rafmacalaba/data-use-mentions-extended huggingface dataset:
you think the Contraceptive Acceptance Rate is for [INSERT WOREDA] according to official administrative<br>data (the baseline of the 2007 Core Plan)?|HSDP Core Plan 2008/09-<br>2013/14|0.59|0.29|-0.23|-0.19|-0.52|-6.38|0.00|||688|
|What do you think the Rate of Births Attended by Skilled Health Personnel is for [INSERT WOREDA] district<br>government according to official administrative data (the baseline of the 2007 Core Plan)?|HSDP Core Plan 2008/09-<br>2013/14|0.33|0.22|2.62|-0.19|-0.60|-8.26|0.00|||688|
|What do you think the Proportion of infants fully immunized is for [INSERT WOREDA] according to official<br>administrative data (the baseline of the 2007 Core Plan)?|HSDP Core Plan 2008/09-<br>2013/14|0.74|0.22|-1.24|-0.08|-0.57|-8.06|0.00|||688|
|**Agriculture indicators**||||||||||||
|How much land do you think is used for agricultural purposes for [INSERT WOREDA] district government according to<br>data from the WCBS?|WCBS Round III (2009/10) &<br>Round V (2012/13)|29,366|33,892|6.49|1.01|1.01|||||203|
|How much land do you think is used for pastoral purposes for [INSERT WOREDA] according to data from the WCBS?|WCBS Round III (2009/10
percent, compared to an actual poverty headcount of 57 percent before the road project was implemented and 48 percent after the road was completed. Thus, the change in poverty incidence from 57 percent to 52 percent was due to road improvement and from 52 percent to 48 percent due to other factors. Similarly, predicted extreme poverty is 30 percent, compared to an actual extreme poverty rate of 35 percent in the base year and 29 percent in the year after the project completion. In other words, a road improvement project in RDP project villages has led to an approximate 5 percent reduction of moderate and extreme poverty; p-values for these reductions are also presented in Table 5, and the differences are statistically significant.

In RRMIMP project villages, improved roads have also modestly reduced the incidence of poverty. Predicted moderate poverty is 46 percent, against an actual poverty headcount of 52 percent in year 0, suggesting a 6 percent reduction in moderate poverty. Similarly, the predicted extreme poverty rate is 31 percent against an actual poverty rate of 38 percent in the base year, indicating an approximate 7 percent gain in extreme poverty reduction in RRMIMP villages that could be attributed to road improvement.<sup>30</sup> Again, the predicted reductions are statistically significant. Thus, overall we see a fairly substantial reduction in moderate and extreme poverty due to rural road development across both samples, with a slightly steeper decline in RRMIMP project villages.

# **7. Conclusions**

Road investment constitutes a major portfolio of public investment in rural areas, reinforcing the notion that rural income and productivity growth depend critically on roads and other public investments. Yet the traditional way of evaluating a rural road project has underestimated the impact of road projects through the use of transport surveys that simply determine the extent of transport cost savings for a road by the likely road users. While this way of estimating the project’s rate of return (the so-called internal rate of return) is a standard practice, roads are important public investments that have
penetration ( _MPj_ ) is computed using bilateral distance data from CEPII (Head et al. 2010); and government effectiveness ( _GEj_ ) is sourced from the Worldwide Governance Indicators (Kaufmann et al. 2011). Trade data to construct the import share and RCA variables are from UN Comtrade. Import tariffs are from UNCTAD TRAINS/WITS. Data on COVID-19 cases are from the WHO <u><mark>(https://covid19.who.int/table)</mark></u> . Appendix Table 2 reports summary statistics for all variables used in the analysis. Apart from COVID-19 cases and deaths that pertain to the third week of July 2020, all control variables are the average for the years 2017 and 2018.

# **6. Results**

Table 1 reports the results from PPML estimation of equations (1) and (2) separately for liberalizing and restrictive measures imposed on medical goods, with standard errors clustered by country in each case. Results reported in Table 2 replicate the analysis in Table 1 but distinguish between measures that were imposed and subsequently removed within the sample period and those that remained in effect as of mid-July 2020. Table 3 reports the results from the PPML estimation of equations (1) and (2) applied to specific types of trade measures, i.e., restriction or liberalization of exports or imports, respectively. Finally, based on the dates of implementation and removal, Table 4 reports the results from estimating equations (1) and (2) using as dependent variable the duration of the respective measure and not the total number of measures.

> 15 Jurisdictions imposing measures include the Eurasian Economic Union, the EU, and the South African Customs Union (SACU). Measures imposed by these blocs are allocated to their member states for the purpose of analysis. 16 Shingal and Ereshchenko (2020) measure the “depth” of procurement provisions in PTAs on the basis of seven broad attributes: non-discrimination; coverage in terms of goods
production sector, and parameters used in the LES functional form to model the households. The values for elasticity of substitution are not available for Ethiopia. The only sources of these parameters are existing studies for developing countries which provide values for the elasticities of substitution. We use the values from Chen et al. (2013) and Mosa (2018). The parameters used in the LES function are taken from Mosa (2018) and Diao et al (2012). Since the values of elasticity of substitution and other parameters are taken from the literature, sensitivity analyses on these values should be conducted to validate the finding of the study. Other parameters such as household saving rates, rates for transfers between the agents, and distribution of total government consumption to individual categories are calibrated from the SAM.

# **3. Design of Scenarios**

The study considers a hypothetical carbon tax with a rate US$20/tCO2. We have developed one baseline scenario and four main scenarios depending on how the carbon tax revenue is recycled into the economy. These scenarios are defined in Table 2. A scenario comes up with multiple subscenarios or cases depending upon the criteria used to recycle the revenue under a given scenario. The selection of a single tax rate is guided by the objective of the study. The objective is to compare the distributional impacts of a carbon tax policy under the alternative schemes for revenue recycling. The rate may not be relevant for this purpose because the comparison is expected to hold unless the tax rates are altered by its multiples (e.g., two times, three times). In practice, tax rates are increased gradually overtime so that the economy can adjust to it. Moreover, if we use multiple rates, we will have too many cases because of the large number of alternative revenue recycling schemes considered in the study. It will introduce complications in the interpretation of the results and articulation of the main message.

**Table 2: Definition of Scenarios**

|**Scenario
# **VII. CONCLUSIONS**

This paper examines the inflation process in Albania by using three different alternative approaches. The first approach decomposes inflation into four components: _seasonal, cyclical, trend,_ and _random._ The second approach relies on the widely employed Granger-causality test by using disaggregated data on both the consumer price index (CPI) and the key economic variables. The third approach applies co-integration and error-correction techniques to the inflation process -- a process which is outlined by a simple theoretical model.

The results showed that inflation exhibits strong seasonal patterns, peaking in February and reaching its through in July. The marked seasonality of prices could be due, in large part, to agricultural seasonality (agriculture comprises more than half of Albania's GDP). An interesting finding was that peaks and troughs of monetary aggregates correspond to those of inflation with a two month lag. We also found that the exchange rate exhibited a stable seasonality, reaching its through in August and showing a tendency to depreciate during the first part of the year. Better infrastructure, and with it lower transport costs, as well as improved customs services may be crucial to reduce seasonality in an open economy such as Albania.

The results of the Granger Causality tests indicated that causality runs from credit to government to the price of non-tradables. This finding has an important policy implication: an increase in credit to government as a result of the fiscal deficit will lead to an increase in the price of non-tradables. This, in turn, will produce an appreciation in 30
With respect to operationalizing and testing the large literature on measuring rent-extraction and economic voting provide clear guidance regarding which variables need to be utilized for this exercise to be valid. Specifically, most of the variables of interest are available from Persson and Tabellini’s (2003) canonical dataset, which includes a dummy variable for high-income democracies<sup>13</sup> . In order to construct the EDD/NEDD ratio the World Bank’s (2011) measure of regulatory quality (regulatory density<sup>14</sup> ) is added to the dataset (1998-2000).

# **The Sample**

The data used to test the model is the same dataset used by Persson and Tabellini (2003) in their seminal analysis of the economic effects of constitutions. The dataset has been augmented to include a measure of regulatory quality (one of the independent variables of interest) as well as additional control variables identified in the literature as being potentially critical determinants of rent extraction. Furthermore, two separate analyses of the dataset are undertaken. One utilizes only high-income democracies (n=19-21)<sup>15</sup> , with the aim of establishing whether the theoretically anticipated relationship between the EDD/NEDD ratio holds, given a relatively homogenous set of countries. The other utilizes all countries available (n=52-58)<sup>16</sup> , which introduces heterogeneity in the sample (hence a need to control for more variables), but also enables more tests of the robustness of the initial results. In both cases, the substantive results should be the same. Specifically, it should be observed that an increase in the EDD/NEDD ratio is associated with a reduced level of rent extraction.

# **The Dependent Variable**

There is a lively debate regarding how to measure corruption and rent extraction (see for example Tresiman, 2007, Olken 2009, and Kauffman et al 2007). However, despite continued controversies over (1) the validity of
) across the five risk-categories created from the continuous risk measure. The average is positive and small for taxpayers in the low and medium-low risk-levels, and negative for taxpayers with higher assessed risk<sup>32</sup> .

# **5.5 Costs of the experiment**

While the marginal cost of sending emails is zero, any campaign involving thousands of taxpayers requires several activities from dozens of workers in the tax authority<sup>33</sup> . These include meetings to define the content of messages, time to prepare databases, train frontline workers who might receive calls or visits from taxpayers, among others. Using back-of-the-envelope calculations of time invested and hourly wage of involved workers,

> 30For a detailed explanation of the causal forest algorithm and application in economics, see Davis & Heller (2017).

> 31We show in Figure A1 that results are similar if we consider the amount of gross revenue declared as the outcome of interest.

> 32Magnitudes are different from our estimated heterogeneous effects obtained in Table 6, where we present results linear regressions that interact the dummy for treatment with baseline assessed risk. In here we estimate a much more flexible model, using causal trees and a richer set of covariates, and then aggregate the predicted effects based on the baseline risk.

> 33While the intervention discussed in this paper required further time from researchers outside the tax authority (e.g. developing experts’ forecast survey, preparation of the pre-analysis plan and preregistration), this cost-benefit analysis focus on what an "usual" intervention, developed entirely by the tax authority, would cost.

18
# **1. Introduction**

1. **Informality is a broad development challenge with multiple features and different layers of analysis.** Informality is associated with many development challenges, such as lower productivity firms, less protected workers, and tax leakages (Loayza 2018; Ohnsorge and Yu 2021; Ulyssea 2020). At the same time, the informal sector provides livelihoods, creativity, and buffers against shocks. The latter was exemplified by the recent shock induced by the COVID-19 pandemic. Despite the significance of informality in emerging markets and developing economies (EMDEs), it remains difficult to measure and study, mainly due to the presence of multiple definitions for informality and the lack of estimates (Elgin et al. 2021a). This is particularly the case in Indonesia where a limited number of  studies have focused on the informal sector.<sup>1</sup>

2. **Against this backdrop, this paper uses several novel datasets and various modeling approaches to measure the extent and nature of informality in Indonesia.** It first briefly summarizes the literature, discusses the multiple existing definitions of informality, and adapts the cross-country analytical framework on informality to the context of Indonesia. It then uses several novel datasets, including an updated cross-country database on informality provided by Elgin et al. (2021a), and a range of modeling approaches to capture the levels and trends of both output and employment informality. It further contributes to the existing literature by estimating output and employment informality in Indonesia at the regional, provincial, and sectoral levels, using a series of labor force surveys (SAKERNAS surveys) provided by Badan Pusat Statistik (BPS). Those estimates were then benchmarked to the levels, trends, and features in EMDEs to examine whether the major features of the informal sector in Indonesia deviate from those in EMDEs.

3. **Despite the recent development, informality remains pervasive in Indonesia.** While being slightly lower than the average of other EMDEs, output informality in Indonesia averaged around
distortion from inter-company transactions or accounts. This is crucial for examining the impact of location-specific factors, such as weather, on firms’ behavior, which would be limited with consolidated data. Finally, We also focus on countries with more than 500 observations and more than single observation for country-year-grid group.

All nominal variables (fixed & total assets) are deflated using the GDP deflator at the country level, sourced from World Bank data.<sup>12</sup>

# 3.3 Construction of variables

Firm-level variable From Orbis data, we construct the variable that indicates the growth rate of the firm’s size. We use the fixed assets as the main size variable. For each firm _i_ in country _c_ and year _t_ , we define the size growth as the log return on the fixed asset _Ki,t_ : where _κ_ is the nominal fixed capital and _pc,t_<sup>_k_is the deflator of country</sup><sup>_c_at time</sup><sup>_t_.</sup> Furthermore, to construct the proxy for investment irreversibility, we use the ratio of tangible fixed assets to total assets, following Gulen & Ion (2016), who argues that companies with significant tangible fixed capital investment often undertake projects requiring substantial initial expenditures, and thus

> 11U1 represents the unconsolidated record without the accompanying consolidated record, while U2 represents the unconsolidated record with the accompanying consolidated record.

> 12 `https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG`

8
**4 DATA**

Based on the methodology previously described, this work required three main sources of data inputs: (i) the public transportation network in order to calculate travel times between OD pairs, (ii) the spatial distribution of employment opportunities and (iii) the spatial distribution of population. Below is a description of the data sources used.

# **4.1 Public Transportation Networks**

Transport data were based on available GTFS feeds and shapefiles. In order to calculate accessibility, first a digital representation of the public transport network is created - either the General Transit Feed Specifications (GTFS) which is the standard data used for planning routes or a Geographic Information System (GIS) shapefile with the information on public transportation travel speeds and frequencies. General Transit Feed Specification (GTFS) is a free and open template for entering data related to basic transit system services, such as routes, stops, fares, and schedules. The information on routes may also be digitally recorded as a GIS shapefile. This shapefile must include each route that is served in the city. Additional to the routes the file must contain information about the frequency and speed, at different times of day, particularly the am peak.

7
# **3.3. Sampling frame**

In the HIES 2016/17, the primary sampling units (PSUs) are the Census Enumeration Areas (EAs) used for Bangladesh’s 2011 Census of Population and Housing. EAs are small geographic areas consisting of an average of 110 households. The sampling frame for the selection of PSUs consists in the list of all EAs covering people residing in non-institutional dwelling units (98.5 percent of the population in Bangladesh).<sup>8</sup> This is an important departure from the HIES 2010, where the sampling frame for the selection of PSUs consisted in the list of all EAs in the Bangladesh IMPS that excluded some geographic areas, such as urban slums.<sup>9</sup> For the HIES 2016/17, the IMPS could not be used because the most recent version of IMPS based on the 2011 Census of Population and Housing included only 2,012 EAs, an insufficient number to serve as a sampling frame for this survey. Table 1 summarizes the main characteristics of the sampling frame for the HIES 2016/17:

**Table 1 Sampling frame for the HIES 2016/17**

|**Area**|**Number of**<br>**households**|**Number of**<br>**EAs**|**Mean number**<br>**of households**<br>**in EA**|
|---|---|---|---|
|Rural|24,588,843|228,202|108|
|Urban|5,020,815|43,872|114|
|City Corporation|2,253,737|21,266|106|
|**Total**|**31,863,395**|**293,340**|**109**
reduction through their fiscal systems, Jordan’s performance (6<sup>th</sup> out of 14) is mid-range as well but less than half of that in the top three performing countries, Mexico (2012), Panama and Uruguay.

**_Figure 10_** _Fiscal Impact on Monetary Poverty (points reduction in poverty headcount, PPP $3.2)_

<!-- Start of picture text --> 6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>Tanzania (2011) Ghana (2012) Armenia (2011) Sri Lanka (2009) Brazil (2008) Namibia (2009) Peru (2011) El Salvador (2011) Dominican Republic (2006) Colombia (2010) Russia (2010) Colombia (2014) Paraguay (2014) Chile (2013) Jordan (2018) Romania (2016) Mexico (2014) Mexico (2012) Panama (2016) Uruguay (2009)<br><!-- End of picture text --> Source: CEQ Institute database (as of May 2020) Why does Jordan’s fiscal policy not have as much impact on equity and in poverty as in many other countries? Jordan’s revenue and spending as a percentage of GDP is relatively high at the aggregate level compared to other countries in the CEQ database. However, the composition of both has historically been less progressive than elsewhere. We first look at the revenue levels and composition. In terms of total fiscal revenue as a percentage of GDP, Jordan performs in the middle of the database; at 24.8 percent of GDP in 2010, it was 18<sup>th</sup> best of the 49 country-years, although that had fallen to 26<sup>th</sup> by 2018. However, this masks Jordan’s significant reliance on non-tax revenue. In terms
price changes originating in wholesale markets are transmitted to retail markets.

Three questions are addressed. First, do wholesale price shocks fully transmit to retail prices in the long run? This would imply a pass‐through coefficient from wholesale to retail prices equal to 1. Second, do retail prices adjust in the short run, to gradually return to the long‐run equilibrium relation with wholesale prices, after a shock that affects the latter? This would imply a speed of adjustment coefficient that is negative (to gradually correct for the disequilibrium). Third, is the short‐run adjustment to return to the long‐run equilibrium after a shock symmetric upward and downward? This would imply that there is no difference between the speed of adjustment that implies an increase of retail prices, and that implying a decrease of retail prices after a shock in wholesale prices.

The hypothesis of full price transmission is rejected for all but one commodity. Yet, the price co‐ movement is substantial. Shocks to wholesale prices are not fully transmitted to retail prices, because the pass‐through coefficient is less than one for all but one commodity. However, the pass‐through is highly significant and ranges from 52 to 97 percent. For example, 89 percent of a price shock at the wholesale market is transmitted, in the long run, to the retail market for gram. In the case of rice, it is not possible to reject the hypothesis that the pass‐through equals one (Figure 18).

The speed of adjustment is both statistically and economically significant for all commodities, and heterogeneous. Prices in the onion retail market adjust back to equilibrium for around 30 percent of the disequilibrium per period caused by a price shock in the onion wholesale market (Figure 19).

> 14 See Government of India “Economic Survey 2015‐16, Volume I” pgs. 79‐83.

> 15 Due to the lack of availability of time series farm‐gate data, this analysis had to focus on the trends in
The last two years of the sample period shows a decline in trade flows, which coincides with a modest growth slowdown in the domestic economy. There is some question to what extent the lower aggregates are the result of incomplete reporting in the last one or two years. It is possible that not all firm-level information had been included in the database yet when we received the data files. A comparison with information from the World Bank’s World Development Indicators (WDI) suggests that some of the trade slowdown could be due to incomplete reporting. The ratio between the firm-aggregates and the WDI statistics declines somewhat from 2015 to 2016 for exports. However, for imports there is some divergence between the firm-level and WDI aggregates between 2014 and 2015, but the two measures converge again in the last year. The possibility of incomplete reporting is something to keep in mind when we look at evolutions over time, but there is no clear break in the last year that would make this an obvious problem.

# **2.2 GVC aggregates**

We next use the information in the EORA global supply chain database that includes a multi-region input-output table, to provide an overview of the GVC integration of the Rwandan economy at an aggregate level.<sup>6</sup> Van Biesebroeck and Mensah (2019) describes how the full table—which contains information for 190 countries over a 26 sector harmonized classification—can be collapsed to construct a country-specific input-output table for Rwanda that shows the interactions of its economy with eight regions in the world.

The left panel of Figure 2 illustrates that the agricultural sector of Rwanda operates rather independently from the rest of the world. The share of total output that consists of intermediates sourced within Rwanda itself and value added created in the agricultural sector is around 97%. This high fraction has not changed noticeably over time. It is not surprising that this share is high in agriculture, but this share for Rwanda is higher than for the rest of sub-Saharan Africa, where it fluctuated between 95