (Ricardo Reis, 2006) and consistent with the “last mile” challenge of promoting financial inclusion.

Our study also allows us to dismiss the importance of some factors often thought important for the demand for financial products, but which have little or no effect on demand in our setting. We assess the impact of a short education module and a set of framing effects from the economics and psychology literature. The education module has no significant effect on 3
<!-- Start of picture text --> Figure 3.3. Deviation from base in 2030 for MDG<br>indicators (% pts or ‰ pts)<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>infhd+tx infhd+eff infhd+fg infhd+mix inf+mix hd+mix pwemin-sd<br>Poverty U5MR (‰) Lack of water access Lack of sanitation access<br><!-- End of picture text -->

**Figure 3.4. Deviations from base GDP growth by sector**

<!-- Start of picture text --> pwemin-sd<br>hd+mix<br>Other government<br>services<br>inf+mix<br>Infrastructure services<br>Human development infhd+mix<br>services<br>Private services<br>infhd+fg<br>Non-mining industry<br>Agriculture infhd+eff<br>infhd+tx<br>-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0<br>%-age points<br><!-- End of picture text --> 22
<!-- Start of picture text --> 0.05%<br>0.00%<br>-0.05% hold<br>-0.10% —Housel income<br>-0.15%<br>-0.20% —Household consumption<br>-0.25% expenditures<br>-0.30%<br>0.35%<br>-0.40%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 3.5%<br>3.0% _<br>2.5% a with only captaliputs<br>2.0% —ScenarScenar io with ‘ o nlynly capitalintermediaryi inputs<br>1.5% —Scenario with only labor inputs<br>0.5%<br>1.0%<br>0.0%<br>von<br>1.0<br><!-- End of picture text --> <!-- Start of picture text -->
1.5%<br>ss<br>1.0%<br>0.5%<br>0.0% : . . .<br>-0.5% ‘only capital inputs<br>—Svcenario with only labor inputs<br>1.0% —~ Scenario with only inlermediary inputs<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> 2.0% —Scenario with only capital<br>15% See ario with only oe<br>: --Scenanotermediary wihi only labor“<br>1.0% input Pea“<br>0.5% ’<br>uw<br>0.0% wl<br>0.5%<br><!-- End of picture text -->
Table 1: Regression results for employment elasticity of growth: world

|Variables||1995 t|o 2010|||1995 t|o 2001|||2002 t|o 2007||20|08 to 20|10|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)<br>OLS|(2)<br>FOLS|(3)<br>ROLS|(4)<br>SGMM|(5)<br>OLS|(6)<br>FOLS|(7)<br>ROLS|(8)<br>SGMM|(9)<br>OLS|(10)<br>FOLS|(11)<br>ROLS|(12)<br>SGMM|(13)<br>OLS|(14)<br>FOLS|(15)<br>ROLS|
|ECA||||||||||||||||
|Output coeff.|0.181|0.120|0.085|0.232|0.074|0.075|0.058|0.062|0.140|0.124|0.060|0.128|0.325|0.221|0.201|
|Output t-stats.|7.2|3.7|3.6|4.5|1.9|2.0|1.9|1.1|2.5|1.4|1.4|1.3|6.8|2.4|3.9|
|Number of obs.|413|413|413|413|161|161|161|161|168|168|168|140|84|84|84|
|R squared|0.111|0.289|0.472||0.023|0.433|0.555||0.036|0.343|0.697||0.358|0.689|0.883|
|AB AR2 test (p value)||||0.411||||0.913||||0.087||||
|Hansen J statistics (p value)||||0.942||||0.231||||0.125||||
|Western Europe||||||||||||||||
|Output coeff.|0.438|0.407|0.494|0.472|0.538|0.320|0.331|0.516|0.448|0.428|0.536|0.572|0.333|0.028||
|Output t-stats.|13.7|7.1|11.1|4.1|8.4|3.3|3.3|5.1|6.9|4.2|5.4|4.5|3.7|0.2||
|Number of obs.|285|285|285|285|114|114|114|114|114|114|114|95|57|57||
|R squared|0.397|0.521|0.681||0.388|0.649|0.647||0.300|0.630|0.680||0.201|0.669||
|AB AR2 test (p value)||||0.154||||0.232||||0.614||||
|Hansen J statistics (p value)||||0.997||||0.316||||0.508||||
|Peers of ECA||||||||||||||||
|Output coeff.|0.046|0.032|0.019|0.103|0.049|0.022|-0.004|0.038|0.028|0.013|0.015|0.062|0.083|0.065|0.004|
|Output t-stats.|4.2|2.7|3.1|2.6|2.8|1.1|-0.5|0.7|1.6|0.6|1.8|0.6|3.2|1.9|0.9|
|Number of obs.|1462|1462|1462|1462|566|566|566|566|596|596|596|498|300|300|300|
|R squared|0.012|0.239|0.587||0.014|0.325|0.872||0.004|0.352|0.810||0.033|0.500|0.986|
|AB AR2 test (p value)||||0.485||||0.228||||0.449||||
|Hansen J statistics (p value)||||0.243||||0.119||||0.977||||
|Peers of Western Europe||||||||||||||||
|Output coeff.|0.070|0.038|-0.008|-0.013|0.007|-0.007|-0.014|-0.010|0.432|0.284|0.266|0.036|0.591|0.087||
|Output t-stats.|3.3|1.8|-0.8|-0.4|0.6|-0.5|-1.3|-1.2|5.9|3.8|10.5|0.1|5.6|0.9||
|Number of obs.|310|310|310|310|121|121|121|121|126|126|126|105|63|63||
|R squared|0.033|0.523|0.538||0.003|0.556|0.685||0.219|0.710|0.951||0.340|0.914||
|AB AR2 test (p value)||||0.52||||0.17||||0.326||||
|Hansen J statistics(pvalue)<br>Note: The dependent variabl<br>OLS stands for cross-sectiona<br>ROLS stands for robust regres<br>The null hypothesis of the Ar<br>The null hypothesis of the Ha|e is emp<br>l bivaria<br>sions. S<br>ellano-B<br>nsen J-s|loymen<br>te regre<br>GMM sta<br>ond AR(<br>tatistics|t growth<br>ssions. F<br>nds for<br>2) test i<br>is that t|0.996<br>. The outp<br>OLS includ<br>system ge<br>s that the f<br>he instrum|ut coeffic<br>e in addi<br>neralized<br>irst-diffe<br>ents are|ient is t<br>tion cou<br>method<br>renced e<br>not corr|he para<br>ntry and<br>of mom<br>rrors ex<br>elated w|0.423<br>meter esti<br>year fixed<br>ents regre<br>hibit no se<br>ith the res|mate of o<br>-effects.<br>ssions.<br>cond-ord<br>iduals.|utput gr<br>er seria|owth.<br>l correlat|0.175<br>ion.||||

12
_β_ = 0 (where _β_ is the vector of coefficients associated with each variable). Standard errors are clustered at village level. We do not reject the null hypothesis of no difference between treatment and control for any of the three groups of variables, which means that we do not reject that these variables are jointly equal in the treatment and control groups. This provides additional evidence that randomization was effective in achieving balance in the characteristics of treatment and control villages.<sup>10</sup> Half the population in our sample consists of females, as shown in Table 1. Almost all household members usually live in the house visited by the interviewer. The population is quite young, with an average age of 22 and an average age of respondents of about 42. Average levels of education in our sample are low: only 19 percent of respondents ever attended school and 76 percent of them attended only primary school. The proportion of literate respondents is equally low (20 percent). Almost all respondents are Muslim and married.

Table 2 shows that average household size in the sample is between 4 and 5, with more than half of household members being below 18 years of age. Respondents living in these villages are very poor: only 43 percent of them has access to drinking water from a public tap, 6 percent has a toilet, 25 percent owns a radio, 95 percent uses firewood as the main source of fuel and the average number of rooms per house is well below 2.

Compliance with treatment was high, but not perfect. Table 3 shows that 6 percent of households living in control villages reported having their dwelling sprayed in the 5 months prior to the survey<sup>11</sup> . The spraying in control villages was not carried out by the government. Most likely, households used simple insecticide sprays purchased from local shops, which have low effectiveness when compared to IRS, since the cost of replicating the IRS provided by the government would be too high for any of these poor households.<sup>12</sup> 10The list we originally used to randomly assign villages to treatment or control group included 116 villages. Some names were changed at the time of the intervention or when the data collection was conducted and some villages had to be replaced because they were not found. A detailed analysis of the treatment allocation is presented in the Online Supplementary Material. Our analysis provides evidence that randomization was effective.

11This is roughly the period of time between treatment and the interviews, allowing for some recall error. 12Respondents were asked whether anyone had sprayed the interior walls of their dwelling against mosquitoes over the previous 12 months. NMCP records report that no IRS campaigns was conducted in control villages over the 12 months prior to the survey. We can also exclude that other organizations conducted an IRS campaign in the region. Since the question did not specify “with DDT” or “by spray9
reproductive ages). Among older women (panel (c)), those with a first-born daughter are ‘better-off’ in terms of their own and their husband’s education, first gave birth at an older age, are more likely to be household heads, and are also characterized by higher weight-for-height and body mass index (BMI).<sup>19</sup> These findings, especially those related to differences in predetermined characteristics among older women, are again consistent with selective mortality.

# **4 Empirical strategy**

## **4.1 Fertility regressions**

Given that the sex of the first-born can reasonably be considered as random, the empirical strategy should be straightforward: unbiased estimates should be obtained by simply regressing the number of children ever born on the sex of the first-born. However, as discussed in the previous section, there are differences in observable characteristics between women with a first-born daughter or son that may have developed over time as a consequence of son-preferring fertility behavior. This would generate a negative correlation between a first-born girl and fertility because surviving women have fewer children on average. In all regressions I include a set of observable covariates in order to reduce the potential (downward) bias. Conditional on the controls, if there is preference for male children, women with first-born daughters should exhibit higher fertility than women with first-born sons.

In order to understand if the sex of the first-born also affects the _desire_ to have more children, I construct a dummy variable based on the NDHS survey question: _‘Would you like to have another child, or would you prefer not to have anymore children?’_ . I also consider the reported use of contraceptives as an outcome.<sup>20</sup> I estimate the following regression: with woman _i_ , born in year _t_ , and resident in region _r_ . _yi,t,r_ is the dependent variable, either the number of children ever born, the desire for more children, and contraceptive use. _firstborngirl_ indicates whether the first child ever born is a girl; _Xi,t,r_ is a set of covariates including: age (of the woman and her husband) and age squared, age at first marriage, age at first birth, number of years

> 19The Body Mass Index (BMI) is a measure of women’s nutritional status. It is the ratio of the weight in kilograms to the square of the height in meters. Weight-for-height is another indicator of women’s nutritional status and it’s defined in terms of standard deviations from the reference median based on the DHS reference standard.

> 20The variable for contraceptive use is defined as equal to one if the woman reports she is currently using _any_ contraceptive method, zero otherwise.

15
port is about twice as large as the number of passengers who depart from Brazzaville – again pointing to considerable misreporting.

_Table 6: Inbound and Outbound Passenger Traffic at the Ports of Brazzaville and Kinshasa, 2009_

||_Passengers_<br>_%_|_Passengers_|_%_|
|---|---|---|---|
|_Data by Brazzaville port:_|To Brazzaville|From Brazz|aville|
|Crossing from/to Kinshasa|101,510<br>95.6|50,733|94.8|
|From/to upriver|4,620<br>4.4|2,787|5.2|
|Total|106,130<br>100|53,520|100|
|_Data by ONATRA:_|To Brazzaville|From Brazz|aville|
|Crossing from/to Kinshasa|141,394<br>99.6|62,611|99.4|
|To/from upriver|520<br>0.4|400|0.6|
|Total|141,914<br>100|63,011|100|

Officially recorded passengers only. Sources Port of Brazzaville (PABPS) and ONATRA.

**The overall number of passenger crossings, around 700,000 annually, is extremely small compared the size of the two cities** . According to ONATRA, fare-paying passengers represent somewhat less than a third of all crossings, as disabled travelers are exempt from paying the fare and many of them make a living from informal trading. This leads us to estimate for the total annual number of trips across Malebo Pool in the region of 700,000. As we show in Section 4, this volume of traffic, scaled to city sizes, is some 175 times smaller than the river-crossing passenger traffic in Kisangani. It is also around five times smaller than the volume of passenger traffic between East and West Berlin in 1988 – well before the dismantling of the Berlin Wall.<sup>12</sup>

# **3. ASSESSING THE POTENTIAL FOR CROSS-BORDER INTEGRATION: NEW EVIDENCE**

**How large is the potential for trade expansion across Malebo Pool?** Compared to the two cities’ size, economic importance and role as gateway to the Lower Congo region, economic exchanges between Kinshasa and Brazzaville are extremely limited. Such limited observed trade could be the result of either substantial barriers to trade or a lack of tradable products. In order to investigate the reasons behind this limited trade, we assess the extent of existing and potential cross-border economic interactions in Kinshasa-Brazzaville using primary data collected for the purpose. We collect and compare retail price data on both sides of the river, testing for symptoms of market segmentation in the form of systematic price differentials that would indicate the existence of trade barriers. In addition, we analyze the results of structured interviews carried out with trading firms in both capitals for a qualitative assessment of barriers to trade, and for an estimation of the latent potential for trade expansion.

> 12 In 1988, there were some 1.9 million passenger crossings between East and West Berlin – about one crossing per three inhabitants of the Berlin metropolitan area. The 700,000 estimated crossings across Malebo pool represent about one crossing per 15 inhabitants of the Kinshasa-Brazzaville conurbation.

15