rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1. OLS Estimates of the Partial Correlation between Unemployment and the Digital Economy across Countries**

|||||**Pa**<br>|**nel A: Full S**<br>|**ample**<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Payments|0.138**|1.024*|-0.066|-0.338*|||||-0.297*|-0.289*|-0.403**|
|Payments(^2)|-0.002***|-0.028|-0.013|||||||||
|Users(%)|||||0.251***|0.487**|0.144|-0.072|||0.054|
|Users(%) (^2)||||-|0.003***|-0.007|-0.005|||||
|Informality (%)|||-0.262***|-0.266***|||-0.201***|-0.206***|-0.254***|-0.255***|-0.221***|
|Other controls|NO|NO|NO|NO|NO|NO|NO|NO|YES|YES|YES|
|Bank credit(%)|NO|NO|NO|NO|NO|NO|NO|NO|NO|YES|YES|
|Observations|151|61|61|61|182|66|66|66|60|60|59|
||**Panel B: Sa**<br>|**mple with Info**<br>|**rmality Impu**<br>|**ted for High In**<br>|**come (=0 for**|**all high-inco**|**me countries**|**without repo**|**rted inform**<br>|**ality)**<br>||
|Payments|0.138**|0.288***|-0.496***|-0.219***|||||-0.206***|-.193***|-0.152**|
|Payments(^2)|-0.002***|-0.004***|0.003**|||||||||
|Users(%)|||||0.251***|0.387***|0.083|-0.215***|||-0.030|
|Users(%) (^2)|||||-0.003*|-0.005***|-0.003*|||||
|Informality (%)|||-0.245***|-0.194***|||-0.133***|-0.194***|-0.187***|-0.190***|<br>-0.180***|
|Other controls|NO|NO|NO|NO|NO|NO|NO|NO|YES|YES|YES|
|Bank credit(%)|NO|NO|NO|NO|NO|NO|NO|NO|NO|YES|YES|
|Observations|151|93|93|93|182|99|99|99|92|92|91|
|||||**Panel C: S**|**mallest Com**|**mon Sample**||||||
|Payments|0.884|-0.139|-0.405**||||-0.366**|-0.358**|-0.403**|||
|Payments(^2)|-0.0267|-0.013||||||||||
|Users(%)||||0.485**|0.114|-0.066|||0.054|||
|Users(%) (^2)||||-0.006|-0.004|||||||
|Informality (%)||-0.249***|-0.252**|*|-0.211***|<br>-0.215***|<br>-0.237***|-0.238***|-0.221***|||
|Other controls|NO|NO|NO|NO|NO|NO|YES|YES|YES|||
|Bank credit(%)|NO|NO|NO|NO|NO|NO|NO|YES|YES|||
|Observations<br>Source:A|59<br>uthors’ calculati|59<br>ons based on da|59<br>ta from ILOSTA|59<br>T, Global Findex D|59<br>atabase, and t|59<br>he World Devel|59<br>opment Indicato|59<br>rs.Notes: * p<|59<br>0.10; ** p<0.05;|*** p<0.01. Th|e table reports|

<u>Source: Authors’ calculations based on data from ILOSTAT, Global Findex Database, and the World Development Indicators. Notes: * p<0.10; ** p<0.05; *** p<0.01. The table reports</u> OLS estimates. “Payments” refers to the share of adult population that reports having made a payment over the internet. “Users” refers to the share of the population with access to the internet. “Other controls” include the following variables: a. GDP % deviation from trend, and b. severance pay of redundancy dismissal (% of annual salary). Bank credit is private bank credit to GDP (%) and it is not statistically significant in any specification. See Appendix for sources and definitions.

27
- Burgess, R., R. Jedwab, E. Miguel, A. Morjaria, and G. Padró i Miqel (2015): “The Value of Democracy: Evidence from Road Building in Kenya,” _American Economic Review_ , 105, 1817–51.

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- Caliendo, L., F. Parro, E. Rossi-Hansberg, and P.-D. Sarte (2018): “The Impact of Regional and Sectoral Productivity Changes on the U.S. Economy,” _The Review of Economic Studies_ , 85, 2042–2096.

- Carlino, G. and W. R. Kerr (2015): “Chapter 6 - Agglomeration and Innovation,” in _Handbook of Regional and Urban Economics_ , ed. by G. Duranton, J. V. Henderson, and W. C. Strange, Elsevier, vol. 5 of _Handbook of Regional and Urban Economics_ , 349 – 404.

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40
There is also the possibility that some companies are deliberately adopting aggressive pricing strategies in order to drive competitors out of the market to gain market share. All the industry participants acknowledge that intermediation margins have been thin and returns on equity low and that is probably the reason why some life insurance companies have decided to exit the annuity market in recent years. Although the industry has become more concentrated and the possibility that the annuity market will undergo a process of consolidation similar to that observed in the pension sector in the 1990s cannot be entirely discarded, some life insurance companies have found a profitable business in the provision of voluntary savings for retirement. Since it is more heterogeneous, less transparent and therefore less competitive, smaller life insurance companies can compete in the voluntary savings market.

The differences between the degrees of competitiveness of the pension fund and insurance sectors are also reflected in the reported returns on equity (ROE) of AFPs and life insurance companies. As shown in Figure 12, while AFPs have generated ROEs above 20 percent and in some years even above 50 percent, the life insurance companies have earned much lower ROEs, despite bearing much higher levels of risk.

The ROEs of the two sectors are not directly comparable year by year, because the returns of pension funds and the ROEs of AFPs reflect a mark-to-market valuation, whereas the portfolios of insurance companies are valued by a combination of book and market values, largely book values. Moreover, the accounting ROEs of annuity providers are affected by a strict capital rule that imposes large provisions when the company sells an annuity and leads to accounting losses at the point of sale. However, the two indicators are more comparable over longer periods of time and the numbers indicate clearly a more competitive annuities industry.

**Figure 12: Returns on Equity of AFPs and Life Insurance Companies, 1997-2008**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>‐10% 1 99 7 1999 2001 2003 2005 2007<br>‐20%<br>‐30%<br>Life Insurance Companies AFPs<br><!-- End of picture text --> Sources: SVS, SAFP

# **4.2 Prudential Regulation**

Chile is the first country in the world that has mandated the use of retirement products with regular income streams over the expected life of beneficiaries (either in the form of life annuities or life expectancy phased withdrawals). As a result, it has introduced a rigorous regulatory 31
status as a result of debt relief at Decision Point. The share of women of the same status, birth year, and country giving birth in the same year is not affected by HIPC at Decision Point and this effect does not seem to differ systematically depending on mothers’ socioeconomic status, as can be seen in Table 8.

In an additional set of sensitivity tests, I analyze if a number of country-level potential confounding factors influence the overall findings in this paper. Table 10 presents the results. In column (1), I control for logged GDP per capita (Feenstra, Inklaar, & Timmer, 2015) which may be an important factor determining child health, as well as a channel through which debt relief affects the same variable. The estimation suggests that income plays a statistically significant role in determining infant mortality, but it does not change the conclusions on debt relief at Decision Point. In fact, the point estimate of Decision Point goes up a little. Aid flows, net aid transfers (NAT) (% of GDP, logged) (Roodman, 2012), and overall government expenditures (% of GDP, logged) (Feenstra et al., 2015) are potential confounders or channels, but neither affect infant mortality or influence the role of debt relief at Decision Point (columns (2) and (3) in Table 10). Furthermore, logged investments (% of GDP) and trade flows (% of GDP) (Feenstra et al., 2015), along with changes in the political and democratic institutions (Polity2 (Marshall, Jaggers, & Gurr, 2011), civil liberties (Freedom House, 2014)), and political corruption (Coppedge et al., 2015) may influence the findings on debt relief. However, columns (4), (5), (6), (7), and (8) do not show any signs of this and there are no changes in the Decision Point variable. Finally, country-level changes in the fertility rate (births per woman, logged) (World Bank, 2015) may be related to changes in infant mortality, but this does not appear to be the case in this sample (column (9)).

# **4.3 Health Mechanisms**

To analyze the potential health mechanisms through which reaching Decision Point of the HICP Initiative influences infant mortality I first test the impact of HIPC on neonatal mortality since the causes of death before one month are often different from those later in the first year of life (For The Million Death Study Collaborators, 2010). The results in Table 11 indicate that reaching Decision Point of the HIPC Initiative does not have any influence on overall neonatal mortality. There does however appear to be some heterogeneity across different sub-samples as there is a 0.33 percentage points decrease in neonatal mortality among poor mothers and 0.25 percentage points reduction among mothers without formal schooling. Both coefficients are statistically significant at the 10 percent level. This represents 8.3 and 5.7 percent of the sample means, respectively. In general, however, the reductions in neonatal mortality do not explain most improvements in infant health from reaching Decision Point. There does thus appear to be implications from reaching Decision Point which affect mortality after the first month of life.

14
1. <u>Introduction</u>

Can civic participation be externally induced? Can a policy intervention deepen democracy? These questions lie at the heart of development “as freedom” (Sen, 1999). Over the last two decades there has been a concerted effort by aid agencies to induce participation around the world with mixed results, with the World Bank alone spending around $85 billion (Mansuri and Rao, 2013). This paper examines the impact of a two-year effort to deepen democracy in the poor and arid region of north Karnataka in southcentral India. The intervention was evaluated using a mixed-method design. Survey data was collected over two rounds on a sample of two hundred villages, with half randomly assigned to be treated by the intervention. In addition, ethnographic methods were used to track a 10 percent subset of the quantitative sample over a four-year period. Thus, the paper also makes a methodological contribution to the literature on impact evaluations by combining a randomized control trial (RCT) with in-depth ethnographic research to understand the mechanisms behind the changes in outcomes measured by the 1 two rounds of quantitative surveys .

Summarizing the findings in brief, the quantitative data show no impact from the intervention. Both treatment and control areas demonstrate considerable change over time, but the differences in the changes between them are not statistically significant. The detailed qualitative data allow us to unpack the reasons why the intervention “failed” highlighting the role of variations in the quality of facilitation, lack of top-down support, and difficulties with confronting the stubborn challenge of persistent inequality. However, the qualitative investigation also uncovered subtle treatment effects that are difficult to observe in structured surveys. The paper thus demonstrates that a concerted effort to use thick description to uncover the process of change using careful and detailed qualitative work can add value to standard impact evaluation techniques.

There is a growing literature on the impact of interventions that attempt to improve the quality of participatory decision-making and planning at the community level. RCTs of such interventions generally find limited impact. Olken (2007) examines an intervention in Indonesia where communities that were part of the large participatory Kecamatan Development Program, were randomly assigned to a top-down effort at monitoring village expenses via an external audit, and to villages where communities were encouraged to participate in village meetings where local officials documented their expenditures.

> 1 Rao and Woolcock 2003, and White 2011, among others make the case for such mixed-methods evaluations to understand both “outcomes” and “processes” but there are very few published examples of such work - particularly with RCTs.

2
# **Table 6. The Investment Dynamics**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from OLS regression of investment on Titling Firm dummy variable for various post-event periods. The independent variable is the averaged investment (capital expenditure divided by PP&E) for a subsample period between 2013 and 2017 net of investment between 2010 and 2012. Column (1) presents parameter estimates associated with the average investment between 2013 and 2014. Column (2) presents parameter estimates associated with the average investment between 2015 and 2016. Column (3) presents parameter estimates associated with the average investment of 2017. Titling Firm is a dummy variable that is 1 if the firm has allocated land before the land title scheme in 2012 and 0 if otherwise. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||∆<br>|(Investment/PP&E)<br>||
|---|---|---|---|
||Year 2013/2014|Year 2015/2016|Year 2017|
||(1)|(2)|(3)|
|Titling Firm|0.130*|0.262***|0.304***|
||(1.888)|(3.395)|(3.443)|
|ROA|0.002**|0.001|0.002*|
||(1.997)|(1.441)|(1.722)|
|Tobin's Q|0.005|0.003|0.003***|
||(1.561)|(1.568)|(2.701)|
|Constant|-0.098**|-0.103**|-0.114**|
||(-2.516)|(-2.426)|(-2.559)|
|Industry FE|Yes|Yes|Yes|
|Exchange FE|Yes|Yes|Yes|
|Adj. R<sup>2</sup>|0.019|0.019|0.023|
|# of Obs.|1250|1250|1250|

52
- Schott, Peter. 2004. “Across-product versus within-product specialization in international trade.” _Quarterly Journal of Economics_ 119 (2):647–678.

- Sutton, John. 1997. “Gibrat’s legacy.” _Journal of Economic Literature_ 35 (1):40–59.

- Timoshenko, Olga A. 2015a. “Learning versus sunk costs explanations of export persistence.” _European Economic Review_ 79:113–128.

- ———. 2015b. “Product switching in a model of learning.” _Journal of International Economics_ 95:233–249.

- Verhoogen, Eric. 2008. “Trade, quality upgrading and wage inequality in the Mexican manufacturing sector.” _Quarterly Journal of Economics_ 123 (2):489–530.

25
Table S1.10: EFFECTS ON FAMILY TRANSFERS OF ULTRA-POOR HOUSEHOLDS

||Transfer<br>in past<br>(Yes=1)|s received<br>12 months<br>(BDT)|Transfe<br>in past 1<br>(Yes=1)|rs given<br>2 months<br>(BDT)|Net trans<br>in past<br>(Yes=1)|fers received<br>12 months<br>(BDT)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Panel A:_||_Transfers_|_From/to W_|_ealthier F_|_amily Memb_|_ers_|
|Treatment|-0.007|3.812|0.002|6.269**|-0.007|-2.457|
||(0.006)|(18.703)|(0.002)|(2.462)|(0.006)|(19.293)|
|Mean in control|0.087|137.473|0.007|2.636|0.087|134.837|
|Adjusted R-squared|0.066|0.032|0.006|-0.000|0.066|0.027|
|Observations|13464|13464|13464|13464|13464|13464|
|_Panel B:_||_Transfers F_|_rom/to No_|_n-wealthy _|_Family Mem_|_bers_|
|Treatment|0.001|-65.533|0.012***|2.983|0.000|-69.394|
||(0.005)|(56.269)|(0.004)|(39.763)|(0.005)|(49.065)|
|Mean in control|0.078|264.466|0.017|104.067|0.076|160.399|
|Adjusted R-squared|0.014|0.015|0.009|0.000|0.013|0.009|
|Observations|13464|13464|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All monetary values are measured in Bangladeshi Takas, deflated to 2007 prices using the annual CPI index published by the Bank of Bangladesh. In 2007, 1USD=69BDT nominal and 1USD=18.5BDT at PPP. *** (**) (*) indicates significance at the 1% (5%) (10%) level. **The dependent variables in Panel A refer to transfers received from or given to wealthier households who live outside the village but are part of the first-degree family (parents, siblings, children, parents-in-law or siblings-in-law) network of the respondent. The relative wealth of the network members is self-declared by the respondent at baseline. In contrast, the dependent variables in Panel B refer to transfers received from or given to poorer households who live outside the village and are not part of the first-degree family network of the respondent’s household.** In particular, the outcome variable in column 1 is a dummy variable (DV) equal to 1 if the respondent’s household received any transfers (in cash or in kind) during the 12 months preceding the survey; in column 2 it is the monetary value of transfers received during the past 12 months; in column 3 it is a DV equal to 1 if the respondent’s household gave any transfers to another household during the past 12 months; in column 4 it is the monetary value of all transfers given by the respondent’s household during the last 12 months; in column 5 it is a DV equal to 1 if the respondent’s household was a net transfer receiver in the past 12 months; in column 6 it is the monetary value of transfers received minus transfers made by the respondent’s household in the past 12 months.

10
37 back to 19 percent in December and has remained high since then.<sup>10</sup> The final effect of this program was thus not very different from previous ones that did much less in terms of fiscal and monetary adjustment. A frustrating outcome.

A new set of measures, the Collor II plan, was announced on January 31, 1991 to deal with the resurgence of inflation. One component of the new program was an attempt to deepen the fiscal adjustment, by dealing with the finances and the debt overhang of the state and local governments. **However,** this orthodox message was accompanied with the old practice of price and wage controls (which proved ineffective in the past), and attempts to new regulate financial markets, this time by eliminating overnight operations.

These mixed signals had a negative effect on the government's image. The initial attitude of the Collor administration of being tough and wilLing to pay the costs of disinflation, gave way to one where the authorities were concerned about reducing the costs of this process. In addition, the use of old failed policies also affected expectations in an adverse way, as they were associated with quick increases in inflation.

# c. Arantina

The Argentine stabilization program of July 1989, the Bunge & Born (83) plan, was the first stage of what has been a long term effort to stop hyperinflation. In contrast to the programs in Brazil and Peru, the 83 plan used the exchange rate as its nominal anchor, though as the others, it was solely based on orthodox measures and explicitly avoided the imposition of any type of controls on prices or wages. It also relied on a major fiscal 10 Monthly data disguises the fact that prices were fully stabilized for around 3 weeks early on.
Figure 2: Probit regression "marginal effects" of social organization activity on governance indicators: Private, Community, Total <!-- Start of picture text --> 50<br>40 _<br>^ 30<br>20 - 1 N<br>.,, 10 iS Private<br>U Social<br>TN '~~~~ 0 Total<br>; -0 -<br>-30<br>-40 -<br><!-- End of picture text --> were created for different purposes (e.g., economic, social, religious, etc.), these groups produce better governance outcomes than _desa_ government organizations, which were explicitly created to channel information and allow for participation in decision-making.

Figure 2 summarizes the results. The private effects are generally positive or very small. The community impacts vary widely both in sign and in magnitude. The net effect is "substantially" positive (greater than a ten percent increase in the indicator) in five cases (more open budgets, more participation in programs, household engagement in protest, expression of voice and responsiveness to economic problems) and only in one (participation in sanctions) is the association substantially negative.

_IV C) Regressions on desa aggregates_ 32
## **Dependent variable: ln(Specialization index for 2000)**

||**_(1)_**|**_(2)_**|
|---|---|---|
|ln(Population 2000)|-0.358|-0.502|
||(-5.28)|(-3.37)|
|ln(Population)*South dummy||0.151<br>(0.91)|
|Dummy for South region||-2.413<br>(-1.12)|
|Constant|-0.220|1.997|
||(-0.25)|(1.03)|
|**Adj. R**<sup>**2**</sup>|0.18|0.22|

t-values in parentheses

# **4. INDUSTRIAL DECENTRALIZATION**

As the urban system develops, typically manufacturing decentralizes out of the biggest cities first into their suburbs and nearby ex-urban transport corridors and then into smaller cities, with their lower cost of living, lower wages, and lower rents (Henderson _et al_ . 1995). Decentralization as noted above is spurred by inter-city and hinterland infrastructure investment and increasing overall sophistication of the labor force. In a modern system of cities the share of manufacturing in local economic activity tends to rise as we move down the urban hierarchy. As part of a domestic product cycle, traditional standardized products are manufactured in smaller cities and more high tech, innovative products in the biggest cities. In contrast to manufacturing, as we move down the urban hierarchy, the share of business services such as financial and legal activities in local economic activity declines. Conversely, the ratio of manufacturing to business services falls as we move up the urban hierarchy, reflecting the service orientation of bigger cities (Kolko 1999).

As suggested by theory, Brazil has experienced a manufacturing decentralization process between 1970 and 2000. In Table 9 we list the ratios of employed population working in the secondary and the tertiary industries in 1970 and 2000 (see also Figure 10). We group agglomerations into five size groups as before based on the relative population cutoff points. A comparison between 1970 24