rafmacalaba/datause-encoder-data huggingface dataset:
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# **A. Cross- Country Studies Using Aggregate Indices**

The first category of studies uses different aggregate indices of institutional quality and economic performance to econometrically estimate the importance of institutions. Many of these studies have been concerned with trying to determine the relative importance of institutions as compared to economic policies or geographic endowments [Rodrik et al 2002]. Before the different influences could be sorted out, reasonable indices or proxies for institutional quality, good policy, and geographic endowment had to be obtained.<sup>54</sup> These proxies by necessity are always somewhat arbitrary, perhaps based on surveys and perceptions, making it difficult to identify all of the complex causal relations among different variables. Despite this Rodrik et al [2002] have effectively demonstrated that institutional quality (measured in different ways) is highly correlated to different measures of economic performance including GDP per capita, growth and growth volatility. They also find that there is an important correlation between good policies (such as greater openness to international trade, stronger domestic competition and stable macroeconomic policies) and good institutions. The sustainability and economic effects of good policies seems to depend on good institutions. Institutions are thus more important than policies or geographical endowment in determining growth. In fact, policies did not seem to have any independent influence on growth, and “geography” seemed to have weak effects. The effectiveness of good policies seemed to depend on the existence of good institutions. Institutions, on the other hand, were an independent determinant of performance and not simply the consequence of higher incomes or better policies.<sup>55</sup> Easterly and Levine [2002] obtain similar results from their econometric analyses. Geographic endowments seem to affect performance only through their effects on institutions while policies do not have any independent effect on income or income growth [IMF 2003]. These studies provide some general confirmation for the institutionalist theory. Nevertheless, the results should be interpreted with caution due to aforementioned measurement problems associated with finding adequate proxies for the various theoretical variables and the econometric problems associated with disentangling the complex causal relations among the variables.

> 54 See Rodrik et al, 2002; IMF World Outlook 2003; and World Development Report 2002. The IMF 2003 chapter 7 has a compact summary of many of these studies. 55 Rodrik et al, p. 4.
He uses 4 repeated cross-sections of the national income-expenditure survey ENIGH (2000, 2004, 2005, 2006) which includes labor outcomes and categories of expenditures. ENIGH data has a total of 76,000 household-heads and 156,247 other members. He does not find a significant result on the probability of being in the formal sector, nor does he find an effect on wages. However, Barros does not control for unobserved characteristics as he uses cross section data. The target intensity may also be a poor measure of the actual intention to enroll households in _SP_ ; the targets are likely to reflect political stances rather than an accurate measure of the availability of _SP_ services in an area.

Bosch et al. (2010) look at the effect of _SP_ over a longer time period and do find a disincentive effect on formal employment. They use aggregate data at the municipality level from the Instituto Mexicano de Seguro Social (IMSS) from 2001 to 2008. They use a difference in difference approach, comparing the differences in formal jobs outcomes in the municipalities with the program and compare to those without the program. They find evidence of a negative impact of the program on formality at late stages of the program (i.e. after 2007) and found that it is larger in small firms compared to micro firms (with already a high level of informality). However, there are limitations to this work. Their data are aggregated at the municipality level, so they cannot control for individual heterogeneity or aggregation biases.

New working papers do use individual level data to look at the impact of _SP_ (Aguilera, 2011; Azuara and Marinescu, 2011; Duval and Smith 2011). However, they only use individual crosssectional data; they do not look at how individuals‘ change their employment status over time nor do they control for unobserved heterogeneity. They also do not take into account that individuals‘ decisions can be affected by the SS status of their spouse, nor do they consider both flows into and out of SS.<sup>5</sup>

> 5 Duval and Smith (2011) look only at the likelihood of whether informal workers shift from being involuntarily informal to voluntarily informal, but having to use observed household characteristics in making this estimation. Azuara and Marinescu (2011) do look at transitions in employment categories, but only over a single quarter so their results would only capture changes that occurred at the precise moment of the rollout.  Otherwise, their results are based on differences between individuals in the cross-section.  They also measure SP on an annual basis rather than quarterly basis.

8
**Table 4: Characteristics of the medium-term cycles**

|||**_------ Duratio_**<br>|**_n(years) ------_**|**_------ Amplit_**<br>|**_ude(std) ------_**|
|---|---|---|---|---|---|
||_Peakyear_|_Expansion_|_Contraction_|_Expansion_|_Contraction_|
|**First**||||||
|Energy|1980|10.7|11.4|3.20|2.29|
|Base metals|1978|7.0|6.6|2.24|2.36|
|Precious metals|1980|11.2|7.5|2.80|1.40|
|Fertilizers|1978|8.0|9.8|3.30|1.66|
|Annual agriculture|1977|7.1|10.1|3.04|1.85|
|Perennial agriculture|1977|7.5|12.9|3.16|2.71|
|**Average**|**1978**|**8.6**|**9.7**|**2.96**|**2.04**|
|**Second**||||||
|Energy|—|—|—|—|—|
|Base metals|1991|6.7|8.5|2.22|2.86|
|Precious metals|1993|4.3|7.7|0.35|1.25|
|Fertilizers|1992|5.2|6.7|0.71|1.36|
|Annual agriculture|1995|7.5|8.3|1.64|2.38|
|Perennial agriculture|1997|6.0|5.9|2.05|2.32|
|**Average**|**1994**|**5.0**|**6.2**|**1.16**|**1.71**|
|**Third**||||||
|Energy|2008|11.7|10.9|2.38|3.34|
|Base metals|2009|7.1|5.6|3.58|3.02|
|Precious metals|2010|10.2|8.1|3.14|3.88|
|Fertilizers|2009|7.8|6.4|3.13|3.99|
|Annual agriculture|2011|8.5|8.6|3.33|3.70|
|Perennial agriculture|2009|6.5|10.5|2.94|3.12|
|**Average**|**2010**|**8.6**|**8.4**|**3.08**|**3.51**|

**Notes** : Commodity-specific statistics are reported in Appendix A (Tables B1-B3). ‘—’ implies that a cycle was not detected. ‘std’ means standard deviation.

_— 21 —_
# **Table A10:  Global E-Governance Index (EGV)**

**Data Provider** Brown University Center for Public Policy **Description** University located in Providence, United States **Website** www.insidepolitics.org **Data Source** Global E-Governance Index **Type** Expert assessment **Respondents** Research team led by Professor Darrell M. West **Frequency** Annual since 2002 **Coverage** Global sample of countries **Public Access** Yes **Description** This source reports an assessment of the quality of e-government based on reviews of official government websites.  Features assessed include online publications, online database, audio clips, video clips, non-native languages or foreign language translation, commercial advertising, premium fees, user payments, disability access, privacy policy, security features, presence of online services, number of different services, digital signatures, credit card payments, email address, comment form, automatic email updates, website personalization, personal digital assistant (PDA) access, and an English version of the website.  Assessments are scored on a 100-point scale with 72 points for availability of publications and databases and 28 points for the number of online services available.

||2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|
|**Voice and Accountability**<br>NA|..|..|..|..|..|..|..|..|
|**Political Stability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||
|Global E-governance Index|X|X|X|X|X|..|..|..|
|**Regulatory Quality**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Control of Corruption**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Country Coverage**|**196**|**195**|**192**|**195**|**194**|**..**|**..**|**..**|
|**Year of Publication**|**2006**|**2005**|**2004**|**2003**|**2002**|**..**|**..**|**..**|

48
government fiscal balance, _GBAL,_ is thus defined as _GBAL = TAX - TR- WSGSGE - ESUU3 - PC3 (Gc + ZG)_<sup>_-_</sup> _icER_<sup>_*_</sup> _FLG, 1_<sup>_._</sup> (84) where TAX denotes total tax revenues, _TR_ government transfers<sup>tohouse-</sup> holds, _WSGSGE_ the wage bill on school teachers, _ESuU3_<sup>totalemployment</sup> subsidies to firms in the private formal sector, Gc other<sup>realcurrentexpendi-</sup> tures on goods and services, _ZG_ real investment<sup>spending,and</sup><sup>_iaER- FLG,__</sup> is interest payments on foreign borrowing.

Total tax revenues consist of revenue generated<sup>byimporttariffs, sales</sup> taxes, income taxes, and payroll taxes on unskilled labor: with atxrr = 0.

Public investment in infrastructure, health, and education,<sup>determinesthe</sup> rate at which the stock of each type of public capital,<sup>_K_</sup> _3_<sup>_,_</sup> with<sup>j</sup><sup>_= E, H, R,_</sup> grows over time. Accumulation of each type of capital<sup>is thusdefinedas:</sup> where 0 < _63_ **_<_** 1 is a depreciation rate.

Infrastructure and health capital are combined through<sup>aCESfunction</sup> to produce the stock of public capital, _KG:_

# **2.10 Balance of Payments**

The external constraint implies that any current account<sup>surplus(or deficit)</sup> must be compensated by a net outflow (or inflow)<sup>offoreigncapital,given</sup> by the sum of changes in net foreign borrowing by the government,<sup>_AFLG,_</sup> and private firms, _A\FL 3 :_ _, (wpe,E,_ - _wpmzM1) + i*(FLG,_l + FL 3 ,_1 ) + L\FLG + AFL3 = 0._ (88) **_i=1,3_** _31_
fixed effects (Table 13, Model 3); unsurprisingly, new topics are 20 percentage points less likely to elicit a response from politicians; however, even when we control for this, women are 18 percentage points less likely than men to receive a response from their elected official.

Table 13: Official Responsiveness, by Gender

||||_Depend_|_ent variable:_|||
|---|---|---|---|---|---|---|
||On Top|ic Politician Re|sponse|O|n Topic Admin.|Response|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Female|_−_0.18<sup>_∗∗∗_</sup><br>(0.05)|_−_0.27<sup>_∗∗∗_</sup><br>(0.03)|_−_0.28<sup>_∗∗∗_</sup><br>(0.04)|_−_0.003<br>(0.06)|_−_0.17<sup>_∗∗∗_</sup><br>(0.05)|_−_0.20<sup>_∗∗∗_</sup><br>(0.04)|
|Female President||_−_0.10|_−_0.12||_−_0.11|_−_0.08|
|||(0.07)|(0.08)||(0.10)|(0.10)|
|New Topic|_−_0.20<sup>_∗∗∗_</sup>|_−_0.20<sup>_∗∗∗_</sup>|_−_0.20<sup>_∗∗∗_</sup>|_−_0.12|_−_0.12|_−_0.09|
||(0.04)|(0.04)|(0.04)|(0.09)|(0.10)|(0.08)|
|Female x Female President||0.18<sup>_∗∗∗_</sup>|0.20<sup>_∗∗∗_</sup>||0.43<sup>_∗∗∗_</sup>|0.42<sup>_∗∗∗_</sup>|
|||(0.08)|(0.09)||(0.07)|(0.08)|
|District FE|✓|✓|✓|✓|✓|✓|
|Backwardness Score Control|✓|✓|✓|✓|✓|✓|
|Topic FE|||✓|||✓|
|Observations|251|251|251|259|259|259|

_Note:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. Robust Standard Errors, clustered at the district, in parenthesis. Te Backwarndess Score is an measure of village level development, calculating using demographic and infrastructal variables, including the share of population belonging to the Scheduled Castes and Tribes, as well as indicators for the presence of a primary or secondary school, hospital or medical clinic, and bank.

# **5.4 Effect of Mandated Representation on Deliberative Equality**

Tese paterns suggest that women, and women citizens in particular, are at a considerable disadvantage in the _gram sabha_ . Tey speak less, are less likely to drive conversation, and are less likely to get a response from government officials. And these disadvantages hold even in _sabhas_ when we control for the issues that are raised. Indeed, it was in recognition of these deeply gendered inequalities that the Government of India pro-actively designed the _gram sabha_ with quotas for women to serve on the village council and as village president. Here, we examine whether the presence of a female incumbent — to lead the discussion, to respond to citizens, and to act as a role model for female villagers — has an impact on the measures of deliberative equality explored above. Importantly, the assignment process for women’s reservations, established by the _Tamil Nadu Panchayats Act_ (1994), is as-if-random — allowing us to interpret these effects in a causal 33
<!-- Start of picture text --> Figure 11: Variation in Company Numbers compared to Insurance Penetration<br>Life Non Life<br>14 8<br>12 7<br>10 6<br>5<br>8<br>4<br>6<br>3<br>4<br>2<br>2 Data Set Average<br>Data Set Average 1<br>0<br>0<br>0 20 40 60 80 100<br>0 20 40 60 80 100<br>Life Company Numbers<br>Non Life Company Numbers<br>Life Penetration (% of GDP)<br>Non Life Penetration (% of GDP)<br><!-- End of picture text --> Source: Staff Analysis Market size overall, in absolute terms, does paint a different picture. The expectation that larger markets do also see more participants is visible in the data set (see Figure 12). This adds further to the proposition that consideration of consolidation has to be measured against expectations of market growth. In effect, larger markets may be able to accommodate more participants.

**<u>Figure 12: Variation in Company Numbers compared to Market Size</u>**

<!-- Start of picture text --> Life Non Life<br>1000000 1000000<br>100000<br>100000<br>10000 Data Set Average<br>10000 Data Set Average<br>1000<br>100 1000<br>10<br>100<br>1<br>10<br>0.1<br>0.01 1<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Life Company Numbers Non Life Company Numbers<br>Life Premium ($US Millions)<br>Non Life Premium ($US Millions)<br><!-- End of picture text --> Source: Staff Analysis It is proposed to conduct further analysis in the area of numbers of market participants. Initial regression analysis was promising but is not ready for presentation at this stage until further work is completed.

# **Summary, Concluding Remarks and Next Steps**

The presentation of the data has been prepared at this stage so as to assist in cross country benchmarking and interpretation of concentration measures in the insurance sector. This is motivated, in particular, because there is not a suitable reference available elsewhere of a similar nature.

20
15

# **Table 4:  Elasticity estimates of the economic and institutional indicators using the [(K, HE, L)/E] production function**

|Variable|Elasticity|Standard error|t-statistic|
|---|---|---|---|
|TOPEN|0.47|0.10|4.53|
|PCREDIT|0.51|0.12|4.25|
|VA|0.01|0.04|0.28|
|PIV|-0.01|0.02|-0.28|
|GE|0.04|0.10|0.40|
|RB|0.03|0.07|0.39|
|RL|-0.07|0.10|-0.73|
|CC|0.01|0.09|0.17|

Legends: TOPEN-trade openness; PCREDIT- variable for private sector investment; VA - voice and accountability; PIV- political instability and violence; GE - government effectiveness; RB - regulatory burden; RL - rule of law; and CC - control of corruption.

**Table 5:  Elasticity estimates of the economic and institutional indicators using the [(K, E, HE)/L] production function**

|Variable|Elasticity|Standard error|t-statistic|
|---|---|---|---|
|TOPEN|0.50|0.09|5.27|
|PCREDIT|0.51|0.11|4.83|
|VA|0.02|0.03|0.45|
|PIV|-0.01|0.02|-0.44|
|GE|0.06|0.09|0.62|
|RB|0.03|0.07|0.37|
|RL|-0.08|0.09|-0.86|
|CC|-0.02|0.08|-0.24|

Legends: TOPEN-trade openness; PCREDIT- variable for private sector investment; VA - voice and accountability; PIV- political instability and violence; GE - government effectiveness; RB - regulatory burden; RL - rule of law; and CC - control of corruption.

**Table 6: Level of Gross National Income per capita given a reduction in the amount of land**

|**Prod. function**|**Baseline***|**R**|**eduction inthe **|**amount of land b**|**y**|
|---|---|---|---|---|---|
|||**20%**|**50%**|**75%**|**92%**|
|**(K,HE,L)/E**|$8,638.10|$8,068.84|$7,019.27|$5,774.25|$4,297.16|
|_Difference_|_from baseline**_|_(-7%)_|_(-19%)_|_(-33%)_|_(-50%)_|
|**(K,E,HE)/L**|$9,096.20|$8,540.27|$7,477.97|$6,147.62|$4,455.06|
|_Difference _|_from baseline**_|_(-6%)_|_(-18%)_|_(-32%)_|_(-51%)_|

*Predicted per capita GNI at the mean values of the explanatory variables. **Rounded off to the nearest whole number Sample size of each production function = 67
more signals are brought to bear on choices than in an autocratic government (Coates et al., 2008).

# **4. Empirical Analysis**

The empirical analysis tests the following set of hypotheses:

- (i) Public investment levels are higher in countries with low institutional quality. (ii) Public investment volatility is higher in countries with low institutional quality. (iii) Infrastructure quality is higher in countries with high institutional quality. (iv) High aid flows, revenues, and natural resources positively affect both the levels and volatility of public investment.

- (v) Public investment levels are higher in resource rich countries with low institutional quality.

- (vi) Corruption is an important channel through which quality of governance affects public investment levels (following Tanzi and Davoodi, 1997, and Mauro, 1998).

# **4.1. Data**

We construct an annual panel dataset for 144 countries over the period 1984-2008 to exploit both cross-sectional and time series variation. The dataset encompasses country-level public investment and quality of infrastructure data, several measures of institutional quality, and a set of control variables (see Table 4). A brief description of the variables used in the analysis is provided below, while a more comprehensive list with sources and descriptive statistics is found in Table A of the Appendix.

# [Table 4 about here]

Deviating from Keefer and Knack (2007), we use gross public fixed capital formation from the IMF’s World Economic Outlook (WEO) database as a measure of public investment. Public gross fixed capital formation is referred to the general government sector, excluding public corporations.<sup>10</sup> We normalize public investment by GDP and by total investment.<sup>11</sup> Although some countries have missing observations, the majority of countries have the full panel set and this ensures that all regions of the world are well represented.

In order to understand how the variance of public investment is affected by institutional quality, we define volatility of public investment as the absolute value of the percentage change in the deviation of the ratio of public investment to GDP and total investment, , from the trend component extrapolated using the Hodrick-Prescott (HP) filter<sup>12</sup> , , minus the same deviation at time , normalized by the trend at time :

> 10 Keefer and Knack (2007) use the IMF’s Government Finance Statistics (GFS) central government capital expenditure data (not including investments by state-owned enterprises). The limited temporal coverage of GFS does not permit the authors to take into account the longitudinal dimension, thus they perform an average crosssectional analysis over the period 1974-1998. Other data sources include the IMF Article IV country reports, which unfortunately are not available on a regular basis.

> 11 As a robustness check public investment has also been normalized by general government total expenditure.

> 12 For the HP filter the smoothing parameter, λ, has been set to 6.25.

7
# **3. Data**

The data for our analysis come from two primary data sets. The first is a database compiled by the team that includes two of this paper’s authors (Demirgüç-Kunt, Lokshin, and Torre 2022). It provides estimates of the social protection measures the government implemented in response to the COVID-19 pandemic in 2020 and 2021 in 154 countries. The data come from the Global Database on Social Protection Responses to COVID-19 (Gentilini and others 2021), budget data from official documents (including IMF Article IV revisions and other international organizations’ related documents), government websites, and news sources. For inclusion in this data set, the key criterion is that social protection spending must have been part of a pandemic response package. In some cases, this spending was part of a new program, in others, it represented the expansion of an existing program. To the extent feasible, we estimated the budget corresponding to expanding existing programs (for example, the cost of extending the eligibility period of unemployment benefits).<sup>1</sup> Social spending programs can be broadly divided into income protection and job protection - policies <u>(Demirgüç Kunt, Lokshin, and Torre 2022). Income protection policies include social</u> assistance measures such as cash transfers, food, and other in-kind transfers; waivers and reductions of utility and financial obligations; some labor market policies, such as public works programs and activation measures; and social insurance policies, such as pensions, unemployment benefits, and out-of-work support. These policies seek to directly increase incomes of beneficiaries. Job protection policies include policies aimed at job retention, such as wage subsidies and other indirect subsidies, including waivers of or reductions in social insurance contributions. These policies represent a more indirect way of supporting individuals’ welfare by preserving their employment.

The two main dependent variables in our analysis are the _size_ of spending on social assistance and income protection measures and the _share_ of this spending in the total social protection response

> 1 To validate our results, we compared our estimates against estimates by the International Monetary Fund (IMF) on total additional public spending (on social protection or other programs) implemented during the pandemic (IMF <u>2021). In no case did our estimates exceed the corresponding IMF figures. Our estimates are also consistent with those</u> of Gentilini and others (2022).

7
US dollars, which we de fl ate by the U.S. CPI to generate a constant dollar series. Summary statistics for China’s exports are presented in Table 1, showing that China’s real exports to the world increased by 500 percent between 1992 and 2005, from US$84.94 billion to US$525.48 billion. Its share of exports to the US increased from 10 percent to 21 percent over the sample period. To check for the accuracy of the China export data, we also use data on US imports from China, from the US Bureau of the Census, Foreign Trade Division. This data also has the advantage of being available at an even higher level of aggregation, at the HS 10-digit, which includes 18,600 product categories.

As there were major reclassi fi cations in the international HS 6-digit classi fi cations in 1996 and 2002, in some cases we aggregate the data up to HS 6-digit codes and convert them to the same HS 6-digit classi fi cations used in 1992 to avoid problems related to reclassi fi cation of codes. This reduces the number of product codes for China’s world exports to 5,000 products. To examine broader export patterns we divide the data into SITC 1-digit codes, which include agriculture (SITC 1 to 4), chemicals (SITC 4), manufactured materials (SITC 5), manufactured materials (SITC 6), machinery (SITC 7) and miscellaneous manufactures (SITC8).

# 3. Reallocation across Industries

China has experienced big changes in its export composition. It has moved from the fi rst stage of agriculture and apparel to more sophisticated manufactured goods. Figure 1 shows this by plotting the export share of each one-digit SITC sector in 1992 and 2005. Rapid export growth has been associated with a move out of agriculture and apparel into the machinery and transport sectors. In Figure 2, we focus on changes within the manufacturing sector. In particular, we look at how trade shares have adjusted in all major 2-digit SITC sectors, where major is de fi ned as accounting for at least 3 percent of exports in 1992 and/or 2005. There is a notable move out of apparel, textiles, footwear, and toys and into electrical machinery, telecom, o ffi ce machines, and to a lesser extent metals.

4