rafmacalaba/datause-encoder-data huggingface dataset:
construction.

These trends have led to a steady decline in the proportion of urban infrastructure investment that is financed from direct budget spending or operating surplus. Figure 5 shows that the proportion of total urban infrastructure construction financed by budgetary funds decreased from 50% in 1991 to 29% in 2001. The decline has continued thereafter, and is projected to continue into the future.

Figure 5: Share of Urban infrastructure investment financed from the budget <!-- Start of picture text --> 800 60<br>700<br>50<br>600<br>40<br>500<br>400 30<br>300<br>20<br>200<br>10<br>100<br>0 0<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>State budgetary appropriation proportion<br><!-- End of picture text --> Source : Authors calculations A second source of direct public financing is off-budget fees. As noted in Part I, central authorities have vigorously sought to rein in local fees, marked by periodic initiatives since 1994 (e.g., in 1996 and 2001) to limit the number of authorized fees to a restricted list and bring them within the formal revenue reporting system. Most of these fees did not take the form of service charges or user fees for service consumption (which have been increasing at central government urging), but 37
<!-- Start of picture text --> Figure 4: Reform of special tariffs: cutting ad-valorem<br>equivalents<br>0.0%<br>-1.0% cap at 25% cap at 15% cap at 10% all zero<br>-2.0%<br>-3.0%<br>-4.0% Change in Tariff Revenue<br>-5.0% Change in Trade Tax Revenue<br>-6.0%<br>-7.0%<br>-8.0%<br>-9.0%<br><!-- End of picture text --> Table 7 shows statutory complex tariffs in the Tanzanian tariff schedule and Figure 4 illustrates the revenue implications of converting them to ad-valorem equivalents and subsequently capping these. As is common, the specific and complex tariffs are not important sources of revenue and their revenue impact is small relative to the costs a country incurs in terms of economic distortions and administrative costs at customs when maintaining them.

Finally, notice that TRIST allows analysis of the impact of sequencing the different elements of a trade reform. An interesting example is presented in the next section in the context of the analysis of trade diversion.

# **4.2. Extensions of TRIST**

Up to this point, this paper has focused on the analysis of revenue changes in response to trade policy reforms. Yet, revenue effects are not the only adjustment cost policymakers may need to address. The following analysis shows how TRIST can be extended to provide input to an analysis of the impact of trade reforms on domestic production and employment, poverty as well as on trade diversion. It is straightforward to adapt TRIST to contribute to these additional and crucially policy relevant topics.

However, again, it is important to note that TRIST uses a partial equilibrium model and thus does not take into account inter- and intra-sectoral linkages and the economy wide impacts of tariff changes. The results presented below are based on tariff-line level partial equilibrium simulations which do not take into account the various economy-wide resource constraints and reallocations, or any inter- and intra-sectoral economic linkages that may be important for the medium to long term impacts of the respective reforms.<sup>24</sup>

> 24 The equilibrium allocation of resources is much less important when there is a large amount of unemployed or underemployed resources, especially labor.

21
# **Table A7: Test for care‐taking behavior**

_Panel A: Caretaking as an outcome_

|Sample|All respondents|Women only|
|---|---|---|
||Hours on child|1(Miss work due|
|Dependent variable|care|to caretaking)|
|Other worker ill|2.782|0.008|
||[3.382]|[0.018]|
|Other worker ill X Female|2.784<br>[4.158]||
|Mean dependent variable|6.555|0.020|
|Observations|3948|2207|
|R‐squared||0.763|

_Panel B: Differential effects among respondents who engage in child care_

|Dependent variable|1(Work)|Days worked|
|---|---|---|
|Other worker ill|0.044|0.085|
||[0.036]|[0.282]|
|Other worker ill X Female|‐0.100|‐0.308|
||[0.070]|[0.403]|
|Any Child Care X Other worker ill|0.100<br>[0.070]|0.760*<br>[0.435]|
|Any Child Care X Other worker ill X Femal|‐0.079|‐0.552|
||[0.110]|[0.568]|
|Mean dependent variable|0.807|4.279|
|Observations|3,951|3,948|
|R‐squared|0.507||

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which s/he was planning to work and the reason for missing work was injury/illness. Column 3 of Panel A and column 1 of Panel B are estimated by OLS and columns 1 of Panel A and columns 2 and 3 of Panel B are estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). All specifications include individual fixed effects. Sample includes respondents employed at baseline. Regressions in column 1 of panel A and panel B include week‐of‐interview dummies interacted with gender and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
reduces the non-primate prefecture population by 1.7 percent but increases the primate prefecture population by 1.1 percent. Highways also affect patterns of specialization. Regional primates specialize more in manufacturing and services through better regional highways. On the other hand, peripheral areas lose manufacturing but gain in agriculture. Better access to international ports promotes greater population, GDP, and private sector wages on average, effects that are probably larger in the hinterland than in primate prefectures. These findings suggest that highways allow regions to specialize in their comparative advantage in China, meaning that centrally located prefectures focus on manufacturing while those in the hinterland concentrate on agriculture.

Similarly, Roberts et al (2012) also analyze the spatial and national impacts of the construction of the Chinese National Expressway Network. The results indicate that aggregate Chinese real income was approximately 6 percent higher than it would have been in 2007, had the expressway network not been built, although this estimation does not consider the opportunity costs associated with expenditure on the network. The construction not only enhanced intra-national trade, but also accelerated the development of the country's poorest inland regions with the aim of promoting their catch-up with the country's leading coastal areas.

Cosar and Fajgelbaum (2016) introduce internal geography to the canonical model of international trade driven by comparative advantages to study the regional effects of external economic integration. Then they empirically test their model using industry level data from Chinese prefectures, proxying for industries’ export orientation with national export-revenue ratios. The direct effect of distance on economic activity is sizable: moving inland by 275 miles (the median distance from the coast across prefectures) decreases industry employment by 17 percent for an industry with an average export-revenue ratio, and by 13 percent for an industry with average labor intensity. But this negative distance gradient is stronger for export-oriented industries: employment shrinks by 32 percent for an industry that has an export-revenue ratio 1 standard deviation higher than average, and by 21 percent for an industry that has labor intensity 1 standard deviation higher than average. Economic activity at the industry-prefecture level is strongly correlated with the interaction between prefecture proximity to coastlines and industry export orientation.

Like China, India had a large-scale highway construction and improvement project, the Golden Quadrilateral (GQ). Datta (2012) estimates the impact on firms' production for those located along the GQ. Such firms reported reducing their inventories by 10.5 days’ worth of production, between the outset of the GQ project in 2002 and its completion towards 2005, when much of the project had been implemented, while no such reduction was reported by firms located further away. Firms located along the GQ also showed a greater propensity to change suppliers 34
_Role of NGOs in Health Delivery Afica Health Policy Study_ _- 15e_ methodological difficulties in making such comparisons, this table gives<sup>only a very rough</sup> idea. Given the volume of NGO involvement, therefore, the major policy consideradon<sup>lies</sup> first in how this existing resource can be integrated more within national<sup>health priorities,</sup> and second whether or not there is potential for expansion of the<sup>NGO role. This</sup> question is far from a technical one, for the relationship between NGOs<sup>and government</sup> and NGOs and donors are primarily political, and in all but very few cases<sup>both tend to</sup> be problematic. Governments are often jealous of resources being channelled<sup>to NGOs</sup> and wary of their oppositionist potential, while NGOs often live in fear<sup>of government</sup> intervention in their activities. Bearing this In mind, however, the following<sup>discussion</sup> tends to emphasize constructive options which enhance, rather than threaten,<sup>the NGO-</sup> government relationship.

# _Defining the Legal Basis for Registration_

41. The fundamental parameters which governments can set for NGO operadons concern the legal status given NGOs and the legal procedure NGOs must follow<sup>in order</sup> to be registered. It is at this stage, before the NGO even begins any health care<sup>actividesb</sup> that governments can select those NGOs appropriate to established health care priorides, lay down the criteria for NGO accountability to government and control the distribution of their activities geographically within the country.

42. In reality, however, in Africa, registration of NGOs tends to be lax and "after-thefact." In many African countries, the only reliable inventories of NGOs active within<sup>the</sup> country are compiled by donors. Frequently, there is no single locus within the government for communication with NGOs, but rather the information may stretch<sup>across</sup> disparate ministries, partly due to the Inter-sectoral nature of NGO activities.<sup>One</sup> exception to this scenario is Mozambique, where the Ministry of Health refuses<sup>to dea</sup> with NGOs which are not willing to conform to government policy, despite the<sup>need for</sup> the resources which these NGOs represent.<sup>6</sup>

## _Monitoring_

43. Ensuring regular contact with NGOs and submisslon of relevant health information is critical for health sector managers at the national level; NGO leaders<sup>have</sup> valuable information of use to managers if a suitable forum for dialogue can be<sup>arranged.</sup> Some governments tend, however, to impose needlessly cumbersome and time-consuming demands on often short-staffed NGOs by demanding detailed financial accoundng<sup>and</sup> planning of activities.

6. Dr. Leonardo Simao, Minister of Health for Mozambique, stated at the Africa Health Policy Study External Technical Advisory Group meeting May 1989.
those who fail drop much more precipitously than those who succeed, and converge to only one out of ten individuals making any deposits at the halfway point of the study.

# _C. Treatment Effects on Smoking Cessation_

We estimate intent-to-treat (ITT) effects of CARES and cue cards on test passage using the OLS specification: t (1): _pass_ i = α + β _cares_ i + χ _cues_ i + δ _X_ i + γ _W_ i + ε i Where i indexes individuals, t refers to the 6-month or 12-month test, _pass_ , _cares_ and _cues_ are all binary variables, _X_ is the vector of baseline covariates, and _W_ is a vector of dummies for the three marketing waves. We report these results in Table 4, Panel A. We also estimate (1) using probit instead of OLS (Appendix Table 3), and after dropping the baseline covariates (Appendix Table 4), and find very similar results.

Each table reports results on 6-month test passage in odd columns, and on 12-month test passage in even columns. We estimate effects under three different assumptions on clients for whom we do not have a test result: i) these clients would have failed the test (Columns 1 and 2), ii) these clients have the average pass rate; i.e., we drop these clients (Columns 3 and 4), iii) these clients have the average pass rate, unless they were found by the technician and refused to take the test, in which case we assume they would have failed (Columns 5 and 6).<sup>19</sup> Table 4 Panel A shows CARES ITT effects on 6-month test passage of 3 to 6 percentage points under these assumptions. These effects are large relative the control group sample mean passage rates of 0.08 to 0.12. The effects on 12-month test passage, which as discussed above are probably a better measure of effects on a lasting quit spell, range from 4 to 6 percentage points. Again these effects are large relative to the control group sample mean passage rates of 0.10 to 0.18.

We do not find any significant effects from the cue cards. Although the point estimates on cue cards are not statistically different than those for CARES, it is important to keep in mind that the takeup rate for cues was nearly 100%. So the maximum feasible ITT effect for cues is 1.0, while for CARES it is only 0.11/1. In other words, even if one

> 19 Six test strips turned blue (off the NicCheck results spectrum) in each of the six- and twelve-month follow-up pools. This is the likely due to the TB medicine Isoniazid.  We coded these blue strips as failures, but Green Bank returned the commitment balance to the one CARES client with a blue result.

13
food insecurity and trust-related behavior. Consequently, the estimates may be biased and inconsistent. Following recent research (Ruyssen and Salomone, 2018; Smith and Floro, 2020), we address potential endogeneity issues using a matched sample of food secure and food insecure respondents with identical variable distributions. Entropy matching methods enable us to compare individuals such that, after matching, the only difference between the two subsamples is their food insecurity status. Given the strong correlation between observable and unobservable characteristics, matching on observable characteristics implies at least some matching on unobservable characteristics (Stuart et al., 2010; Ferraro and Miranda, 2014; Ruyssen and Salomone, 2018). Matching produces an unbiased measure of the influence of food insecurity on trust if the entropy algorithm captures all relevant differences between individuals who are food insecure and those who are food secure (see Appendix A for more details).

# **_2.2. Data_**

The data for the study draws from the 2014-17 waves of the Gallup World Poll, including FAO’s FIES. The GWP collects information on individuals’ labor force participation, income, educational attainment, future aspirations, subjective well-being, demographic characteristics, and countryidentifiers. In most countries, the GWP interviews 1,000 individuals and is nationally representative. Researchers use a random route procedure to select sample households within each country and select the respondent randomly within each household using a Kish grid method (Gallup, 2016). Observations for respondents without valid food insecurity responses or who failed to provide valid information on one or more questions used to construct the control variables were dropped from the sample. The final sample is 387,385 individuals aged 15 years and older in 134 countries.

# **_2.3. Measures of Vertical and Horizontal Trust_**

Trust is defined as holding a positive perception about the actions of an individual or an organization (OECD, 2013). Generally, trust has two components: 1) vertical (political) trust, citizens’ faith in government and its institutions; and 2) horizontal (social) trust, citizens’ interpersonal confidence in their social community (OECD, 2013). Trust is harder to quantify and model formally than traditional economic measures (Barrett, 1997). Despite the difficulty of 5
According to the 2011 National Population Census, conducted by the Bangladesh Bureau of Statistics, the total population of the country was 144 million with 76% of the total population live in rural areas in Bangladesh. The spatial distribution of poor population shows that the north-central (Mymensingh to Rangpur), southwestern (Barisal to Khulna) and some parts of southeastern (Comilla) Bangladesh are generally poor.

Upazila-level, percentage of households with access to tubewell and tapwater supplies in Bangladesh are shown in Figure S2. The data on access to water supply come from the 2011 Census of Population and Housing. The national average of households with an access to tubewell and tapwater supply (town or municipal water supply via piped network) is 82% and 10% respectively. Tapwater supply is limited to towns and large metropolitan cities such as Dhaka, Chittagong, Rajshahi, Sylhet, Barisal and Rangpur (Figure 1). The absence of tubewell-based water supply in these cities suggests that drinking water and domestic water come from municipal water supplies managed by the city authorities such as the Dhaka Water Supply and Sewerage Authority (DWASA) in Dhaka city.

Access to groundwater-fed water supplies for irrigation is an important indicator for measuring food security in Bangladesh. Currently groundwater meets 80% of all irrigation water supplies and has been sustaining the dry-season “Boro” rice cultivation since the 1970s that has made the country self-sufficient in food production and has led to major economic development.<sup>16</sup> Upazila-level, groundwater use for irrigation for the year of 2006–07 Boro rice season is shown in Figure S2c. Groundwater irrigation has been estimated using reported information on irrigated area and the number of irrigation pumps surveyed under the minor irrigation campaign by the Bangladesh Agricultural Development Corporation (BADC). Additional information on irrigation requirement for dry-season rice cultivation under various soil types and their infiltration capacity<sup>17</sup> has been used to estimate groundwater irrigation.

The social vulnerability is defined here by the lack of nutrition in children under the age of five. Four indicators measuring the level of under-nutrition in children: the number of underweight and severely underweight, and stunted and severely stunted children at the upazila level of Bangladesh (Figure S3) are used here to characterize social vulnerabilities. According to the 2012 Undernutrition Maps of Bangladesh, underweight and severely underweight children are those, whose standardized weight-for-age is more than two and three standard deviations, respectively, below the median for the international reference 28
receive grants, and only then do these communities choose the _types_ of projects they wish to receive. The information available in the FISE program documents suggests that selection processes were _not_ that clear-cut. There were villages to which no menu was offered, on the grounds of having too few poor people. There also appear to have been villages that were eligible for the program, but from which no applications were received.<sup>33</sup> We therefore prefer the multinomial probit specification, where a hierarchical structure of selection is not imposed, and the three possible outcomes are not ranked.

Be that as it may, it turns out that the key poverty and inequality results are remarkably robust to the alternative, Heckman probit specification. Table 7 shows the selection probit equation and five alternative specifications for the main equation. The variable excluded from the main equation is the proportion of people in the province that voted for the incumbent.<sup>34</sup> As before, the incidence of poverty is positive and significant in both the selection equation (likely due to spatial poverty targeting from the center) and in the main equation, consistent with the model prediction.<sup>35</sup> The expenditure share of the elite (or inequality) variable also remains negative and statistically significant (for the top 1%, 3% or 5% of the population), with the coefficients remarkably similar to those from the multinomial probit estimation. Interestingly, inequality is not significant in the selection equation, suggesting that the effect of inequality really bears on the choice of project _type_ – again consistent with the model in Section 3.<sup>36</sup>

> 33 Although, unfortunately and as indicated in Section 4, the administrative FISE data set on which we draw does not include data on unsuccessful applications, or on communities visited by FISE teams from which no applications were received.

> 34 Under a sequential view of the selection process, where communities are first identified to receive a project, and the type of project is chosen subsequently within the community, it makes sense to expect electoral variables at the central level to affect selection, but not to have any impact on the type of project a community applies for.

> 35 Significance levels are, if anything, larger than in the multinomial probit specification.

> 36 Several papers suggest that the relationship between local inequality, participation, and collective action is complex, and that while some inequality may be necessary to mobilize collective action, too much inequality may be harmful (see, for example, Dayton-Johnson and Bardhan 2002 and Khwaja 2002). In Appendix Figure 4, we present evidence reminiscent of this literature when we allow for a more flexible (non-linear) relationship between inequality and probability of project receipt. Conditional on various community characteristics, the likelihood that a community receives a FISE project increases initially with inequality, then declines.

27
_b) If you had NOT won YouWin!, and were running a business today, how many employees in total do you think you would have today?_ _c) If you had NOT won YouWin!, and were running a business today, how much do you think your sales would have been for the last month?_ Question a) in both cases is a probabilistic expectation following the percent chance formulation of Dominitz and Manski (1997) and Manski (2004), which has been used with tertiary‐educated individuals in a developing country context by McKenzie et al. (2013). In contrast b) and c) are non‐probabilistic expectations questions, following the approach of directly asking what you expect used by Jensen (2009) among others. A potential critique of such questions is that it is unclear whether respondents are answering with a mean, median, mode, or something else. The reason for using such a method was to enable surveys to be done by telephone or online, in addition to face‐to‐face.<sup>2</sup> Delavande et al. (2011) provide evidence that this form of direct expectations question is strongly correlated with the mean of a subjective probability distribution, but can be more susceptible to outliers. As a result, I examine the responses for large outliers and trim one extreme outlier (noted below).

# **3.3 Assessing the Accuracy of These Counterfactuals**

Let _Yi_ be the realized outcome in the third follow‐up survey for individual _i_ , and _Ei_ be the counterfactual they express when answering the questions described above. Let _T_ denote the treatment group and _C_ the control group. Then the mean expected treatment effect of changing treatment status for group _gε {T, C}_ is given by: Where _Ng_ denotes the number of individuals in group _g_ . This is estimated separately for the new firm applicants and existing firm applicants. The first approach to assessing the accuracy of the counterfactuals expressed by applicants is then to compare this estimate of the treatment effect to experimental treatment effects reported in McKenzie (2015).

> 2 Surveys were typically asked face‐to‐face using tablets programmed with the questionnaire. However travel restrictions in some parts of Nigeria due to the security situation meant some individuals were given phone or online surveys, with these alternative methods also used to chase up individuals who were not found on multiple attempts to interview them in person.

7