rafmacalaba/datause-encoder-data huggingface dataset:
them about the loan products may have primed them to be more accepting of the sharia-compliant product, leading us to over-estimate true demand.

The main outcome variable is applications for the loan but due to the implementation delays from the partner we were unable to see observe how this would have translated into actual loans made to those in the sample. It is possible that the preliminary application rates could have differed from the final disbursement. It is difficult to predict in which direction this potential difference may have gone.

Relatedly a lack of repayment data limits the ability to make strong statements about selection on unobservables and profitability for the bank. Although there is no evidence of compositional observable differences between those that apply for the sharia-compliant product and those that apply for the conventional loan, there may be unobservable differences. Unobservable characteristics could lead to adverse or advantageous selection. For example, those who take up sharia-compliant loans may be more pious and thus creditworthy, and thus less likely to engage in moral hazard behavior particularly at the repayment decision; on the other hand, those who take up shariacompliant loans may default more, as they could be used to more forgiving faith-based services. In the latter case, the increases in application rates would actually be detrimental to the financial institution.

6. Conclusion This paper shows that demand for sharia-compliant loans from borrowers in a Muslim-majority country is stronger than demand for conventional loans. This study also provides evidence that individuals are willing to pay more for this type of product, even though its financial characteristics are very similar to its conventional counterpart. While there seems to be no impact from different types of authorizing entities on demand for this product, this study finds that price elasticity differs by religiosity, as proxied by whether or not the respondent watches religious TV.

When comparing these impacts to what is implied by survey data, we show that survey data overestimate the exclusionary power of religious objectives by about one third, and that religious and social considerations are at least as important as economic factors like loan size and interest rates when we consider the reasons why individuals claim that they are not utilizing credit.

Together, these findings imply several lessons for improving financial inclusion across the world, and lead to several avenues for future research. First, improving our understanding of how non-financial preferences impact financial decisions can be an effective way to increase access to financial services for those that turn them down for social reasons. Second, even if the preferences are for products that are more costly than the conventional alternative, 23
<!-- Start of picture text --> 2000<br>Data - interquartile range<br>1800 | YY) Simulation - interquartile interquartile range<br>| -———~ Simulation Simulation average<br>= 1600 +sse«Data average average<br>=<br>S 1400 -<br>5 1000 *<br>2°° isi:i: Sosos ie5.5. oa<br>3 ed Ni Ge<br>=<br>200 *.)<br>0 » «fl _“Suara“Suara at” — ntece<br>0 10 20 30 40 50<br>Distance to the the center (km)<br><!-- End of picture text -->

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ii costly, firms may circumvent the regulation by "unhooking" the equipment when regulators are not looking.

We discuss below the implications of costly monitoring and eBnforcement for the case where regulators set emission or abatement istandards. Much of what we have to say applies with equal force 1io price-type policy instruments. The analysis is based on the sconomics-of-crime analysis of Becker (1968). Becker argued that "r4tional" criminals will commit crimes as long as the private maz!ginal benefit of the crimes exceeds the expected marginal ctst of committing the crimes."3 Similarly, when deciding whether to comply with an environmental regulation, firms will compare the cos8t of compliance with the expected value of the consequences of none:ompliance.

Denote the probability of detection and prosecution of noncompliance as p (determined by the enforcement budget and strategy of the regulator) and suppose further that, if found in violation of an emission standard, the firm must pay a fine F and comply with the standard incurring abatement costs of C. A riskneutral firm will choose to comply if C < p(F + C) or if C < (pF/(1-p)). This simple model of firm behavior suggests that the lower the costs of compliance, the greater the chance that a firm will comply. It also suggests that where firms have heterogeneous costrs, some will comply while others, which have the higher 50
# **1. Introduction**

Agriculture remains the main economic activity in Timor-Leste. The sector provides subsistence to a majority (72 percent in 2007, 52 percent in 2014, Figure 1) of the population, and accounts for over 90 percent of total exports (mainly coffee).<sup>2</sup> Yet it represents only 11.4 percent of the GDP. This suggests that, given the number of people employed in the sector, most farmers in the country remain locked in low-productivity, subsistence agriculture.

**Figure 1: Distribution of the population by poverty status and sector, 2007–2014**

<!-- Start of picture text --> 90 83<br>80<br>72<br>70 62 63<br>60 55<br>52<br>48<br>50 45<br>38 37<br>40<br>28<br>30<br>17<br>20<br>10<br>0<br>Poor Non-poor Total Poor Non-poor Total<br>2007 2014<br>Agriculture Non-agriculture<br>Population (%)<br><!-- End of picture text --> _Source:_ 2007 and 2014 Timor-Leste Survey of Living Standards A majority (83 percent in 2007, 63 percent in 2014) of the poor depend on agriculture for their livelihood (Figure 1). Across the three regions of the country, agricultural households tend to be concentrated in the west and east (61 percent in 2014); and among agricultural households, poverty tends to be more prevalent in the west than in the east or the center (Figure 2). Because most of the poor depend on agriculture, the sector is widely regarded as a major channel through which poverty can be reduced in Timor-Leste. This notion is supported by historical evidence that agriculture has played an integral role in successful poverty reduction elsewhere in Asia, and that growth in agriculture tends to be more beneficial to the poor than growth in other sectors of agrarian, developing economies (DFID 2004). In Myanmar, growth of the agriculture sector accounted for at least 46 percent of the poverty reduction seen between 2005 and 2015, much of which was achieved by enhanced productivity and crop diversification (World Bank, 2019). The experience of Cambodia also shows that a 10 percent increase in agricultural land productivity reduces the head count poverty rate by one percentage point (World Bank, 2019). Experiences from Thailand show that agricultural competitiveness is key to support the development of the sector, which requires sound and consistent macroeconomic policies, including an open

> 2 Fanzo and Bonis-Profumo (2017) provide a detailed overview of the kinds of crops produced and the nature of employment in Timor-Leste.

2
Policy Research Working Paper 7848

# **Abstract**

This paper studies how migration from a poor to a rich country affects key economic beliefs, preference parameters, and transnational household decision-making efficiency. The setting is the migration of Tongans to New Zealand through a migration lottery program. In a 10-year followup survey of individuals applying for this program, the study elicited risk and time preferences and pro-market beliefs. It also linked migrants and potential migrants to a partner household consisting of family members who would stay behind if the migrants moved. Survey participants played lab-in-the-field games designed to measure the degree of intra-family trust and the efficiency of intra-family decision-making. Migration provides a large and permanent positive shock to income, a large change in economic institutions, and a reduction in interactions with partner household members. Despite these changes, the study finds no significant impacts of migration on risk and time preferences, pro-market beliefs, or the decision-making efficiency of transnational households. This stability in the face of such a large and life-changing event lends credence to economic models of migration that treat these determinants of decision-making as time-invariant, and contrasts with recent evidence on preference changes after negative shocks.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dmckenzie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
market shocks that may hit specific educational groups. We cluster the standard errors by group, and weight the regressions by the number of observations used in calculating group-level exposure.

The results of the difference-in-difference estimations using the grouping estimator are displayed in Table 10. The estimate reported in Column 1 shows a negative effect of the minimum wage on total employment (including both formal and informal employment).This indicates that the minimum-wage increase had negative effects on employment overall – although the estimated effect is only statistically significant at the 10 percent level. As with the estimates using the EIS data, estimates from the HLFS data show a highly significant negative effect of the minimum wage on formal employment – which includes both private and public sector. The estimates are of similar magnitude with using the HLFS data as with the EIS data (Column 2). As shown in Column 3, we do not find that that the minimum-wage hike increased informal employment. This finding contrasts with the findings of some studies in other contexts.<sup>28</sup> Our findings seem incompatible with the notion that formal workers who were employed by firms that wound up exiting the market transitioned instead into informal employment – at least in the short run. One possible explanation for our finding is the higher degree of segmentation between formal and informal employment in Turkey, documented by some studies (Tansel and Acar, 2017; Duman, 2020). This higher level of segmentation contrasts with the situation that characterizes other contexts, such as in some Latin American countries, where frequent transitions between formal and informal employment have been evidenced.<sup>29</sup> The difference-in-difference coefficient in Column 4 indicates that raising the minimum wage led to a sizeable increase in unemployment – although the coefficient is insignificant due to large standard errors at the group level. The estimate reported in Column 5 also shows a significant effect of the minimum-wage increase in raising levels of inactivity. This suggests that the destruction of formal firms and formal jobs was accompanied by an increase in the degree to which workers were discouraged, leading them to drop out of the labor force as a result. Thus, the

> 28 The minimum wage has been found to increase informal employment at least for some groups of workers in various studies: Honduras (Ham, 2017), Indonesia (Comola and de Mello, 2011), Russia (Muravyev and Oschchepkov, 2016), and Thailand (Del Carpio et al., 2019).  In contrast, Lemos finds no such employment effects in Brazil and Madruger (2013) finds a positive effect of the minimum wage on formal employment in Indonesia. In Turkey, a recent paper by Isik et al. (2020) finds that the minimum-wage increase boosts informal employment among workers who are young and those who have low levels of education, but not for other groups.

> 29 See for example, Maloney (1999) for evidence from Mexico, among others.

28
|Table 3: Summary statistics ofc|enter-lev<br>Kinder<br>(N=2|elvaria<br>garten<br>21)|bles by <br>Isla<br>kinder<br>(N=|type of<br>mic<br>garten<br>50)|ECEDs<br>Non-p<br>playg<br>(N=|ervice<br>roject<br>roup<br>70)|Proj<br>playg<br>(N=2|ect<br>roup<br>36)|To<br>(N=|tal<br>578)|
|---|---|---|---|---|---|---|---|---|---|---|
||Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|
|**Observed classroom quality**|||||||||||
|ECERS-R: Space & furnishing|2.915|1.223|2.636|0.993|2.522|1.075|3.235|1.251|2.974|1.223|
|<br>ECERS-R: Personal care & routine|2.534|1.113|2.195|0.811|2.435|0.990|2.612|1.091|2.524|1.070|
|ECERS-R: Language-reasoning|3.762|1.593|3.023|1.131|3.204|1.401|3.518|1.215|3.531|1.404|
|ECERS-R: Activities|2.429|0.974|1.920|0.689|2.122|0.924|2.752|0.893|2.480|0.951|
|ECERS-R: Interactions|4.076|1.581|3.926|1.305|3.849|1.559|4.158|1.436|4.069|1.498|
|ECERS-R: Program structure|2.743|1.470|2.178|1.138|2.510|1.422|2.883|1.314|2.723|1.387|
|ECERS-R: Parents & staff|2.784|0.961|2.387|0.821|2.473|1.065|2.465|0.867|2.582|0.938|
|ECERS-R: Total (mean) score|3.035|1.049|2.609|0.725|2.731|0.995|3.089|0.911|2.983<br>(N=|0.974<br>566|
|**Teacher characteristics**|||||||||cent|ers)|
|Highest edu. level: Primary|0.000|0.000|0.000|0.000|0.014|0.084|0.006|0.051|0.004|0.044|
|Highest edu. level: Junior secondary|0.005|0.067|0.017|0.084|0.029|0.145|0.019|0.090|0.014|0.091|
|<br>Highest edu. level: Senior secondary|0.347|0.402|0.502|0.393|0.684|0.392|0.742|0.342|0.563|0.417|
|Highest edu. level: Post secondary|0.648|0.403|0.481|0.402|0.273|0.388|0.233|0.328|0.419|0.418|
|Mean years of teaching|9.815|6.392|8.249|5.429|5.799|4.372|5.189|2.614|7.300|5.273|
|Teacher’s prior experience is:|||||||||||
|ECED teacher experience|0.425|0.437|0.330|0.422|0.334|0.423|0.245|0.389|0.333|0.422|
|ECED teacher & non-teacher exp.|0.142|0.309|0.163|0.324|0.133|0.310|0.132|0.308|0.139|0.309|
|<br>ECED non-teacher experience|0.171|0.333|0.152|0.269|0.173|0.316|0.318|0.403|0.229|0.363|
|No ECED experience|0.262|0.390|0.355|0.414|0.361|0.427|0.304|0.396|0.299|0.400|
|Teacher’s training is:|||||||||||
|No training|0.164|0.317|0.323|0.398|0.144|0.287|0.117|0.255|0.157|0.303|
|Non-project training|0.834|0.317|0.677|0.398|0.856|0.287|0.259|0.347|0.592|0.433|
|<br>100 hours of Project training|0.000|0.000|0.000|0.000|0.000|0.000|0.101|0.265|0.040|0.175|
|<br>200 hours of Project training|0.002|0.022|0.000|0.000|0.000|0.000|0.522|0.412|0.210|0.366|
|**Structural characteristics of center**|||||||||||
|Student-to-staff ratio|12.162|5.788|9.419|5.152|9.182|4.821|10.214|4.383|10.769|5.199|
|Hours of operation per week|15.197|2.163|14.790|3.077|11.289|3.810|9.834|3.792|12.508|4.063|

Note: N represents number of centers. ECERS-R scores are the averages of two observers who each rated the same centers at the same time using the stop-score rule. All teacher characteristics are averaged at the center-level and thus, continuous. Each variable is constructed by dividing the # of observed teachers in a center who have the particular characteristic by the total number of observed teachers in that center.

20
**Table 6 :  Maximum Likelihood Estimation of Probit Model of Unemployment**

|**Age group 15-64 only**|
|---|

||Initial select<br>by year of|ion equation<br>observation|Dynamic s|election equ|ati|on by year o|f observation|
|---|---|---|---|---|---|---|---|
||1994|2000|1997|2000||2004|2004|
|Unemployed1994|||3.195<br>(8.53)**|1.345<br>(5.14)**||0.672<br>(2.01)*||
|Unemployed2000|||||||1.349|
||||||||(4.06)**|
|Genresidualunemployed1994|||0.112|-0.029||-0.039||
||||(0.50)|(0.18)||(0.17)||
|Genresidualunemployed2000|||||||-0.429<br>|
||||||||(2.13)*|
|Female|-0.039|-0.071|0.106|-0.207||-0.022|0.125|
||(0.73)|(1.33)|(0.78)|(2.13)*||(0.18)|(1.37)|
|Married1994|-0.761||-0.338|-0.282||-0.381||
||(10.47)**||(1.61)|(1.88)||(2.06)*||
|Married2000||-0.650|||||-0.380|
|||(8.86)**|||||(2.54)*|
|**Age groups (reference group=Age 15-19):**||||||||
|Age 30-44|-0.599|-0.547|-0.180|-0.238||-0.502|-0.354|
||(9.50)**|(8.71)**|(1.00)|(2.14)*||(3.75)**|(3.46)**|
|Age 45-54|-0.759|-0.678|0.262|-0.276||-0.737|-0.729|
||(6.64)**|(6.08)**|(1.00)|(1.52)||(3.37)**|(4.01)**|
|Age 55-64|-0.915|-0.837|-0.000|-0.414||-0.728|-0.828|
||(4.87)**|(4.53)**|(0.00)|(1.44)||(2.23)*|(2.56)*|
|**Education (reference group=no formal**||||||||
|**schooling or primary incomplete):**||||||||
|Primary school completed|0.709|-0.265|0.062|0.138||0.048|-0.358|
||(9.60)**|(2.18)*|(0.28)|(0.94)||(0.25)|(1.61)|
|Secondary school completed|1.187|-0.246|0.572|0.120||0.118|-0.137|
||(16.60)**|(1.93)|(2.26)*|(0.70)||(0.54)|(0.46)|
|Some tertiary education|0.123|0.145|0.318|-0.842||0.037|-1.014|
||(1.04)|(1.03)|(1.03)|(2.90)**||(0.14)|(2.88)**|
|City dummies|Yes|Yes|Yes|Yes||Yes|Yes|
|Ethnicity and religion dummies|Yes|Yes|Yes|Yes||Yes|Yes|
|Paternal education dummies|Yes|Yes|No|No||No|No|
|Paternal occupation dummies|Yes|Yes|No|No||No|No|
|Constant|-0.692|-0.577|-2.304|-1.197||-0.418|-0.980|
||(5.21)**|(4.63)**|(6.10)**|(4.60)**||(1.30)|(3.74)**|
|Observations|3312|3138|1430|1228||726|1253|
|Log likelihood|-1540|-1561|-218||-|474|-301 -536|
|Pseudo R-squared|0.26|0.18|0.76||0|.29|0.18 0.21|

Absolute value of z statistics in parentheses * significant at 5%; ** significant at 1% 34
**_Energy demand models for policy_** **_<u>formulation</u>_**

The end-use model has been widely used in energy demand forecasting and analysis throughout the world. Some examples of end-use engineering models with rich technological representation are presented in table 3.

Table 3: Energy end-use models for industrial energy demand analysis

|Name of the model|Country of origin|Technology<br>representation|Modelling approach|
|---|---|---|---|
|AMIGA|US|Explicit|Simulation|
|EERA|NewZealand|Unknown|Simulation|
|ENUSIM|UK|Explicit|Simulation|
|ENPEP|US|Explicit/ stylistic|Simulation|
|MAED|Austria|Explicit|Simulation|
|MEDEE|France|Explicit|Simulation|
|LEAP|US|Explicit|Simulation|

Source: Worrel et al (2002) and Fletcher and Marshall (1995).

Fig. 2: Industrial energy demand estimation in end-use method Source: UN (1991).

Fletcher and Marshall (1995) present a study of industrial energy demand forecast of an English region using a disaggregated end-use model, ENUSIM. “This is a technologybased, ‘bottom-up’ industrial energy use simulation model which considers both 56
<!-- Start of picture text --> Pollcy Research<br>Education and Employment<br>WPS 1075<br><!-- End of picture text -->

- This paper a product of the Education and Employment Division, Population and Human Resources Department- is part of a larger effort in the departmen **t** o study the operation of the labor market and its impact on human resources development. Copies of the paper are available free from the World Ba&k, 1818 H Street NW, Washington, DC 20433. Please contact PHREE, room S6-214, extension 33680 (January 1993, 35 pages).

Ziderman and Horn use the reverse tracer study technique to identify altemative training paths for selected skilled and semi-skilled occupations in Colombia.

The study, confirming earlier research for the United States, shows that workers pursue many different training paths to acquire the skills they need in a given occupation. Ziderman and Horn provide an occupational training map format to analyze these training paths.

They conclude that strong public intervenion that narrows the effective range of available training should be discouraged; such intervention will not only reduce choices but will also lead to a less flexible and efficient training system.

A reduction in training alternatives is the result of the manpower requirements forecasting approach to planning for the provision of na- tional vocational education and training yet that approach is still popular in the planning ministries in developing countries.

The more training options available to workers, the better they can arrange their own training packages.

**T'hc Policy Rcsearch Working PaperoScriesdisseminates th iondingsof work uder way** in theBank. **Anobjectiveof the series** is **to get these findings out quickly, even if presentations aTe less than fully polished. The** findings. interpretations, **and conclusions** in **these papers do not necessarily Tepresent official Bank policy.**

**Produced by the Policy Research Disserniination Center**
PHN 1991 Sector Review 42

# lTble 10: (continued)

|Region/Country|Operations|Nutrition Act|
|---|---|---|
|EMENA|||
|Egypt|SAL|Companion Social Fund to<br>compensate for adjustment's<br>negative impact on nutrition.|
|Poland<br>LAC|SAL I|-<br>Define health policy through<br>Implementation of studies of<br>food consumption and school<br>feedingprograms.|
|El Salvador|SAL I|-<br>Provide infant foods through<br>health posts; introduce a pilot<br>food coupon program and<br>implement a fortified biscuit<br>program for primary schools.<br>-<br>Establish a Social Investment<br>Fund.|
|Honduras|SAL II|-<br>Implement a food coupon<br>program.<br>-<br>*Agree on parameters for<br>measuring the effect of the<br>food program.|
|Mexico|Agriculture SECAL|-<br>Continue reform of food and<br>nutrition programs begun<br>under AGSAL L<br>Implement a pilot nutrition<br>and health project to evaluate<br>procedures, operational<br>viability and cost-effectiveness<br>of current nutrition and<br>health programs.<br>Use the results of the pilot<br>program to implement an<br>expanded national nutrition<br>and health program.<br>Continue to reduce<br>untargeted subsidies to urban<br>consumers and replace them<br>with targeted distnbution of<br>maize.<br>Present to the Bank an<br>evaluation of food and<br>nutrition programs and agree<br>with the Bank on general and<br>agency-specific action plans<br>for nutrition for 1993 to 1994.|

*Condition for tranche release.