rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Econometric Methods

|Section|Method|Outcome<br>Measure|Borrower<br>Measure|Method|Table|
|---|---|---|---|---|---|
|5.1|Linear Probability Model|Binary|Binary|OLS/FE<br>2SLS|Table 5<br>Table 6|
|5.1|Linear Probability Model|Multiple|Binary|OLS/FE<br>2SLS|Table 7<br>Table 8|
|5.2.1|Logit and Probit|Binary|Binary|OLS/FE<br>2SLS|Table 9<br>Table 10|
|5.2.1|Ordered Logit and Probit|Multiple|Binary|OLS/FE<br>2SLS|Table 11<br>Table 12|

Note: the column “Section” refers to the part of next chapter which contains the estimations through the method reported in the column “method”. The “Outcome Measure” and “Borrower Measure” columns explain what type of rating is used, either binary (0 unsuccessful or 1 successful) or multiple (from 1 very unsuccessful to 6 very successful), for more references on these measures refer to section 3. OLS means Ordinary Least Squares, FE fixed effects method, 2SLS two stage least squares.

Anticipating on the results we notice that across the estimations there are minor differences and the results of 5.1 seem to be pretty robust. The last estimation is more complex, especially for what regards its interpretation, for this reason it is more deeply developed in section 5.2.2.

# **5 Results**

# **5.1 The Simplest Estimation**

In this section we report the linear probability estimations of equation (1) and the two equations (1) and (2), using binary measures both for outcome and borrower (Table 5 and 6) and a multiple measure for outcome and binary for borrower and bank performance (Table 7 and 8). An intuitive feature of these estimations is given by their coefficients, which are directly interpretable as marginal effects (or probabilities). In general the model seems to explain a significant portion of variability as the adjusted R<sup>2</sup> swings around 0.5; while the two stage approach seems to be useful as the instruments are always very significant and, though we can never validate them, the overidentification tests (Sargan and Basman) do not reject the null hypothesis of their validity, both in Table 6 and in Table 8.

By analyzing tables 5 and 6, we notice that borrower performance is the only significant variable across all estimations (including country fixed effects in FE(1) and both country and year fixed 12
subsection, it is shown that the channel through which their return rate is lower could be their search

# **5.3 Is search intensity related to change in GHQ ?**

The BHPS has a section designed in a similar way to the LFS (Labor Force Survey), intended to measure unemployment by the ILO standards. In this section, unemployed persons are being asked if they have been searching for a job in the last week, and/or in the last month, their response being limited to Yes/No. Their answer can provide some information on the search intensity of the group as a whole. Using the previous sample of GHQ differences, two groups are created. The first group consists of those who report a large utility loss (higher than 1 point of SWB) when they lose their job. The second group consists of those who report either no utility change or are happier being unemployed.

Search intensity varies slightly between the two groups. It is observed that 62% of the group suffering from being unemployed searched for work last week, whereas this drops to 55% for the other group. These results suggest that the unemployed modify their search behavior according to how much they report being hurt by the job loss<sup>19</sup> . A means test confirms that the two means are different, at the 95% confidence level, the results of the test are in table A.11 of the appendix.

We provide in the appendix a probit of ”search last week”, in which we use the same controls as for duration. Unfortunately, our variables of SWB do not seem to be significant, exept for _bigloss_ , whose effect is positive and significant, but only at the 10% level. Further data could confirm our prediction, and show that those suffering a bigloss are indeed searching more frequently. Table A.10 presents the results of the search probit.

Table 2: Search intensity : ”Have you looked for any kind of paid job in the last week?” - population mean by <u>groups</u>

|**Variable**|**Mean**|**Std. Err.**|**N.Obs**|
|---|---|---|---|
|High GHQ drop|0.625|.021|554|
|No GHQ drop|0.55|.028|324|

> 19An reverse causality issue can arise : maybe search is causing unhappiness. As soon as search stops, happiness increases (true when they find a job or when they search less). If this holds, then all the results/conclusions in the present thesis are wrong. The reason why this does not hold is because we are using the _initial_ drop in happiness.

28
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In the context of monetary poverty, equality can be defined as an equal distribution of consumption across the population, with inequality being the departure from that equal distribution. Measures of inequality are thus defined over the entire population, aiming instead to capture the full consumption distribution without depending on the mean of the consumption distribution. It is important to note that measuring inequality with consumption, instead of income, tends to underestimate inequality in the population as consumption‐based measures do not consider savings or wealth (Beegle et al., 2016).

The Gini index or coefficient is the primary measure of inequality presented in this analysis. It ranges between 0 and 1, such that a coefficient equal to 0 indicates perfect equality and equal to 1 complete inequality. The Gini index is graphically represented by the Lorenz curve, a visual representation of the distribution of consumption across the population. It plots the cumulative population distribution by consumption percentile against the cumulative consumption distribution. The Gini index is the area between perfect equality, as represented by the 45‐degree line, and the Lorenz curve observed from the data, relative to the maximum area that would be attained given perfect inequality (Figure A.7). Formally, where y denotes the cumulative proportion of the total country‐wide consumption expenditure for the _ith_ person and x the cumulative proportion of the total population for the _ith_ person. An alternative measure of inequality presented below is the Theil index. It is part of a larger family of measures referred to as the general entropy class (Coudouel et al., 2002), with the general formula given by: <mark>where</mark> y� <mark>denotes the total consumption for individual</mark> _<mark>i,</mark>_ <mark>y</mark> � the mean expenditure per capita and _N_ is the total population. The parameter α regulates the emphasis placed on higher or lower incomes. As with the Gini index, higher values of the Theil index represent higher levels of inequality, but unlike the Gini coefficient, this measure is not bounded between 0 and 1. Moreover, the Theil index is sensitive to inequality among the poor, and has the advantage of being additive across different subgroups in the country, allowing to decompose inequality into how much of it is explained by differences within groups and how much by differences between groups.

# 6.2. Results

As data collection in wave 2 of the SHFS was restricted to accessible areas, survey poverty headcount estimates are representative of only of the population living in these areas (Table 1; Figure 1). The SHFS filled this critical gap by imputing poverty based on data extracted from satellite images for inaccessible areas. Section 5 describes the imputation methodology in detail. Survey and satellite imputation estimates for all population types were combined to compute a poverty headcount rate representing the entire 31
where the parameters _θ_ 0<sup>_g,i_</sup> and _κg,i_ corresponds to the initial level and growth rate in agricultural yield of cellulosic feedstocks. We obtain the agricultural yield growth rate based on production-weighted average of econometric estimates of Cassman et al. (2010) for major grain yields using global data over 1966 - 2009 period. We obtain the yield data for cellulosic feedstocks (miscanthus giganteus) from Taheripour and Tyner (2011).

We also account for the potential impact of climate change on the growth rate of agricultural yields. In the baseline we assume moderate temperature increase by the end of the century following Representative Concentration Pathways (RCP) 2.6 GHG forcing scenario (Moss et al. 2008). We assume that potential crop yields remain unchanged by the end of the century, as a decline in yields due to moderate increase in temperature is likely to be oset by CO 2 fertilization eect (Long et al. 2006). In contrast, second generation biofuels feed stocks benet from higher temperatures and yields increase strongly (Brown et al. 2000). Based on the simulation results for switchgrass yields in the upper Midwest of the United States (Brown et al. 2000), agricultural yield growth for cellulosic feedstocks is expected to increase by 50% in 2100. We annualize the growth by assuming that agricultural yield growth rate of cellulosic feed stocks relative to 2005 increases by 10% every twenty years until 2100.

It can be shown (see, e.g., Rutherford (2002) for complete derivation) that non-land costs per ton of agricultural product, _c_<sup>_g,i_</sup> _,_ are given by where _c_<sup>_C,i_</sup> _t_ are endogenous shadow prices (rents) of agricultural lands dedicated to food crops and cellulosic feedstocks. These prices are determined from a conditional input demand function for agricultural lands:

# C.1.5 Livestock Sector

The initial output of livestock products (measured as the global physical production of meat, diary and eggs) in 2004 come from the FAOSTAT database.

17
Figure 1. Fiscal policy

# A. Government debt

<!-- Start of picture text --> Percent of GDP<br>2018 2007<br>70<br>60<br>50<br>40<br>30<br>20<br>EMDEs ECA EAP LAC MNA SAR SSA<br><!-- End of picture text -->

# C. Primary fiscal balance sustainability gap

<!-- Start of picture text --> Percent of GDP<br>8 2018 2007<br>6<br>4<br>2<br>0<br>-2<br>-4<br>E. Fiscal multiplier, by debt level<br>Multiplier<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>Low debt High debt<br>EMDEs ECA EAP LAC MNA SAR SSA<br><!-- End of picture text -->

# B. Fiscal balance

<!-- Start of picture text --> Percent of GDP<br>2007<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>EMDEs Commodity Commodity<br>exporters importers<br>2009 2011 2013 2015 2017 2009 2011 2013 2015 2017 2009 2011 2013 2015 2017<br><!-- End of picture text -->

# D. Sovereign ratings

<!-- Start of picture text --> Index (1-21, higher better)<br>14 Interquartile range mean<br>13<br>12<br>11<br>10<br>9<br>8<br>7<br>6<br>2007 2018<br>F. Fiscal rules<br>Number of countries<br>80<br>60<br>40<br>20<br>0<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> Source: Huidrom et al. (2019), International Monetary Fund, Kose et al. (2017), World Bank. Note: EMDEs = Emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A.-D. Unweighted averages.

A. Gross government debt.

C. The sustainability gap indicates the difference between the current primary fiscal balance and the debt-stabilizing primary fiscal balance at current growth rates and interest rates. A negative value indicates that government is on a rising trajectory. See Kose et al. (2017) for details.

D. Based on data for 97 EMDEs.
across the different sections of the Excel file, the user is provided with a button for the _Navigation Tree_ , where the analyst can click on the element of interest (see Figure B.1).

In addition, (advanced) users can add new country data sets to ISIM and change other aspects of the GEM‐Core program by editing relevant files in the ISIM\GEM‐Core installation folder, using Excel and a text editor.

# **The reference scenario**

<mark>The first step in performing counterfactual simulations with ISIM is the construction of a (dynamic) baseline scenario. To help the user carry out this task, ISIM includes, for each country data set, a pre‐defined reference scenario. Key parameters of this scenario can be changed inside ISIM, including t</mark> he GDP growth rate, all model elasticities (including those related to trade, household expenditure, the reservation wage, and the impact of share of trade in real GDP on total factor productivity), as well as closures and other rules, the latter covering the government budget, the balance of payments, the savings‐investment balance, factor markets, and various payments, split into government and non‐government depending on whether the government is involved or not. The rule chosen for any payment is overwritten if, according to the related closure setting, the payment in question is a free variable; for example, the specification that direct taxes are determined on the basis of exogenous tax rates is overwritten if, according to the government closure rules, changes in direct taxes clear the government budget. In addition, the user can configure the ISIM Poverty Module (see Lofgren and Cicowiez (2017b)). Specifically, the parameters of the Poverty Module allow the user to change: (1) the approach to compute poverty; (2) the welfare index (income/consumption); (3) the initial poverty rate; and (4) the growth elasticity of poverty.

To exemplify, Figures B.3a and B.3b demonstrate how the analyst can change the elasticities of substitution between primary factors of production; the default values can always be restored. Once defined, the reference scenario can be run by clicking the _Run Setup_ button; ISIM will automatically call GAMS in order to run GEM‐Core.

‐57‐
An analysis of the returns outstanding as a percentage of the population of active registered taxpayers showed that SARS has made progress in some areas, notably income tax, but not in others, notably pay as you earn (PAYE). SARS also continued its efforts to increase filing compliance though an on-going clean up of the taxpayer registry. One of the difficulties faced by SARS was that there was no single taxpayer identification number (TIN). SARS initiated changes to improve taxpayer registration, including the establishment of an electronic link with the Registrar of Companies in 2000, such that firms registering with the Registrar were automatically registered with SARS.

SARS, like many other tax administrations, was less successful reducing the amount of taxpayers’ arrears. In fact, arrears grew over the SARS period, from 4.01 billion Rand in March 1996 to 13.24 billion in March 2001 for income tax, 2.70 billion to 11.86 for VAT, and 0.14 to 4.38 billion for PAYE. Moreover, more than two thirds (68%) of the arrears were over nine months of age.

SARS also made some progress with the establishment of a large taxpayers office (Corporate Tax Center, CTC), though the results were not wholly satisfactory. Overall implementation of the CTC reform was disappointing, in part because of SARS’ inability to attract and retain high quality staff.

The registration picture was clouded in Kenya due to a botched PIN implementation, and poor follow through thereafter, in the pre-KRA period. Overregistration led to a low proportion of active taxpayers as a percentage of the total number registered. The problem was that it was difficult for the KRA to ascertain whether firms that did not file or pay taxes were legally or illegally inactive.

The KRA, however, was quite successful in increasing the number of VAT taxpayers in its registry, which grew from 17,106 to 26,591, an increase of more than 55% from 1997 to 2000. Yet, the KRA faced problems enforcing filing compliance among VAT taxpayers. The percentage of non-filers stood at 29% at the end of 2000. The major problem confronting the KRA in the area of registration and filing was the lack of integration of its databases, which required that staff carry out important work manually. Management also failed to integrate the registration function, as separate identification numbers were required for different taxes (VAT and income) and separate offices were charged with the registration function (management argued that a new IT system was 32
for HIV jumped from 12% in 2004 to 84% in 2014)<sup>37</sup> and modern contraceptive use (increased from 35% in 2004 to 60% in 2014, among married women), the continuing high prevalence of HIV/AIDS in the country makes intensifying prevention efforts as pressing as ever.

# 4. Financial Inclusion

For migrants, the need for inclusive financial services is particularly essential. Not only do migrants need to send remittances to their families but they also would greatly benefit from increased access to other financial services, including bank accounts to receive payments (for formal workers), funeral insurance, long‐term savings and asset accumulation, and short‐term borrowing.<sup>38</sup> Most Basotho still primarily access informal financial services. In 2011, only 18% of adults in Lesotho had accounts at a financial institution, compared to 29% in Swaziland and 54% in South Africa.<sup>39</sup> Women were also less likely than men to own an account (17% vs. 20%), as were low‐income individuals (10% vs. 25%), making poor women the least likely to have access to formal financial services. This is not to say that they do not engage in financial transactions, but rather that their financial lives are characterized by informality. For example, while only 3% of women borrowed from a financial institution, 5% did so from a loan shark, and an overwhelming 51% of women borrowed from family or friends in 2011.<sup>40</sup> Only 10% of Basotho adults used an account to receive remittances, and only 6% used an account to send money, with an even lower share among women as, unlike male migrants, they were not enrolled in any remittance‐sending schemes.<sup>41</sup> Female migrants are consequently even more reliant than men on informal channels to send money home, carrying cash across the border or sending cash or goods home with friends or relatives.<sup>42</sup>

> 37 DHS 2004, 2014.

> 38 CENFRI MAP study (http://cenfri.org/making‐access‐possible/map‐lesotho).

> 39 FINDEX, 2011. In 2014, 70.31% of adults in South Africa had bank accounts. 40 FINDEX 2011.

> 41 Based on Lesotho legislation, a third of male miners’ salaries in South Africa is automatically remitted to their households back home. 42 Other studies have found higher rates of financial inclusion. The FinScope survey, conducted in 2011, found that only 19% of Lesotho adults were not financially served, as opposed to 27% in South Africa and 37% in Swaziland. 38% of adults have a bank account, 23% have another form of formal financial service, and a further 20% are only served by informal financial services. 62% of adults have funeral insurance, either formal or informal. The differences in these rates are definitional: this high level of inclusion is driven by the use of insurance, especially funeral insurance.

45
results discussed below using the alternative growth measure ( _Employment Growth_ 1) are available on request from the author.

# 4.2 _Additional controls_

Starting with the final baseline specification, we check if the results are robust to additional controls. Thus, in Table 6, we add controls for region dummies (LAC, EAP, etc.) in column 1; in column 2, we include controls for R&D activity, foreign ownership, purchase of fixed assets, and city-size dummies; controls for country-size and several dimensions of the business environment quality (corruption, regulation, etc.) are included in columns 3 and 4. Our result for the negative relationship between informal competition and employment growth survives. That is, the estimated coefficient value of informal competition remains negative, large and it is significant at the 1 percent level in all the specifications. The coefficient value is larger (more negative) due to the additional controls. For instance, starting with the coefficient value of -5.7 in the final baseline specification (column 5, Table 4), it rises to -7.1 with all the additional controls included in the specification (column 4, Table 6).

# 4.3 _Within-Country Variation_

The next robustness check involves using cell averages of informal competition at a more disaggregated level. We experimented with country times industry and city times industry level, where city is the sub-national sampling region used for stratification purposes by the ES. The industry groupings used is the same as above (2-digit ISIC Rev. 3.1). Regressions using cell averages at the country times industry level control for all country-wide factors via country dummies (Country fixed effects). Similarly, regressions using city-industry level cell averages 23
-3-

to improving the educational level and discipline of the labor force<sup>and may</sup> act as a stabilizing influence in the society. Likewise, it has been argued (see, for example, Thompson (1974)) that military expenditure can be economically productive to the extent that it enhances the state of national security and improves the enforcement of property rights, thereby encouraging private investment and growth. Capital expenditure<sup>on the</sup> military can also have productive uses: many developing countries still benefit from extensive transport networks that were originally<sup>constructed</sup> for military purposes. These counter-examples suggest that the question<sup>of</sup> whether, and to what extent, military spending is economically unproductive cannot be resolved by recourse to anecdotal evidence and historical generalizations, but instead requires rigorous theoretical and<sup>empirical</sup> analysis.

The analysis must also be able to confront formidable estimation problems: even if cuts in military spending do improve growth performance substantially, these effects are likely to appear with a long lag.<sup>Thus in</sup> empirical studies the beneficial effects from large military spending<sup>cuts</sup> may be hard to disentangle in the estimation work from other factors<sup>that</sup> influence economic growth. Given these considerations, it is not<sup>surprising</sup> that the existing empirical literature yields ambiguous results, not<sup>only on</sup> the magnitude of the impact of military expenditure on long-term economic growth, but even on whether the effect is positive or negative. Nevertheless, if national governments are to be convinced that it is to their economic advantage to stockpile fewer guns in order to make room for more investment in productive capital, they need to be presented with robust