rafmacalaba/datause-encoder-data huggingface dataset:
**Table 13. Guatemala: Decomposition of GDP Growth with Education Level Disaggregation, 1951-2002** (in percent)<sup>a/</sup>

|Time|GDP<br>Growth|||<br>|Contribution of<br>Education|||
|---|---|---|---|---|---|---|---|
|<br>Period|<br>Rates|Capital|Labor||||TFP|
|||||_Primary_|_Secondary_|_Tertiary_||
|1951-55|2.3|0.6|-1.1|_0.0_|_0.7_|_-1.1_|3.2|
|1956-60|5.4|1.7|2.0|_1.3_|_1.1_|_1.1_|-1.7|
|1961-65|5.3|1.1|1.9|_1.2_|_1.2_|_0.7_|-0.8|
|1966-70|5.8|1.7|1.8|_1.4_|_0.8_|_0.7_|-0.5|
|1971-75|5.6|1.6|1.1|_0.9_|_0.9_|_0.7_|0.5|
|1976-80|5.7|2.3|2.7|_1.5_|_1.7_|_0.8_|-3.4|
|1981-85|-1.1|0.6|-1.0|_-0.3_|_0.1_|_0.0_|-0.6|
|1986-90|2.9|0.4|1.5|_0.8_|_0.8_|_0.6_|-1.3|
|1991-95|4.3|0.9|0.6|_0.1_|_0.2_|_0.5_|2.0|
|1996-00|4.0|1.5|0.4|_0.7_|_0.5_|_0.2_|0.6|
|2001-02|2.3|1.3|0.8|_0.6_|_1.3_|_0.3_|-2.1|
|Averae|39|1.2|1.0|_0.8_|_0.8_|_0.4_|-0.3|
|g|.|32%|25%|_19%_|_21%_|_10%_|-7%|

_Source:_ Author’s calculations. a/ Discrepancies are due to rounding.

**Table 14. Guatemala: Decomposition of GDP Growth with Education Level Disaggregation and Adjusted for Quality of Capital, 1971-2002** (in percent)<sup>a/</sup>

|Ti|GDP<br>Growth|||C<br>|ontribution of<br>Edti|||
|---|---|---|---|---|---|---|---|
|me<br>Period|<br>Rates|Capital|Labor||ucaon||TFP|
|||||_Primary_|_Secondary_|_Tertiary_||
|1971-75|5.6|1.5|1.1|_0.9_|_0.9_|_0.7_|0.6|
|1976-80|5.7|1.5|2.7|_1.5_|_1.7_|_0.8_|-2.5|
|1981-85|-1.1|0.2|-1.0|_-0.3_|_0.1_|_0.0_|-0.2|
|1986-90|2.9|-0.1|1.5|_0.8_|_0.8_|_0.6_|-0.7|
|1991-95|4.3|1.1|0.6|_0.1_|_0.2_|_0.5_|1.8|
|1996-00|4.0|1.8|0.4|_0.7_|_0.5_|_0.2_|0.4|
|2001-02|2.3|1.1|0.8|_0.6_|_1.3_|_0.3_|-1.8|
|||1.0|0.9|_0.6_|_0.7_|_0.5_|-0.2|
|A|35|||||||
|verage|.|28%|25%|_18%_|_21%_|_14%_|-6%|

_Source:_ Author’s calculations. a/ Discrepancies are due to rounding.

-57-
3 The model incorporates elements from Baumol (1952), Tobin (1956), Lagos and Wright (2005) and Williamson (2012). Risk neutral agents with heterogeneous labor productivities can bridge the gap between receipts and expenditures either by using money or by using bonds. Bonds have a higher rate of return but there is a fixed cost for using them and therefore agents will use bonds only when their income is sufficiently high. Inflation affects the labor supply of agents who use money but does not affect the labor supply of agents who use bonds.

The welfare cost of inflation has two components: Transaction costs that are paid by bond users and the distortion to the labor supply of money users. The area under the demand for money curve is equal to the welfare cost of inflation only in the special case in which there are no direct taxes and no proportional intermediation costs. Otherwise, the area under the demand for money curve overstates the welfare cost if money substitutes are not important and understates the welfare cost if money substitutes are important. The importance of money substitutes declines with the nominal interest rate and therefore for low nominal interest rate the area under the demand for money overstates the welfare cost of inflation.

A "weak" social planner who can tell agents how to invest their wage payment (but cannot tell them how much to work) faces a tradeoff between two distortions: the labor-leisure choice and the cost of using bonds. The social cost of the labor-leisure distortion is higher for more productive workers: consequently the "weak" social planner will have sufficiently productive workers save in bonds, and sufficiently unproductive workers save in money, consistent with their privately optimal choice. However, for agents with intermediate productivity levels, the privately optimal saving choice is different from the choice of the "weak" planner because agents ignore the effect of their choice on seigniorage revenues (and the lump sum that they get). From the social point of view there is excessive use of bonds in the decentralized equilibrium.
Third, the average years of schooling show up and down relationships with each of the quality measures, and do not have a clear pattern. Forth, the local government expenditures on education and health (per 1000 people) are positively related to the city per capita income level. Also local governments in agglomerations with lower human capital accumulation spend more on education. This may contribute to diminishing inter-city disparity in educational attainment. Last, the local government expenditures on security, housing and transportation (per 1000 people) do not vary according to city size, per capita income and average years of schooling. The cities in the Southern region spend more on education and transportation when we control for other variables.

**Table 10: The OLS results of the Quality of Life: Infrastructure in 2000**

||The number<br>of banks<br>(per 1000)|The number<br>of libraries<br>(per 1000)|The number<br>of hospitals<br>(per 1000)|Garbage<br>collection<br>(%)|Electricity<br>use (%)|Piped<br>water use<br>(%)|
|---|---|---|---|---|---|---|
|log(population)|-.0002|0.002|-0.001|-0.846|-0.136|-1.656|
||(0.11)|(0.83)|(0.34)|(2.56)*|(0.69)|(3.19)**|
|log(per capita income)|0.089|0.006|-0.008|10.051|3.02|15.489|
||(6.27)**|(0.43)|(0.62)|(4.32)**|(2.17)*|(4.25)**|
|Avg. years of schooling|-0.017|-0.005|-0.012|-0.529|0.544|0.563|
||(3.47)**|(1.03)|(2.95)**|(0.68)|(1.16)|(0.46)|
|Dummy (Southern)|0.017|0.006|0.011|3.295|1.28|9.045|
||(2.41)*|(0.79)|(1.77)|(2.77)**|(1.80)|(4.85)**|
|Constant|-0.321|0.032|0.138|48.762|78.379|13.425|
||(5.97)**|(0.59)|(2.93)**|(5.52)**|(14.85)**|(0.97)|
|Observations|123|123|123|123|123|123|
|Adjusted R^2|0.61|0.01|0.21|0.60|0.44|0.74|

Absolute value of t statistics in parentheses; * significant at 5%; ** significant at 1% 28
# **Appendix A. Historical Financial Data Cleaning Procedure**

Following Bureau van Dijk (2011), Kalemli-Ozcan et al. (2015), Cusolito and Didier (2020), and Kalemli-Ozcan et al. (2023), we document the steps we apply to clean the financial information.

1. _Fill time-invariant data gaps_ : for a given BvD.ID-year combination, with BvD.ID standing for firm unique identifier, replace missing highly likely time-invariant information with information available for previous years (e.g., US SIC code, NAICS, NACE, NACE main sector, company name, city, region, postal code, legal form, incorporation date). To perform this step, the team first worked with auxiliary raw tables, which collect legal and sectoral information of the firm, and collapsed the time-invariant variables at the BvD.ID level.

2. _Harmonize timeframe_ : convert variable closedate from string to numeric format. Then create a new variable, name it year, and assign a year to the observation according to the following rule. If closing month corresponding to the observation is June or any other month after June, then make Year take the year reported in closedate. Otherwise, make Year the year reported in closedate minus 1.

3. _Drop duplicates_ : the raw database presents a large number of duplicates at the BvD.ID-year level. The team noticed that the information was the same, except in the SIC primary code variable. Thus, we collapsed all the SIC primary codes reported by the same BvD.ID-year in one variable, using semicolons to list all the SIC primary codes, and eliminated duplicates.

4. _Drop firms with missing relevant information_ : drop all the firms with no information for the following set of variables: US SIC code, NAICS, NACE core code, NACE main sector.

5. _Drop observations with missing information for the currency code_ : eliminate observations with missing information for the currency code.

6. _Drop observations with missing information for variable closedate_ : eliminate observations with missing information for the close date of the financial statement.

31
> bodi SPECIAL Topic II: DEMOGRAPHIC TRENDS IN FORMER SOVET-UNION

FPgure S2. Population Growth Rates, 1990 <!-- Start of picture text -->
*'M -<br>0.. |<br><!-- End of picture text -->

# Figure S3. TFR in Former Soviet-Union Republics

Figure S2 compares annual population growth rates for the period ... 1990-94. Five countries have growth rates over 1 percent, whereas most of the other countries' growth rates hover around 0.5 percent per year. These rates show a clear geographic pattern, with the Central Asian countries mostly having high growth rates. Tajikistan, Uzbekistan, Turkmenistan, Kyrgyzstan, and Azerbaijan, which together have only 14 percent of the population of the former Soviet Union account for 40 percent of the increase in population in all fifteen countries. One consequence of the differences in growth rates is that the composition of the region will gradually change. For example, the share of the Russian Federation of the region's total will drop from just over 51 percent now to 47 percent by 2025.

## _Fertlity_

Figure 84. Trends in TFR, low fertility countries <!-- Start of picture text --> . 'Uu.w<br>. ~<br><!-- End of picture text --> The main reason for the differences in population growth rates is the variation in fertility. Figure S3 compares the TFR in 1992 for al 15 countries. Figures S4 and S5 show trends in the TFR from 1960 to 1990. Two sets of countries can clearly be distinguished: those with high and medium-high fertility (Figure _S5)_ and those with fertility near replacement (Figure S4). For most of the high and medium high-fertility countries, trends since 1980 are generally downwards, although at a slow rate. The low fertility countries have been mostly stable at low levels since the 1960s. An exception is Armenia, which joined the low fertility countries only in the mid1970s.
# **5) Scaling up business training**

Classroom-based business training programs typically provide training to 15 to 40 small businesses at a time. Developing countries have hundreds of thousands, or millions of such small firms – for example, the Mexican Economic Census enumerated 4.1 million firms with 0 to 10 workers (INEGI, 2016). The big challenge for policy makers is then how to provide good quality business training services at a scale that can reach vast numbers of firms while not being prohibitively expensive for the government. I see three (potentially complimentary) approaches for achieving this goal.

# _Approach 1: Charge firms for training and develop the market for business services_

A first general approach to scaling is to develop and use the market for business service provision and having firms pay at least part of the costs of training. Many governments and NGOs have offered business training for free or for a nominal fee. Charging for training can make such programs more financially sustainable, and may be a way of selecting entrepreneurs who value the training more and who will exert more effort. However, it may also prevent poorer and liquidityconstrained entrepreneurs from getting assistance. Recent work by Maffioli et al. (2020) in Jamaica explores these issues by randomly varying the price charged for training, enabling them to assess how the demand for training varies with price, and to investigate whether those who pay more for training attend training more regularly. Their business training course consisted of 40 hours divided into ten lessons of four hours each, and cost $150 to provide. They find demand falls sharply as price increases: 76 percent attend at least one class when it is offered for free, 65 percent when they have to pay a token fee of 5% ($7.50), 29 percent when charged half of the cost ($75), and only 11 percent when charged the full cost. Offering the opportunity to pay in three installments did not change willingness to pay. Poorer and smaller businesses are less likely to pay and thus attend once the price rises. However, the authors do find that those who pay higher prices attend more of the 10 sessions, suggesting a potential sunk cost or psychological effect where paying for business training makes firms value them more. Unfortunately, this study is unable to measure whether those who pay higher prices see larger business impacts from the training.

A key factor to consider when charging for business training is that business training may be an experience good, which firm owners need to receive in order to assess the quality and potential 26
<!-- Start of picture text --> Share of total country payments for CEE countries<br>2000-2006 programming period<br>Bulgaria Czech Republic Estonia<br>”<br>“4 eee e. ee? e °° e e . ° e ° 6 e e<br>ote ° e e ee? ee?<br>Hungary Latvia Poland<br>”<br>a7eg N e . e e . ce e ° e e e . e e° . ee ° bd<br>ole e ® ee? ee?<br>Romania Slovakia Slovenia<br>on<br>a 7 e . ; ee . of ee<br>I e e e e e<br>. e e e e<br>ole te? oe? ee?<br>a a a, Ss ie<br>2000 2003 +=.2006 = 20092000 += 2003S 2006 = 20092000 += 2003-=S 2006 = 2009<br>Year<br>Share of total country payments for non CEE countries<br>2000-2006 programming period<br>Austria Belgium Denmark Finland<br>7 _° °<br>. % e ° fe?<br>France Germany Greece Italy<br>a 8 o—* - ° ° .° e ° e<br>Netherlands Portugal Spain Sweden<br>2000 ©2003 2006 82009 «2000 82003 2006 =2009 $2000 2003 2006 2009 2000 2003 2006 = 2009<br>Year<br><!-- End of picture text -->
_Ingo Borchert and Mattia Di Ubaldo_ is found both for services VA in services exports and services VA in total exports, in columns (1) and (3) of table 6. The magnitude of these effects is rather small, however: the coefficient estimated for services exports suggest that the average PTA effect is to increase the within-PTA services VA share by _e_<sup>0</sup><sup>_._0125</sup> − 1 = 0 _._ 0126%.

Table 6: Service PTA effect on value-added shares

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Services|VA in serv|ices exp.|Services|VA in tot|al exp.|
|PTA|0.0125**|||0.0069**|||
|Bilateral PTA||0.0309***|||0.0474***||
|EU bilateral PTA||0.0124**|||0.0067**||
|Plurilateral PTA||0.0123**|||0.0061*||
|Depth Q1|||0.0013|||0.0012|
|Depth Q2|||0.0129**|||0.0077|
|Depth Q3|||0.0041|||0.0035|
|Depth Q4|||0.0312***|||0.0576*|
|EU Pair|0.113|0.113|0.111|0.133**|0.133**|0.133**|
|Depth non-serv.|||0.0000*|||0.0000|
|Observations|165,066|165,066|165,066|165,066|165,066|165,066|
|Exporter-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

_Note_ : ‘Depth’ measures the number of provisions in a PTA’s services and non-services chapters, respectively. Q1-Q4 denotes quartiles. PPML estimation with two-way standard errors clustered at exporter and importer level. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01 In columns (2) and (4) we unpack the PTA effect depending on the bilateral versus plurilateral dimension of the agreements. The coefficients on EU-bilateral and plurilateral PTAs are similar to the aggregate one, as they pick up the effect for the majority of the observations in our data. Bilateral agreements, however, although accounting for a small fraction of our data, appear to lead to a twice as large an increase in the VA share from PTA partners, with this effect being also much more precisely estimated.

In columns (3) and (6), lastly, we exploit the detailed information about the _vertical depth_ of services PTAs, i.e. the number of services-related provisions in the agreement.

39
in human history. In practice, however, social scientists studying this experiment have uncovered a variety of problems including elite dominance, corruption, high levels of clientalism, poor tax compliance and, consequently, an almost total dependence on outside grants for revenues (e.g. Bardhan and Mookherjee 2011; Rajaraman 2003).

Studies of deliberative decision-making within the panchayat system in South India have found that gram sabhas can benefit citizens in several ways. Ban, Jha and Rao (2012), analyzing transcripts of 300 gram sabhas, find that the issues raised within them reflect the preferences of the median citizen. Discourse within gram sabhas could thus be interpreted as honing close to the interests of the median voter and thus as “efficient democracies”. Rao and Sanyal (2010) in a qualitative analysis of the same data find that underprivileged castes tactically use the gram sabha as a forum to make identity claims and to question the criteria for poverty targeting. Despite their potential value to citizens, Besley et al (2005), however, find that gram sabhas are often not held regularly. But, when they are held, benefits from public programs tend to be better targeted towards the poor.

Besley et al (2005) are unable to draw causal links in their analysis because gram sabhas are not randomly assigned. Thus, while there is evidence to suggest that gram sabhas have intrinsic discursive value and show considerable potential for democratic deepening, it is unclear whether holding them results in a consequent improvement in the quality of village government. And, therein lies the rub; can gram sabhas and other forms of everyday democratic participation play an instrumental role in holding village governments accountable and make them more responsive to the needs of citizens? Is it possible to strengthen village level deliberative processes via an external intervention to deepen democracy?

The key to the success of such interventions according to Fung and Wright (2003) is to build “empowered participatory governance” which is characterized by three important principles: (a) it must have a practical orientation, (b) it must involve all the people most affected by and knowledgeable about the problems being addressed, and (c) it must be deliberative. Such interventions, they find, require Fox’s “sandwich” in order to work with meaningful devolution of functions and finances and centralized supervision and control. The People’s Campaign in the Indian state of Kerala was one such intervention.

Kerala state, which has the highest levels of human development in India, pioneered an important attempt to deepen democracy in the process of implementing the 73rd amendment. There were two elements to this “People Campaign”: First, the state government devolved significant resources to the GPs with 40% of the state’s total expenditures allocated to them; second, a grassroots training and 4
- 37 -

> e1, the units term in the production function, **is** arblitrarily **set** equal to one allowing equation (25) to be solved for al. **The** value for 72 is then derived by residual using equation (9), given the units' definitioa for output.

> Demand side parameters **are** determined In an analogous fashion

> using calibration **techziques,** except that **first** order conditions for utility maximization are used. **Taking the** derivative of (10) with respect to Kj yields:

pj Pi fXjl L (27) - _ j , j - 1,2, k - 1,2, Pk Pk LxkJ 2 Normalizing so that E j - 1, individual values can be obtained. i-1 Because £2 can be arbitrarily **set** equal to one in the base case data, the value for 6 can be derived from (16). l can then be determined directly from the equal utility condition linking **the** manufacturing and agricultural sectors.

> The microconsistent data **sets** to which we calibrate our model are

> built for the two years of 1962 and 1982, **each** chosen to reflect different stages of Korean growth. One is largely pro-outward orientation, and the

> other post-outward orientation and for a more recent year. In **constructing** these data sets, **different basic** data **sources** have been used **and various** incompatibilities between source **materials** have had to be dealt with. Adjustments have been made to the **data,** both to resolve incompatibilities (differences in definition, end measuremnt differences) end to ensure that the equilibrium conditions of the model **are satisfied in** the data.
ceptually the questions of how AIDS impinges on the economy and how its effects can be combatted by suitable policies. The basic model is set out in section 2.1, with special attention paid to the effects of premature adult mortality on (nuclear) family structure and the schooling of children. The dynamics of the system under an exogenous mortality profile, corresponding to some given disease environment, are analyzed in section 2.2. Pooling, where members of the extended family take in orphans, as an alternative form of organization that has both advantages and drawbacks in such a setting, is analyzed in section 2.3.

This sets the stage for the analysis of the policy problem in section 3, where the outbreak of the AIDS epidemic is modeled as an adverse shock to an existing profile of premature adult mortality. Interventions in the spheres of health and education are examined separately in section 3.2, and jointly in section 3.3. Finding the right balance between these two sets of measures is the central policy problem, and the results in these sections attempt to illuminate how the balance should be struck. The possibilities of multiple equilibria and self-fulfilling prophecies, together with the associated credibility of public policy, are pursued in section 3.4. An extensive treatment of what can be called ‘fairly good’ policy programs is the subject of section 4, in which the aim is to develop simpler alternatives than those requiring the computation of a full optimum.

In the third part of the paper we apply the model to South Africa. The choice of South Africa as a test-bed is a natural one on several grounds. First, the very nature of the model demands that the available economic and demographic series be long and fairly reliable if there is to be a solid base for calibration. Second, South Africa is a middle-income country that has experienced substantial growth over much of the past half century. A collapse of the kind analyzed in the theoretical sections of this paper, were it to occur, would therefore mean that there is a long way to fall. Third, the epidemic has progressed rapidly in South Africa, from a prevalence rate among the population aged 15 to 49 of about one per cent in 1990 to just over 20 per cent a decade later (UNAIDS, 2002). The demographic writing is already on the wall (see especially Dorrington _et al._ , 2001).

9