rafmacalaba/datause-encoder-data huggingface dataset:
# **4. The increase in socially-oriented policy concerns**

Thus far we have focused our analysis on the discussion of poverty and inequality. But perhaps, the enlightenment we have documented regarding these two topics is part of a broader shift towards greater socially-oriented policy concerns on the governments’ side. In this section, we seek to address the following question: Is the poverty and inequality political enlightenment part of a broader shift towards an increased concern for social policy? As a first step in examining this question, in Figure 9 we explore how references to some social issues selected ad-hoc have evolved over time.

## **Figure 9. Evidence of increasing socially-oriented policy concerns**

_(% of speeches that contain each word by decade)_

## (a) Long-standing social issues

## (b) Emerging social issues

<!-- Start of picture text --> 100 100<br>Development<br>75 Education 75 Drugs<br>Global Terrorism<br>50 Violence 50 warming<br>Crime<br>25 25<br>Health Democracy Illegal<br>0 0<br>(c) Household (d) Labor market<br>100 100<br>Work<br>75 75<br>Growth<br>Family<br>50 50<br>Jobs<br>Women<br>25 25<br>Elderly<br>0 0 Unemployment<br>1830 1850 1870 1890 1910 1930 1950 1970 1990 2010 1830 1850 1870 1890 1910 1930 1950 1970 1990 2010<br>1830 1850 1870 1890 1910 1930 1950 1970 1990 2010 1830 1850 1870 1890 1910 1930 1950 1970 1990 2010<br><!-- End of picture text --> Note: This Figure shows the percentage of presidential speeches on each decade that contained at least once each of the keywords and were calculated pooling the presidential speeches of 10 Latin American countries. See Appendix A for more details about the data.

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Lowenstein, Sunstein and Golman (2014) argue that disclosure policy implemented successfully may have little effect on consumers but large effects on financial institutions. Financial disclosure regulation in the countries we study could however be improved. For example, basic guidance could be provided to consumers on the key questions to ask when looking for a credit or savings product. In addition, information could be provided more transparently as tested in a follow-up to this study in Mexico (Giné, Martinez and Mazer, In process). Of course, lenders may choose in equilibrium to spend little time with low-income customers because they are expensive to service, but aggregate terms such as the APR and APY should be adequately disclosed voluntarily as required by law. The correlation between the disclosed cost in printed materials and the total cost of credit and savings products is 0.34 and 0.81, respectively, in Peru and Mexico but only 0.28 and 0.24 in Ghana where the APR/APY do not have to be disclosed. In addition to disclosing the APR and APY to enhance comparability across similar products, staff should also disclose usage fees (not reflected in the APR and APY) because they can significantly impact the total yield and cost of products.

One may think that clients will anticipate the motives of self-interested staff and therefore discount their advice, but empirical research has shown that clients follow financial advice blindly (Engelmann et al. 2009) not aware of the strategic aspects in the advice relationship.

The second key finding is that staff responds to incentives by limiting the offer of basic savings accounts in Mexico and by reducing the loan amounts approved in applications from over-indebted auditors. This finding suggests that by lending responsibly in the context of consumer lending, financial institutions are not contributing to financial instability.

This study covers consumer credit and savings products contracted at the branch and may therefore be expensive to offer, especially to low-income populations. A promising avenue is the take-up of low-cost mobile or online savings accounts, or access to financial products as a byproduct of government-to-person payment programs.

Finally, this study focuses on the disclosure of information during the period until the product is contracted (pre-sale period). While the terms of the credit products studied cannot be changed, those of savings products could and as a result, assessing post-sale disclosure may also be important in future research.

# **References**

Agarwal S., Driscoll J, Gabaix X, Laibson D. 2013. “Learning in the Credit Card Market”, mimeo. Available at http://dx.doi.org/10.2139/ssrn.1091623 Anagol S, Cole S, Sarkar S. 2012. <mark>“Understanding the Advice of Commissions-Motivated Agents: Evidence from the Indian Life Insurance Market.”</mark> Harvard Business School Finance Working Paper No. 12-055.

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BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE attracted private investors not only to ICT but also to other sectors. The Democratic Republic of Congo, Liberia, Nigeria, Uganda, Kenya, and Senegal all have captured between 1.8 and 2.5 percent of GDP.

**Figure 26. Private investment by sector in various African countries**

As percentage of GDP <!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Water Transport Power ICT<br>Percentage of GDP<br>Senegal Kenya Uganda Nigeria Liberia Sierra Leone Mozambique Tanzania Rwanda Ghana Zambia Burkina Faso Mali Benin Seychelles South Africa Cameroon Niger Lesotho Gabon Madagascar Guinea Malawi Mauritania Cote d'Ivoire Zimbabwe Angola Burundi Congo, Rep. Mauritius Chad Botswana Togo Swaziland Namibia<br>Sao Tome and ... Guinea-Bissau Central African ...<br>DRC Congo, Dem. ... Equatorial Guinea<br><!-- End of picture text --> _Source_ : AICD calculations.

Adopting lower-cost technologies could substantially reduce the cost of meeting infrastructure targets; in fact, it could eliminate the funding gap. The overall savings from adopting alternative technologies to provide a given level of service would amount to $227 million, or as much as 108 percent of the country’s total infrastructure funding gap, underscoring the importance of optimal technology choices (table 21).

**Table 21. Savings from innovation**

|US$ million|**Before**<br>**innovation**|**After**<br>**innovation**|**Savings**|**Savings as % of**<br>**sector funding gap**|**Savings as % of total**<br>**funding gap**|
|---|---|---|---|---|---|
|WSS, appropriate technology|283|144|139|82%|66%|
|Power trade|222|178|44|106%|21%|
|Roads, appropriate technology|116|72|44|696%|21%|
|Total|621|394|227|108%|108%|

_Source_ : AICD calculations.

The greatest saving in both absolute and relative terms would come in the water supply and sanitation sectors. Meeting the Millennium Development Goals for water supply and sanitation with lower-cost technologies than previously used could reduce the associated price tag from $283 million to $144 million each year, reducing the funding gap in the water sector by as much as 80 percent (table 22).

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**Table 3. The determinants of government expropriation**

||(1) Tax/Sale|(2) Fee/Sale|(3) Special<br>Assessment/Sale|(4) ETC/Sale|
|---|---|---|---|---|
|Ln asset|-0.001**|-0.003***|-0.001***|-0.001***|
||(0.001)|(0.001)|(0.000)|(0.000)|
|Firm age|-0.000|-0.000|-0.000***|-0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Ln employee|-0.002**|-0.002***|-0.002***|-0.003***|
||(0.001)|(0.001)|(0.000)|(0.000)|
|Female|0.001|0.006**|0.003|0.003|
||(0.002)|(0.003)|(0.002)|(0.002)|
|Year of schooling|-0.000|-0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Former cadre|0.003|0.001|0.002|0.002|
||(0.003)|(0.002)|(0.001)|(0.002)|
|Former|0.001|0.000|-0.001|-0.002*|
|manager|(0.002)|(0.002)|(0.001)|(0.001)|
|In Congress or|0.004**|0.001|-0.000|-0.001|
|Consultative Conf.|(0.002)|(0.001)|(0.001)|(0.001)|
|Party member|0.000|-0.001|-0.002*|-0.001|
||(0.002)|(0.001)|(0.001)|(0.001)|
|Prov., Sector, year|Yes|Yes|Yes|Yes|
|Observations|11,166|9,184|7,545|9,244|
|R-squared|0.034|0.050|0.031|0.048|

_Data sources: National Surveys of Privately Owned Enterprises in China._ Notes: Standard errors are clustered at provincial level and reported in parentheses. Significance levels 0.1, 0.05 and 0.01 are noted by *, **, and ***, respectively.

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mostly dynamic orientation that naturally calls for policies that affect the path leading to a crisis. Instead, interconnectedness externalities are mostly associated with structural or architectural reforms, while information externalities naturally call for information-based reforms.

**Table 1: Marginal Regulatory Responses as Frictions and Volatility Accumulate**

|||**Age**<br>|**Frict**<br>**ncy**<br>|**ions**<br>**Agency + Co**<br>|**llective Action**<br>|
|---|---|---|---|---|---|
|||**_Full Rationality_**|**_Bounded Rationality_**|**_Full Rationality_**|**_Bounded Rationality_**|
|||**IFA**|**IBA**|**IFC**|**IBC**|
|**Aggregate**|**_No_**|Market Integrity and<br>Transpacency|Consumer Protection<br>Microprudential|Market Correction<br>Microprudential|Basel II<br>Microprudential|
|**Volatility**||**GFA**|**GBA**|**GFC**|**GBC**|
||**_Yes_**|Time Consistency<br>Macroprudential|Dynamic Alignment<br>Macroprudential|Collective Action<br>Macroprudential|Collective Cognition<br>Macroprudential|

**Table 2: Macroprudential Policy Map**

_(Aggregate volatility (_ **_G_** _) permeates all cells)_

||**Agency Frictions**<br>**(A)**|**Agency + Collective Action Frictions**<br>**(C)**|
|---|---|---|
||**GFA**|**GFC**|
|**Full Rationality**<br>**(F)**|Private Sector - Public Sector Wedge<br>**_Time Consistency Macroprudential_**<br>Correcting incentive distortions due to<br>expectation of post-crisis interventions|Individual - Social Wedge<br>**_Collective Action Macroprudential_**<br>Internalizing externalities (controlling<br>crowds and crowding risks)|
||**GBA**|**GBC**|
|**Full + Bounded**<br>**Rationality**<br>**(B)**|Sophisticated - Unsophisticated Wedge<br>**_Dynamic Alignment Macroprudential_**|Collective Perception - Reality Wedge<br>**_Collective Cognition Macroprudential_**|
||Aligning principal-agent incentives<br>on behalf of the unsophisticated|Tempering mood swings<br>where rational arbitrageurs fail|

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A mixed-method approach can also contribute to incorporating different dimensions of particular indicators (Shaffer, 2013). Standard quantitative measurements of poverty such as consumption per capita, for example, can be weighted according to local or contextual definitions or perceptions of what ‘poverty’ means (Kristjanson et al, 2010).

**c) Causation and model specification:** By having detailed knowledge of a particular context, qualitative work can be helpful in solving endogeneity problems<sup>11</sup> and can reveal the direction of causality by identifying instrumental variables (Ravallion, 2000; Rao and Woolcock, 2003). (Some qualitative researchers also argue that techniques such as process tracing can be used to make causal claims of their own—e.g., Bennett (2010)—and note that case study evidence is routinely the basis on which causal arguments are made and defended in ‘real world’ settings such as court rooms—Honore (2010). However, we shall not address such matters here.)

**d) Quality and reliability of data collection:** Understanding the context through qualitative analysis is not only useful with regards to knowing what should be assessed in a survey or what should be included in an equation. It also contributes insights as to how and to whom questions should be asked or assessed, given that the quality of the data obtained depends on the collection methods used with specific objectives in specific contexts. As an example, Sana et al. (2012) conducted a study in the Dominican Republic and found that respondents answered differently depending on the type of questions asked by type of interviewer (local or external). They found that respondents reported higher income and higher tolerance towards marginalized groups to external interviewers compared to the responses given to local interviewers. Hence, qualitative methods can help to improve the quality of the data by exploring the best ways in which a question should be asked, how and to whom it should be asked, and by whom.

Parallel qualitative data collection techniques such as those generated by participant observation or case studies can also help to assess the reliability and quality of the data collected through surveys. The IFC Lima Tracer Study, for example, which assessed the impact of firm formalization on the performance of micro firms in Lima, found significant divergence from survey responses when the team conducted in-depth interviews to try to understand the low

> 11 In evaluation, endogeneity problems stem from biased estimates of impact due to issues such as omitted variables or measurement error, which weaken the claims of attribution. In principle, experimental designs greatly reduce these problems by ensuring that any such biases are at least equally present in the treatment and control groups.

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Entrepreneurship Monitor, researchers and policymakers can better understand the variables that affect the business creation process.

Despite effort to minimize disparities and make the data comparable across countries, certain limitations preclude a completely systematic analysis of entrepreneurial development. The following represent the most frequently faced problems in the process of gathering and processing the data:

- _Data availability_ **.** Several countries were only able to provide data on newly registered companies—and not able to provide data on total registered companies—since recent registration information is stored electronically, but historical data was not digitalized. Some countries were excluded from this survey because they lacked tools or resources to measure business registration. For instance, some countries have decentralized business registries that make aggregation at the national level extremely difficult. In other cases, the data are archived only in paper format.

 _Limitations regarding data on firm closures_ **.** Although approximately 80 percent of surveyed countries require businesses to report closures, only a few were actually able to report the number of business closures. The reasons differ from country to country, but are mainly due to the fact that the registrars generally have no enforcement mechanisms to compel businesses to report closures. In other cases, the number of closed businesses was reported may be imprecise because only a low percentage of reporting businesses. Although the number of closed companies is essential to paint a clear picture of the economic and entrepreneurial activities of a country, it is not yet feasible to obtain comparable data.

In this regard, while information on “active” companies—excluding closed or inoperative businesses—is sometimes available from national tax agencies and labor ministries, the research shows that only a low number of countries can actually provide this data. Therefore, it was decided to focus on the business registries. Yet, this is an important indicator of private sector activity for business registrars to measure.

- _Shell corporations_ **.** Shell companies are defined as companies that are registered for tax purposes, but are not active businesses.  These corporations do not fit into

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Table 2. Datt-Ravallion Decompositions

|||Growth|Redistribution|
|---|---|---|---|
||Bangladesh,2000–10|85%|15%|
|US$1.25-a-day|Ghana,1998–2005|103%|-3%|
||Nepal,1996–2003|132%|-32%|
||Argentina,2000–10|43%|57%|
||Brazil,2001–09|54%|46%|
||Chile,2000–09|57%|43%|
||Colombia,2002–10|84%|16%|
||Costa Rica,2000–08|113%|-13%|
|US$4-a-day|Ecuador,2003–10|64%|36%|
||Honduras,1999–2009|105%|-5%|
||Panama,2001–09|63%|37%|
||Paraguay,1999–2010|43%|57%|
||Peru,2004–10|84%|16%|
||Thailand,2000–09|105%|-5%|
|US$5-a-day|Moldova,2001–10|98%|2%|
||Romania,2001–09|98%|2%|

_<mark>Source:</mark>_ <mark>Authors’ calculations with data from SEDLAC (CEDLAS and the World Bank), RIGA for Ghana and Nepal, and from household surveys for Bangladesh, Moldova, Romania, Peru, and Thailand.</mark> <mark>Consumption-based measures of poverty are used in the case of Bangladesh, Ghana, Nepal, Peru, Thailand, Moldova, and Romania. Income-based measures of poverty are used in the case of Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Honduras, Panama, and Paraguay.</mark> 26
As has been noted, the richness of the Enterprise Survey data provides for direct ways to test for the links between quantitative and qualitative indicators, and to link them to firm characteristics and firm performance. This is done in the next section.

# **4.    How well do subjective responses reflect objective conditions?**

A first step in determining the validity of subjective responses is to see how well they correspond to more quantitative measures of conditions facing firms. One would expect that within an area, those facing worse objective conditions would complain more about that area. We run the following regressions for each of the relative measures of subjective investment climate constraints: Where Xi is a set of k dummy variables identifying firms’ characteristics: size, firm age, location , ownership (domestic, foreign and government), exporter (yes-no);  s and  c are sector and country dummies We find strong empirical evidence that perceptions reflect the real investment climate. Within the thirteen issues areas for which the survey has related quantitative follow-on questions,<sup>8</sup> we tested if the firms that report longer delays, greater costs or lower quality service are the same ones that report that issue as being more constraining. In each case, the answer is yes. The coefficients _ß1_ are extremely significant and with the appropriate sign.

> within country variations as indications of different firms’ demand for these goods.  They predict that more productive firms will likely complain more when comparing within a country, but that they will complain less when comparing across countries.   In this paper, country and sector dummies are included.   As the evidence presented shows, there is actually considerable variation within country in the quantitative measures that can be exploited and is meaningfully related to the subjective rankings reported.

> 8 The survey did include four other issue areas.  However, for two of the areas (macroeconomic instability and access to land) there are no additional objective indicators included in the survey.  For two others (courts and operative licenses) there are too few firms that either used the court system or applied for a new operative license in the year prior to the survey.

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# **1. Introduction**

Since the early 1990s, many countries in the Arab world have embarked on significant financial and economic reforms, involving internal and external financial liberalization, as well as efforts to increase the depth, scope, and efficiency of their financial systems. At the same time, Arab financial systems have shown considerable improvements over the last two decades. Although policy makers in the region have realized the importance of expanding the breadth of their financial systems and operating active capital markets, Arab financial systems are often perceived to be underdeveloped. In particular, financial markets are still highly bank-based, thin, tightly regulated, and dominated by government ownership. Within the Arab world, financial systems in the Gulf Cooperation Council (GCC) tend to be more developed and globally integrated than those of other countries in the region.<sup>1</sup> This paper uses a unique data set to provide a first documentation of the extent to which firms in the Arab region use capital and syndicated loan markets to obtain financing and grow. We address three main questions as follows. (1) What is the total amount raised in equity, bond, and syndicated loan markets by firms in the Arab region and how does their issuance activity stand with respect to the rest of the world? (2) How many and which firms actually issue equity, bonds, and syndicated loans? (3) How do the assets, turnover, and number of employees evolve for issuing relative to nonissuing firms?

To address these questions, we assemble a comprehensive transaction-level data set on equity, corporate bonds, and syndicated loans issued in domestic and international markets over the 19912014 period. The data include 719,242 individual security issuances conducted by 138,091 (listed and non-listed) firms from 96 countries. For Arab countries, we then match these data with balance sheet information on publicly listed firms. Our matched data comprise 1,462 firms over the period 2003-

> 1 T he Gulf Cooperation Council states include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates.

1
procedure. To the extent that the conditional mean of the distribution may be far away from the food insecurity line and that marginal effects may effectively differ across the distribution, this can be problematic for our food insecurity estimation method. We test the plausibility of our assumption by investigating the heterogeneity in the RSQ effects along the food consumption distribution and we estimate quantile regression models to uncover differences in RSQ effects across conditional quantiles. We first consider our initial specification without interaction and we estimate the marginal effect of the RSQ on the FCS for quantiles ranging from 0.05 to 0.95. Results presented in Figure 6a reveal an inverted U-shaped pattern with a marginal RSQ effect peaking at above 4 around the 6<sup>_th_</sup> decile and falling to around 2 at the 5<sup>_th_</sup> percentile. We are particularly interested in better understanding how the marginal effect varies within the domain of the distribution that corresponds broadly to the subset of households around the food insecurity line and thus susceptible of coming in and out of food insecurity due to climate variability. Based on historical national food insecurity rates, we evaluate this is roughly the 25<sup>_th_</sup> -45<sup>_th_</sup> percentile – interval.<sup>35</sup> Our estimated marginal effects vary only slightly on this interval between – approximately 3 and 3.5 and is broadly in line with our OLS estimate, which does not invalidate the assumption held in the food insecurity estimation method. We also estimate quantile regressions on logged food consumption which permits a utility interpretation of the results. Interestingly, we find the highest marginal effects (around 9%) at the bottom of the distribution, which then decreases at around 7% between the 3<sup>_rd_</sup> and 6<sup>_th_</sup> deciles before markedly declining to approximately 2.5% at the top of the distribution. This is broadly consistent with the notion that, in relative terms, the poorest are more vulnerable to climate variability. We replicate the analysis on the specification including a regional fixed-effects interaction and leave results in appendix C.4 for the sake of conciseness. It is worth noting that, for some regions, we find that the OLS estimate differs quite significantly from marginal effects estimated in the percentile interval of interest. In our final prediction model calibration, we partially address this issue by allowing for distinct vulnerability functions across household groups within regions, defined based on characteristics that also capture differences in food consumption ceteris paribus. Also, we estimate our final model with quantile regressions at different percentile values and choose the value that minimizes prediction errors at the regional level.

> 35We estimate moderate food insecurity rates at the national-level from FSMS for the period 20112015 and we find a minimum of 28.8% in 2013 and a maximum of 41.9% in 2012 as a result of the 2011 drought. The 25<sup>_th_</sup> -45<sup>_th_</sup> percentile interval considered is thus a conservative interval.

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