rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6.2 DHS**

This section reports the results of the analyses performed on the combination of DHS survey and the road survey. I mapped the GPS locations of DHS survey clusters together with the road survey vector dataset and other geographic raster data layers.

Table 4 shows the estimates of the first-stage regression - following the equation (2). The dependent variable is the endogenous variable _LogDistGRs_ - log of distance to the nearest road in good condition. It is instrumented with _LogDistRoads_ 1903 _s_ - log distance to the nearest primary military and postal road in 1903.

Table 4: First-stage regression.

||Coef.|s.e|
|---|---|---|
||Logdist. good road||
|Logdist. Road 1903|0.423***|(0.0252)|
|Logdist. regional center|-0.316***|(0.0446)|
|Logdist. capital city|0.612***|(0.0846)|
|Altitude|0.00101***|(0.0001)|
|Slope|-0.0716***|(0.0199)|
|Population Density 2015|0.00438***|(0.00025)|
|Nightlights Composite|-1.652***|(0.0971)|
|Aridity|-0.00689|(0.00665)|
|Individual controls|Yes||
|Household controls|Yes||
|Region dummies|Yes||
|Observations|3,801||
|R-squared|0.350||
|First stage F-stat|186.38||

Individual controls include: age, age squared, gender, years of education, individual weights. Household controls include: owns land usable for agriculture, wealth index, number of children of age 5 and lower, number of family members. Robust standard errors in parentheses
**The size of expenditure: national vs. subnational governments.** According to our micro data, subnational governments account for a significantly higher fraction of awarded procurement value than national governments in most regions. When computing a simple average across regions, we find that around 87% of procurement value is awarded by subnational governments. In 33/38 regions, national governments account for less than 20% of the awarded procurement value. The only two regions in which the share of procurement value awarded by national governments is significantly higher are the two regions where the capitals are: Madrid (73%) and and Ile de France (64%). To have a sense on how well our micro data reproduces the procurement value awarded by national vs. subnational governments at the national level in Spain and France, we compare the share of expenditure done by the two governments with the one reported by the OECD.<sup>13</sup>

According to the OECD, the “Sub-central government procurement as a percentage of general government procurement, excluding social security funds” was around 56% for France and 81% for Spain in the year 2010. We find slightly larger numbers in our micro-data, especially for France. We believe the differences come from the following reasons. First, the OECD subcentral government share of procurement is computed at the country level rather than as an average of the subnational share in each region. This means that larger regions account for a higher weight in the average. In the case of France, the number is dominated by the share of subcentral procurement expenditure in Ille de France, that is lower than average. In addition, our data only contains a subset of projects whose value is above a certain threshold whereas, in principle, the OECD should report numbers based on the universe of procurement contracts. Third, our classification of agencies into types of governments may not be the same as the one used by the OECD. And fourth, because we are not sure about how to
|_Waged worker_|0.29|0.45|0.56|0.50|||
|_Self-employed_|0.54|0.50|0.28|0.45|||
|Annual income, 2010 constant dollar|1,280|499|1,163|1,174|||
|Observations|4,|709|47,|137||3,571|

_Note:_ Sample is restricted to males aged 18-59. _†_ Here, the mean is the share of males in the full sample. *Conditional on having taken a loan to migrate. **For columns 3 to 6, the GCC does not include Bahrain, which is coded as “others” in the original HIES data. ***These statistics exclude individuals who have been back in Bangladesh for less than a year at the time of the survey. 12
also in world markets through electronic platforms.

The remainder of this paper is organized as follows. Section 2 describes the sample that responded to the TPO survey. We divide our sample into four groups: high-income countries (17 TPOs), upper-middle-income countries (20 TPOs), lower-middle-income countries (11 TPOs) and low-income countries (9 TPOs). Section 3 explores differences in TPO size 1Although the export promotion literature has shown that a focus on small exporters may generate higher returns (Volpe Martincus and Carballo, 2010).

3
accounting framework. One approach is to account for the contribution of measured inputs, and label the “residual” – how much output is produced taking into account the volume of inputs – as an estimate of TFP. Another approach is to estimate the production function to try to measure the additional factors that affect productivity and output: when a characteristic of firm such as private ownership is associated with higher output, we say that after accounting for the contribution of the factors of production (capital and labor) to output, private ownership has a positive impact on TFP.

To obtain estimates of the average TFP level by country, the production function equation is first estimated on _country-averages_ : averages were calculated for firm capital, labor and output were calculated for each of the 55 country surveys, and a regression estimated using these 55 observations<sup>4</sup> . This approach is known in econometrics as the “between estimator” because the variation between countries is the source of the information for estimating the effects on productivity; compare to the “within” approach presented below.

The basic estimation results are reported in column 1 of Table 1; the coefficients in the parentheses show the productivity gaps between Serbia and selected countries. Croatia, Hungary, Poland and Slovenia all had higher TFP than Serbia in 2002 or 2003.<sup>5</sup> The TFP “residuals” as defined above are plotted in Figure 2 versus GDP per capita. The figure shows the strong positive correlation between country-average TFP and GDP per capita: richer countries have more productive firms.<sup>6</sup>

**Table 1: Basic TFP regressions using country-survey-averages and firm-level data** **<u>(manufacturing only)</u>**

||Regression on country-<br>survey-averages|Regression on firms with<br>country-survey dummies|
|---|---|---|
|Fixed capital|0.574**|0.387
mask (Copernicus Global Land Cover Layers, 2019) was applied to exclude hazard values in areas that would not affect crop yields. Data on seasonal timing were taken from the UN Food and Agriculture Organization (FAO) for each country. To aggregate the hazard data to an appropriate geographic unit for merging with the welfare data, additional data were incorporated in the data set, including country and administrative area boundaries from the World Bank Group, Global Administration Areas (GADM), and Global Administrative Unit Layers (GAUL) of the FAO.

The standardized anomaly measure is defined as _(level – mean for local area)/standard deviation_ for the local area, where the mean and standard deviation for the local area are calculated based on the 20 years of hazard data, or the maximum data available in the case of soil moisture. The local area is defined by aggregating the data for a 20 km buffer of the household location or for the administrative area if the former is not available.

The unconditional correlation between different hazard measures is not particularly high. Table 3.2 presents results for each livelihood zone used in the analysis. The independent variation across these hazard measures highlights their capture of different atmospheric conditions; hence the difficulty of identifying a measure capturing drought events well. But as the analysis will show, in a regression framework the results are more consistent across most hazard measures (with some exceptions). This suggests that despite capturing different atmospheric conditions, the hazard measures are reasonably effective at capturing situations making households worse off.

10
# **_4.3  Poverty correlates_**

This section considers a few province- and district-level correlates of local headcount rates, in an attempt to look beyond its spatial distribution to more generalizable patterns. The results refer to figures 1-5 in the appendix.

The data displays a clear positive relationship between poverty rate and Gini index. A more equal distribution is associated with a lower poverty rate (Figure A.1 appendix). However, what is also clear from this Figure is that at any particular poverty rate there is heterogeneity across provinces and districts in terms of inequality outcomes.

Although Vietnam remains a rural country, the urbanization process has been accelerating in recent years. About 30 percent of people now reside in urban areas (General Statistics Office, 2011). Overall, urban areas tend to have lower poverty, and as a result it is a general tendency that poverty is lower as the urban population share increases (Ravallion et al., 2007). In Vietnam, poverty is negatively correlated with the urban population share at the provincial and district level (Figure A.2 Appendix).

Also, poverty is substantially higher in areas with large concentrations of ethnic minorities (Figure A.3 Appendix). This finding relates to the earlier one that the poor are now concentrated in Northern mountain and central highlands where there are large population shares of such ethnic minorities. It also follows that poverty rates are higher in areas with low population and low population density (Figures A.4 and A.5 Appendix).

# **5. The 2009 inequality and wealth maps**

We now turn, briefly, to the spatial distribution of inequality across provinces and districts. Table A.9 in the Appendix presents the Gini index and the ratio of the 90<sup>th</sup> to 10<sup>th</sup> expenditure percentile (a measure of “absolute” inequality) for all provinces. In addition, we also estimate the percentage of people belonging to the
Some specifications replace the location-specific characteristics ( _Xjd_<sup>_l_) with fixed effects for</sup> household primary sampling unit (PSU) that corresponds to the household’s village, town, or neighborhood. These fixed effects capture unobservable local factors that may influence sanitation practices in a geographic area. PSU fixed effects absorb the control variables for household location, as the household location characteristics are homogeneous within a PSU.

All specifications limit the sample to households that are headed by a Hindu or Muslim. Standard errors are clustered at the PSU level (village, town, or neighborhood), which adjusts for the spatial correlation in sanitation practices within nearby areas.

# **IV Results**

# **IV.A Main Estimates**

Table 2 reports results from estimating equation (1), using the IHDS data set which observes three sanitation practices. Panel A reports estimated differences in latrine ownership for Hindus, relative to Muslims. Panel B reports estimated differences in handwashing. Panel C reports estimated differences in whether no fecal material was observed around the house.

Column 1, panel A, reports that Hindus are 17.9 percentage points less likely to own a latrine than Muslims, without adjustment for other covariates (95% confidence interval: – 13.0pp less likely 22.9pp less likely). Column 2 reports similar differences between Hindus and Muslims when controlling for household characteristics, such as household demographics and proxies for household wealth, though these controls do contribute substantial explanatory power (raising the adjusted R-squared from 0.014 to 0.341).

Column 3 reports that Hindus are 5.6 percentage points less likely to own a latrine than Muslims, after controlling for differences in household location (95% confidence interval: 1.2pp less likely – 10.0pp less likely). This estimate is statistically smaller than the unconditional difference between Hindus and Muslims, and represents a 68.7 percent decline in the unconditional difference (reported in column 1). Similarly, column 4
operating costs, and sales and other income. We use the data from phase II for both surveys. The reference year for phase II of the 2003 NIC is the calendar year 2003 and the reference year for phase II of the 2014 IBES is the calendar year 2013.<sup>5</sup>

> 3 The ETD is a joint initiative of the Groningen Growth and Development Centre (GGDC) and United Nations University-World Institute for Development Economics Research (UNU-WIDER). It is publicly available at <u>https://www.wider.unu.edu/project/etd-economic-transformation-database, along with documentation on its</u> contents and construction.

> 4 For survey design and implementation details, see GSS (2006) and (2018).

> 5 Phase II of 2003 NIC was fielded between December 2004 and February 2005, and phase II of 2014 IBES was fielded between November 2015 and April 2016.

6
|Proper foor<br>(7)<br>(8)<br>0.11_∗∗∗_<br>0.10_∗∗∗_<br>(0.02)<br>(0.02)<br>0.03<br>0.04_∗_<br>(0.02)<br>(0.02)<br>0.08_∗∗∗_<br>0.07_∗∗∗_<br>(0.02)<br>(0.02)<br>0.08_∗∗_<br>(0.03)<br>0.02<br>(0.04)<br>_−_0.07_∗_<br>(0.04)<br>0.06_∗∗_<br>(0.03)<br>0.44<br>0.44<br>1,971<br>1,971<br>4,202<br>4,202<br>, World Bank geocoded<br>d control variables from<br>ountry-year fxed efects<br>de the feature reported|
|---|
|ness and Missions<br>Radio/TV<br>(5)<br>(6)<br>0.06_∗∗∗_<br>0.06_∗∗∗_<br>(0.01)<br>(0.01)<br>0.02<br>0.02<br>(0.01)<br>(0.01)<br>0.04_∗∗∗_<br>0.04_∗∗∗_<br>(0.01)<br>(0.01)<br>0.09_∗∗∗_
43 Second, radio markets such as Benin‘s, dependent on donor and government purchases of programming slots, may be particularly inhospitable to accountability-based programming. Stations that depend on government programming (or on programming by donors who are in turn concerned about relations with government) are less likely to broadcast programs that question government performance in education or other areas. The data from Benin are unique in identifying the extent of education programming in radio broadcasts, but are insufficient to establish the programming balance between government education performance and the desirability of household investments in education. This remains an important topic for future research.

Third, even in the absence of an accountability channel, media interventions can have an effect on service delivery outcomes through their influence on household incentives to optimize their use of public services. However, the particular type of programming that can have these effects may not emerge everywhere. It is the product of media markets in which media providers want to broadcast the information and households want to listen to it. The radio market in northern Benin provides such a setting, but other markets may not.

In particular, a market in which commercial radio, dependent on traditional commercial advertising, is more firmly entrenched, may be less hospitable to extensive public interest programming. Airtime is more expensive, raising the cost of information interventions. Stations may regard informative programming as harmful to a franchise that depends on pure entertainment programming to attract advertisers. In contrast, commercial stations may regard ―accountability‖ programming, which may have ―sensational‖ news characteristics, as more attractive. The degree to which this may enable commercial radio to deliver accountability effects, through their greater independence from government and ability to package accountability programming in entertaining ways, remains another important topic for future research.
for other insurance sector developmental levers see Lester 2010). However, this innovation needs to be handled carefully, because mis-selling and overly aggressive use of financial arbitrage (for example, arising from different capital rules) can lead to strong and potentially negative official reactions. Examples of strong regulatory and supervisory responses have already been seen in the United Kingdom, the Middle East, and North Asia.

# **Bancassurance in Emerging Markets**

## **Selecting Representative Emerging Markets**

Representative emerging markets were selected using the following criteria (table 2.4):

- A credible regulatory and supervisory regime is in place

- Bancassurance data are available

- Population is more than 10 million

- M2/GDP is greater than 50 percent

33
_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return, and is restricted to returnees from Iraq (the country with the highest share of return migration). For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

45
|
|**Pragmatic s**|**cenario**||||||||
|National|874|5,372|0|160|3|119|282|1.3|
|Regional|2,010|2,893|113|33|4|117|268|1.3|
|UAI|50|1,839|63|17|5|15|100|0.5|
|RAI||34,201|0|116|2|258|376|1.8|
|Total|2,934|44,305|176|326|13|509|1,025|4.8|

_Source:_ Adapted from Carruthers, Krishnamani, and Murray (2009). _Note:_ RAI = Rural Accessibility Index ; UAI = Urban Accessibility Index

# Ports

## **Achievements**

The Sudanese Port Authority (SPC) has used its large revenues to invest in infrastructure improvements at Port Sudan. The SPC revenue per ton of throughput increased by 47 percent in dollar terms between 2000 and 2005. A combination of a higher realization per tonne handled and large increases in throughput has led to the generation of substantial surpluses. Information from the mid-2000s suggests that the SPC invested around $187 million in development projects. This resulted in the main areas being in good condition with a

**Figure 10. Port Sudan faces serious congestion problems**

<!-- Start of picture text --> 150%<br>125%<br>100%<br>75%<br>50%<br>25%<br>0%<br>Source: AICD ports database.<br>reported capacity<br>Ratio of current demand to<br>Cotonou Cape Town Luanda Port Sudan Douala Dar es Salaam Mombasa<br><!-- End of picture text --> number of new berths added and
|
|R-squared|0.323|0.023|0.045|0.043|0.042|<br>0.045|0.044|0.042|
|Number of households|581|581|581|581|581|581|581|581|
|Controls|NO|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|NO|NO|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-round FE|NO|NO|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variable is the standardized CSI. Data used are from the six visits of the GHS and telephone survey for conflict and an additional telephone survey with the CSI administered to 581 households in 2017. All regressions are conducted using weights specifically calibrated for the sample. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ <0.01; ** _p_ < 0.05; * _p_ < 0.1 xiv
|
|Treatment|3_._559***|3_._080***|0_._298|_−_0_._046|0_._044|0_._182**|
||(0_._933)<br>[0_._000]|(0_._876)<br>[0_._001]|(0_._309)<br>[0_._335]|(0_._277)<br>[0_._869]|(0_._159)<br>[0_._782]|(0_._073)<br>[0_._013]|
|Control Mean|_−_0_._314|_−_0_._331|0_._047|0_._065|_−_0_._294|0_._200|
|Observations (Markets)|252|252|252|252|252|252|
|Observations (Facilities)|1303|1303|1303|1303|1303|1303|

_Notes_ . Robust standard errors are reported in parentheses and p-values are reported in brackets. *** (**) (*) denotes significance at 1% (5%) (10%) level. Regressions include randomization strata controls (by county and market size) and control for the percentage of health facilities of each level in the market.

# **IV.4 Cost**

The operational cost of this intervention during the pilot phase was USD 165 per visit, which includes inspections and visits for the enforcement of warnings and sanctions, as well as closures of facilities and/or departments within facilities. Multiple factors would allow us to reduce the costs in a scaled-up version to USD 95 per visit. With an average of 3 visits (2 inspections) per

> 22This analysis includes 92% of the markets identified at randomization. We restrict the sample to markets that were active at both baseline and endline and exclude markets where missing data accounts for more than 30% of the
parameters and data from various sources. In that process, it is first necessary to make decisions concerning the basic geographic units of analysis, the set of crops, and the time period for which to run the inversion. We use Sri Lanka’s 25 districts (i.e. _I_ = 25), which are the country’s second-level administrative divisions, because that is the most granular level of aggregation for which we have data on important variables such as wages and producer prices. We use the eight crops (i.e. _K_ = 8) for which we have data on both fertilizer intensity and producer prices, namely: cinnamon, cloves, groundnut, maize, onions, potatoes, rice, and tea. We choose to use 2019 for inversion because it is prior to the 2021 fertilizer import ban (and to other potentially influential events such as the Covid-19 pandemic and Russia’s invasion of Ukraine) while also offering rich household survey data.

Preference parameters ( _σA_ , _σK_ , _σM_ ) are taken from Table II and production parameters ( _{γk_<sup>_f_,</sup>

> 117Blue variables can be recovered from black and red variables by using equations (5), (8), (14), (15), (17), (18), (19), and (21).

> 118Region-specific fertilizer endowments ( _{Fi}Ri_ =1<sup>)arerecoveredbyapportioningtheglobalfertilizerendowment</sup> ( _F_<sup>_G_</sup> ) across regions in two stages. First, total domestic fertilizer endowment (<sup>�</sup><sup>_I_</sup> _i_ =1<sup>_Fi_) is set to 3.5% of total domestic</sup> fertilizer use, with the remainder of the global fertilizer endowment being apportioned to the RoW (i.e., _FRoW_ = _F_<sup>_G_</sup> _−_<sup>