rafmacalaba/datause-encoder-data huggingface dataset:
**_<u>________________________________________________________</u>_**

# **2.  Existing Data Sets and Analytical Tools**

Performance indicators are necessary to encourage accountability by public agencies, measure the quality of their management, and evaluate the service to users. In addition, performance indicators can help in comparing a country’s efficiency with other countries as well as with acceptable benchmarks. They are also helpful in monitoring the impact of policy reform and other measures taken to reduce costs and facilitate trade.

Trade facilitation has most often been proxied by a single indicator, such as (1) import prices, (2) international transport costs, or (3) the productivity of a particular transportation mode (air, maritime, road). The traditional measures have to be replaced by something more consolidated that can be used more widely and that reflects transport and logistics efficiency. In a broader context, however, trade facilitation clearly involves policies and activities that accelerate the flow of goods and services across borders. This includes customs regimes, port infrastructure, transport systems and related infrastructure as well as information technology (as it relates to transport and logistics). Transport infrastructure includes road, railway, and inland waterway networks, as well as warehousing facilities and supporting communication systems that are essential for efficient transport services. With modern inland transportation networks countries are in a better position to supply foreign markets. Exporters can choose among different transport modes, and alternative ports and other transport platforms. Other barriers to trade are institutional and involve unreasonable customs delays or demands for bribes to expedite the movement of goods across borders.

A set of benchmark indicators to measure progress and outcomes based upon comparisons can provide a starting basis for quantifying trade facilitation. A compendium of these data sets and indicators of trade facilitation is provided in the following sections.

## **2.1 General Economic Indicators 2.1.1 Investment Climate Indicators**

## _<u>World Bank’s Rapid Response Unit</u>_

Rapid Response is a knowledge service specializing in policy advice on business environment reform and privatization policy in developing countries. Areas of expertise include comprehensive assessments of the business environment in developing countries, through both country-specific reports as well as comparative data used for benchmarking purposes. Their products include:

## _<u>Doing Business database</u>_

The Doing Business database provides objective measures of business regulations and their enforcement. The Doing Business indicators are comparable across 155 economies. They indicate the regulatory costs of business and can be used to analyze specific regulations that enhance or constrain investment, productivity and growth.

The topics covered include: 5 ____________________________________________________________________________________________________________
Policy Research Working Paper 9620

# **Abstract**

This paper evaluates the performance of different small area estimation methods using model and design-based simulation experiments. Design-based simulation experiments are carried out using the Mexican Intra Censal survey as a census of roughly 3.9 million households from which 500 samples are drawn using a two-stage selection procedure similar to that of Living Standards Measurement Study surveys. Several unit-level methods are considered as well as a method that combines unit and area level information, which has been proposed as an alternative when the available census data is outdated. The findings show the importance of selecting a proper model and data transformation so that the model assumptions hold. A proper data transformation can lead to a considerable improvement in mean squared errors. The results from design-based validation show that all small area estimation methods represent an improvement, in terms of mean squared errors, over direct estimates. However, methods that model unit level welfare using only area level information suffer from considerable bias. Because the magnitude and direction of the bias are unknown ex ante, methods that rely only on aggregated covariates should be used with caution, but they may be an alternative to traditional area level models when these are not applicable.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pcorralrodas@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Data and Methods**

The 1998 Enquête Niveau de Vie des Ménages, a nationally representative household survey in Côte d’Ivoire, is used to analyze the distributional aspects of public spending. From the raw file, an initial working file was extracted containing individuals of school age (6-30 years old). Information about characteristics of the head of household, the spouse of the head, as well as other household information was extracted and merged with the initial working file. The sample contains approximately 9,000 observations relating to family members of school age of whom approximately 5,100 attended school in school year 1997-98; of those, 4,560 attended public schools and 550 (or approximately 11 percent) attended private schools. Among those who attended school, 965 (19 percent) received some form of education subsidy, 836 (or 86.5 percent) going to public schools and 129 (or 13.5 percent) going to private schools.

The overall gross enrollment rate (for school age children 6-29 years old), was about 56 percent in 1997-98: 61 percent for boys and 50.5 percent for girls (Table 2). The gross primary enrollment rate was just over 70 percent (73 percent for boys and 66 percent for girls). About 57 percent of secondary school age children were attending school (66 percent of boys and 46.5 percent of girls), 25 percent of whom were still in primary school (28 percent of boys and 22 percent of girls). About 27 percent those over 18 years were attending school (32 percent of boys and 21 percent of girls), 14 percent of whom were still attending secondary school (17 percent of boys and 10.5 percent of girls). The above estimates confirm the reported high repetition rates in Côte d’Ivoire.

Overall school attendance varies between urban and rural areas, with 62 percent attending 10
**_Figure 4_**

_Average Price Changes during Ban and No Ban Periods_

**Source** : Authors’ calculation based on _unadjusted_ price data.

**_Figure 5_**

_Nominal Maize Prices, International Comparison_ <!-- Start of picture text --> USD/kg<br>0.6<br>Nairobi South Africa US Gulf<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14<br><!-- End of picture text -->

**Source** : Ministry of Industry and Trade, Tanzania,  FAO GIEWS, World Bank

35
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53
and a 2.2 percentage point increase in the share of sales reported for tax purpose (relative to a mean of 80.9%). The wedge between the OLS and the IV coefficients suggests that firms depress their reported size in order to reduce tax compliance.

# **4.2 Non-linearities of Tax Inspection and Compliance with Size**

In Section 4.1 we show that tax inspection and tax compliance increase with industry size. Here we study whether this relation is linear or concentrated among specific segments of the size distribution. We present non-parametrically the reduced form relation between tax outcomes and the industry size ranking in the US. We first residualize the industry size-ranking in the US Census, _NUS_ , with respect to controls and country and year fixed effects as in equation 5. Similarly, we residualize the tax outcome of interest (e.g. tax inspection likelihood) with respect to the controls and fixed effects. We then split the residualized industry size-ranking _N_<sup>�</sup> _US_ into the size deciles, and normalize to zero the median industry’s size.

Figure 5 shows for each decile of industry size how tax inspection and compliance compare to the level of the 5th decile. Panel A shows that tax inspection is concentrated at the top of the industry size distribution and is flat in the bottom and middle of the distribution. Industries at the top of the size distribution appear 3% more likely to be inspected compared to the median industry. Panel B shows that the tax compliance rate mirrors inspection: an industry at the top of the size distribution reports 2% more sales compared to an industry in the middle. The mirroring pattern of tax inspection and tax compliance suggests that tax compliance is partly driven by size-dependent tax inspection.

# **4.3 Heterogeneity across Development Levels**

Reliance on size-dependent policies for tax enforcement could originate in state capacity: at lower levels of development the tax authority is constrained by a lack of information 18
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- 11 -

Figure 3. Observed Temperature and Precipitation: Vishnugad Pipalkoti, India <!-- Start of picture text --> 1970m1 1975m1 1980m1 1985m1 1990m1 1995m1 2000m1 2005m1 1970m1 1975m1 1980m1 1985m1 1990m1 1995m1 2000m1 2005m1<br>Year and month Year and month<br>800<br>20<br>600<br>15<br>400<br>10<br>Temperature (oC)<br>5 Precipitation (mm per month) 200<br>0 0<br><!-- End of picture text --> More formally, it has been found that in the Vishnugad Pipalkoti case, hydrological series looks similar to the correlogram of an autoregressive multiplicative seasonal series, 1( − α _L_ )(1 − φ _L_ 12 )<sup>_x_</sup> _t_ , where<sup>_x_</sup> _t_<sup>is a random variable at period</sup><sup>_t_.</sup><sup>_L_denotes the lag operator.</sup> When applying correlogram, which is a plot of the autocorrelation coefficient as a function of the number of lags included in the assumed first-order autoregressive (AR(1)) process, the autocorrelation coefficients first decline and then increase again to a peak in a 12-month cycle. The partial autocorrelations have a positive and negative spike at 1 and 13 lags, respectively (Figure 4). These features are reasonable because hydrology and weather data would likely have a 12-month cycle with some white noise and display some continuity over adjacent months (Johnston and DiNard, 1997). The other two cases, the Kotmale River and the Thac Mo Reservoir, show the same pattern of correlograms.

Figure 4. Correlograms: Vishnugad Pipalkoti, India <!-- Start of picture text --> 0 10 20 30 40 0 10 20 30 40<br>Lag Lag<br>Bartlett's formula for MA(q) 95% confidence bands 95% Confidence bands [se = 1/sqrt(n)]<br>1.00 1.00<br>0.50 0.50<br>0.00 0.00<br>Autocorrelations of lnHYDRO -0.50 -0.50<br>Partial autocorrelations of lnHYDRO<br>-1.00 -1.00<br><!-- End of picture text -->
**Table 1: Sample households’ socio-economic characteristics**

||**Total**|**Regi**|**on**|||**Province**|||**Head m**|**arried**|
|---|---|---|---|---|---|---|---|---|---|---|
|||**Rural**|**Urban**|**Lusaka**|**North**|**East**|**West**|**South**|**Yes**|**No**|
|Household size|5.290|5.199|5.513|5.707|5.495|5.068|4.857|5.393|5.794|4.147|
|No. of adults|2.576|2.369|3.085|3.208|2.451|2.314|2.315|2.480|2.764|2.151|
|Share high school|0.355|0.270|0.563|0.562|0.260|0.162|0.296|0.399|0.350|0.364|
|Share salaried|0.323|0.279|0.431|0.475|0.212|0.402|0.186|0.284|0.326|0.316|
|Share self-employed|0.241|0.257|0.202|0.232|0.218|0.350|0.152|0.202|0.237|0.251|
|<br>Share unemployed|0.273|0.273|0.274|0.238|0.284|0.259|0.293|0.279|0.252|0.320|
|Traditional hut|0.361|0.485|0.055|0.064|0.505|0.501|0.767|0.226|0.348|0.390|
|Traditional house|0.263|0.318|0.125|0.107|0.291|0.318|0.166|0.282|0.280|0.223|
|Detached house|0.327|0.193|0.656|0.546|0.202|0.170|0.048|0.477|0.330|0.320|
|Flat/apartment|0.049|0.003|0.164|0.283|0.002|0.012|0.019|0.015|0.042|0.067|
|No. of rooms|3.182|2.809|4.099|3.933|3.345|2.796|2.321|2.876|3.279|2.963|
|Electricity access|0.229|0.037|0.702|0.705|0.133|0.030|0.106|0.127|0.221|0.249|
|Access to tap water|0.247|0.049|0.734|0.777|0.193|0.044|0.166|0.133|0.222|0.304|
|**Household head char**|**acteristics**||||||||||
|Male|0.711|0.720|0.689|0.677|0.800|0.741|0.549|0.706|0.950|0.170|
|Age|46.800|45.355|50.347|50.723|46.020|42.491|46.959|46.313|44.141|52.829|
|Married|0.694|0.717|0.637|0.634|0.794|0.754|0.523|0.702|1.000|0.000|
|Separated|0.102|0.107|0.089|0.109|0.074|0.085|0.170|0.112|0.000|0.333|
|Widowed|0.175|0.151|0.235|0.229|0.125|0.146|0.187|0.167|0.000|0.574|
|No education|0.186|0.232|0.071|0.057|0.243|0.422|0.188|0.122|0.159|0.245|
|Primary school|0.251|0.292|0.151|0.172|0.291|0.309|0.342|0.213|0.226|0.308|
|High school|0.410|0.409|0.413|0.432|0.389|0.215|0.359|0.540|0.444|0.334|
|College or higher|0.153|0.066|0.365|0.340|0.077|0.054|0.111|0.124|0.171|0.113|
|Self-employed|0.542|0.627|0.334|0.239|0.669|0.510|0.741|0.615|0.520|0.593|
|... farming|0.642|0.718|0.291|0.337|0.812|0.547|0.848|0.630|0.668|0.592|
|<br>... non-farming|0.357|0.281|0.709|0.663|0.188|0.453|0.152|0.368|0.332|0.408|
|Work for salary|0.458|0.373|0.666|0.761|0.331|0.490|0.259|0.385|0.480|0.407|
|No. of obs.|4,888|3,703|1185|491|525|761|491|566|3,455|1,433|
|Est.population|1,709,593|1,214,953|494,640|184,147|167,765|327,931|150,558|251,872|1,186,526|523,067|

_Source_ : Own computation from 2018 Quarterly Labor Force Survey conducted by Central Statistical Office of Zambia. Sampling weights adjusted with non-response probabilities are used to estimate the mean values.

21
# Table 7: Latrine construction and availability

|VARIABLES|Household<br>constructe|has a [...] latri<br>d within the l|ne that was<br>ast 3years|Household<br>construct|has a [...] latrin<br>ed more than 3|e that was<br> years ago|Household<br>uses a shared<br>|
|---|---|---|---|---|---|---|---|
||any|private|shared|any|private|shared|latrine|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|HWWS (β1)|-0.015|0.026|-0.042**|0.027|0.008|0.019|-0.031|
||(0.035)|(0.030)|(0.020)|(0.024)|(0.017)|(0.013)|(0.030)|
|TSSM ( β2)|0.082**|0.124***|-0.042**|0.035|0.036**|-0.001|-0.092***|
||(0.034)|(0.032)|(0.019)|(0.022)|(0.018)|(0.011)|(0.027)|
|HWWS + TSSM (β3)|0.077**|0.100***|-0.022|-0.009|-0.002|-0.007|-0.076***|
||(0.031)|(0.028)|(0.019)|(0.022)|(0.018)|(0.010)|(0.026)|
|p-values β1= β2|0.006|0.003|0.972|0.731|0.083|0.158|0.028|
|for F-test β1= β3|0.006|0.013|0.302|0.150|0.592|0.052|0.098|
|β2=β3|0.892|0.434|0.267|0.067|0.04|0.602|0.465|
|_Observations_|3,469|3,469|3,469|3,469|3,469|3,469|2,974|
|_R-squared_|0.121|0.108|0.041|0.044|0.039|0.032|0.074|
|_Control Mean_|0.571|0.386|0.185|0.181|0.134|0.046|0.332|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects.

43
Table A.2: Descriptive statistics: the migration experience of documented and undocumented return migrants

||Docu<br>mi|mented<br>grants|Undoc<br>mi|umented<br>grants||
|---|---|---|---|---|---|
||(1)<br>Mean|(2)<br>St. Dev.|(3)<br>Mean|(4)<br>St. Dev.|(5)<br>Diference|
|Monthly income abroad in EGP|2,112|2,195|1,278|1,294|834.335***|
|Monthly savings abroad in EGP|1,606|2,121|1,167|1,639|439.384**|
|Incidence of remittances|0.604|0.490|0.521|0.500|0.083**|
|Monthly remittances in EGP|2,002|3,247|1,090|1,932|911.367**|
|Migration duration|4.344|4.251|3.468|3.561|0.876***|
|Number of migration episodes|1.220|0.817|1.174|0.448|0.046|
|**_Year of migration_**||||||
|1970-1979|0.032|0.176|0.050|0.218|-0.018|
|1980-1989|0.309|0.463|0.475|0.500|-0.166***|
|1990-1999|0.243|0.429|0.170|0.376|0.072**|
|2000-2009|0.389|0.488|0.284|0.452|0.105***|
|2010-2012|0.023|0.150|0.018|0.132|0.005|
|**_Year of return_**||||||
|1970-1979|0.007|0.083|0.011|0.103|-0.004|
|1980-1989|0.166|0.372|0.306|0.462|-0.140***|
|1990-1999|0.295|0.457|0.302|0.460|-0.008|
|2000-2009|0.380|0.486|0.263|0.441|0.117***|
|2010-2012|0.138|0.346|0.117|0.323|0.021|
|**_Countries of destination_**||||||
|Libya|0.131|0.337|0.292|0.455|-0.161***|
|Jordan|0.128|0.335|0.149|0.357|-0.020|
|Saudi Arabia|0.359|0.480|0.039|0.194|0.320***|
|Iraq|0.181|0.386|0.445|0.498|-0.264***|
|United Arab Emirates|0.064|0.245|0.028|0.167|0.036**|
|Kuwait|0.067|0.249|0.011|0.103|0.056***|
|Other countries|0.071|0.257|0.039|0.194|0.032*|
|Observations||437||282||

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Column 5 is t-test for whether the difference in means between the two groups is statistically significant.

50