rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3. Growth rate of assets of financial institutions in Poland (%)**

|Institutions|2003|2004|2005|2006|2007|2008|2009|2010|
|---|---|---|---|---|---|---|---|---|
|Commercial<br>and<br>cooperative<br>banks|4.8|10.1|8.9|16.2|16.3|30.6|2.1|9.5|
|Credit<br>unions|32.0|27.3|26.2|13.2|21.7|28.8|23.4|21.8|
|Insurance<br>companies|14.1|18.6|15.0|21.2|16.9|8.7|0.8|4.4|
|Investment<br>funds|47.0|11.2|63.8|61.0|34.9|-44.8|26.4|30.4|
|Open<br>pension<br>funds|41.8|39.7|37.5|35.4|20.1|-1.2|29.1|25.0|
|Brokerage<br>entities|32.1|48.6|25.5|56.5|9.3|-27.1|15.1|-7.1|
|**Total**|**9.6**|**13.4**|**15.1**|**22.4**|**18.5**|**15.7**|**6.2**|**12.2**|

Source: NBP, KNF, KSKOK.

**Table 4. Number of banks and branches of credit institutions carrying out operational**

**activities**

|Institutions|2002|2003|2004|2005|2006|2007|2008|2009|2010|
|---|---|---|---|---|---|---|---|---|---|
|Commercial<br>banks|59|58|54|54|51|50|52|49|49|
|Cooperative<br>banks|605|600|596|588|584|581|579|576|576|
|Branches of<br>credit<br>institutions|x|x|3|7|12|14|18|18|21|
|**Total**|**664**|**658**|**653**|**649**|**647**|**645**|**649**|**643**|**646**|

Source: NBP, KNF.

27
# _MDG and education module_

MAMS includes a set of HD (MDG and education) indicators. A built-in poverty module computes the three standard Foster-Green-Thorbecke poverty indicators (headcount poverty, poverty gap, and severity of poverty) on the basis of the assumption that, for the household types in the database (or for aggregations of these household types, per-capita consumption follows a lognormal distribution parameterized on the basis of an initial headcount poverty rate and an additional parameter (standard deviation, Gini coefficient, or initial poverty elasticity).<sup>8</sup> A different treatment is used for other MDG and education indicators: MDGs 4 (under-five mortality), 7w (improved water access), 7s (improved sanitation access); net intake rate to first grade of primary, promotion rate to next grade (one rate for each of the three education cycles), and continuation into next cycle (the share of those who are promoted from the last grade who continue to next cycle; for primary and secondary). For these indicators, a two-level formulation is used. At the bottom level, an intermediate variable specific to each indicator is expressed as a CE function of a set of determinants, summarized in Table 3.1 and extracted from the literature on determinants of these outcomes.

**Table 3.1. Determinants of selected MDG outcomes**

|**MDG**|**Service**<br>**delivery**|**Household**<br>**consumption**<br>**per capita**|**Wage**<br>**incentives**|**Public**<br>**infra-**<br>**structure**|**Other**<br>**MDGs**|
|---|---|---|---|---|---|
|**2. Primary education**|x|x|x|x|4|
|**4. Under-five mortality**|x|x||x|7w, 7s|
|**7w. Access to safe water**|x|x||x||
|**7s. Access to basic sanitation**|x|x||x||

**Note: Service delivery refers to services in the relevant area (primary education, health, water, or sanitation).**

At the top level, each MDG and education indicator is a logistic function of its intermediate variable. The parameters of these two-level functions are calibrated to replicate data on projected progress for the top-level indicators under the _base_ scenario, with upper or lower limits imposed on the basis of logic or cross-country experience (e.g. the net intake rate has an upper limit of 100 percent; the under-five mortality rates cannot fall below some low positive value). As shown in Table 3.1, the determinants include the supply of services by government or private sector (measured per student for each cycle of education and per-capita for health), the stocks of different types of relevant government infrastructure (energy and other), real household consumption per-capita (an indicator of the ability of households to make purchases in support of stronger MDG and education outcomes), and other MDG indicators (to reflect the fact that progress for one MDG may have a positive impact on other MDGs). A wage incentive

> 8 It is widely accepted that the lognormal provides a good approximation for within-country income and consumption distributions even though it may fail to account for phenomena such as consumption smoothing (Easterly 2007, pp. 5-6; Lopez and Servén 2006, p. 2).

19
# **Appendix B: Estimated impact for floods** **_or_ droughts, and for all shocks, in the LSMS-ISA**

Table B1. Estimated impact of all adverse shocks captured in the LSMS-ISA

|Country|Year|Estimate number of<br>individuals affected<br>by all shocks|Estimate share of<br>total population<br>affected by all<br>shocks|Estimate damages<br>from all shocks|
|---|---|---|---|---|
||2011-2012|14 million<br>[ 12 ; 17 ]|15.00 %<br>[ 12.40 % ; 17.60 % ]||
|Ethiopia|2013-2014|34 million<br>[ 30 ; 39 ]|34.20 %<br>[ 29.80 % ; 38.60 % ]<br>|34 million<br>[ 30 ; 39 ]|
||2015-2016|52 million<br>[ 46 ; 57 ]|49.30 %<br>[ 43.90 % ; 54.60 % ]|52 million<br>[ 46 ; 57 ]|
||2018-2019|31 million<br>[ 27 ; 36 ]|27.60 %<br>[ 23.60 % ; 31.60 % ]|31 million<br>[ 27 ; 36 ]|
||2009-2010|6 million<br>[ 5 ; 6 ]|39.00 %<br>[ 34.20 % ; 43.90 % ]||
|Malawi|2012-2013|6 million<br>[ 5 ; 7 ]<br>|36.40 %<br>[ 32.00 % ; 40.80 % ]<br>||
||2015-2016|9 million<br>[ 7 ; 11 ]|52.30 %<br>[ 41.70 % ; 62.80 % ]||
||2018-2019|9 million<br>[ 7 ; 11 ]|49.30 %<br>[ 39.00 % ; 59.50 % ]|9 million<br>[ 7 ; 11 ]|
|||4 million|21.90 %|4 million|
|Mali|2014|[ 3 ; 4 ]|[ 18.90 % ; 25.00 % ]<br>|[ 3 ; 4 ]|
||2017|5 million<br>[ 4 ; 6 ]|26.20 %<br>[ 23.10 % ; 29.30 % ]|5 million<br>[ 4 ; 6 ]|
|Nier|2011|12 million<br>[ 11 ; 13 ]|70.80 %<br>[ 63.70 % ; 77.90 % ]|12 million<br>[ 11 ; 13 ]|
|g||7 million|36.70 %|7 million|
||2014|[ 6 ; 8 ]|[ 30.00 % ; 43.40 % ]|[ 6 ; 8 ]|
|Nigeria|2019|18 million<br>[ 15 ; 22 ]|9.20 %<br>[ 7.50 % ; 11.00 % ]|18 million<br>[ 15 ; 22 ]|
||2008|11 million<br>[ 10 ; 12 ]|25.20 %<br>[ 22.50 % ; 28.00 % ]||
||2010|14 million<br>[ 13 ; 15 ]|30.80 %<br>[ 27.80 % ; 33.90 % ]||
|Tanzania|2012|11 million<br>[ 10 ; 13 ]|23.90 %<br>[ 21.00 % ; 26.90 % ]||
||2014|9 million<br>[ 5 ; 14 ]|18.30 %<br>[ 9.90 % ; 26.60 % ]||
||2019|14 million<br>[ 10 ; 19 ]|23.70 %<br>[16.20 % ; 31.20 % ]||
|All countries|2008 - 2019|268 million<br>[ 251 ; 285 ]||10,611 million<br>[ 8,892 ; 12,330 ]|

_Note: both point estimates and 95% confidence intervals are reported_ 46
policy in place, insufficient incentives exist for creating and diffusing new technologies. While there is a large literature demonstrating the need for both policies in developed country settings (see Popp, 2010 for a review), the appropriate balance of environmental and technology policies within developing countries has not received similar attention. However, within developing countries, the focus will be on technology transfer and adaptive R&D, rather than on the creation of new green technologies. Thus, the relative importance of environmental and technology policies may differ in a developing country setting. Moreover, even within developed countries, there is still debate whether it is sufficient to use broad technology policies that correct market failures for all innovations, or whether targeted policies, such as product-specific R&D subsidies or tax credits, are needed.

The appropriate role for intellectual property rights for green innovations provides one example where additional research is needed. While IPR will encourage innovation, it also slows diffusion once innovations exist. For developing countries whose interests are diffusion, rather than the creation, of technology, weaker patent rights may appear to be a panacea. However, empirical evidence to support recent calls for weaker IPR for green technologies is lacking. To date, there has been little work directly studying the effect of intellectual property rights on technology transfer of eco-innovations. A Copenhagen Economics (2009) study on climate change concludes that IPR are not a barrier to the transfer of carbon emission-reducing technologies, and that the high costs of these technologies are due more to the immaturity of the technologies, rather than IPR. Hall and Helmers (2010) provide an extensive review of the literature on patent protection. While they find evidence that stronger IPR encourages innovation in general, this effect is strongest in chemical-related sectors such as pharmaceuticals. Regarding technology transfer, they cite the work by Copenhagen Economics (2009), as well as 42
|||_LATINAMERIC_<br>|_A_<br>||
|---|---|---|---|---|
|**SAT**|•Fiscal Career<br>Service<br>Commission|•Fiscal Career<br>Service<br>(regulations<br>issued by board<br>of directors)|•Yes (percentage<br>undetermined)|•Board must have<br>decisions<br>approved by<br>MOF|
|**SENIAT**|•Human Resource<br>Department|•Professional<br>Human Resource<br>System<br>(presidential<br>decree)|•Yes (only<br>superintendent<br>and adjunct<br>superintendent)|•Completely<br>autonomous<br>(initially, though<br>eroded over<br>time)|
|**SUNAT**|•Human Resource<br>Department and<br>Institute for Tax<br>Administration|•Internal<br>regulations<br>(issued by<br>presidential<br>decree)|•Yes (less than<br>3% of total staff)|•Completely<br>autonomous|

_Source: ARA laws, acts, and decrees._ In the case of the KRA, the CG is authorized to appoint all staff members, except the commissioners of customs and excise, income, and value added taxes, who are appointed by the board. The board has established a Staff Committee, composed of the chairperson, three other board members, the CG, and the chief human resources and legal officers. It is the role of the committee to approve all hiring decisions for senior management positions. The KRA’s Code of Conduct is the principal instrument guiding staff behavior and spelling out the procedures for handling disciplinary matters.

The terms and conditions of employment are determined by the board through its Staff Committee, which, overall, has played a major role in the development of the KRA personnel system. The KRA board is empowered to determine the salary structure and benefits package for agency personnel (the foundation for this aspect of autonomy is the KRA’s exemption from the State Corporations Act).

The nature of the KRA’s personnel autonomy, in spite of the apparent clarity of the KRA act, has not been crystal clear in practice: there have been cases in which the Public Service Commission has infringed on the KRA’s autonomy. During the retrenchment exercise, for example, the commission took legal action against the KRA over the program (though the matter was subsequently resolved). The commission also issued a circular in 2001 on harmonizing housing allowances in the public sector, which seemed to apply to the KRA, in spite of the KRA’s exemption from the State Corporations Act. Whether the commission will insist on the applicability of the policy to 68
in T2 and T3 to assess whether risk mitigation (T1) or targeted savings (T2 and T3) is more effective at boosting investment in productive assets, and encouraging welfare gains in the long-run. Karlan et al. (2014) suggest that investments in managing risk may be more effective at encouraging productive investment.

- **_The difference between saving for emergencies and saving for investment in affecting ability to manage risk and investment outcomes:_** We will compare participants in T3 and T4 to assess what is the impact of savings for emergencies (T4) rather than investments (T3) on investment in productive activities and ability to manage risk.

- **_The role of commitment in savings products in ensuring outcomes:_** By comparing outcomes between T2 and T3 we will look at the impact of high commitment (T3) over low commitment (T2) on investment in productive activities and ability to manage risk. By undertaking this comparison we will explore the question of what level of commitment is beneficial. As Dupas and Robinson (2013) note: “Since much of the value of a savings product appears to be in the mental labeling it facilitates, a product which does not severely limit liquidity is preferred to one that does, especially for people living in an environment in which income shocks are common, such as rural Kenya.” We therefore explore whether the earmarking product (T2) did raise more demand than the higher-commitment savings product (T3) and which of both had a higher impact on investment.

# **4.** **<u>Selection of participants and data</u>**

The experiment was conducted with 806 individuals in rural areas in the Departement de Kaffrine in Senegal and around Bobo-Dioulasso in Burkina Faso. Farmers groups were chosen where a vast majority of members, if not all, cultivated less than 6 hectares of land. ROSCAS had to hold regular meetings in order to be included in the sample. As shown in Table 2, 14 ROSCAS and 17 farmers’ groups participated in the study. The membership of ROSCAS in both countries was entirely female, whilst farmers groups were entirely male in Senegal and mixed in Burkina Faso.

Individuals participating in the experiment were members of the selected farmers’ groups and ROSCAs. Group leaders were systematically included in the study, the rest of the participants were selected randomly out of a list of other group members. We conducted 20 sessions with 20 individuals each in both countries. Not more than 40 individuals (two sessions) per group were included in the study in order to limit learning and spillovers. For that same reason, when one group was split into two sessions, the sessions were conducted on the same day.

**Table 2 - Sample Description**

||Senegal|Burkina<br>Faso|
|---|---|---|
|Panel A: Baseline Sample|||
|Total number of individuals surveyed at baseline|403|403|

9
investments. This was followed by the Presidential Decree 77/2007, which provided the first official NIL in Indonesia.<sup>12</sup> In addition to having a more coherent legal basis for enforcing the NIL, this Decree was the first to explicitly list the conditions on investments across product markets.<sup>13</sup> In addition, for the first time the Decree provided a consistent definition of product market based on the 5-digit KBLI classification.<sup>14</sup> To ensure the consistency of these regulatory changes, we center our analysis around the NIL revision in 2011.<sup>15</sup> There are four main types of restriction coded in the NIL. The first concerns markets that are fully closed to private investment, both domestic and foreign. These included products and services that are considered sensitive from political, security or moral point of views, such as chemical weapons, alcoholic beverages, radio and television public broadcasting services, and casinos. The second includes limitations to foreign equity ownership, which vary between 0 percent (i.e. open only to domestic investors) and 95 percent. In 2008, this list included hundreds of business fields and 13.3 percent of the universe of KBLI 5-digit product markets. The third type of restrictions consists of reserving certain business activities to Micro-Small and Medium Enterprises (MSMEs) in full, or of requiring investors to partner with MSMEs. The reservation to MSMEs _de jure_ excludes foreign firms from the activity, as foreign investors cannot operate an MSME in Indonesia. Finally, special licenses, usually from a Ministry, are required to produce certain products or services. For example, in 2008 investors in plywood industries needed a permit that ensured the country had a sufficient supply of raw materials, and producers of narcotics needed a special license/permit from the Minister of Health.

We obtain a combined indicator of investment restrictiveness, our measure of product

> 12 All Presidential Decrees are announced at the end of the calendar year, which implies that they become effective at the beginning of the next year. We follow this convention in constructing our dataset.

> 13 Only a few sectoral laws included investment restrictions which were not included in the Presidential Decree. These restrictions were still legally valid as laws have higher legal standings than decrees in Indonesia. As discussed below, we complement the investment restrictions data in the Presidential Decrees with these sectoral restrictions.

> 14 This differs from the definition in the previous Presidential Decision, which used only general industry descriptions, thus making it difficult to identify variation across product markets.

> 15 Further revisions were enacted since 2015, but these extend beyond the coverage of our manufacturing plants’ survey. It is also worth noting that the majority of the restrictions in the NIL was eventually eliminated through the so-called Omnibus Law for Job Creation in 2021.

11
Section 5 provides the results of the interventions on the rate of formalization among informal firms, and Section 6 concludes with discussion of implications for both policy and further studies in this area.

# **2. Context and the Process of Formalizing**

Belo Horizonte is the capital city of the state of Minas Gerais in Brazil, and has a city population of almost 2.5 million, with 5.5 million in the official metropolitan area (the third largest in Brazil after São Paulo and Rio de Janeiro). A 2009 survey by the Brazilian statistical agency IBGE along with government records was used by SEBRAE, the government agency for supporting micro and small businesses, to estimate that Belo Horizonte has a total of 561,310 businesses, of which 402,744 were informal (72 percent).

# **2.1. Registering a Microenterprise in Belo Horizonte**

The Complementary Federal Law 123 defines micro-enterprises as firms with annual revenues up to R$ 360,000 (US$177,000)<sup>3</sup> , provided they are not the subsidiary of another firm. Microenterprises which meet several other conditions – the key ones being they do not have a foreign owner or partner, they are not in certain sectors like financial services, consulting, alcohol or tobacco, or transportation – are eligible to register their businesses formally under a national simplified taxation system called SIMPLES. The SIMPLES regime combines several ongoing tax and contribution payments into a single payment (including employee taxes and ICMS, the State sales tax), but does not simplify the registration process itself. In addition, at the time of beginning our study, enterprises with one or fewer employees and which had R$36,000 or under in annual revenues could instead register as individual microentrepreneurs (MEIs). This was changed after our intervention had begun, with the eligibility threshold being raised to R$60,000 after a law change in September 2011.

The State government created a unit called Minas Fácil in June 2008, in order to simplify the registration process. Registration under this new system involves registering at the federal, state and municipal levels all through a single process (set out in Appendix 1). Many steps in the process are online, and the entire process is estimated to take 7 days for an average firm. The key documents obtained are federal tax registration, evidenced by obtaining a CNPJ (Cadastro Nacional de Pessoas Jurídicas) number; state level registration with the Chamber of Commerce (JUCEMG); and a municipal license (Alvará de Localização e Funcionamento, or ALF). The initial cost of registration is R$236 for a sole proprietorship and R$320 for a limited liability company.

> 3 1 US dollar is approximately 2 Reais during the period of our intervention.

5
_5_

# I. OVERALL IMPORTS, PRIMARY COMMODITIES AND ENERGY.

1. A funeral choir of economists has bemoaned the demise of free trade and the rise of protectionism in industrial countries in the 1980s. Yet meanwhile, the growth of merchandise imports into industrial countries accelerated sharply, during the very decade when the growth rate of GDP fell to its lowest decennial level since the Great Depression. From 4.4 percent during the period 1965-1980, the annual average growth rate of imports into OECD countries rose to 5.2 percent during the 1980s, while the growth rate of GDP fell from 3.7 percent to 3.1 percent<sup>4</sup> .

2. Table 1 shows the implied apparent increase in import elasticity from 1.11 during the 1965-80 period to 1.67 during the 198(;s - hardly a sign of effective import restraints or of surging protectionism. Import elasticity rose in every OECD country except Switzerland. In the US, it rose from 2.03 to 2.23. The palm went to the EC's two newest members, Spain and Portugals. Both had below unit import elasticities before 1980, and 2.9 and 3.0 respectively in the last decade when they moved to the EC's liberal import policies. Even Australia and New Zealand, though they still have abnormally low import ratios, liberalized their trade policies and raised their import elasticities, Australia from .25 to 1.38 and New Zealand from .46 to 1.9.

3. The elasticities in Table 1 are derived from of import and GDP growth in constant prices. While often useful, such estimates are necessarily ambiguous; they depend, notably, on the chosen base year. Also, by abstracting from price fluctuations, they neither describe the impact of import growth on exporters (whose earnings may precede or lag behind volume changes), nor the relationship of import expenditure to income in the importing coantries. Growth rates in current prices are influenced by general inflation; but ratios (notably, those of imports to GDP and to various components of it) do not have that defect and have the additional merit of being urmnbiguous and precise. Of course, they too must be treated with caution. In particular, a given import-dependency, relative to GDP, signifies more open trade policies for a larger than for a smaller economy: it stands to reason that, with a given tightness of trade restraints, the ratio of imports to GDP would be lower for the US than for Monaco! This is well illustrated in Chart 1, which shows the logarithms of GDPs on the X-axis, and the ratio of GDP to all imports and to manufactures imports on the Y-axis. With a few outliers (Belgium an the Netherlands for high ratios, New Zealand, Australia and Japan for low ones) the inverse relationship is striking.

> _4 WorldeDvelopmenRepon 1992,_ **_The_** World **Bank,** Washington DC, WDI Tables 2 **and** 14. Growth rates are in constant 1980 **prices.**

I Portugal and Greece are EC members; they and Turkey **are** OECD members but the **World Bank** classifies them with _Developing Cowwies;_ they are excluded from the averages for OECD members given **earliet** in this paragraph.