rafmacalaba/datause-encoder-data huggingface dataset:
The pattern of giving to Senate candidates appears to be very similar to the pattern of giving to candidates up for House elections. That is, candidates on the top-20 Ag PAC Senate list get roughly similar contributions as do candidates on the top-20 Ag PAC House list. Hence, Ag PACs view these top-20 senators and representatives approximately equally. If the theory that contributions are made in _exchange_ for political favors is correct, then Ag PACs view senators as having as much power to provide political favors as do representatives. If the theory that contributions mainly target _access_ to political influence is correct, then Ag PACs view senators and representatives approximately equally in the amount of influence each dollar of contributions is likely to buy. This quantity of influence, in turn, depends on the provision of _information_ to politicians that is perceived by them to be both, costly and valuable.

One important difference between Ag PAC contributions to Senate and House candidates is that Ag PAC contributions to Senate candidates as a percentage of their total PAC receipts do not exceed 25% and is generally lower than 10% even for the largest recipients of Ag PAC contributions. This is not surprising since, on average a Senate election costs roughly ten times as much as a house election<sup>16</sup> . This is depicted in Figure 6.1 and 6.2 for the House and in Figures 8.1 and 8.2 for the Senate. However, the evidence that Ag PACs view senators and representatives equally while making campaign contributions appears to be fairly strong evidence that Ag PACs do not generally seek to influence election outcomes. If influencing elections were the true motive, then we should see Ag PAC receipts as a proportion of total PAC receipts to be roughly similar across House and Senate candidates. But this implies that contribution to Senate candidates should be ten times higher than they actually are.

> 16 In 1992 the average winning Senate candidate spent $3.9 mn. while the average House winning candidate spent $0.5 mn., approximately an 8:1 ratio,.  The average Senate loser spent $2.0 mn. while the average House loser spent $.2 mn., a 10:1 ratio. In other election cycles the average Senate to House spending ratios (winners plus losers) 10:1 in 1994, 8:1 in 1996, 9.4:1 in 1998, 10:1 in 2000. Further, total PAC receipts as a proportion of total campaign spending averaged approximately 20% for winning Senate candidates while they averaged approximately 50% for winning House candidates. These figures are computed from in formation on the opensecrets.org (opensecrets 2002) web site.

32
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|---|---|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|Second Serbia Health Project|Dorothee Chen|
|ImprovingDisaster Risk Management in Mongolia|Artessa Saldivar-Sali|
|ImprovingDisaster Risk Management in Mongolia|Artessa Saldivar-Sali|
|ImprovingDisaster Risk Management in Mongolia|Artessa Saldivar-Sali|
|ImprovingDisaster Risk Management in Mongolia|Artessa Saldivar-Sali|
|ImprovingDisaster Risk Management in Mongolia|Artessa Saldivar-Sali|
|ImprovingDisaster Risk Management in Mongolia|Artessa Saldivar-Sali|
|Market Instruments for Climate Change Mitigation in Mexico|Gabriela Elizondo Azuela|
|Guyana EarlyChildhood Education Project|Hongyu Yang|
|Guyana EarlyChildhood Education Project|Hongyu Yang|
|Guyana EarlyChildhood Education Project|Hongyu Yang|
|Guyana EarlyChildhood Education Project|Hongyu Yang|
|Guyana EarlyChildhood Education Project|Hongyu Yang|
|Guyana EarlyChildhood Education Project|Hongyu Yang|
|Guyana EarlyChildhood Education Project|Hongyu Yang|
|PE Mainstreaminginclusive design and universal mobilityin Lima|Georges Bianco Darido|
|PE Mainstreaminginclusive design and universal mobilityin Lima|Georges Bianco Darido|
|PE Mainstreaminginclusive design and universal mobilityin Lima|Georges Bianco Darido|
|PE Mainstreaminginclusive design and universal mobilityin Lima|Georges Bianco Darido|
|PE Mainstreaminginclusive design and universal mobilityin Lima|Georges Bianco Darido|
|PE Mainstreaminginclusive design and universal mobilityin Lima|Georges Bianco Darido|
43 Toble 13 econtinued)

||Stationary<br>p|<br>opulat|ion||Stationary p|opulatio|n|
|---|---|---|---|---|---|---|---|
||||Year||||Year|
|Country<br>or economy|<br> <br>Nittions<br>1|As of<br> <br>985pop|when<br>NRR=1|<br> <br>Country<br>or economy|A<br> <br>Millions1|s of<br>985 pop|when<br>NRR=1|
|Czechoslovakia|19|120|2030|Yugoslavia|27|118|2030|
|Denmark|4|77|2030|other Europe|0.4|<br>142|2020|
|Finlard|4|91|2030|<br>U.S.S.R.|398|143|2020|
|France|58|105|2030|||||
|German<br>Dem. Rep.|15|93|2030|<br>Oceania|40|165|2030|
|Germany,<br>Fed. Rep. of|40|66|2030|<br>Australia|20|130|2030|
|Greece|10|102|2030|<br>Fiji|1|193|2010|
|Nungary|10|92|2030|French Polynesia|0.5|284|2015|
|Iceland|0.3|131|2030|Guam|0.3|221|2020|
|Ireland|5|144|2020|<br>Kirabati|0.2|<br>245|<br>2025|
|Italy|46|8l|2030|<br>New Caledonia|0.3|<br>198|2010|
|Luxewbourg|0.3|<br>73|2030|<br>New Zealand|4|123|<br>2030|
|mlta|0.3|96|2030|<br>Pacific<br>Islands|0.4|<br>270|<br>2015|
|Netherlands|13|88|2030|PapuaNewGuinea|10|298|2025|
|Norway|4|94|2030|SolomonIslands|1|529|2035|
|Poland|48|130|2020|Tonga|0.2|245|2025|
|Portugal|11|104|2030|Vanuatu|1|441|2035|
|Raawnia|28|123|2030|Westerm Samoa|0.4|240|2015|
|Spain|41|106|2030|<br>Other<br>Micronesia|0.1|<br>230|<br>2020|
|Sweden|7|87|2030|Other Polynesia|0.2|224|2010|
|Switzerland|5|79|2030|||||
|United<br>Kingdom|56|98|2030|<br>World<br>total|10837|224|<br>2045|

Note: Populations of more than I million have been rounded to the nearest million.

(a) For a listing of cotntries in the "other" category, see Table 12.
# **4. Financing Patterns and Firm Performance – Empirical Strategy**

We first estimate the following regression model: _Sales Growth/Reinvestment Rate/Productivity Growth =_ α _+_ β _1Bank Dummy+_ β _2Firm Size dummies +_ β _3 Age dummies +_ β _4Corporations +_ β _5Collectives +_ β _6State Ownership +_ β _7Competition Dummies +_ β _8City Dummies +_ ε _(1)_ The bank loans for our sample were approved prior to 2002. Accordingly, we report regressions for which the dependent variable is sales or productivity growth rate between 2001 and 2002 or the reinvestment rate measured in 2002. However, because annual growth rates are likely to be subject to random shocks, we also report regressions using sales growth and productivity growth of firms for the period 1999-2002. These regressions are descriptive, showing the association between growth and firm access to the formal sector over a longer horizon.

Our main independent variable of interest is the Bank Dummy. We include a number of firm level control variables - Firm Size dummies, Age dummies, State Ownership dummy, Corporation dummy, Cooperatives dummy and Competition dummies - that are described in detail in Section 3.4. We include city dummies to control for unobserved heterogeneity at the city level. The data on industries are available for a very narrow definition of business activity giving us 81 industry dummies. Hence, while our results are robust to controlling for industry dummies, we do not include industry dummies in our baseline specification so as to not lose too many degrees of freedom. We also report alternative specifications using the other financing variables described in Section 3.3 - Access Dummy, Bank Financing Dummy, and the two informal financing variables, Self Financing1 and Self Financing2.

24
-6-

Yugoslavia. But of these four countries, Moro'co is the country where the decreasing trend was less apparent; and Portugal and Yugoslavia where it was more apparent. The second pattern consisted of a constantly increasing trend in the level of public indebtedness. Examples are the cases of Algeria and Pakistan. However, this pattern was slightly more pronounced in Algeria than it was in Pakistan.

Concerning the level of total public debt, Morocco is the country with the highest level. By the mid-1980s, Morocco's ratio of total public debt to GDP reached about 130 percent. Although the _-ther countries' level of total debt was lower, it was very high by international sta.ndards. For instance, the ratio of total public debt to GDP reached a maximum of about 92 percent in 1989 for Algeria; 109 percent in 1984 for Portugal and 95 percent in 1989 for Pakistan. These ratios compare closely to the total public debt to GDP ratios for Italy and Belgium, which are the OECD countries with the highest level of public indebtedness. (In 1986, Italy's ratio was 99 percent and Belgium's was 128 percent.<sup>4</sup> ) The other two cruntries, Turkey and Yugoslavia, had a lower ratio of total public debt to GDP thus indicating a lower level of public indebtedness. In Turkey this ratio reached a maximum of 63 percent in 1988 and in Yugoslavia it reached 52 percent in 1984.

External Public Sector Debt. Another important characteristic of the evolution of total public debt has been the large contribution of external public debt. In most of the six countries the external public debt accounted for more than half of the total public debt. This characteristic has been particularly clear during the periods of the highest level of public indebtedness, such as 4/ See: OECD (1990).
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# **6.3 Impacts on Firm Performance**

Table 4 shows that we generally do not observe significant treatment impacts on business performance outcomes; although due to power limitations, we cannot rule out some sizeable impacts.<sup>12</sup> Treated firms report insignificantly higher unconditional profits than control firms in the last year (Column 1). A large part of this difference is driven by the larger number of control-group entrepreneurs who closed their original firm, did not open a new firm, and hence report zero profits, and so differences in conditional profits are smaller and also insignificant (Column 2).<sup>13</sup> Almost all firms who stay open report positive profits in the last year; thus there is no evidence of access to psychometric-appraised loans keeping “zombie firms” alive during the challenging macroeconomic circumstances. That said, treatmentgroup firms report significantly _lower_ profits in the last 30 days prior to the endline survey in October 2021 compared to control group firms (Column 4 randomization inference p-value _<_ 0.1; and Columns 3 and 4 of Appendix Table A3 show significant reductions in levels).

It therefore appears that access to psychometric-appraised loans kept more, and slightly smaller, firms alive; that this at least did not lead to weaker average performance among the treatment group in the last year; but that treatment-group firms were performing worse specifically around the time of the endline survey. It may be that firms in the treatment group are in specific product or service lines that were particularly exposed to the challenging macroeconomic climate around the time of the endline survey — which took place just after national elections, and prior to those a late peak of COVID-19 and restrictions. We do not see evidence, however, that the new firms that opened up in the control group are those with the highest profits in the last 30 days. Nor, as mentioned above, do we see that the new firms are distributed differently across sectors. Thus if the control group had greater flexibility to

> 12The firm performance outcomes in Tables 4 and 5 and Appendix Tables A3, A5 and A7 are measured for the respondent’s main business (whether the original business, or a new one she opened); but results are almost identical across all businesses (Appendix Table A4), and 85% of women who are still operating a business at endline have only one.

> 13Table 4 shows the pre-specified Inverse Hyperbolic Sine transformation of the value outcomes, but we also present the values in Ethiopian birr in Appendix Table A3.

16
In the end, the selection criterion does not appear critical. We replicate the Mexico results defining very discrete categories based on both worker protections and firm size to ensure that, in fact, we are studying job transitions and the basic patterns remain. The reason is that a relatively small share (around 5-7%) of the workforce in firms of over 10 (presumably formal) reports being without coverage, and the contributions of changes in protection status within firm size categories to overall measured transitions is low. Likewise, we find there are relatively few workers who change protection status and graduate to an adjacent firm size category, capturing, potentially, a position being formalized as a firm formalizes. The bulk of our informal to formal transition are in fact, also transitions from micro firms of under 5 employees to substantially larger firms (non-adjacent firm categories). Given the short time span or our panels, the likelihood of large jumps in firm size seem unlikely. Hence, most of the transitions we are studying are, in fact, transitions among jobs.

The second group associated to informality is the large proportion (between 20% and 30% of the labor force) of independent or self-employed workers (S). This micro entrepreneurial group operating outside the regulatory system has been the focus of much of the literature dating from early work by Hart (1972). More recently, several authors have focused on the decisions of these individuals to work within or outside the regulatory system (most recently, see De Paula and Scheinkman (2007), Loayza Rigolini (2006), Fajnzylber et. al.(2007). Both Mexican and Brazilian labor surveys include two categories to capture this micro entrepreneurial sector, self-employment and owners of firms. Ideally, we would consider all those self employed (excluding professionals and technicians without social security contributions)<sup>12</sup> and the owners of small micro firms. However again, in Brazil, we lack a measure of firm size in the data. We are left here with the choice of dropping all owners of firms or including them as informal selfemployment. We again cross checked with the Mexican sample to get some insight of what proportion of self reported owners correspond to large formal firms. It turns out that only 1.5% of the universe of owners run firms with more than 15 employees. Therefore, for Brazil we choose to include all owners as informal self employed aware

> 12 There is also a relatively small group of self-employed in Mexico (less than 2% of all self-employed) that although under no legal obligation they satisfy the IMSS quotas. We consider them formal sector workers. In Brazil, however, self-employed do have the legal obligation to pay for social security contributions. This survey however does not provide this information. However, Henley et al. (2006) report that around 95% do not do so.

9
# Section 18: Transport Services and Road Infrastructure

<!-- Start of picture text --> SER. Do you have a [.......] What is the commonest mode | How long does it take you to Is the road usable all the What is the distance Why was the road<br>NO. in your community? of transport used to reach the | travel to the nearest [ROAD]? year round? from your household to | unusable?<br>nearest [ROAD]? an all year usable road<br>1=Bad weather<br>1=Yes 1= Walking 1=Yes 2=Bad terrain<br>2=No (>>NEXT 2= Taxi (car) 2=No (>>6) 3=Potholes<br>ROAD) 3= Boda-boda (if yes in qn 5) 4=Poor drainage<br>4= Bus/minibus 5=Bushy roads<br>5= Motorcycle 6=Insecurity<br>6= Bicycle 8=Other (specify)<br>T= Boat TIME IN MINUTES<br>8= Other (Specify) KILOMETERS<br>eeGr GS GS Sd,<br>Fa<br>What is Roadthe distance from | What type of road is this public AWas [ACTIVITY] affected by How was [ACTIVITY] affected?<br>your household to the transportation point/stage? HOUSEHOLD ACTIVITY your local road conditions?<br>nearest public transport INTERVIEWER: IF NEGATIVELY,<br>point/stage? 1=Yes PROBE FOR SEVERITY.<br>1= Trunk road (Tarmac) 2=No (>>NEXT ACTIVITY)<br>2= Trunk road (Murram) 1=Made it easier<br>3= District/feeder road 2=Did not affect much<br>4= Community Access Road 3=Made it a little more difficult<br>8=Other (specify) 4=Made it much more difficult<br>KILOMETERS 5=Made it impossible / almost impossible<br>tt<br>a i OO<br>a<br>education, etc.<br>END TIME | T ts] fT ones? es<br><!-- End of picture text -->
of the financial industry under the plan (People’s Bank of China et al. 2012).<sup>3</sup>

# **2.c. Assessing the quality of financial sector strategies**

The quality of financial sector strategies can be assessed on the basis of four categories of strategic objectives: financial development, systemic risk management, implementation arrangements, and policy trade-offs (Table 1 provides the rating criteria used, based on Maimbo and Melecky 2014).

Key evaluation criteria for the financial development objective are whether a strategy has clear and well-quantified objectives and whether it identifies tools to support its development goals. For instance, the financial strategy for Rwanda in 2007<sup>4</sup> states that “Rwanda seeks to develop a financial sector that is effective, in particular, by: (1) Expanding access to credit and financial services; (2) Enhancing savings mobilization, especially long-term savings; and (3) Mobilizing long-term capital for investment” (Murgatroyd et al. 2007). This description is clear, but it does not provide quantitative objectives or tools to achieve them. By contrast, to strengthen banks’ role in providing better access to financial services, Rwanda’s strategy quantifies the development objectives for the market shares and the pricing of bank products and supports them with one main action, namely the transformation of Union des Banques Populaires du Rwanda into a commercial bank, Banque Populaire du Rwanda.

Elements of an effective strategy to address systemic risk include: (a) the identification of potential risks, such as a significant increase in private sector indebtedness or imprudent behavior of financial institutions that could lay the foundations for instability; and (b)

> 3 China’s 12th five-year plan for the development and reform of the financial industry.

> 4 A FIRST initiative-funded team was recruited and prepared the Rwanda financial sector development strategy under the guidance of the central bank (BNR) and working closely with a national steering committee composed of key stakeholders in each of the areas addressed by the FSDP.

11
# **Table 4. Service EXPY and growth: 3 year panel data regressions, 1990-2007**

**(Controlling for Size of Service sector and Goods export sophistication)** (dependent variable – GDP per capita growth)

||FE|System GMM|FE|System GMM|FE|System GMM|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|log lag initial GDP/cap|-0.04***|0.009|-0.04***|-0.05***|-0.05***|-0.05***|
||(0.01)|(0.04)|(0.01)|(0.01)|(0.01)|(0.02)|
|**log lag initial service EXPY**|**0.03*****|**0.10***|**0.03****|**0.05***|**0.03*****|**0.06****|
||**(0.01)**|**(0.05)**|**(0.01)**|**(0.03)**|**(0.01)**|**(0.03)**|
|log human capital|0.0003|-0.02|-0.01|0.02|-0.02|0.03|
||(0.02)|(0.06)|(0.02)|(0.03)|(0.02)|(0.03)|
|log financial development|0.01**|-0.01|0.01|0.03|0.01|0.03*|
||(0.009)|(0.04)|(0.008)|(0.01)|(0.009)|(0.01)|
|Trade (% of GDP)|0.04***|-0.006|0.04***|0.01|0.04***|0.007|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Rule of Law|-0.009|0.02|-0.006|0.03***|-0.01|0.04***|
||(0.007)|(0.04)|(0.007)|(0.01)|(0.008)|(0.01)|
|log service value added (% of GDP)|0.02*|-0.06|0.02*|0.002|0.02*|-0.009|
||(0.01)|(0.10)|(0.009)|(0.05)|(0.01)|(0.05)|
|log lag initial goods EXPY|||0.02**|0.009|0.01**|0.007|
||||(0.009)|(0.01)|(0.009)|(0.01)|
|Drop India|No|No|No|No|Yes|Yes|
|Constant|-0.27**|-0.66|-0.28***|-0.38|-0.36|-0.38|
||(0.12)|(0.46)|(0.11)|(0.26)|(0.13)|(0.27)|
|R-squared|0.21||0.22||0.24||
|observations|357|357|362|349|345|345|
|Countries|93|93|95|93|92|92|
|Rho|0.90||0.90||0.92||
|Hansen J-statistic|||||||
|(p) value||0.54||0.40||0.27|
|Second-order serial|||||||
|correlation (p-value)||0.02||0.004||0.005|
|Note: All equations include period<br>Number of Instruments|dummies.|Fixed effects in<br>12|clude dumm|ies for countrie<br>38|s. GMM is|the Blundell-<br>38|

<mark>Note: All equations include period dummies. Fixed effects include dummies for countries. GMM is the Blundell-</mark> Bond System-GMM estimator using lagged growth rates and levels as instruments. The GMM estimation also uses log population and log area as additional instruments. * Significant at 10% level ** Significant at 5% level *** Significant at 1% level. Robust t-statistics are given in parentheses.

21