rafmacalaba/datause-encoder-data huggingface dataset:
Another potential worry for consumers is the authenticity of the sharia-compliant service (El-Zoghbi and Alvarez 2015). There is significant variation across the Muslim world in the interpretation of Islamic law and corresponding variation in what types of financial products are accepted as sharia-compliant and which are not. Since there is no central authority in Islam, different places follow different religious interpretations, and may have different thresholds for whether a product is sufficiently “Islamic”. Even if individuals have access to sharia-compliant loans, and the price is in line with their expectations, they may worry that the product is not different enough from conventional products and so not actually religiously permissible and they will continue to turn down sharia-compliant loans for religious reasons.

Returning to the larger literature on financial inclusion, in an analysis of the patterns across several microcredit evaluations, Banerjee, Karlan and Zinman (2015) notes how low take-up is an important issue facing providers across a variety of contexts. Similarly Field et al. (2013) showcases how traditional microfinance products may lead to less than optimal outcomes for borrowers. One way to interpret these two facts is to say that there is a need to develop new modes of microcredit that address the needs of borrowers, both from the side of personal preferences (e.g. religious beliefs, behavioral biases, etc.) and personal financial needs (e.g. in-kind loans, longer grace periods, etc.). This experiment allows us to investigate how products that address personal preferences of borrowers in a particular context can contribute to improving the outreach of financial products to the poor, and in turn shed light on potential mechanisms that are also important in more advanced economies.

_The Cultural and Financial Landscape in Jordan_ Only a quarter of adults in Jordan had any account at a financial institution, with this proportion decreasing to 16% for the poorest forty percent of the country. While 14% had borrowed from a financial institution during 2014, 32% had borrowed money in general, with the difference being made up by borrowing from friends and families as well as informal lenders (Demirgüç-Kunt et al. 2015). Part of the reason for these low rates of financial inclusion could be because there are important information asymmetries– only 2.5% of the population is covered by the country’s public credit registry, making it difficult for lenders to assess credit risk (World Bank Group 2016). Microfinance institutions are often seen as one avenue through which countries can increase access to financial services.

In Jordan, 97% of the population of 7.5 million identify as Muslim. The World Bank estimates that 14% of the population lives in poverty. Religion plays a large role in the country. Most of the populace, 85%, say that religion 8
this approach are discussed in Section 3. We are not aware of other studies that have used this approach.

In addition to the impact on output, interest also focuses on how additional credit affects investment into fixed capital and the use of variable factors such as fertlizer and labor. Gandhi (1986), for example, estimates agricultural investment equations using aggregate time series data for India with credit as an exogenous variable. His study assumes that credit demand is equal to credit supply and that, therefore, credit use is exogenous. He assumes away the possible joint dependence of investment demand and credit use, a problem addressed in Section 3. Schluter, 1974, estimated input demand functions for labor, modem varieties, fertilizer, crop area, animal and machine power with respect to credit, using cross section data for Surat district in India. He did not account for possible simultaneity or selectivity bias. Another cross-section study in the Philippines estimated the impact of ciedit use on allocative efficiency (Mandac and Herdt, 1978).

Finally, the expansion of rural financial institutions may reduce liquidity constraints in the rural nonfarm sectors. And credit to agriculture may have secondary spillover effects on the nonfarm economy via input, labor and output linkages (Haggblade and Hazell, 1989). We are not aware of any studies investigating these second-round effects of rural finance.

3
**Table A8 – Impact of treatments on willingness to get vaccinated (with inverse probability weights)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Expert advice treatment|0.0397|||0.0344|
||(0.0272)|||(0.0289)|
|Social norms treatment||0.0858**||0.0902**|
|||(0.0290)||(0.0295)|
|Relative safety treatment|||0.104***|0.103***|
||||(0.0288)|(0.0297)|
|Male|0.0987***|0.0340|0.118***|0.0616**|
||(0.0265)|(0.0279)|(0.0278)|(0.0218)|
|Over 40 years|-0.0119|-0.0205|-0.0636*|-0.0321|
||(0.0297)|(0.0310)|(0.0303)|(0.0238)|
|Highlands region|0.0605|-0.132***|0.0363|-0.0666*|
||(0.0383)|(0.0392)|(0.0416)|(0.0311)|
|Islands region|0.0246|-0.159**|-0.0191|-0.127***|
||(0.0460)|(0.0487)|(0.0494)|(0.0374)|
|Momase region|0.000459|-0.155***|0.00284|-0.0969**|
||(0.0404)|(0.0419)|(0.0436)|(0.0328)|
|Secondary school or less|-0.0892***|-0.0832**|-0.0723**|-0.0777***|
||(0.0269)|(0.0281)|(0.0280)|(0.0220)|
|Constant|0.161***|0.344***|0.172***|0.282***|
||(0.0412)|(0.0414)|(0.0443)|(0.0351)|
|Observations|915|876|891|1514|

Note: This chart is based on answers to question 34 in Appendix B.***, **, and * indicate significance at the 1, 5, and 10 percent critical level. Standard errors in parentheses.

46
significant at 10% level. In contrast, male refugees from Somalia are significantly less likely to participate in family farm activities (18 percentage points) compared to their hosts.

In general, the disaggregated results suggest that self-employment and agricultural employment are driving the observed differences among refugees and hosts, reflecting the limited availability of paid employment opportunities in refugee-hosting regions. Among women, we only see significant differences in Benishangul-Gumuz region where refugees are less likely to participate in self-employment and/or farm activities compared to women in hosts. This difference directly links with refugees’ lack of agricultural land, while women in hosts have greater access to land and about half participates in farm activities.

# <mark>Individual-Level Participation</mark>

We now turn to examine the determinants of wage employment for working age individuals (aged 15-65). Estimates are reported in as average (marginal) effects for women and men by refugee status.

The results from <mark>Table</mark> _<mark>5</mark>_ suggests that there are significant gender differences, at individual level, both among refugee and host community population. The gender gap is large among hosts where women are 15 percentage points less likely to participate in wage employment compared to men. Similarly, there is a 9-percentage points gender difference among refugees, in which women are significantly less likely to be employed. Overall, our results suggest that women (both refugee and hosts) are more likely to suffer from lack of employment opportunities compared to men. These results are consistent with findings in the descriptive statistics (Table 1), which show large differences in employment between women and men.

Again, at the individual level, education increases the probability of participation in wage employment for both women and men, both among hosts and refugees. While secondary and tertiary education are strongly associated with female employment, tertiary education has a large and strong effect on male employment.

Displacement duration also significantly increases participation in employment, with women who are displaced for at least three years more likely to participate in the labor market compared to those displaced for less than three years. In contrast, among men, duration only plays a role for those who are displaced for more than six years.

Lack of physical safety – measured as feeling moderately or very unsafe when alone at home or walking around during the day or after dark – significantly reduces wage employment for women (but not men) in the host community and have no effect on refugees.

_Table 5: Individual participation in wage employment: probit model_

||Hosts|||Refugees|
|---|---|---|---|---|
|All|Female|Male|All|Female<br>Male|
|||||20|
8 increase efficiency given the expected contemporaneous correlation in the residuals of each equation .8 Lastly, by pooling the data we minimize sample size problems.

The specification is as follows<sup>9</sup> :

**_In_** _n p_ **_p q_** A4Y, _= Z/, 1_<sup>_A_</sup> _4_<sup>_Y,,_</sup> _4_<sup>_+Za_</sup> _1_<sup>_A_</sup> _4_<sup>_TO1I,,+÷ JVUS1i,_</sup> _+±S CA1, G j + L AjA4SPD, j_ /=1 _,j=0 j=0_ 1=0 _j=O +± )jISO, 1 +C,_ .J=0

Where, Y},: Output _TOTit:_ Terms of Trade _USIi,:_ US Federal Funds real interest rate. CAIt: Current Account Industrial Countries (millions of US dollars at 1996 prices). _SPDit:_ Non Latin lrady Bond Sovereign spreads. _ISOit:_ Southern Oscillation Index. Cj: Constant term. And, A4: year-over-year variation _Subscript i:_ ith country Estimation results (Appendix 1) show that coefficients are significant and with the expected sign: terms of trade improvements and better climatic condition (as measured by the ISO index) have a positive effect on growth whereas higher external interest rates and spreads have a negative effect, as does a lower availability of funds.

The estimated coefficients are then used to calculate 1998 and 1999 growth rates under two scenarios: _Actual_ (external variables take actual values) and _Constant_ (assuming external variables remain at their 1997 average level). That is, we estimate the N-step forecast under these two alternative external conditions, and the difference between the two is the effect of the external factors on growth. For the region as a whole, we calculate that external factors account for 4.6 percentage points of the slowdown (Table 2, Col. 3).

To calculate the total slowdown we use two alternative procedures: one possibility is to use the N-Step forecast using the actual conditions in the external variables (Actual scenario above); or, alternatively, we can use the most likely outcome for the year that can be estimated given that we already have third quarter GDP data for most countries

> 8 Another reason to expect efficiency gains from the SUR methodology arises because El Nino did not affect all countries. Hence the set of explanatory variables in the countries not affected by it is a subset of the explanatory variables included for Peru and Ecuador.

> 9 Initially, the specification did not include the current account in industrial countries. Adding this variable improved the explicatory power of the model and was used as the final specification.. US growth rate was dropped out since it turned out to be not significant. This result coincides with Ahmed (1999). However, it might be the consequence of estimating a panel without allowing for slope heterogeneity. A next revision of this estimation should allow for this flexibility to verify this result.
since the ATI project worked through CBOs to raise awareness about the law. One would expect that this would have created stronger incentives for group membership in treated more than in control municipalities. One possible reason why it did not could be a substitution effect. Where individual CBO members become more empowered to request information, there is likely to be lower demand for collective action.

However, the fact that group membership has grown over the two rounds of the survey in both treatment and control municipalities is interesting in itself and probably a reflection of the Municipal Law 176-07 that complements the FOI law 200-04. A qualitative baseline assessment reports that the Municipal Law 176-07 promoted the development of CSOs, through greater access to public information, use of public media and financial assistance, to propel their participation in the municipal administration.<sup>14</sup> Engagement in other transparency projects such as the Participatory Budgeting has similarly increased in both ATI and non-ATI municipalities, which is to say, independently of the ATI project intervention.

# **Table 2 here**

# _Local governance_

As Table 3 reports, the impacts of the ATI on local governance appear to be the strongest so far. They indicate a higher rate of consultation of citizens in government decision making across samples and periods. The general decline from the baseline to the follow up survey is somewhat puzzling. It may have occurred due to the local elections that took place just before the collection of the baseline survey, thus reflecting candidates’ campaign or commitment to greater accountability than previous administrations.

Nonetheless, looking at the differential effect of the ATI, we observe a positive and large effect, i.e. the reported consultation rate of citizens by local governments in the treatment municipalities is higher by 7.8 percentage points compared to consultation rate

> 14 The financial incentives to the creation and development of non-profit organizations consist of a competitive grant in which the organizations with the project which could benefit mostly the community would win, as established by art. 227 of the Law No. 176-07.

19
Focusing now on firms in establishments, the proportion of firms that are formal according to the basic definition is comparable across the two ELMPS waves (49%-55%), somewhat lower in the EsC (39%), and much lower in the EcC (14%), likely because of the way that some EcC survey instruments excluded the question about registration. Moving to the extended definition more than doubles the share of firms that are formal in the EcC (34%), does not change the estimate of 39% for EsC, and raises the estimate of formality by a few percentage points (to 54-61%) in the two ELMPS waves. Thus, the extended estimates are broadly consistent with each other and suggest that somewhere from over a third to half of all private non-agricultural firms of fewer than 25 workers operating in establishments are formal, depending on the data source. Hereafter, we use the extended definition of formality as our primary measure and focus on firms that operate inside fixed establishments in our analysis. We note any exceptions to this approach.

Although Figure 1 shows the rate of formality for firms, since we have the number of employees in each firm, we can also calculate the number of employees in each data source within the universe of nonagricultural firms with 1-24 workers in establishments. Likewise, among these workers, we can calculate the number within formal firms versus informal firms. In the ELMPS 2012, using the extended definition, there are 3.5 million workers overall and 2.4 million within formal firms – although not necessarily formal employment. This compares to 5.8 million workers in the contemporaneous Economic Census, of whom 2.6 million were within formal firms. In the ELMPS 2018, there were 3.0 million workers, of whom 1.9 million would be within formal firms. The Establishment Census generates a much larger number of workers, 9.2 million, of whom 4.5 million were within formal firms.

# **4.2.2. Distribution of firm characteristics across data sources**

We now examine firm characteristics across the four data sets to determine whether we are observing roughly the same firms. As shown in Figure 2, the two waves of the ELMPS have a somewhat higher proportion of one-person firms and are conversely somewhat under-represented in the three larger size categories. This may be due to branches of firms, which tend to be larger, being included as separate establishments in the EcC and EsC. Despite this tilt toward smaller firm sizes, the ELMPS data produces higher estimates of formality than the other two sources, as we have seen above. Besides the fact that EcC has a higher representation of two-person firms (35%) and the EsC a higher representation of threeor four-person firms (25%), these two data sources produce fairly comparable estimates in the three other size categories. Overall, firms in our universe skew toward the smaller size categories, with one or twoperson firms making up 66% to 86% of all firms in the universe and firms of 5-24 workers making up at most 10%.

12
# **REFERENCES**

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- Adams R et al., 1999. The economic effects of climate change on US agriculture. In Mendelsohn R & Neumann, J (eds), _The Impact of Climate Change on the United States Economy_ , Cambridge, UK: Cambridge University Press.

- Boer G, Flato G & Ramsden D, 2000. A transient climate change simulation with greenhouse gas and aerosol forcing: Projected climate for the 21st century. _Climate Dynamics_ 16: 427– 450.

- Bourguignon F, Fournier M & Gurgand M, 2004. Selection bias corrections based on the multinomial logit model: Monte-Carlo comparisons. DELTA Working Paper No. 20, École Normale Supérieure.

- Chib S & Greenberg E, 1998. An analysis of multivariate probit models. _Biometrika_ 85: 347– 361.

Chow G, 1983. _Econometrics_ . New York: McGraw-Hill.

- Dinar A, Hassan R, Kurukulasuriya P, Benhin J & Mendelsohn R, 2006.  The policy nexus between agriculture and climate change in Africa.  A synthesis of the investigation under the GEF / WB Project, _Regional Climate, Water and Agriculture:  Impacts on and Adaptation of Agro-ecological Systems in Africa._ CEEPA Discussion Paper No. 39, Centre for Environmental Economics and Policy in Africa, University of Pretoria.

- Dubin JA & McFadden DL, 1984. An econometric analysis of residential electric appliance holdings and consumption. _Econometrica_ 52(2): 345–362.

- Emori, S, Nozawa T, Abe-Ouchi A, Namaguti A & Kimoto M, 1999. Coupled ocean-atmospheric model experiments of future climate change with an explicit representation of sulfate aerosol scattering. _Journal of the Meteorological Society of Japan_ 77: 1299–1307.

- Ford J & Katondo K, 1977. The distribution of tsetse flies in Africa. Organization of African Unity, Nairobi, Kenya.

Greene WH, 1998. _Econometric Analysis (3rd edition)._ New Jersey: Prentice Hall.

Heckman JJ, 1979. Sample selection bias as a specification error. _Econometrica_ 47(1): 153–162.

- Houghton JT et al. (eds), 2001. _Climate Change 2001: The Scientific Basis._ Contribution of Working Group I to the third assessment report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.

- Kurukulasuriya P & Mendelsohn R, 2006.  A Ricardian analysis of the impact of climate change on African cropland. CEEPA Discussion Paper No. 8, Centre for Environmental

21
These minor revisions to the historical data result in only trivial changes in the six aggregate WGI over the period 1996-2006 when compared with last year's data. In all but one case over the past 10 years, the correlation between the original and the revised indicators is 0.999 or higher. The only minor exception is Regulatory Quality in 1998 where the correlation is still extremely high at 0.97.

# **2.3  Aggregation Methodology**

We combine the many individual data sources into six aggregate governance indicators, corresponding to the six dimensions of governance described above. The premise underlying this statistical approach should not be too controversial – each of the individual data sources we have provides an imperfect signal of some deep underlying notion of governance that is difficult to observe directly. This means that as users of the individual sources, we face a signal-extraction problem – how do we isolate an informative signal about governance from each individual data source, and how do we optimally combine the many data sources to get the best possible signal of governance in a country based on all the available data? The statistical procedure we use to perform this aggregation, known as the unobserved components model, is described in detail in our past work (see for example Kaufmann, Kraay and Mastruzzi (2004), as well as Appendix D). The main advantage of this approach is that the aggregate indicators are more informative about unobserved governance than any individual data source. Moreover, the methodology allows us to be explicit about the precision – or imprecision – of our estimates of governance in each country. This imprecision is not merely a consequence of our reliance on subjective or perceptions data on governance – rather imprecision is an issue that should be squarely addressed in all efforts to measure the quality of governance, recognizing the inherent complexity and imprecision associated with such a task.

The aggregation procedure we use in effect first rescales the individual indicators from each underlying source in order to make them comparable across data sources. It then constructs a weighted average of each of these rescaled data sources to arrive at an aggregate indicator of governance. The weights assigned to each data source are in turn based on the estimates of the precision of each source that are produced by the unobserved components model. In brief, the identifying assumption in the unobserved 13
8

# **II. INITIATING AND DESIGNING REFORM**

Identifying priorities for reform and countering resistance from interest groups, politicians, and political parties are often the biggest challenges in the early stages of the reform process. Our case studies suggest that reformers can deal with these challenges by: relying more on new diagnostic tools and practices to identify priorities for reform; seizing windows of opportunity to get reform onto the policy agenda; sequencing and packaging reforms so that they are both credible and feasible; and using various measures to build coalitions for supporting reform.

## **IDENTIFYING PRIORITIES FOR REFORM**

Investment climate reforms cover a wide agenda, and no country can tackle everything at once. Establishing priorities is especially important for less developed countries, which tend to face all the possible problems but have the least capacity to tackle them. In the past, choices were based on a standard list of reforms known as the “Washington Consensus” (macroeconomic stabilization, privatization, trade liberalization, protection of property rights). But this approach has not always triggered an adequate supply response. Reformers often tried to do too much at once, and often failed, or started with reforms that were not of high priority; and, sometimes, reform in one area created unexpected distortions in another, often leading to weak or even adverse effects (Hausmann, Rodrik, and Velasco 2006, p. 12).

Over the past few years, however, the process of identifying priorities for reform has become much more context-specific, and increasingly more targeted. Reformers can make use of a growing body of new analytical tools and information to identify priorities with greater precision, and in a way that helps deliver results and build momentum for more reform. These tools include:

- Surveys of investors that help identify some of the most important issues;

- Detailed benchmarking data, such as those provided by the World Bank Group’s Doing Business indicators, which benchmark and rank the cost and quality of business regulations for key cross-cutting investment climate issues;

- Market information on prices and return on investments which provide important information. For example, high real interest rates indicate that access to finance is a key constraint, while low returns to education suggest that aggregate skills are not, at least for now (Hausmann, Rodrik, and Velasco 2005); and

- In-depth industry analyses which can be used to identify industry-specific policy issues, such as entry barriers, undue protection and subsidies, inadequate price and product regulations, and issues relating to government certification and protection of intellectual property rights (Palmade 2005).