rafmacalaba/datause-encoder-data huggingface dataset:
- 40 -

# Table 3: Additional Variables in Regression for Rural Households

|WALLADOBE|One if dwelling<br>has adobe walls,<br>zero otherwise.|
|---|---|
|WALLBAM|One if dwelling<br>has bamboowalls,zero otherwise.|
|FLOORBAM|One if dwelling<br>has bamboo floors,<br>zero otherwise.|
|PUMPWELL|One if household's<br>drinking<br>water<br>comes from a well with a pump,<br>zero otherwise.|
|OPWELLRIV|One if household's<br>drinking<br>water<br>comes from a well without<br>pump<br>or from a river,<br>zero otherwise.|
|NOWINDOW|One if dwelling<br>has no windows, zero otherwise.|
|DISTAB,<br>DISTABSQ|The distance<br>by air<br>(kilometers)<br>of the community from Abidjan,<br>and its<br>square,<br>respectively.|
|LMKTDIST|Log distance<br>(kilometers)<br>to nearest<br>market.|
|LMANWAGE|Log male agricultural<br>wage rate<br>(thousands<br>CFA Francs<br>perday).|
|MUSLIM|One if Islam<br>is predominant<br>religion<br>in community,<br>zero<br>otherwise.|
|COCOAREA|One if cocoa is the leading<br>crop in the community,<br>zero<br>otherwise.|
|LCOCOHAR|Loghousehold's<br>land (hectares)<br>with mature cocoa trees.|
|LCAFEHAR|Log household's<br>land (hectares)<br>with mature coffee<br>trees.|
|LLANDUSE|Log household's<br>land (hectares),<br>excluding<br>cocoa and coffee<br>trees,<br>under cultivation.|
|AKAN,NMANDE<br>NONIVOR|, Dummy variables<br>which take the value of one if the head of<br>household<br>is an Akan, Northern<br>Mande or Non-Ivorian<br>(i.e.<br>immigrant),<br>respectively,<br>zero otherwise.|
22 DASGUPTA AND GROVER petition that non-primary regions provide to domestic industries, thereby making them relatively less attractive. This hypothesis is borne out in the Indian data at the aggregate level during 2000-09 (Stylized Fact 2) and in the evidence provided in Table 1, which shows a relative decline in output, employment and plant count in the Secondary region during 2000-09. Unlike in the model, however, internal trade costs did not fall uniformly but rather for a subset of districts across all the regions that got connected along the GQ network. This can lead to re-allocation _within_ each region as districts that experience a lower internal trade cost due to two opposing forces: while districts along the highway network are not protected from import competition,they benefit from increased market access due to better connectivity. The overall outcome on reallocation within (and across) regions depends on the net of the two effects.

We define districts “on GQ” as those that are within the 10 kilometer range of the highway network.<sup>24</sup> Given that a large part of GQ passes along the coast, it is not surprising that 38 of 68 “on GQ” districts are in the primary region. Following the empirical specification in the previous section, we additionally introduce a third dummy variable, GQ status, and include the full set of double interaction terms and the triple interaction terms of the form Period _×_ Region _×_ GQ status. As before, we allow the coefficients to be region, period and GQ-status specific. Specifically, we run the following regression: where _αg_ is a dummy variable that takes a value of one if the district is on GQ.

# **5.2 Results**

Our estimations in Table 3 suggests that following key points: _First_ , prior to GQ upgrades, economic activity in secondary region did not vary by their location on a potential highway network. Said differently, there were no pre-trends in economic activity within the secondary region. While output, employment and plant count in off-GQ districts of the secondary region grew faster than their counterparts in the primary region pre-GQ upgrades (coefficient on _1994-00×Secondary_ ), these growth rates were the same when measured relative to those on GQ (coefficient on _1994-00×Secondary × On GQ_ is not significantly different from zero).

_Second_ , while activity in the secondary region declined relative to the primary region after GQ upgrades, the effect was heterogeneous across districts based on their location along the

> 24Table 12 in Appendix D shows the distribution of districts across regions and GQ status that results from this
# **I.** INTRODUCTION

Low population densities and low income per capita put Sub-Sahara Africa (SSA) at a natural disadvantage where investment in transport infrastructure is concerned. Since demand for transport increases with area and with population dispersion, the burden of the investment in and maintenance of the transport network on the economy's resources is inversely related to output per unit of area (GDP per square km, i.e., the product of GDP per capita and population per square km). SSA is in a unique position: low levels of income per capita increase this burden compared with the better-off Latin American countries (e.g., Brazil and Argentina) although the latter are more sparsely populated, and low population densities result in a higher burden compared with densely populated countries in Asia (e.g., India and China) although they are poorer (Table 1).

**Table 1.** Population Density, GDP per Capita and GDP per Square km

||Population<br>km<sup>2</sup>|GDP per capita<br>1987 (US$)|GDP<br>km<sup>2</sup>|
|---|---|---|---|
|Sub-Sahara|20.3|340|6,902|
|Sub-Sahara, excluding||||
|Nigeria|16.2|330|5,346|
|Brazil|16.6|1,960|32,536|
|Argentina|11.2|2,400|26,880|
|India|242.6|310|75,206|
|China|111.7|300|34,627|
|Japan|322.8|15,800|5,100,240|
|France|101.7|12,910|1,312,947|
|Germany, Fed. Rep.|245.8|14,440|3,549,352|
|U. K.|232.7|10,540|2,452,658|
|U. S. A.|25.9|18,580|481,222|

Sources: Sub-Saharan Africa - From Crisis to Sustainable Growth, Long-term Perspective Study, 1989.

The World Bank Atlas, 1989. World Tables, 1991. World Development Report, 1989.
librium?: Theory and Evidence from Colombia’s La Violencia,” _Journal of Conflict Resolution_ , 55.

- Chassamboulli, Andri and Giovanni Peri (2015) “The Labor Market Effects of Reducing the Number of Illegal Immigrants,” _Review of Economic Dynamics_ , 18 (4), 792–821.

- Chatruc, Marisol Rodr ́ıguez and Sandra V Rozo (2024) “In someone else’s shoes: Reducing prejudice through perspective taking,” _Journal of Development Economics_ , 170, 103309.

- Clemens, Michael A. and Jennifer Hunt (2019) “The Labor Market Effects of Refugee Waves: Reconciling Conflicting Results,” _ILR Review_ , 72 (4), 818–857.

- Cobb-Clark, Deborah A., Clinton R. Shiells, and B. Lindsay Lowell (1995) “Immigration Reform: The Effects of Employer Sanctions and Legalization on Wages,” _Journal of Labor Economics_ , 13 (3), 472–498.

- Cohodes, Sarah, Sean Corcoran, Jennifer Jennings, and Carolyn Sattin-Bajaj (2022) “When Do Informational Interventions Work? Experimental Evidence from New York City High School Choice,” NBER Working Papers 29690, National Bureau of Economic Research, Inc, https://ideas.repec.org/p/nbr/nberwo/29690.html.

- Crowne, Douglas and David Marlowe (1960) “A New Scale of Social Desirability Independent of Psychopathology,” _Journal of consulting psychology_ , 24, 349–54.

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49
ratio is mainly discussed in the context of the preference that many South and East Asian families have for sons over daughters (‘son preference’) and related issues that concern the marriage market, fertility, and dowry (Edlund, 1999; Bhat and Zavier, 2003; Sen, 2001; Rao, 1993; Bloch and Rao, 2002; Botticini and Siow, 2003; Das Gupta, 2005; Jacoby and Mansuri, 2006). The demographic differences between Hindus and Muslims, on the other hand, have usually been discussed in terms of the number of children in Muslim families and the higher population growth rates of this community. However, the debate often overlooked the fact that infant mortality among Muslims (at 59 per 1000) is much lower than that among Hindus (at 77 per 1000) as documented in Borooah and Iyer (2005a). Similarly child mortality, which is 83 per 1000 for Muslims, is substantially lower than child mortality among Hindus, at 107 per 1000 (IIPS and ORC Macro International, 2000). The survival advantage of Muslim children despite the higher fertility of their mothers is particularly puzzling in light of the fact that Muslims in India are typically poorer than Hindus as highlighted by the Sachar Committee Report (Government of India, 2006). Even a cursory glance at these facts and figures suggests that religious differentials in infant mortality need to be examined more closely.

In this paper, we link these two separately-studied demographic realities into a comparative analysis of infant mortality across religious and caste groups, using data from the newly released National Family Health Survey of 2005-06. To do so, we first divide our population into three subgroups: non-Dalit Hindus (also known as upper-caste Hindus), Dalit Hindus (also known as lower-caste Hindus), and Muslims. Our preliminary comparative analysis finds that with respect to observable socio-economic characteristics, Muslims are similar to Hindu Dalits in that they have lower levels of education, are poorer than non-Dalit Hindus. They also have more children, are less likely to experience the death of a child (particularly a girl), have higher female-male sex ratios among children alive as well as among children ever-born, are less likely to use contraception, and have preferences for a greater number of girls as well as boys. The next step of our analysis is to estimate survival probabilities for children and control for a number of important socio-economic, geographic and demographic variables. Our findings confirm that Muslim infant and child mortality is considerably _lower_ than that for the Hindus, even after accounting for the wide range of socio-economic characteristics available to us. The effect is particularly strong for girls. Female infant mortality rates are lower in Muslim families than in Hindu families even with the inclusion of numerous controls for household and community characteristics. The final 2
**Table D.4:** Satisfaction

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Satisfaction index**(SUR)||0.064<br>(0.030)<sup>_∗∗_</sup><br>[0.058]<sup>_∗_</sup>|0.059<br>(0.034)<sup>_∗_</sup><br>[0.139]|0.069<br>(0.034)<sup>_∗∗_</sup><br>[0.080]<sup>_∗_</sup>|5052|
|(1) Satisfaction with family health|3.439|0.055|0.051|0.059|5052|
||(0.670)|(0.027)<sup>_∗∗_</sup>|(0.030)<sup>_∗_</sup>|(0.029)<sup>_∗∗_</sup>||
|||[0.096]<sup>_∗_</sup>|[0.642]|[0.102]||
|(2) Satisfaction with public health workers|3.258|0.069|0.052|0.083|4994|
||(0.802)|(0.034)<sup>_∗∗_</sup>|(0.041)|(0.039)<sup>_∗∗_</sup>||
|||[0.096]<sup>_∗_</sup>|[0.642]|[0.102]||
|(3) Satisfaction with care|3.646|0.034|0.039|0.030|2535|
||(0.696)|(0.035)|(0.039)|(0.039)||
|||[0.196]|[0.642]|[0.287]||
|(4) Would return to clinic|0.967|0.007|0.007|0.007|2527|
||(0.174)|(0.007)|(0.008)|(0.008)||
|||[0.196]|[0.642]|[0.287]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

**Table D.5:** Clinic Quality

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Clinic quality index**(SUR)||0.055|_−_0.002|0.112|254|
|||(0.064)|(0.074)|(0.075)||
|||[0.322]|[0.644]|[0.157]||
|(1) Clinic service provision index|0.000|0.149|0.095|0.203|254|
||(1.000)|(0.153)|(0.180)|(0.175)||
|||[1.000]|[1.000]|[1.000]||
|(2) Clinic aware of free health care|0.798|0.005|_−_0.023|0.033|254|
||(0.404)|(0.052)|(0.060)|(0.058)||
|||[1.000]|[1.000]|[1.000]||
|(3) Employee satisfaction index|0.000|0.003|_−_0.043|0.049|254|
||(1.000)|(0.127)|(0.141)|(0.152)||
|||[1.000]|[1.000]|[1.000]||

_Notes:_ Treatment effects are estimated using ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors are shown in parentheses. Multiple-inference corrected q- values that adjust for the false discovery rate within treatment arm are shown in square brackets. The Clinic service provision index is composed of measures on facility maintenance (mainly cleanliness, orderly medicine storage and signposting) and whether required services like pre- and post-natal care, immunization, reproductive health and other forms of consultation are provided. The employee satisfaction index consists of employees’ satisfaction with their job, with the communities’ participation in the clinic and the extend to which they feel supported by the communities. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A18
# **2. Motivating Theory**

This section first discusses several general features of malapportioment and then lays out our theoretical argument for the origins and consequences of malapportionment.

A long standing literature in political science (Taagepera and Shugart, 1989; Samuels and Snyder, 2001; Snyder and Samuels, 2004) identifies malapportionment as a formal and often deliberate “pathology of electoral systems”. Malapportionment – a discrepancy between the share of legislative seats and the share of population held by electoral districts – violates the “one person, one vote” principle that authors like Robert Dahl (1971, 1989) consider to be a basic pillar of fair democratic regimes. Even though this principle is often guaranteed by constitutional charts, in many countries it has been disregarded or implemented only after judicial intervention<sup>5</sup> .

As Snyder and Samuels (2004) point out, countries with a bicameral system may display a high level of malapportionment in the upper chamber since this chamber usually represents all the geographical constituencies more or less equally. While upper chamber malapportionment is, therefore, normatively justifiable, there is no _a priori_ reason for weighing the votes of citizens unequally in the lower chamber. This paper thus focuses only on lower chamber malapportionment.

Lower chamber malapportionment can arise spontaneously over time due to migration or different regional patterns of population growth. Western European and North American democracies typically have low levels of malapportionment since they periodically reapportion the number of seats attributed to electoral districts in response to these demographic changes (Snyder and Samuels, 2004).

On the other hand, the data from Samuels and Snyder (2001) reported in Table 1 show that many of the countries with high legislative malapportionment are newly established or consolidating democracies. In addition, data from Snyder and Samuels (2004) on Latin America

> 5 For example, with two verdicts, _Baker v. Carr_ in 1962 and _Wesberry v. Sanders_ in 1964, the US Supreme Court ruled in favor of redesigning electoral districts since they were characterized by high levels of malapportionment. The Supreme Court’s motivation for these sentences was the necessity to safeguard the “one man, one vote” principle (see Casper, 1973).

4
# **TABLE 11: Impacts on CBCC Outcomes - 36-Month Follow-Up**

|||||<br>**Depende**|<br>**nt variable:**||
|---|---|---|---|---|---|---|
|||**Class**<br>**Obser**<br>**Ind**|**room**<br>**vation**<br>**ex**|**T**<br>**Enro**<br>**(rep**|**otal**<br>**llment**<br>**orted)**|**Number of**<br>**STC-trained**<br>**Teachers**|
|**Variables**||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|T2 (careiver trainin)||0.284|0.301|4.597|5.845|1.272***|
|g g||(0.303)|(0.305)|(7.963)|(6.795)|(0.143)|
|T3 (T2 + rir innti)||0.335|0.324|-1.589|1.049|1.233***|
|caegve ceves||(0.290)|(0.292)|(7.624)|(6.514)|(0.137)|
|T4 (T2 + arentin trainin)||0.167|0.135|10.259|7.426|1.237***|
|pg g||(0.291)|(0.298)|(7.645)|(6.534)|(0.137)|
|Lagged Dependent Variable (Baseline)|||0.046<br>(0.089)||0.614***<br>(0.089)||
|Mean (standard deviation) of dependent variable for the||0.0|00|67|.545|0.000|
|controlgroup||(1.0|00)|(42|.738)|(0.000)|
||T2=T3|0.864|0.940|0.432|0.475|0.782|
|F-test for Equality of Parameters - p-value|T2=T4|0.697|0.601|0.476|0.816|0.801|
||T3=T4|0.557|0.517|0.118|0.326|0.981|
|District-bin Fixed Effects?||Yes|Yes|Yes|Yes|Yes|
|Lagged Dependent Variable?||No|Yes|No|Yes|No|
|Number of observations||178|178|178|178|178|

Notes: .01 - ***; .05 - **; .1 - *; OLS regressions at the CBCC level using baseline and 36-month data on Classroom Observations, Enrollment and STC training of teachers. Standard errors in parentheses. Classroom Observation Index is obtained by weighting underlying variables with Inverse Covariance Weights (ICW) as in Casey et al. (2012) and described in detail in the Appendix. For each round, indices are stardardized using the mean and standard deviation for the control group in that round. We use all CBCCs for whom the relevant variables are available for baseline and the 36-Month FollowUp. STC stands for 'Save the Children'.

54
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- 14 -

# V. RESULTS OF THE HOUSEHOLD INTERVIEWS

5.01 Following the interviews with water vendors, the enumeration teams gqnducted 235 in-depth household interviews throughout Onitsha.-/ The questionnaire focused on the willingness of the household to pay for water. After explaining that the survey was part of a World Bank study, the enumerator read each respondent a carefully-worded statement that was designed to set the scene for the "bidding game" in which respondents would tell whether or not they would be willing to pay certain specified amounts for water under certain circumstances. The statement said that when the New Onitsha Water Scheme was commissioned and when distribution pipes reached the respondent's neighborhood, households with private connections would have safe, reliable water 24 hours per day, all year around. The enumerator next described a situation in which each family with a private connection would have a meter installed, and the household's monthly water bill would be determined by the amount of water which was used. The enumerator compared this billing procedure to that currently used for electricity.

- 8/ The household interview had five parts. The first dealt with the basic socioeconomic characteristics of the household, such as the number of adults and children in the household. The second part was concerned with household water-use practices. For each of seven possible sources of water (tanker trucks, neighbors and small retail water vendors, distributing vendors, shallow wells, rainwater collection, surface water, and the public piped distribution system), respondents were asked questions about the following: (1) whether the particular source was available in the neighborhood; (2) the prices charged for water from this source; (3) the quality of water from this source; (4) whether this household obtains water from this source, and, if so, how much is used; (5) what water from this source is used for.

The third part of the interview consisted of a series of highly structured questions designed to determine how much households were willing to pay for improved water supplies. The fourth part of the questionnaire dealt with household assets. The enumerator asked for information on the monthly rent, the monthly electric bill, the number of rooms in the house, and whether the household owned each of a series of consumer durable goods (such as a refrigerator, radio, air conditioner). In the fifth part of the interview, the respondent was asked to provide information on the occupations of different family members and their total monthly cash income. Respondents were not asked to specify precisely the monthly cash income of family members, but rather to indicate the category into which their income fell.