rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Extensive credit information sharing can have several benefits for the financial system. First, economic theory predicts that reliable credit information can address the fundamental problem of asymmetric information between borrowers and lenders (Padilla and Pagano, 2000, and Stiglitz and Weiss, 1981). Cross-country evidence confirms that the availability of detailed information about the borrowing and repayment behavior of prospective clients places banks in a better position to assess default risk, counter adverse selection, and monitor institutional exposures to credit risk (Pagano and Jappelli, 1993, Japelli and Pagano, 2002, Miller 2003). A recent study of a Guatemalan micro-lender provides evidence that information sharing is also effective at altering borrower behavior, countering moral hazard, and improving repayment rates (de Janvry, McIntosh, and Sadoulet, 2010). Perhaps most importantly, credit reporting allows borrowers to build a credit history and to use a documented track record of responsible borrowing and repayment as “reputational collateral” to access credit outside established lending relationships (Love and Mylenko 2003).<sup>2</sup> In addition, financial regulators can draw on credit reporting systems to understand the credit risk faced by financial institutions and systemically important borrowers, to define capital provisioning requirements, and to conduct essential oversight functions.

Despite the numerous benefits of information sharing for credit market efficiency, credit reporting institutions do not always emerge spontaneously. About 26 percent of countries do not have any credit reporting institution at all. Another 19 percent have only a public (i.e. government mandated) credit registry, but no credit bureau<sup>3</sup> . One reason why a credit bureau may not emerge voluntarily is that the private returns to information sharing are not necessarily aligned with its public returns. This paper examines two features of the banking market – competition and concentration – that can influence private returns to joining a credit bureau and that can thus affect the probability of a credit bureau emerging.

Pagano and Jappelli (1993) develop a model which predicts that the incentive to share information is greater when lenders are protected from competition by barriers to entry. Brown and Zehnder (2010) find empirical evidence for a negative relationship between competition and

> 2 See also Padilla and Pagano (2000) 3 Based on the Doing Business dataset that is described in more detail in section 3.

2
26 approach, which exploits differences in conditions within countries. Changes in income distribution respond to so many different stimuli—in a general equilibrium environment—that no single method has yet been developed to fully identify the causes of all observed changes. Instead, researchers have relied on a variety of different approaches. Sub-national regression analysis (using geographical panel data) sheds light on the relative importance of sectoral growth patterns, and of initial differences in the distribution of land or human capital. Micro-simulation based decompositions of growth incidence curves can help us understand the relative roles of changes in household endowments; changes in returns to those endowments; and changes in participation and occupational choices. Finally, combining such micro-simulations with models capable to capturing the general equilibrium transmission of initial shocks can help us understand the distributional impact of broad, economy-wide policy changes.

As we move forward, more research is needed on all of these fronts, and in their integration. It is only from such research that we can hope to learn what enables some countries (such as Vietnam) to grow rapidly with little or no rise in inequality, and thus to enjoy dramatic rates of poverty reduction. The diversity of country experience has established that equitable growth is possible, and that it is particularly pro-poor. But much remains to be learned about both the general economic conditions and the policy context within which it is achievable.
# Table (1.3) Economic Structure and Performance in Sub-Saharan Africa

|1970-81|1982-85|1986-89|
|---|---|---|
|Real arowth rates|||
|GDP<br>3.7|1.4|2.0|
|Exports<br>3.6|1.1|3.3|
|Share in**GDP**|||
|Agriculture<br>39.3|36.7|36.3|
|Manufacturing<br>20.1|22.0|21.7|
|Services<br>37.9|41.3|**41.2**|
|Share in Labor Force|||
|Agriculture<br>78.5|||
|Manufacturing<br>7.8|..||
|Services<br>13.7|||
|Rural population (% of total population)<br>79.6|74.4|71.2|
|Source: World Bank Data Bank (BESD)|||
captured by a firm time invariant effect and control for firm heterogeneity. Unfortunately, our data do not have a longitudinal dimension for most of the countries and we are thus unable to account for this heterogeneity.<sup>11</sup> Alternatively, we propose to proxy the degree of firm openness with sector and regional aggregates for foreign direct investment and exports (which are arguably more exogenous to firm outcomes). In particular, we have computed regional and sector averages for foreign presence and export intensity.<sup>12</sup> Finally, we will test the robustness of our findings to different samples. We specifically will consider the following diverse samples: country level, low income versus middle income (high income), manufacturing versus services, high tech versus low tech, and capital city versus other cities. We will also test the robustness of the main results by exploring alternative measures of technological innovation in the firm.

# **4. Main Empirical Findings**

Before discussing the main findings, we summarize briefly the evidence on the two main channels of the demand for skilled labor discussed in the paper: firm openness and firm technological innovation. Tables 2 through 4 report the share of firms that export, share of firms with FDI and the share of firms adopting new technology , by country and manufacturing sector of activity, respectively.

The last row in table 2 reports the mean share of exporting firms for all countries in each manufacturing sector. We measure export intensity using a dummy variable equal to one when firms export and foreign ownership with a dummy variable equal to one for firms with more than 10 per cent of foreign capital.<sup>13</sup> Cambodia and Malaysia are the countries with the greatest share of exporting firms. In Malaysia exports are also present across a significant share of all the manufacturing sectors. On average, 82 per cent of the firms in Malaysia export, and this share is never below 74 per cent in each manufacturing sector. Cambodia is also a country with a high export intensity in our

> 11 The only exception is the _Enterprise Survey_ in Cambodia where there surveys were conducted in two different years and some firms can be traced over time. However, we only two waves, there is little time variation on the share of exports or foreign direct investment at the firm level to precisely identify the effect of openness.

> 12 In these specifications, we do not control for regional sector-fixed effects because of perfect colinearity. The results (not reported but available on request) are similar to the ones presented in our base specification except that they tend to be statistically weaker for all the variables of interest.

> 13 In table A1 in the appendix, we report definitions for all the variables used throughout the paper.

12
informal, temporary/irregular, or both. Almost 30 percent of jobs were for the government or public enterprises, and 16.4 percent were in self-employment or family workers (on farm or off).

In Georgia, in contrast, 35 percent of jobs in 2019 were in self-employment and 22.7 percent in unpaid family work. Wage jobs for the public sector and for the private sector both comprised lower shares of jobs (17.2 and 24.6 percent, respectively) than in Jordan. Wage work for the private sector in both countries was _Table 3-2: Employment Structure, Jordan (2016) and Georgia (2019)_

|Mode of Employment|Share of employed (all ages)<br>Jordan|Georgia|
|---|---|---|
|1. Farmer(owns a farm/self-employed)|0.4|35.5|
|2. Business owner/self-employed(but not a farmer)|14.4||
|3. Unpaid familyworker on a farm|1.2|22.7|
|4. Unpaid familyworker(but not a farmer)|0.4||
|5. Wage worker for Government/ public enterprises|29.6|17.2|
|6. Wage Worker for theprivate sector|54.1|24.6|
|Formal and regular|22.1|-|
|Permanent<sup>1/</sup>|21.7|11.0|
|Formal irregular or temporary|0.7|0.5|
|Informal regular orpermanent|23.2|11.0|
|Informal Irregular or temporary|8.1|2.1|

Source: Jordan Labor Market Panel Survey (LMPS) 2016 and Georgia Labor Force Survey (GLFS) 2019. Notes: GLFS 2019 does not distinguish between owner-operation of or unpaid family work on an on-farm versus an off-farm business Since GLFS 2019 does not include questions on regular/irregular workers, only temporary/permanent classification is reported in the table. For Georgia, permanent workers are defined as those reporting that they are permanent workers. For Jordan, permanent workers are defined as individuals who either report they are permanent workers or who work full-time as regular employees.

overwhelmingly regular or permanent, and temporary work represented a very small share of private sector jobs (Table 3-2).

Informal employment constituted an important component of jobs in both countries. Beyond selfemployment and employment by the family, much of which may be informal, informal wage employment represented 31.3 percent of jobs in Jordan and 13.1 percent of them in Georgia (Table 3-2).

3.3 Response to the Pandemic

# **3.3.1 Jordan**

Beginning on March 21 of 2020, the Government of Jordan put in place rigorous measures to contain the spread of COVID-19, including the closure of businesses and work stoppages for all but essential economic activities, as well as non-essential movement restrictions. In an effort to stem job losses, Defense Order #6 (DF6), also issued in the spring of 2020, prohibited layoffs by registered private firms, unless they were “frozen” or permanently closed. Because many workers still experienced significant wage reductions and others suffered from the cessation of their employers’ operations, Jordan also launched subsidy schemes—the most important of which started in January of 2021. According to the CFUWBES data, despite DF6, even firms 10
sources of innovative manpower are nationals from the regional market (28.6% of companies who report a lack of such skills).

**Table 4.4.4: Characteristics of most likely source of innovators**

||Percentage of firms who lack<br>a skill/ability|
|---|---|
|National from regional market|28.6|
|National from local market|19.6|
|Expat from N.America/Europe|16.1|
|Expat from regional market|14.3|
|National from N.America/Europe|14.3|
|Expat from local market|7.1|
|Total|100.0|

_Note_ : One company ticked both national from local market and expat from N. America/Europe, and two companies ticked both National or expat from N. America/Europe. Their responses were split half to each Overall, 52.4% of companies have employed people from other cities as a substitute for skills which are scarce in Singapore, although KIBS companies do so much less frequently than those in the other two sectors. Firms which employ this generally find this arrangement works well ( **Table 4.4.5** ). Such arrangements vary according to the needs of the firm: it may involve employing staff from overseas, engaging consultants from other countries, sending staff for training, or outsourcing. The countries involved are widespread, including the US, Europe, ASEAN, China, India, Canada, Taiwan and Japan.

**Table 4.4.5: Substitution of skills that are scarce in Singapore by using people located elsewhere**

|Has the firm tried to substitute for skills that are scarce<br>inSingapore by using peoplelocated elsewhere?|High-tech|KIBS|Media/<br>creative|Overall|
|---|---|---|---|---|
|No|40.0|64.3|38.5|47.6|
|Yes|60.0|35.7|61.5|52.4|
|Total|100.0|100.0|100.0|100.0|
|||Mea|n score*||
|Ifyes, success ofarrangementisrated as|3.8|4.2|4.1|4.0|

Measured on a Likert scale of 1 (not at all) to 5 (very well) The above findings are consistent with earlier studies on the supply of IT professional manpower and R&D scientists and engineers in Singapore in the late 1990s that reported a high degree of dependence on foreigners (Wong 2004). While the small domestic population is clearly a contributing factor, another reason is the need to tap people with international work experience or regional market exposure, due to the regional hub role of many of the surveyed firms.

41
4 Table 3: Trade Reduction Indexes, Asian, African, Latin American, Europe’s transition economies and high-income regions<sup>a</sup> , all covered tradable farm products, 1960 to 2007

||||(per|cent)|||||||
|---|---|---|---|---|---|---|---|---|---|---|
||1960-64|1965-69|1970-74|1975-79|1980-84|1985-89|1990-94|1995-99|2000-04|2005-07|
|**Import-competing products**|||||||||||
|Africa|-28|-23|-19|3|0|112|7|10|4|na|
|Asia|11|25|19|26|38|70|68|63|76|na|
|Latin America|28|27|11|2|6|1|32|11|20|na|
|All developing countries|-1|20|10|11|7|48|26|10|16|na|
|Europe’s transition economies|na|na|na|na|na|na|13|23|26|29|
|High-income countries|79|80|52|72|88|89|83|84|81|63|
|World|64|55|42|56|58|80|59|60|62|na|
|**Exportable products**|||||||||||
|Africa|29|39|43|47|41|36|38|24|30|na|
|Asia|14|27|26|23|35|20|17|8|0|na|
|Latin America|20|15|28|22|23|21|5|2|3|na|
|All developing countries|22|29|32|30|34|25|17|9|6|na|
|Europe’s transition economies|na|na|na|na|na|na|0|2|-2|-9|
|High-income countries|-8|-12|-9|-5|-8|-21|-13|-4|-2|-2|
|World|3|7|11|12|17|8|4|4|3|na|
|**All covered farm tradables**|||||||||||
|Africa|32|33|33|34|18|54|17|16|23|na|
|Asia|15|28|23|28|34|28|18|8|6|na|
|Latin America|22|8|19|17|19|13|23|7|8|na|
|All developing countries|26|28|26|28|28|29|22|9|10|na|
|Europe’s transition economies|na|na|na|na|na|na|-4|13|14|2|
|High-income countries|19|9|16|21|27|30|28|18|18|7|
|World|21|17|20|24|28|30|21|14|14|na|

Source: Authors’ calculations based on product NRAs and CTEs in Anderson and Valenzuela (2008). a Regional aggregates are weighted using the absolute value of net imports (computed as the difference between the value of consumption and the value of production) at undistorted prices.
some cases the correlation is surprisingly negative. This result contrasts with that found by Cruces et al., who find that the rank in the reference group is strongly and positively correlated with the perceived position in the overall population’s income distribution and, moreover, the objective position has no explanatory power. The main difference between their and our results may lie in the fact that, as mentioned before, their reference group is geographically narrower than ours.

Table 2: Testing for the reference <u>groups</u> hypothesis <u>(consumption)</u>

||(1)<br>Dependent|(2)<br> variable:|(3)<br> perceived d|(4)<br>ecile in the|(5)<br> income di|(6)<br>stribution|
|---|---|---|---|---|---|---|
|Objective decile|0.116<sup>∗∗</sup>|||0.154<sup>∗∗</sup>|0.135<sup>∗∗</sup>|0.185<sup>∗∗</sup>|
|(consumption)|(0.00806)|||(0.0184)|(0.0144)|(0.0173)|
|Decile in Region/Educ.||0.108<sup>∗∗</sup>||-0.0274<sup>∗</sup>||-0.00854|
|ref. group||(0.00674)||(0.0146)||(0.0173)|
|Decile in Age/Educ.|||0.0995<sup>∗∗</sup>||-0.0223|-0.0595<sup>∗∗</sup>|
|ref. group|||(0.00923)||(0.0158)|(0.0184)|
|_Indiv.Controls_|Yes|Yes|Yes|Yes|Yes|Yes|
|_CountryFE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Y earFE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_N_|60726|60990|60987|60729|60726|60729|
|_R_<sup>2</sup>|0.130|0.107|0.120|0.118|0.131|0.120|

Standard errors clustered at the country-year level in parentheses. Individual controls include age, gender, religion and household composition of the respondent ∗ _p_ < 0 _._ 10, ∗∗ _p_ < 0 _._ 05, ∗∗∗ _p_ < 0 _._ 01 Powdthavee (2009) has argued that individuals’ subjective ranking may be influenced by their relative position within their reference group in terms of assets - which are more visible than income. In table 3 we repeat the analysis of table 32 but using instead information on individuals’ and their reference group’s asset index. Here the results show that both the respondent’s position in the population’s asset index distribution and its position within either reference group are correlated with the subjective ranking. In this sense, these results are in line with those of Powdthavee (2009). However, the partial correlation coefficients are low - not exceeding 0.2. This suggests that, still, variables other than the individual’s position in the whole population or the reference group explain a substantial amount of the subjective ranking. We conclude, thus, by arguing that while the reference group hypothesis -particularly when it refers to assets rather than consumptionmay explain some of the “bunching in the middle”, it is far from an exhaustive explanation.

13
The Effects of Health Insurance within Families: Experimental

# Evidence from Nicaragua

_Anne Fitzpatrick and Rebecca Thornton_<sup></sup>

**JEL classification codes:** I13, I18, I38, O15, D1

**Keywords:** Health insurance; randomized controlled trial; spillovers; children;

complementarities.

>  Anne Fitzpatrick is an assistant professor at the University of Massachusetts-Boston; her email address is <u>anne.fitzpatrick@umb.edu. Rebecca Thornton (corresponding author) is an associate professor at the</u> University of Illinois at Urbana-Champaign; her email address is rebeccat@illinois.edu. Funding for this study was provided by USAID’s Private Sector Partnerships- _One_ (PSP- _One_ ) project, the Global Development Network (GDN), and the ILO Microinsurance Facility.  The evaluation was coordinated for GDN by EA Consultants. The authors thank the extensive contributions of the field team at ALVA Consultores including Dr. Ana del Carmen Rojas and Rosario Duarte, as well as the project coordination and support of Barbara Magnoni, EA Consultants, and the contributions in the project design of Tania Dmytraczenko, the World Bank. This paper has benefitted from comments from Taryn Dinkelman, Emily Oster, Jeff Smith, seminar participants at the University of Michigan and two anonymous referees.
|||(0.592)|(0.610)||
|---|---|---|---|---|
|Process innovation (Predicted)||-0.201||0.202|
|||(0.232)||(0.179)|
|Process innovation spillover||0.858||0.222|
|||(0.577)||(0.566)|
|N|2,607|2,607|2,609|2,607|
|***p<0.01,**p<0.05,*p<0.1. Or|dinary least squa|res. Bootstrapped st|andard errors in pa|rentheses.|

We also explore whether the strengths of innovation spillovers vary depending on the extent to which a firm innovates, as shown in Table 7. We add a dummy variable that takes value 1 for firms with above-average propensity to innovate and the interaction of such dummy with innovation spillovers. A positive interaction coefficient suggests that a firm’s productivity benefits from innovation spillovers when the firm’s propensity to innovate is above the sectoral average. We find some evidence of this for innovation (Column 1) and process innovation when product innovation and its spillover are controlled (Column 2), but not for product innovation.

_Table 7 The relation between innovation spillovers on productivity: robustness check_

|||**China and AS**<br>|**EAN countrie**<br>|**s**<br>|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Materials|0.538***|0.540***|0.540***|0.541***|
||(0.0244)|(0.0244)|(0.0186)|(0.0240)|
|Capital|0.0811***|0.0862***|0.0884***|0.0841***|
||(0.0122)|(0.0135)|(0.0142)|(0.0127)|
|Human capital|0.00646|0.00947|0.00905|0.00867|
||(0.00601)|(0.00601)|(0.00599)|(0.00630)|
|Firm size|0.0201|0.0201|0.0257|0.0246|
||(0.0165)|(0.0194)|(0.0187)|(0.0201)|
|Manager experience|-0.0261***|-0.0261***|-0.0260***|-0.0256***|
||(0.00270)|(0.00274)|(0.00244)|(0.00274)|
|Age|0.0202***|0.0198***|0.0200***|0.0193***|
||(0.00328)|(0.00345)|(0.00282)|(0.00349)|
|Innovation|0.170||||
||(0.217)||||
|Innovation spillover|0.449||||
||(0.657)||||
|Dummy of innovation > sect. avg.? (Yes=1)|-0.294*||||
||(0.166)||||
|(Innovation spillover) x (Dummy of Innovation >|0.569**||||
|sect. avg.?)|(0.234)||||
|Product innovation||0.887***|0.699***||
|||(0.268)|(0.204)||
|Product innovation spillover||-1.462**|-1.158*||
|||(0.706)|(0.687)||
|Dummy of product innovation > sect. avg.?||-0.0529|-0.104||
|(Yes=1)||(0.148)|(0.110)||
|(Product innovation spillover) x (Dummy of||-0.455|-0.103||
|product innovation > sect. avg.?)||(0.377)|(0.284)||
|Process innovation||-0.357||0.00418|
|||(0.279)||(0.246)|
|Process innovation spillover||0.915||0.295|
|||(0.622)||(0.639)|

12
Table 2: Summary statistics for the geographic concentration measures

||||**Arrondi**|**ssement**|**Level**|
|---|---|---|---|---|---|
|**Statistics**|**Region Level**|All|Informal|Formal|New Entrants|
||**Al**|**l industr**|**ies**|||
|Mean|0.091|0.007|-0.020|-0.042|-0.124|
|Median|0.010|0.012|0.007|0.000|0.029|
|Minimum|-4.363|-2.081|-2.002|-1.647|-3.536|
|Maximum|6.903|1.757|1.146|0.418|1.975|
|Standard deviation|1.200|0.358|0.311|0.299|0.755|
|Share_<_0|42.424|39.394|41.414|39.190|28.790|
|Share_∈_(0, 0.05]|24.242|31.313|32.323|12.160|13.630|
|Share_>_0.05|33.333|29.293|26.263|48.650|57.580|
||**Manufac**|**turing i**|**ndustries**|||
|Mean|-0.138|-0.078|-0.075|-0.162|-0.120|
|Median|-0.015|-0.001|-0.016|-0.044|-0.006|
|Minimum|-4.927|-1.272|-1.225|-1.201|-0.941|
|Maximum|4.481|0.387|0.387|0.143|0.182|
|Standard deviation|1.211|0.292|0.286|0.300|0.284|
|Share_<_0|53.490|51.170|53.490|44.830|36.000|
|Share_∈_(0, 0.05]|13.950|39.530|32.560|13.790|16.000|
|Share_>_0.05|32.560|9.300|13.950|41.380|48.000|

_Notes:_ Authors’ computations using gec data. Mean and median values are for 99 fourdigit nace classification of industries (43 manufacturing, 74 formal, and 66 new entrants). Share _<_ 0 means ‘not clustered’; Share _∈_ (0, 0.05] means ‘weakly clustered’; Share _>_ 0.05 means ‘strongly clustered’. See Ellison and Glaeser (1997) for details.

show that almost 60 percent of new entrants are strongly concentrated. This result is consistent with Glaeser, Kerr, and Ponzetto (2010).

Looking at the geographical concentration of industries within cities, the following facts emerge. Douala and Bamenda exhibit significant geographical localization compared to the overall distribution of industries in Cameroon. Out of our 99 four-digit types of industries in Cameroon, 95 are operating in Douala, and 56 in Bamenda. The mean eg index of the overall industries in Cameroon is only 4.3 and 1.2 percent of that in Yaoundé and Bamenda respectively. Sugar manufacture, fabrics and stitches, and the growth of other industrial or export products are among the most localized industries in Douala. In Bamenda, the most localized industries are related to soap factories, perfume, detergents and cleaning products, the raising of other animals, and other wholesale trade. As expected, general public administration activities are the 11