rafmacalaba/datause-encoder-data huggingface dataset:
- **Hubbard, Thomas N.** 2003. “Information, Decisions, and Productivity: On-board Computers and Capacity Utilization in Trucking.” _American Economic Review_ , 93(4): 1328– 1353.

- **Klenow, Pete, and Andrés Rodríguez-Clare.** 1997. “The Neoclassical Revival in Growth Economics: Has It Gone Too Far?” _NBER Macroeconomics Annual_ , 12.

- **Mansfield, Edwin.** 1963. “Intrafirm Rates of Diffusion of an Innovation.” _The Review of Economics and Statistics_ , 348–359.

- **Romer, Paul M.** 1990. “Endogenous Technological Change.” _Journal of political Economy_ , 98(5, Part 2): S71–S102.

- **Ryan, B., and N. Gross.** 1943. “The Diffusion of Hybrid Seed Corn in Two Iowa Communities.” _Rural Sociology_ , 8(1): 15–24.

- **Särndal, Carl-Erik, Bengt Swensson, and Jan Wretman.** 1992. _Model Assisted Survey Sampling._ Springer Science & Business Media.

- **Stiroh, Kevin J.** 2002. “Information Technology and the U.S. Productivity Revival: What Do the Industry Data Say?” _American Economic Review_ , 92(5): 1559–1576.

- **Syverson, Chad.** 2011. “What Determines Productivity?” _Journal of Economic Literature_ , 2(49): 326–365.

- **Trajtenberg, Manuel.** 1990. _Economic Analysis of Product Innovation: The Case of CT Scanners._ Vol. 160, Harvard University Press.

- **Ulyssea, Gabriel.** 2018. “Firms, Informality, and Development: Theory and Evidence from Brazil.” _American Economic Review_ , 108(8): 2015–47.

- **Williamson, Oliver E.** 1979. “Transaction-Cost Economics: The Governance of Contractual Relations.” _The Journal of Law and Economics_ , 22(2): 233–261.

- **Williamson, Oliver E.** 1988. “Technology and transaction cost economics: A reply.” _Journal of Economic Behavior Organization_ , 10(3): 355–363.

- **Wood, Lexi.** 2024. “How Much Does ERP Cost?” https://softwareconnect.com/learn/erppricing/.

- **World Bank.** 2009. “Enterprise Survey and Indicator Surveys: Sampling Methodology.” _The World Bank, Manuscript_ .

- **Zeira, Joseph.** 1998. “Workers, machines, and economic growth.” _The Quarterly Journal of Economics_ , 113(4): 1091–1117.

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2

# **1. Introduction**

In the late 1970s and in the 1980s, there was much discussion of “urban bias” in development circles. Following Lipton (1977), development economists increasingly recognized a widespread tendency among (almost always urban-based) governments to pursue policies that – explicitly or implicitly – taxed agriculture and transferred resources to industry and other urban activities. The motivation was not exclusively urban selfinterest. There was a widespread belief, based on the influential early views of Rosenstein-Rodan (1943), Prebisch (1950), and others, that development was to a large extent synonymous with industrialization – and that industrialization inevitably implied urbanization. As markets could not solely be relied upon to allocate resources to that most dynamic sector, government was required to provide a “big push” to help economies along the righteous path of urban growth.

Against that view, Lipton and his followers argued that urban bias implied a “sacrifice of efficient and equitable growth to rapid urban advance” (p.310). By distorting relative prices and the “intersectoral terms of trade”, such policies induced an inefficient allocation of resources that could lead to perpetually infant industries, at the expense of farmers, many of whom were the poorest people in the land.<sup>2</sup> That was a time when an estimated 80-90% of the world’s poor lived in rural areas, and an important part of the argument against urban bias was that, in addition to distorting the allocation of capital and other resources, these policies were also anti-poor.<sup>3</sup> In 2010, the situation is somewhat different. Urbanization has proceeded apace in the last quarter century such that the world’s urban population is now as large as its rural population. Extreme poverty remains a predominantly rural phenomenon, with some 75% of those who subsist on expenditures below $1-a-day still residing in rural areas in 2002, even when higher cost of living in urban areas is taken into account.<sup>4</sup> But urban poverty has been falling more slowly than rural poverty – in part because urbanization has been a

> 2 A classic study by Bates (1981) documented the use of price regulation and marketing boards in Ghana, Nigeria and Zambia to extract surplus from farmers to the benefit of urban food consumers. 3 Ravallion et al. (2007) produced arguably the first global poverty statistics that cover the majority of the world’s population and disaggregate between urban and rural areas. They estimate that the urban share of the world’s extreme poor in 1993 was 19%. 4 See Ravallion et al. (2007).
by between 0.2 percent and 0.3 percent of GDP (relative to the baseline) which leads to deficit of above 4 percent of GDP in 2021 and 2022. Under the “Adverse world” scenario the current account deteriorates quickly by about 0.2 percent of GDP relative to the baseline and declines slowly thereafter. The decline is driven by higher global uncertainty which partially offsets assumed slower rate of changes in oil prices and in the terms of trade.

The scenario analysis suggests that the current account dynamics are more sensitive to changes in the domestic conditions relative to changes in the global environment. The results therefore suggest the importance of policy measures needed to reduce the domestic saving-investment gap; restrict further credit expansion and alleviate inflation pressures; and structural policies to improve the overall competitiveness (which also lead to higher trade openness). The results also show that in the absence of such measures the future path of the current account and the accumulated levels of the net foreign liabilities may pose an increasing risk to the external sustainability.

<!-- Start of picture text --> Figure 30: Projected current account relative to the baseline<br>0.00<br>-0.10<br>Adverse world<br>Domestic expansion<br>-0.20<br>-0.30<br>-0.40<br>2018 2019 2020 2021 2022<br>Predicted Change in Current Account Balance compared to baseline (Percent of GDP)<br><!-- End of picture text --> Source: Authors’ calculations.

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Table A5: Excluding outliers: robotization and dependent variables censored

|||OLS|||IV||
|---|---|---|---|---|---|---|
|Dependent variable:|_log_(1 +_imp_)|_log_(1 +_exp_)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup>|_log_(1 +_imp_)|_log_(1 +_exp_)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.1771**|0.4384***|-0.2725***|0.5830**|1.1704***|-0.5942***|
||(0.0738)|(0.1327)|(0.0720)|(0.2173)|(0.3223)|(0.1370)|
|First stage :|||||||
|_Replaceability_ * _initial GDPpc_ * _global robot stock_||||0.0027***<br>(0.0002)|0.0027***<br>(0.0002)|0.0027***<br>(0.0002)|
|Observations|870,826|871,295|870,942|870,826|871,295|870,942|
|_R_<sup>2</sup>|0.6205|0.7624|0.4095|0.6179|0.7559|0.4070|
|Kleibergen-Paap rk Wald-F-stat||||129.528|128.008|128.850|
|importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, excluding outliers from the baseline estimation sample (robotization and dependent variables censored). Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
only at the beginning of the meeting, if at all. Where available, our data on atendance at the time the meeting began were cross-validated with this official data by the external enumerator.

Te audio recordings of meetings were transcribed and translated into a corpus of textual data by an independent survey firm. Transcripts included verbatim transcriptions and translations of the assemblies, as well identifiers on the gender and position of each speaker.<sup>12</sup> Tese transcripts form the backbone of the following analysis. Each “document” in the corpus consists of an uninterrupted speech by a administrator, elected official, or citizen. From the 50 village assemblies, we have 1,736 such documents, each of which is identified by the position and gender. Table 1 presents descriptive information about the number and character of documents within each village. Assemblies have relatively good atendance (with 123 people atending on average), and consist of roughly 34 speeches, of which one-third are made by women. Citizens deliver just over half (54 percent) of speeches, with the remainder distributed between administrators (29 percent) and politicians (16 percent).

Table 1: Village-Level Summary Statistics

||Mean|Std. Dev.|Median|Min|Max.|
|---|---|---|---|---|---|
|Total Atendance|123.51|83.77|103.00|25.00|462.00|
|Number of Speeches|34.72|22.27|29.50|4.00|97.00|
|Speech Length|109.92|158.22|71.68|25.60|1090.75|
|Percent Female|0.32|0.21|0.30|0.00|0.92|
|Percent Citizen|0.53|0.14|0.53|0.20|0.88|
|Percent Admin|0.31|0.17|0.28|0.00|0.75|
|Percent Politician|0.16|0.16|0.14|0.00|0.50|

# **4.1 A** **_Text-As-Data_ Approach to Deliberation**

While these descriptive statistics allow us to examine _who_ speaks within the _gram sabha_ , to understand the agenda-seting power of speakers and the state’s responsiveness to citizen issues, we 12Te original data contain rich information on the position of each speaker, from school headmasters and ration shop owners, to elected officials and administrators. For the purpose of our analysis, we code the speaker into three types: (1) administrators, who include all persons employed by the state or local government (e.g. _panchayat_ secretary, block development officer, school headmaster, village administrative officer, etc.); (2) elected officials, who include all persons who are in elected office (e.g. president, vice president, ward member); and (3) citizens, all people who neither hold a formal government job or elected office. Tese may include members of social groups (e.g. SHGs) and other organizations, but are not direct employees of the state.

17
groups known to exploit natural resources for revenue generation. Numerous qualitative descriptions of the conflict events occurring in and around protected areas after they are transferred to AP confirm this intuition:

- “Around 100 heavily armed poachers lead by LRA rebel killed a park ranger and 2 FARDC soldiers, and injured another ranger, patrolling the Garamba National Park during an ambush. 2 of the poachers were also killed, who were also allegedly made up of a number of foreigners, including a Sudanese army deserter. Reports conflicted as to the date of the attack.” (ACLED Event ID: DRC9371)

- “An LRA rebel involved in poaching was killed by Garamba park guards.” (ACLED Event ID: DRC10020)

- “On 26 July 2022, suspected JNIM militants and APN [African Parks Network] Park rangers exchanged gunfire in the village of Dassari (Materi, Atacora). There were no casualties.” (ACLED Event ID: BEN739)

- “On 4 April 2022, overnight presumed (Jama’at Nusrat al Islam wal Muslimeen) JNIM or ISWAP abducted a farmer and a motor taxi driver from the Fulani community in the village of Kangara [Kangara Peulh], in Arrondissement of Birni-Lafia (Karimama, Alibori). The gunmen took the abductees to the interior of Park W.” (ACLED Event ID: BEN655)

- “On 9 June 2021, presumed JNIM militants detained (some were tied up) and interrogated road users in the Arly National Park (Logobou, Tapoa).” (ACLED Event ID: BFO4773)

- “On 15 October 2022, unspecified security forces (described as ‘soldiers’, and provisionally coded as SSPDF) shot and wounded two people (reported to be from the Aliab Dinka community) and demolished 34 dwellings in Gumbo (Juba county, Central Equatoria state). An opposition politician from Awerial county has alleged that the security forces also stole money from the houses that were destroyed.” (ACLED Event ID: SSD8852)

- “On 2 October 2022, three FARDC soldiers shot at a young businessman at his home in Kamanyola (Walungu, Walungu, Sud-Kivu), presumably to rob him. The man was wounded, but survived.” (ACLED Event ID: DRC27036)

It is plausible that AP’s law enforcement components are related to these conflict events. For example, rangers deployed in Garamba National Park could have increased their patrolling efforts once the park was transferred to AP, given the substantial resources AP dedicates to anti-poaching efforts (Table 1). Subsequently, AP rangers may have been more likely to discover armed groups engaged in poaching. The roadblocks, kidnapping, and extortion described above are also consistent with our proposed mechanism, lending further credence to our finding of an increase in the probability of civilian targeting. Both government forces and rebel groups active in the regions where AP manages protected areas use these strategies to generate revenue, especially when exclusively controlling areas rich in natural resources is difficult [86, 110]. Moreover, JNIM—the rebel group responsible for some of the civilian targeting described above—is known to rely on the trafficking of natural resources to generate revenue [111, 112].

12
poorest decile obtain a higher share of their income from wages than all except the two highest deciles, probably reflecting landlessness among the poorest households in Cambodia. The most striking thing to note from Table 1 is the dramatic differences between the top two deciles and the rest of the population. The rich in Cambodia (and in many other countries) really are different, typically earning a far higher share of their income from non-farming activities, wages, and rental income than the rest of the population. Given that the top two deciles of most populations tend to live in the major urban centers and are much more closely connected to the policy process than most other groups, it is important to analyse policies to see if they are really serving the interests of the majority of the population (and in particular the poor) rather than those of the, rather different, top two deciles.

**Table 2: Expenditure shares in Cambodia by decile of per capita consumption (percent)**

|**_Consumption_**<br>**_item_**|**_1_**|**_2_**|**_3_**|**_4_**|_5_|_6_|**_7_**|**_8_**|**_9_**|**_10_**|**_avg_**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|FoodTotal|75.7|76.3|76.0|73.7|73.7|72.6|72.0|70.3|64.4|38.6|63.5|
|*Rice,all<br>varietes|28.4|23.3|22.7|20.7|20.6|18.9|17.5|15.1|12.5|5.8|15.6|
|*Fish**&**fish<br>products|9.9|11.1|10.6|10.5|10.9|10.8|10.0|10.0|9.2|5.0|8.9|
|*All other<br>consumption<br>items|37.4|41.9|42.7|42.5|42.2|43.0|44.5|45.2|42.7|27.8|39.0|
|Non FoodTotal|24.4|23.8|24.1|26.4|26.3|27.5|28.0|29.7|35.6|61.4|36.5|
|*Housing,fuel<br>and<br>transportationa|15.7|15.5|15.2|17.1|17.3|17.4|17.5|19.4|24.1|46.7|25.5|
|*Clothingb|2.9|2.8|3.0|3.0|2.8|3.4|3.4|3.2|2.9|2.3|2.9|
|*Other<br>expendituresc|5.8|5.4|5.9|6.2|6.1|6.6|7.1|7.1|8.6|12.4|8.2|

> a Includes house rent (rental value of subsidized housing, rental value of owner-occupied housing, hotel charges), house maintenance and repair, water and fuel, medical care, transportation and communication, and personal care b Clothing and footwear (tailored clothes, ready-made clothes, shoes, etc.) **c** Includes fumiture and household equipment and operation, expenditures in recreation, education , personal effects and miscellaneous items.

> Source: CSES Survey 2001.

Table 2 shows the expenditure shares on different commodity groups broken down by the same per capita consumption decile groups as above. Again we see a remarkable homogeneity across the bottom eight deciles and dramatic differences for the top two deciles. Food expenditure accounts for over 70% of total expenditure for the bottom 80% of the population, but rice consumption increases in importance for poorer households, whilst the share of all other consumption items (except fish and fish products) declines. Similarly non-food shares are quite similar for the bottom eight deciles, with the share of housing, fuel and transportation rising dramatically for the top two deciles.

8
poor and food poor households who do not receive family assistance are over-represented in Sikasso and Segou, while the malnourished without family remittances are overrepresented in Sikasso, Segou and Mopti. The incidence of public transfers is marginal with less than 1% of the population receiving transfers.

**Table** **<u>6:  Transfers received by the poor, food poor and the malnourished</u>**

||**Whole**<br>**population**|**Poor**|**Food poor**|**Chronically**<br>**malnourished**|
|---|---|---|---|---|
|Proportion receiving domestic remittances|22%|22%|21%|22%|
|Value (median per capita, FCFA)|4167||||
|Proportion receiving foreign remittances|19%|14%|12%|19%|
|Value ((median per capita, FCFA)|10,000|5,556|5,000|9,339|
|Proportion receiving public transfers|0.8%|0.7%|0.3%|0.6%|
|Value((medianper capita,FCFA)|13,636|9,545|11,604|8,438|

_Source: authors’ calculations using ELIM –MICS 2010 household survey data Note: percentages are across columns, i.e. 22 % of the whole population receives domestic remittances_ **_._** 9
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These results assume accurate reporting of income. If Thai-owned companies were to report low revenues or high costs to avoid company taxes, the uscr cost of capital as estimated in Table 8 would have to be adjusted downward. In fact, accounting data suggest that Thai-owned companies tend to pay proportionately less taxes than foreign companies. The user cost of capital for Thai firms may therefore be over-estimated.

2. ExemRtion versus Deferral Taxation. The user cost of capital for a company of a specific country is often lower under deferral taxation than under exemption (Table 8). Much of this is due to the advantages afforded to foreign-owned companies to finance investment in Thailand at low real rates of interest in their own country. Under the deferral case, the benefit of a lower cost of finance is greater for the multinational than under the exemption case especially when a significant amount of dividends are remitted to the parent which increases the value of capital cost allowance deductions. Thus, much of the advantages for foreign companies is their access to a lower cost of funds compared to Thai companies.

To gain a better understanding of the effect of deferral, we use the effective tax calculations in Table 9. For the "trade-off" model, when real interest rates are assumed to be the same, tax rates on a deferral basis are generally higher than those on an exemption basis. This would be expected since the effective statutory tax rate, which includes the impact of capital income on the repatriation tax rate imposed by the home country, would be higher than the Thai statutory tax rate. Moreover, the capital cost allowances are partly based on the home country tax writeoffs for foreign investments of multinationals. In general, home countries tend to provide less liberal writeoffs so that capital cost allowances are less valuable on a present value basis to the extent that dividends are remitted.
## **Table 6: Compensation Schemes for Depositors, Investors and Policyholders**

|**Country**|**Depositors**|**Investors**|**Policyholders on**<br>**MTPL**|**Other Policyholders**|
|---|---|---|---|---|
|Azerbaijan|30,000 AZN<br>(€25,100)|None|None|None|
|Bulgaria|100,000 BGN<br>(€51,200)|40,000 BGN<br>(€20,500 up to 90%)|100%|8,000 BGN (€5,000 up to<br>70%) for life insurance<br>only|
|Croatia|100,000 HRK<br>(€13,700)|150,000 HRK<br>(€20,500)|100%|None|
|Czech Republic|€50,000|€20,000<br>(up to 90%)|100%|None|
|Latvia|€50,000|€20,000<br>(up to 90%)|100%|2,000 LVL (€2,800 up to<br>50 – 100%)|
|Lithuania|€100,000|€20,000|100%|None|
|Romania|€50,000|€9,000 (to increase to<br>€20,000)|100%|25 - 70%|
|Russia|700,000 RUB<br>(€16,300)|None|100%|None|
|Slovakia|€20,000|€20,000<br>(up to 90%)|100%|None|

_Source:_ Diagnostic Reviews, National Laws

# **Complaints & Dispute Resolution**

**Customer complaints provide a valuable source of information** . Complaints from financial customers reveal what information consumers receive about their financial services and how and to what extent consumers understand the disclosure that is provided. This information is valuable to all stakeholders, including managers of financial institutions, supervisors, consumers and market participants.

**If complaints are not satisfactorily addressed, they undermine public confidence in the financial sector.** A financial literacy survey conducted in Russia in June 2008 found that more than half of respondents did not have confidence that in case of a dispute with a bank or insurance company, the case would be handled quickly, efficiently and fairly. Where consumers lack confidence that problems can be easily and quickly solved, they will be reluctant to invest their money or take a loan. To build a deep financial sector, consumers need to be assured that they are protected and that solutions will be found to problems if they arise.

**At a minimum, all financial institutions should be obliged to have a designated department (or at least an officer) responsible for handling complaints.** The Diagnostic Reviews found that in all nine countries, the large well-managed financial institutions generally had set up specialized complaint departments to receive complaint letters from consumers and investigate if an error had been made by the institution. All financial institutions—large and small—should have at least one officer designated to receive customer complaints. When opening a new account and buying any new service, the consumer should be advised in writing as to where complaints, inquiries and disputes should be submitted.

**As part of the supervision process, financial supervisors should review the complaint files of the financial institutions they oversee.** The Diagnostic Reviews found that in all nine countries, banking supervisors, while conducting on-site supervision visits at commercial banks, reviewed each bank’s complaint files. This is a useful approach. Financial supervisors should review the ways in which

27