rafmacalaba/datause-encoder-data huggingface dataset:
treat the implementation or removal of caps in different countries as comparable, binary events and establish average effects.

**Lack of uniform data source and definition:** The problems are aggravated by the lack of a uniform and regularly updated data source,<sup>17</sup> which uses a standardized taxonomy to classify interest rate restrictions. Such a database, potentially based on surveys of regulators and market participants, would allow to follow trends in the use of restriction and enable a panel analysis of their effects.

If interest rate caps are set so low that lenders cannot recoup at least their cost of funding and overhead costs and make a non‐zero risk adjusted return, it will not be economically viable for them to lend. If binding restrictions apply exclusively on the level of nominal interest rates, banks and MFIs may try to compensate for the impact of mandatory low rates by **increasing non‐interest fees and charges** related to the loan. This can also be done by a bundling of products and requiring borrowers to buy costly additional services, such as credit insurance, in order to obtain a loan. Such “hidden extra costs” reduce transparency and make it more difficult for borrowers, especially those with limited financial literacy, to assess the overall cost of borrowing and “comparison shop” for loans (Helms and Reille, 2004).

Another response of lenders to low interest rate caps is to **reduce credit extension** . Studies (e.g. Staten, 2008) show that credit supply is highly elastic to price changes and consequently a ceiling resulting in lower lending rates can trigger a swift reduction in the quantity of loans available. The reduction in credit supply caused by interest rate caps may not be uniform across borrowers, but fall predominantly on high‐risk borrowers. In particular, too low ceilings on lending rates can lead to a reallocation from small borrowers to large commercial borrowers or the government, which are less risky and cheaper to administer (IMF, 2017). As lending institutions move to larger loan sizes to be more efficient, clients can also feel pressed to increase their borrowing amounts in order to maintain access to external finance. This can result in borrowing amounts exceeding the client’s repayment capacity and thus, increase the **risks of over indebtedness** . In the micro‐finance context, where lenders often expand outreach by funding network expansion through profits from existing

> 17 The Global Microscope database covers only 55 emerging and developing countries.

19
First, the data available on non-monetary outcomes xij can be noisier than the data ˆ available on monetary outcomes. When selecting a variable xij capturing a non-monetary dimension j, there often is a trade-off between at least three conflicting goals. We present these three goals when assuming that health is the dimension j for which variables must be selected.

1. xˆij approximates xij with limited noise.

- Self-reported health surveys lack the objectivity of medical check-ups and thus noisily capture the health status of respondents. Many variables like vaccines administred or access to clinics relate to health inputs and thus noisily capture health outcomes.

2. xˆij offers a good coverage of dimension j.

Having received a vaccine or having delivered a baby in a clinic only capture very limited aspects of a person’s health. Indeed, they both ignore disabilities, nutritional status, chronic diseases, etc. Arguably, self-reported surveys like EQ5D provide a larger coverage of a person’s health (Szende et al., 2014).

3. xˆij is available or cheaply collected.

Medical check-ups could provide a broad coverage of a person’s health with limited noise, but their cost could be prohibitive.

In most cases, the variable xˆij that provides the best trade-off between these three conflicting goals is not cardinal, but rather ordinal or even categorical. As a result, the practitioner must rely on indicators that can accommodate ordinal data.

Second, the joint distribution between outcomes in different dimensions j and j<sup>′</sup> is often missing. The inter-personal comparisons presented in Figure 2 require not only partial distributions (xi1)i∈R and (xi2)i∈R but also the joint distribution. Without the joint distribution, an individual’s achievement vector (xi1, xi2) is not known and we thus ignore whether an individual cumulates low achievements in several dimensions. One problem is that non-monetary outcomes are often collected through different surveys than monetary outcomes. At global level, monetary outcomes are collected through LSMS-type surveys while non-monetary outcomes are collected through DHS or MICS surveys.<sup>10</sup> As a result, the joint distribution between monetary outcomes (consumption or income) and non-monetary outcomes is often missing. A similar problem arises for the mortality dimension. The joint distribution between mortality risks and other well-

> 10At country level, this constraint may not be present, which allows some countries like Mexico to construct multidimensional poverty indicators capturing both monetary outcomes and non-monetary outcomes (Bank et al., 2021).

9
Preceding outcome was:

|||All<br>periods|Initial<br>periods|(_Stag_,_Stag_)|(_Stag_,_Hare_)|(_Hare_,_Stag_)|(_Hare_,_Hare_)|
|---|---|---|---|---|---|---|---|
|_L_||**0.74**|**0.68**|**0.88**|**0.71**|**0.58**|**0.52**|
|in|_LL_|0.74|0.65|0.86|0.68|0.66|0.50|
|in|_LH_|0.74|0.70|0.89|0.72|0.47|0.53|
|_H_||**0.58**|**0.53**|**0.87**|**0.42**|**0.46**|**0.31**|
|in|_LH_|0.64|0.55|0.86|0.56|0.46|0.40|
|in|_HH_|0.53|0.52|0.88|0.32|0.47|0.26|
|_L −_|_H_|**0.16**|**0.15**|**0.01**|**0.29**|**0.12**|**0.21**|
|_LL _|_−HH_|0.21|0.13|-0.02|0.36|0.19|0.24|
|_N_||1,210|242|452|181|181|154|

Table 1: Proportion of players who chose _Stag_ .

percentage points. These differences contributed to the divergence in pair outcomes, though the data indicate the particular importance of the last-period outcome ( _Stag_ , _Hare_ ).

Caste status is strongly correlated with wealth and education. We next consider whether these covariates explain the variation in behavior by caste status. We collected data in post-play interviews on land ownership, education, and housing.<sup>7,8</sup> Figure 4 shows, by caste status, the distribution of land owned by the households represented in our sample, as well as the proportions of subjects who completed high school and live in houses constructed only of mud (with thatch or tiled roofs). High-caste subjects own more land, are more likely to have a high school education, and are more likely to live in a house that is not made out of mud. However, there is substantial within-group variation, and the distributions of these characteristics overlap between high and low castes. For example, approximately 15 percent of the low-caste sample completed high school, compared to 18 of the high castes; 63 percent of low-caste subjects live in a mud house, compared to 19 of high-caste subjects.

> 7Houses were classified as either brick ( _pucca_ ), impermanent materials like sticks and mud, or a mixture of the two.

> 8Using data from the 1997-98 Survey of Living Conditions in Uttar Pradesh, we find that land ownership, high-school education, and housing type together explain between 30 and 40 percent of the variation in consumption for both high and low castes (Hoff, Kshetramade and Fehr, 2011).

13
**Annex table 3: Number of cattle, chickens, pigs and sheep in ECA countries, 1995, 2000, 2005 and 2009**

|||Cattle|( ̳000)|||Chicken|s ( ̳000)||
|---|---|---|---|---|---|---|---|---|
|Country|1995|2000|2005|2009|1995|2000|2005|2009|
|Albania|840|728|655|494|2,624|4,087|4,671|5,138|
|Armenia|504|479|573|585|2,700|4,100|4,590|3,950|
|Azerbaijan|1,341|1,664|2,007|2,281|13,600|14,200|17,500|21,450|
|<br>Belarus|5,403|4,326|3,963|4,131|29,900|26,400|23,500|29,200|
|Bosnia & Herzegovina|403|462|460|458|3,700|9,000|9,540|17,260|
|Bulgaria|638|682|672|565|17,822|13,300|17,159|15,765|
|Croatia|493|427|471|447|10,724|10,351|7,267|6,707|
|Czech Republic|2,030|1,574|1,397|1,349|25,522|13,658|14,322|24,042|
|Estonia|420|267|250|238|3,130|2,414|2,161|1,757|
|Georgia|944|1,122|1,145|1,028|7,346|8,000|9,300|6,200|
|<br>Hungary|910|857|723|701|33,906|25,890|32,814|31,165|
|Kazakhstan|8,073|3,998|5,204|5,992|32,450|17,880|25,530|30,000|
|Kyrgyzstan|920|932|1,035|1,225|1,978|2,659|4,121|4,000|
|Latvia|551|378|371|380|3,700|3,100|3,450|4,000|
|Lithuania|1,152|898|792|771|8,650|6,226|8,227|8,841|
|Macedonia, FYR|281|270|248|253|4,880|3,350|2,617|2,172|
|Moldova|832|423|331|218|14,362|12,535|17,420|18,220|
|Poland|7,306|6,083|5,483|5,700|46,395|49,526|134,424|124,129|
|Romania|3,481|3,051|2,808|2,684|70,157|69,143|87,014|84,373|
|Russian Federation|43,296|28,032|22,988|21,038|483,200|339,000|328,707|366,282|
|Serbia & Montenegro|1,950|1,427|1,196|1,163|23,491|18,948|14,816|17,116|
|Slovakia|916|664|540|484|7,322|6,080|13,262|13,249|
|Slovenia|477|471|451|470|5,415|4,256|3,114|4,387|
|Tajikistan|1,199|1,037|1,303|1,800|1,473|770|2,296|3,682|
|<br>Turkey|11,901|11,054|10,069|10,860|183,684|239,748|296,876|244,280|
|Turkmenistan|1,181|1,400|2,025|2,154|4,000|5,500|10,000|14,570|
|Ukraine|19,624|10,627|6,903|5,079|136,000|118,000|131,976|158,800|
|Uzbekistan|5,484|5,268|6,571|8,025|18,000|14,407|20,540|29,100|

Source: FAO (2011) 56
**Figure 3 Structure of SSA GDP by countries in 2010 and 2025.**

<!-- Start of picture text --> Cameroon, 2.6 Côte d’Ivoire, 2.0<br>Ghana, 3.7<br>2025<br>Rest of SSA, 25.9<br>Nigeria, 22.0<br>Rest of SSA, 24.6<br>Nigeria, 18.4<br>2010<br>Senegal, 1.3<br>South Africa, 32.0<br>Ethiopia, 2.9<br>Kenya, 3.1<br>Madagascar, 0.8<br>South Africa, 24.7<br>Mauritius, 0.7<br>Malawi, 0.5<br>Zambia, 1.4 Botswana, 1.3 Uganda, 1.7 Mozambique, 1.2<br>Zimbabwe, 0.5 Namibia, 0.9 Tanzania, 2.5<br><!-- End of picture text --> Source: Authors’ simulations and calculations.

Although developing countries will be growing faster, the gaps in income levels will take longer time to bridge than the 15 years in the baseline scenario. The convergence between developing- and developed-country incomes using 2007 PPP exchange rates<sup>4</sup> is progressing, but at speeds varying significantly across regions. At 2007 PPP exchange rates, the average developing-country resident earns about 17 percent of the average income of high income countries—$5,943 versus $35,302. This ratio would rise to 26 percent in 2025, representing an average developing-country income of $11,188 versus $42,767 for high-income countries. There would be only a modest catch-up in Sub-Saharan Africa. The average SSA resident was receiving about 6% of the income of an average resident of high income countries. By 2025 this ratio would improve to 7.4% with the average SSA resident receiving an income of $3,148. Other developing countries residents’ income would improve relatively faster. If China is excluded from the sample of developing countries, the average income of a resident of SSA remains relatively stable as compared to that sample of developing countries with an income equivalent to 38% in 2010 and 36% in 2025.

> 4 Using the market dollar exchange rate of an economy provides a biased estimate of individual wellbeing because prices differ substantially across economies—particularly for non-traded goods such as personal and housing services. For this reason, it is more appropriate to use the PPP exchange rates, which take into account these differences in prices.

6
The survey covered six self-regulatory and trade association organizations such as ICMA, JSDA, and KSDA (now KOFIA). Of these, only two had separate reporting lines for the two functions, and none had separate entities or premises.

Of the 25 exchanges, 17 reported they had formal procedures for managing conflicts of interest, and the government regulator reviews the procedures of 15 of the 17.

NYSE Regulation’s independent status was outlined previously. Similarly, the Tokyo Stock Exchange (TSE) established a TSE Regulation subsidiary. ASX established ASX Markets Supervision in 2006 to oversee supervision of issuers, participants, futures, and prudential risk and enforcement. The ASXMS board has three directors from the ASX board and two independent directors. As noted earlier, Brazil’s Bovespa recently created a new independent entity, now BM&FBovespa Market Supervision, to perform SRO functions.

Other exchanges have addressed conflicts by formally separating management of regulation functions from business operations within a single corporate structure. This group includes HKEx, which has strong firewalls between the business and the Stock Exchange’s listings department and the Nasdaq OMX Nordic Exchanges in Scandinavia.

Several independent SROs have organizationally separated their regulatory and trade association operations. Arguably, the best practice is to formally separate the roles in different organizations. But like exchanges, some associations perform dual roles with separate governance and organizational structures for regulation functions. The AFEI survey cited above showed that six industry associations have both self-regulatory and trade association functions, including the JSDA, KOFIA and ICMA.

In Japan, the JSDA remains a dual-purpose institution, but in 2004, it adopted a new organizational structure to separate the governance of its SRO operations. An independent SelfRegulation Board is entrusted with decision making on SRO matters. Its 11-member board consists of the JSDA chairman, the vice chairman in charge of regulation, five representatives of member firms, and four public members. Separate departments conduct industry association and SRO operations. Finally, an independent board that includes public representatives oversees JSDA’s self-regulatory operations.

In Canada, the IDA (now IIROC) became a pure SRO in 2006 after splitting off its trade association arm into a new organization.

# **Conflicts and supervision committees**

To reinforce the independence of SRO functions, a number of SROs have created board-level regulatory oversight committees. Their job is to supervise the SRO’s regulation programs and to ensure the independence and effectiveness of the SRO department, particularly in light of the conflicts of interest previously noted. Several US exchanges have established such committees, which typically have a majority of independent directors.

Other exchange SROs have created conflicts committees to address any conflicts between a listed exchange and listed companies that it regulates. This appears to be an emerging best 45
Table S.7: Semiparametric specification: Corruption

||TI|WGI|WGImed|
|---|---|---|---|
||(1)|(2)|(3)|
|TI Corruption|0.053<sup>_∗∗_</sup><br>(0.023)|||
|WGI Corruption||0.081<sup>_∗∗_</sup><br>(0.04)||
|WGI Corruption>Median|||0.129<sup>_∗_</sup><br>(0.066)|
|Log Ruggedness|0.119<sup>_∗∗∗_</sup>|0.132<sup>_∗∗∗_</sup>|0.125<sup>_∗∗∗_</sup>|
||(0.041)|(0.04)|(0.04)|
|Log of Rainfall|-.089|-.074|-.062|
||(0.077)|(0.072)|(0.071)|
|Log dist to coast|-.028|-.018|-.021|
||(0.044)|(0.042)|(0.041)|
|Log of Surface Area|0.062<sup>_∗∗∗_</sup>|0.065<sup>_∗∗∗_</sup>|0.07<sup>_∗∗∗_</sup>|
||(0.018)|(0.017)|(0.017)|
|Length>than 50km|-.095<sup>_∗∗_</sup>|-.097<sup>_∗∗_</sup>|-.101<sup>_∗∗_</sup>|
||(0.044)|(0.044)|(0.044)|
|Estimate|-.021|-.020|-.016|
||(0.06)|(0.061)|(0.061)|
|Contract|-.046|-.048|-.047|
||(0.073)|(0.073)|(0.073)|
|Log of GDP pc (1985)|-.019|-.019|0.0001|
||(0.044)|(0.045)|(0.047)|
|Obs.|3322|3322|3322|
|_R_<sup>2</sup>|0.886|0.886|0.886|

_Notes_ : Dependent variable is the log of cost per km; all models control for work activity fixed effects, year fixed effects, an interaction between work type and five-year period fixed effects and region fixed effects; base categories are actual costs; robust standard errors in parentheses, clustered at the country;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.

10
**Table 2a.  Sector Value Added in Kenya  (Percentage unless otherwise indicated)**

||<br>La|<br>bor|||<br>G|<br>DP|
|---|---|---|---|---|---|---|
||Skilled<br>labor|Unskilled<br>labor|Capital|Natural<br>Resources|USD in<br>billion|% of total|
|**Business Services**|||||||
|Air Transport|9.6|41.6|48.8|0.0|0.3|1.3|
|Communication|12.0|17.5|70.6|0.0|0.6|2.5|
|Insurance|27.0|39.3|33.8|0.0|0.2|0.6|
|Business Services nec|24.1|35.0|40.9|0.0|0.8|3.3|
|Financial Services nec|24.7|36.0|39.2|0.0|0.5|2.0|
|Transport nec|10.4|45.0|44.6|0.0|0.8|3.4|
|Water Transport|9.1|39.5|51.5|0.0|0.1|0.4|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|6.6|35.2|58.3|0.0|0.7|2.9|
|Energy and Minerals|6.8|47.1|21.9|24.2|0.0|0.1|
|Food Products|4.5|23.5|71.9|0.0|7.1|28.3|
|Petroleum and Coal Products|8.2|37.8|54.0|0.0|0.0|0.0|
|Other Manufacturing|5.8|34.7|59.5|0.0|0.5|2.1|
|Textile and Apparel|9.1|60.0|31.0|0.0|0.3|1.3|
|Wood and Paper Products|7.5|46.0|46.5|0.0|0.5|2.2|
|**CRTS**|||||||
|Agriculture and Forestry|0.6|68.7|28.7|2.1|6.1|24.2|
|Other Services|33.2|34.4|32.4|0.0|4.8|19.2|
|Trade|7.1|30.8|62.1|0.0|1.3|5.2|
|Utilities|26.0|49.2|24.8|0.0|0.3|1.2|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

40
environment, many firms might find it optimal to evade regulations and operate in the informal sector. Firms that choose to stay small and informal might be unable to realize their full growth potential. Our dataset allows us to examine the growth of the formal private sector, relative to the informal sector, and identify factors that encourage firms to begin operations in or transition to the formal sector.

For instance, a large cross-country study finds that increases in product market and labor regulations have been linked to increases in the size of the informal sector (Loayza et al., 2006). Furthermore, a study in the United States finds that increases in the top marginal income tax rate and decreases in tax audit probabilities and penalties might increase the size of the shadow economy (Cebula, 1997). Another cross-country analysis finds that entrepreneurs are most likely to operate informally to avoid the burdens of bureaucracy and corruption and that increases in regulation – and, importantly, the enforcement of regulations – is associated with larger informal sectors (Johnson et al., 1998a, Johnson et al., 1998b, Friedman et al., 1999). This suggests that regulations can affect the creation of new firms, the average size of firms, and the dynamism of incumbent firms.<sup>7</sup> Our empirical analysis examines the relationship between growth in the formal sector and improvements in the tax, regulatory, and governance environment.

Evidence on the relationship between the informal sector and economic growth is mixed. On the one hand, a larger shadow economy is related to less tax revenue, which might lead to less investment in public infrastructure and economic growth, particularly in developing countries (i.e. Loayza, 1996). On the other hand, evidence in OECD countries finds that the informal economy has a strongly positive effect on consumer purchases of both durable and nondurable expenditures, and an indirectly positive effect on tax revenue and economic growth (Schneider, 1998; Bhattacharyya, 1999). In our longitudinal analysis we find no significant relationship between economic growth and new firm creation, but this might be due to our short panel series.

> 7 However, informal microenterprises might not lag behind formal microenterprises in terms of growth or dynamism – for instance, studies of Latin America suggests that in developing counties, with low levels of formal sector labor productivity, entrepreneurs with low levels of human capital might optimally choose to operate in the informal sector (Maloney, 2004).

5
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22