rafmacalaba/datause-encoder-data huggingface dataset:
infrastructure investments in developing economies (e.g., Duflo and Pande 2007, Dinkelman 2011).

This study also contributes to a literature seeking to understand the development of the manufacturing sector in India. Several studies evaluate the performance of Indian manufacturing, especially after the liberalization reforms (e.g. Kochhar et al. 2006, Ahluwalia 2000, Besley and Burgess 2004). Some authors argue that Indian manufacturing has been constrained by inadequate infrastructure and that industries that are dependent upon infrastructure have not been able to reap the maximum benefits of the liberalization’s reforms (e.g. Gupta et al. 2008, Gupta and Kumar 2010, Mitra et al. 1998). Our work regarding the GQ upgrades supports many of these propositions with respect to the organized sector, while the unorganized sector’s advancement appears more closely link to within-district infrastructure (like paved roads in villages). Our work also links to studies seeking to understand the allocation of activity across regions and the productivity of firms (e.g., Desmet et al. 2011, Hsieh and Klenow 2009).

The remainder of this paper is as follows: Section 2 describes the data used for this paper and its development. This section also includes a more extensive discussion of the GQ upgrades and the definitions of urban/rural areas. Section 3 presents the empirical work of the paper, determining the impact of highway improvements on economic activity. Section 4 concludes.

# **2. Indian Manufacturing Data for the Organized and Unorganized Sectors**

We employ repeated cross-sectional surveys of manufacturing establishments carried out by the Government of India. Our work studies surveys that were conducted in fiscal years 1994, 2000, 2005, and 2007 for the organized sector. For the unorganized sector, our surveys consider 1994, 2000, and 2005. In all cases, the survey was undertaken over two fiscal years (e.g., the 1994 survey was conducted during 1994-1995), but we will only refer to the initial year for simplicity. This time span allows us two surveys before and after the GQ upgrades for the organized sector. For the unorganized sector, we have two surveys before and one after the upgrades. This section describes some key features of these data for our study.

5
**<u>Table 4: Disaggregated Resource Rents and Billionaire Net Worth</u>**

|**Model**|**Country and Year**<br>**Fixed Effects**|**Billionaire and Year**<br>**Fixed Effects**|**Billionaire and Year**<br>**Fixed Effects**|
|---|---|---|---|
|**Dependent Variable**|**Log of**|**Billionaire Net Worth (in B**|**illions)**|
||**(1)**|**(2)**|**(3)**|
|Oil and Natural Gas Rents (% of GDP)|0.011***|0.014***|0.012***|
||(0.004)|(0.003)|(0.003)|
|Mineral and Coal Rents (% of GDP)|0.024**|0.026**|0.028***|
||(0.009)|(0.010)|(0.010)|
|Forest rents (% of GDP)|-0.117**|-0.297***|-0.285***|
||(0.059)|(0.058)|(0.058)|
|Log of GDP (constant 2010 US$)|||0.089*<br>(0.046)|
|Constant|0.752***|0.494***|-2.046|
||(0.088)|(0.032)|(1.322)|
|Country Fixed Effects|YES|NO|NO|
|Billionaire Fixed effects|NO|YES|YES|
|Year Fixed Effects|YES|YES|YES|
|Number of observations|18,351|18,351|18,351|
|R2|0.066|0.281|0.282|
|Adjusted R2|0.061|0.280|0.282|

note: *** p<0.01, ** p<0.05, * p<0.1, Robust Standard Errors Clustered at the Billionaire level 16
of high-risk declarations for which the accuracy of the declared import value is questionable, GasyNet issues a valuation advice: a detailed report on what the value of the specific declaration is likely to be.

3. **Inspector assignment.** The third, and for our purposes, crucial step is the assignment of the declaration to a particular inspector by the ASYCUDA IT system. Official rules prescribe that a newly registered declaration should be assigned to whichever inspector has the lowest workload (i.e., has the fewest pending declarations on his/her desk) and is active (i.e., is connected to the IT system and can therefore receive new declarations). Official rules do allow for productivity differences across inspectors: a highly productive inspector will get, on average, more declarations than a poorly productive inspector. Yet, the assignment of declarations to inspectors is supposed to be random conditional on her/his productivity. We will exploit this feature of the official rules for identifying deviant declarations in Section 4.

However, the customs port manager, the _Chef des Opérations Commerciales_ (COPCO), has the authority to override the IT system’s initial assignment and re-assign a declaration to a different active inspector. Such re-assignments are warranted in case of unanticipated absenteeism (due to illness for example) and, should, _a priori_ , happen only randomly.<sup>13</sup> 4. **Assessment.** The fourth step is the assessment of the declaration by the assigned inspector based on the documentation submitted by the broker on behalf of the importer, the risk analysis diagnostics provided by the risk management unit and GasyNet, and the results of a potential physical inspection. She has to decide which (if any) adjustments to the import value, quantity, product classification and/or origin are to be made and report whether fraud was perpetrated. She then assesses what duties, taxes and potential penalties are to be paid based on the (potentially revised) final value and product classification of the import declaration.

5. **Clearance.** In the final step in which goods are cleared, the importer (or the broker on behalf of the importer) pays the taxes, duties and potential penalties and goods are released from customs.

Our analysis of corruption will focus both on manipulation of the assignment of declarations to inspectors (by IT department staff and/or the customs port manager) done in step 3, and on differential treatment of manipulated declarations by inspectors during assessment in step 4.

# **3 Data**

Our study combines the following databases.

> 13Such re-assignments occur for 6 percent of the import declarations.

7
Growth or differenced versions of this equation and the population supply one have very poor IV results, which is mainly due to a weak instrument problem. For the growth specifications, we only focus on the final reduced form specification (Table 5).

Results for population supply are provided in Table 3. Again, for the estimation we pool three years (1980, 1991, and 2000). Columns 1 and 2 give the GMM-IV and then OLS results. The instruments, listed in the footnote of the table, pass specification tests and produce strong first-stage regression results. All terms have strong, expected sign coefficients. In column 1, a 1% increase in a city’s total income per capita increases city population by 2.4%. The gravity measures of surrounding rural population supply and rural income opportunities have the expected opposite effects with similar magnitudes. A 1% increase in surrounding rural population supply increases city population by 5.9%, and a 1% increase in surrounding rural income opportunities decreases city population by 5.2%. Thus, city populations are very sensitive to rural population supply and earning opportunities.

In columns 3-5, we present supply elasticities by year. The coefficients of all the three covariates increase over time, indicating increasing mobility. Population supply to a city has become more elastic to changes in attributes of the city and nearby rural areas. However, even in 2000, the elasticity, 2.9, is far from perfect mobility elasticity.<sup>7</sup>

> 7 Under perfect labor mobility, we expect a horizontal population supply curve. All the cities offer the same utility level, and city sizes are only determined by demand-side factors.

14
markets. Consequently, we assume that Russian exporters in seven sectors which have been subject to antidumping actions in Russia’s export market, will receive and improvement in their terms of trade by either 1.5 percent or 0.5 percent. (See table 3 for details.)<sup>21</sup>

# **Input-output Tables**

The core input-output model is the 2001 table produced by Rosstat. The official table contained only 22 sectors, and importantly has little service sector disaggregation. In order to disaggregate the table, we used costs and use shares from our 35 sector Russian input-output table for 1995 prepared by expert S. P. Baranov. (For details see <u>www.worldbank.org/trade/russia-wto.) When we broke up a sector such as oil</u> and gas into oil, gas and oil processing, we assumed that the cost shares and use shares of the sector were the same in 2001 as they were in the 1995 table. For example, steel is an input to the oil and gas sector. Suppose in 1995, that oil purchased 55 percent of the steel used in oil and gas. Then we assume that in 2001, oil purchased 55 percent of the steel used in oil and gas.

# **Regional IO tables**

We constructed input-output tables of the 88 oblasts that are based on data from the oblasts (described below) and the national input-output table. The input-output tables for our ten federal okrugs are aggregates of the input-output tables of the oblasts in their respective federal okrugs.

We assume that the technology of production is common across oblasts, so that the input-output coefficients from the national input-output table apply across all oblasts. As a first step, for each industrial sector, we took the national output from the national input-output table for 2001, and we used the data in Regions of Russia to allocate the shares of that output across the 88 oblasts. That is, we have, by oblast, the value of total industrial output and industry shares of oblast industrial output for the year 2000 (Regions of Russia 2001, <mark>table 13.3)</mark> ; thousands of tons of oil recovery, including gas condensate, for the year 2000 (Regions of Russia 2001, <mark>table 13.13)</mark> ; extraction of natural gas (in millions of cubic meters for the year 2000, (Regions of Russia 2001, <mark>table 13.14);</mark> thousands of tons of mined coal for the year 2000 (Regions of Russia 2001, <mark>table 13.15)</mark> . This allows us to calculate the value of industry output by sector and oblast. For each industrial sector, we then proportionally scaled the value of oblast level output so that the sum of industrial output across all oblasts was equal to the value of national output of the sector from the national input-output table.

> 21 WTO accession will grant an “injury determination” to Russia in antidumping cases in WTO members countries. Combined with the decision by the US and the EU to treat Russia as a market economy this will imply Russian exporters may have considerably improved rights in these cases in the US. But market economy status may be denied in particular cases, so it will be necessary to see how this is implemented in practice.

16
- Hertwig, Ralph, Jennifer Nerissa Davis, and Frank J. Sulloway. “Parental Investment:

- How an Equity Motive Can Produce Inequality.” _Psychological Bulletin_ 128.5 (2002): 728.

- <mark>Hatt, Laurel, Rebecca Thornton, Barbara Magnoni, and Mursaleena Islam. “Extending Social Insurance to Informal Sector Workers in Nicaragua via Microfinance Institutions: Results from a Randomized Evaluation.” Bethesda, MD: Private Sector Partnerships-</mark> _<mark>One</mark>_ <mark>project, Abt Associates Inc. (2009).</mark>

- <mark>Imbens, Guido and Joshua Angrist. “Identification and Estimation of Local Average Treatment Effects”.</mark> _<mark>Econometrica</mark>_ <mark>. 62, no. 2 (1994): 467-475.</mark>

- Instituto Nacional de Información de Desarrollo (INIDE) and Ministerio de Salud (MINSA). 2007. Encuesta Nicaragüense de Demografía y Salud (ENDESA) 2006/07, Informe Preliminar [Nicaragua Demographic and Health Survey 2006/07, Preliminary Report]. _http://microdata.worldbank.org/index.php/catalog/988.._ Last accessed: June 9, 2017.

- <mark>Ishdorj, Ariun, Helen H. Jensen, and Justin Tobias. “Intra-household Allocation and Consumption of WIC-Approved Foods: A Bayesian Approach.”</mark> _<mark>Advances in Econometrics</mark>_ <mark>23 (2008): 157-182.</mark>

- <mark>Jacobson, Lena. “The Family As A Producer of Health- An Extended Grossman Model.”</mark> _<mark>Journal of Health Economics.</mark>_ <mark>19 (2000): 611–637.</mark>

- Kaiser Family Foundation. “Children’s Health—Why Health Insurance Matters.” Washington, DC: KFF, 2002.

- King, Gary, Emmanuela Gakidou, Kosuke Imai, Jason Lakin, Ryan T. Moore, Clayton Nall, Nirmala Ravishankar et al. “Public Policy for the Poor? A Randomised

21
answerable research questions (Sala et al., 2018; Englander, 2019), but they do not measure the most important outcomes caused by fishing: the quantities and types of fish that vessels catch. I obtained from the regulator two administrative datasets that contain these variables. Both datasets contain the tons of anchoveta and the percentage juvenile caught by all vessels in the North-Central zone during the six fishing seasons of 2017, 2018, and 2019. However, they differ in important ways which allow me to accurately calculate the number of juvenile anchoveta each vessel catches each time it sets its net (an individual fishing operation).

The first dataset is _electronic logbook data_ (Englander, 2022). Vessels report to the regulator when a fishing trip begins, when a fishing trip ends, and the location, time, tons caught, and percentage juvenile caught from each set during a fishing trip. Vessels record this information on a smartphone or tablet application, which transmits data in real-time to the regulator through the vessel’s onboard GPS transponder. Vessels perform sets once they have located anchoveta in the water. They encircle the anchoveta with a large net (a “purse seine”), close the net, and transfer the anchoveta from the net into the vessel’s hold. While transferring anchoveta from the net into the hold, a trained fisher estimates the percentage of juveniles by taking three samples using a standardized bucket: once during the first 30% of transference and two more times during the remaining 70% (PRODUCE, 2016 _b_ ). The fisher measures each fish in the sample in half-cm intervals, producing data on the length distribution of anchoveta caught by that set (e.g., 10 individuals between 11 and 11.5 cm, 17 individuals between 11.5 and 12 cm, etc.). Out of the several million individual anchoveta caught in a typical set, fishers measure approximately 200. The percentage juvenile for the set is the percentage of measured individuals that are shorter than 12 cm. Vessels report percentage juvenile to the regulator, but they do not report the length distribution.

The second dataset is _landings data_ (Englander, 2022). When vessels finish fishing, they return to shore and transfer (“land”) the anchoveta they caught on their trip to a fishmeal and fish oil processing plant. Each time a vessel lands its catch at a processing plant, a thirdparty inspector measures percentage juvenile and tons landed and reports these data to the 13
16 Table I - Shareholder versus Stakeholder Centered CG Issues

|**_Model_**|**_Control_**|**_Fiduciary Duty_**|**_Firm’s Objective_**|
|---|---|---|---|
|**Shareholders**<br>**Centered**|With shareholders|To shareholders|Maximize shareholders’<br>wealth|
|**Stakeholders**<br>**Centered**|Stakeholders have right<br>to participate in<br>decisions|To all stakeholders|Promote the interests of<br>all stakeholders|
|**IIFS Current**<br>**Practice**|With shareholders|-To all stakeholders<br>-To board of_Shariah_<br>scholars|- Compliance with<br>Islamic_Shariah_<br>- Excellence of service to<br>all stakeholders|
|**IIFS Options**<br>Source: Derived from Boa|Stakeholders have right<br>to participate in<br>decisions<br>tright, J. R. (2002) and extended to incl|-To all stakeholders<br>-To board of_Shariah_<br>scholars<br>ude IIFS.|- Compliance with<br>Islamic_Shariah_<br>- Excellence of service to<br>all stakeholders|

# **III. Shariah Compliance and Stakeholders’ Ethical Interests**

The Governor of the Bahrain Monetary Agency conveyed the sense of IIFS’ roots in stating that “Islamic banks have grown primarily by providing services to a captive market, people who will only deal with a financial institution that strictly adheres to Islamic principles”.<sup>46</sup> This pledge to conduct activities in accordance with _Shariah_ principles entails that IIFS would: (i) not engage in interest-based debt transactions, (ii) not conduct a purely financial transaction disconnected from real economic activity<sup>47</sup> , (iii) not participate in a transaction where there is exploitation of any party, and (iv) not participate in activities regarded as harmful to society. Failure of individual institutions to ensure compliance would entail a reputational risk for the Islamic finance industry.<sup>48</sup> To mitigate such risk, IIFS have created CG structures and processes that reassure

> 46 As reported by _Middle-East-Online.com_ on February 17, 2004. <u>http://www.middle-eastonline.com/english/bahrain/?id=8922=8922&format=0</u> (last visited April 11, 2005) 47 This concept is sometimes referred to as “materiality”. 48 In a survey of depositors’ preferences, Chapra and Ahmed (2002) show that 81.7% of surveyed depositors in Bahrain would withdraw funds from their Islamic bank and chose an alternative Islamic bank, should failure to comply with to _Shariah_ emerge. In the case of Bangladesh, the percentage is 46.6. In Sudan, it raises to 94.6.
# Table 2

# Benefits of Tobacco Club Membership

(percentage of respondents)

||Malawi<br>Househ<br>(|Integrated<br>old Survey<br>IHS)|Tobac<br>Su<br>(T|co Clubs<br>rvey<br>CS)|
|---|---|---|---|---|
||Yes|No|Yes|No|
|_A) INSTITUTIONAL ACCESS_|||||
|- Improved access to auction foor|73|27|–|–|
|- Higher prices|–|–|33|67|
|- Better and more extension advice<sup>(</sup><sup>_a_)</sup>|69|31|90|10|
|_B) COLLECTIVE ACTIONS_|||||
|- Improved access to credit|65|35|–|–|
|- Lower prices for inputs|13|87|11|89|
|- Better input quality|–|–|66|34|
|_C) ECONOMIES OF SCALE_|||||
|- Transport of bales to auction foors|67|33|–|–|
|- Lower transport cost to auction foors|–|–|5|95|
|_D) SUPPORTING NETWORK_|||||
|- Enhanced farming ability due to fellow advice|–|–|98|2|
|- Provision of labor assistance to club members|–|–|65|35|
|- Assistance on farming activities from club members|–|–|44|56|
|- Advice on non-farm activities from club members|–|–|49|51|
|- Motivation to work harder|–|–|96|4|

Notes: The table shows weighted averages computed from the Malawi Integrated Household Survey (IHS) of 2004/2005 and the Tobacco Clubs Survey (TCS) of June 2007. In the Malawi Integrated Household Survey (2004/05), 280 club members answered the question: “Which of the following benefits did you enjoy from membership in a tobacco club?” In the TCS, 240 clubs responded specific questions on the prerogative of clubs. See Appendix 1. (a): refers to visits from government officers or people from NASFAM/TAMA/etc.

12
(Engelschalk, 2007).

A recent study by Wei & Wen (2023) proposes the optimal TOT rate for STRs from a revenue maximization perspective to be around 2.5 percent.<sup>8</sup> They find that this rate balances the risk of bunching just below the threshold separating the TOT regime from profit taxes (if the rate is too low) and of choosing to produce in the informal sector (if the rate is too high).

The size of tax obligations and taxpayers’ responses to them affect the equity and revenue yield of the tax system as a whole. High tax obligations may result in relatively higher tax burdens for the smallest businesses, leading to inequitable outcomes. Similar to the dynamics discussed in response to thresholds, taxpayers often adjust their behavior in response to the size of tax obligations. When tax burdens are too high, this may sustain informality and incentivize under-reporting by relatively larger businesses, affecting tax equity and resulting in lower revenue for the tax system as a whole.

In summary, when designing an STR, there generally are a number of alternatives for each of the three categories (eligibility thresholds, whether set fee or percentage of turnover, and the size of tax obligations). There are forms of STRs that are simple but not fair, forms that are fair but, in fact, not simple at all, and various mixes in between. The next section examines how design choices of STRs have been designed de jure in Sub-Saharan Africa.

# **2.2 Simplified Tax Regimes in Sub-Saharan Africa**

In this section, we focus on three key areas where there is considerable variation in the design of STRs in Sub-Saharan Africa. Our analysis is based on a comprehensive review of STRs across countries in the region, using the most recent tax legislation and policy guidance notes on business taxation in these countries. The review shows that two-thirds of countries in the region had some form of STR for small businesses in March 2024, and in the remaining countries, small businesses that reach a certain size

> 8Based on simulations set largely on evidence from countries in Sub-Saharan Africa.

11
implementation. Interviewers did not visit all of their assigned areas, and did not observe the entire area when they did visit. Some supervisors required supervision themselves by the survey coordinator. Overall, monitoring in this study was difficult compared to the standard household survey as, without a household listing operation, the paper trail on total cluster size was limited, and the populations are mobile, which limits the usefulness of repeat visits to verify the data collected. The project was also highly dependent on the cooperation of local guides, which are outside of the management structure and may be unreliable in some areas. Perhaps most importantly, the terrain in which the survey was implemented is difficult. The weather was extremely hot and numerous natural obstacles to successfully completing the survey tasks occurred. Although these factors affect any survey in Afar, they were particularly troublesome for the RGCS, which required a good deal of driving and walking to reach the selected areas. ‘High effort’ was required from supervisors and interviewers throughout the project to implement the design as developed. Unfortunately we are not able to thoroughly evaluate how much these factors impacted the quality of the data collected. We have concerns that weighted estimates from the data do not accurately capture the number of livestock held in Afar, but we have no reliable comparable standard against which to compare our numbers.

Based on our experience in Afar with the RGCS, we have a number of suggestions that would improve the implementation. In the particular, we recommend more careful planning to avoid conducting the survey during the Ramadan period and the completion of data collection before the onset of the seasonal rains. In addition, training should better explain to supervisors and interviewers the goals of the survey in order to elicit more ‘high effort’ fieldwork. Providing incentives to teams that complete more circles and observe greater percentages of assigned circles may further increase effort. Though it is possible that the RGCS approach may work better in a different country context, the limited capacity of statistics bureaus and the potentially dangerous terrain of the study area are common to nearly all pastoralist areas in the developing world.

Despite the limitations noted above, drylands areas remain difficult to survey and the RGCS offers a viable alternative to traditional approaches. The Ethiopia CSA has decided to extend the method and include it as part of the data collection method in pastoralist areas nationwide as part of the upcoming Agricultural Census. Beyond the specific livestock in drylands context, we wonder if this technique might have applications to other contexts, such as the measuring of homeless persons. We believe that the RGCS approach deserves more study in both the developed and the developing world.

15