rafmacalaba/datause-encoder-data huggingface dataset:
Other types of household credit demonstrate a similar pattern: few households take out a loan and these belong mainly to the richer social stratum. Around 33 percent of rural households report owing money or goods to another person, institution or business.<sup>9</sup> Only a minority of loans originates from formal credit institutions<sup>10</sup> and is used for business expansion; further, only households from the top quintile appear likely to have access to such forms of credit and use loans for business expansion (Table 5).

**Table 5: Proportion of loans taken by household quintile (%)**

||Loans with form<br>|alcredit institutions<br>|Loansforbus<br>|iness expansion<br>|
|---|---|---|---|---|
||All areas|Rural areas|All areas|Rural areas|
|1st quintile|10.0|10.1|2.2|2.3|
|2d quintile|13.3|13.3|2.3|2.1|
|3d quintile<br>|15.2|15.4|2.0|2.1|
|4<sup>th</sup>quintile<br>|14.9|14.8|5.6|5.5|
|5<sup>th</sup>quintile|28.1|22.1|9.8|5.8|
|Total|16.9|15.0|4.7|3.5|

# **4. Empirical strategy**

# _Determinants of participation in the non-farm sector_

In the first part of the empirical analysis, we are interested in identifying the factors which influence the decision to participate in the non-farm sector. An individual’s decision to participate in the non-farm sector is a function of his productive assets (ability, skills and motivation, the financial and structural status of his household), and the array of opportunities available in the environment in which they live.

In rural areas, individuals enter the non-farm sector until the utility from working in the non-farm sector is equal to that from working in agriculture. In cases where there is an insufficient number of individuals in the non-farm sector, those working in this sector will earn a rent, so that the utility of working in the non-farm sector will be higher than that of working in the agricultural sector. Furthermore, within the non-farm sector, those holding a more highly “protected” activity will earn a higher rent.

We model the probability that an individual is observed working in the non-farm sector. We initially distinguish between self-employed and employed workers.<sup>11</sup> Individuals’ preference to work in the non-farm sector are not observed; we use a standard probit to model the latent variable (<sup>_y_</sup> _i_ *<sup>) that represents these preferences:</sup>

> 9 Including loans already reported for non-farm enterprises.

> 10 Those are: state banks, private banks, rural credit, agricultural associations, cooperatives, NGOs, enterprises and others

> 11 For this, we rely on the answers provided by respondents to direct questions concerning their employment status.

7
# **Figure 3. Study Timeline**

<!-- Start of picture text --> NUSAF2 and<br>Obtain NUSAF2 and social Information<br>list of social accountabili scorecard<br>NUSAF2 accountabil ty trainings<br>presented to<br>projects ity end communities<br>Nov 2013 trainings Oct 2015 Feb to March<br>begin 2016<br>June 2014<br>Survey Randomizatio Mean Project Household<br>with n of completion assessment level<br>local projects date of Dec 2015 survey<br>official Jan 2014 NUSAF2 to Feb June to<br>s projects 2016 July 2016<br>Jan 2013 June 2015<br><!-- End of picture text --> 35
# **Data Appendix**

Data come from the World Economic Forum, _Global Competitiveness Report, 2001-02_ (GCR), IMD Lausanne, _World Competitiveness Yearbook 2000_ (WCY), and Kaufmann, Kraay and Zoido-Lobaton (2002) (KKZ). All survey data in GCR comes from the World Economic Forum’s Executive Opinion Survey. A total of 4022 firms were surveyed. “In order to provide the basis for a comparative assessment on a global basis, it is essential that we interview a sufficient number of senior business leaders in individual countries and that the sample in each country is not biased in favor of any particular business group. We have taken a number of steps to ensure this. First, we have asked each of our partner institutes, the organizations that administer the surveys in each country, to start with a comprehensive register of firms. From this, they were asked to choose a sample whose distribution across economic sectors was proportional to the distribution of the country’s labor force across sectors, excluding agriculture. They were then asked to choose firms randomly within these broad sectors (for example, by choosing firms at regular intervals from an alphabetic list), and to pursue face-to-face interviews, following up for clarifications where necessary. The employment distribution was taken from data in the 1998 _Yearbook of Labour Statistics_ of the International Labour Office. The respondents to the survey are typically a company’s CEO or a member of its senior management.” The WCY uses a 115 question survey sent to executives in top and middle management of firms in all 49 countries of the WCY. The sample size of each country is proportional to GDP, and firms "normally have an international dimension." The firms are selected to be a cross section of manufacturing, service, and primary industries. There were 3532 responses to the Survey.

KKZ (2002) updates the data on governance that were developed in Kaufmann, Kraay and Zoido-Lobaton (1999) “Governance Matters.” The database contain more than 300 governance indicators for 175 countries compiled from a variety of sources in 2000/2001. Six aggregate indicators are constructed corresponding to six basic governance concepts: nVoice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law and Control of Corruption.

The various raw data series were chosen because of their relevance to the four concepts of trade facilitation.

- Port efficiency” for each country _J_ is the average of two indexed inputs (all GCR): `o` Port facilities and inland waterways are :(1=underdeveloped, 7=as developed as the world's best, GCR)

- Air transport is :(1=infrequent and inefficient, 7=as extensive and efficient as the world's best, GCR)

- “Customs environment” for each country _J_ is the average of two indexed inputs (all GCR):

- Hidden import barriers other than published tariffs and quotas

28
# **2. Context and Curriculum**

# **2.1. Rwanda Context**

Financial inclusion and financial education are a national priority in Rwanda, with the Government of Rwanda (GoR) announcing a National Financial Education Strategy (NFES) in 2013. According to the 2017 Global Findex, 50 percent of Rwandan adults (ages 15+) have an account with a regulated financial service provider, up from 33 percent in 2011. In comparison, the average rate of account ownership across Sub-Saharan Africa is 43 percent as of 2017.

SACCOs – member based cooperatives – have played a significant role in Rwanda’s financial inclusion progress. In response to the 2008 FinScope survey that found only 21 percent of adults were participating in the formal financial sector, the government of Rwanda set a policy goal of establishing a SACCO in each of the country’s 416 sectors, locally known as _Umurenge SACCOs_ . By 2012, FinScope data showed a significant increase in financial inclusion indicators, with more than 20 percent of adults using a SACCO to save or borrow. The initiative was particularly successful in expanding financial inclusion outside of urban centers. According to the FinScope data, more than 80 percent of SACCO members are from rural areas and over 90 percent of Rwandans live within 5 kilometers of a SACCO.

As part of the GoR’s financial inclusion agenda, the NFES identifies SACCOs as a key delivery channel for financial education given their wide reach and “newly-banked” membership base. SACCOs also offer an opportunity to leverage “teachable moments” in the delivery of financial education as SACCO clients are already engaged in the selection and use of financial services.

Beginning in December 2014, the Ministry of Finance and Economic Planning (MINECOFIN) led the design and piloting of the _Financial Education through SACCOs_ program with financial and technical support from the World Bank. The goal of the program was to establish a consistent, interactive financial education program that could be sustainably delivered to SACCO members through a decentralized delivery model.

# **2.2.** **_Financial Education through SACCOs_ Curriculum**

6
In multi-stage, stratified clustered designs, the probability of being selected must be reflected in each of the different sampling stages. In the case of a two-stage, stratified clustered design, such as HIES 2016/17, the probability of being selected into the sample is a function of: 1) the probability of a PSU being selected in the first stage plus; and 2) the probability of a household being selected within each PSU in the second stage. This can be calculated as follows:<sup>27</sup> where ���� is the probability of household _j_ , in stratum _h,_ and PSU _i_ to be included in the sample; p1 is the probability of the PSU to be selected in the first stage; p2 is the probability of a household being selected in the second stage; kh is the number of PSUs selected in stratum _h_ ; mhi is the number of households selected in PSU _hi;_ and �� is the total number of households in stratum _h_ .

The probability sampling weights in Equation (3) are called _ex-ante_ weights—that is, done before the survey field work—and closely follow the sampling design. It is common, however, to adjust sampling weights _ex-post_ —that is, after survey data collection—to correct for imperfections and bias in the sample identified during field work. If not corrected, these imperfections can lead to errors that detract from understanding the reference population. These adjustments usually include the following: (i) corrections for household non-response conducted at the PSU level; (ii) corrections for sample frame errors generally linked to outdated information, which are usually take place at the PSU level; and (iii) post-stratification to match official projected population statistics at a chosen level of aggregation.<sup>28</sup> The final expression below accounts for the _ex-post_ adjusted sampling weights:

> 27 Since the probability of being selected in each stage is a function of the cluster size and the number of units sampled in each stage, the overall probability of being selected in most living standards household surveys is constant at the level in which PSUs are allocated (i.e. sub-stratum in the case of the HIES 2016/17). This is because PSUs are generally allocated across stratum using proportional allocation based on population size or number of households, which results in a constant probability in the first stage as long as no adjustments are made, such as post-stratification or update of the population based on the listing exercise.

> 28 Depending on the sampling frame used for the sampling design, and whether a listing exercise or poststratification actually takes place or not, the sum of the household-level probability weights in the survey will

33
dummy variable indicating a general clause on equal treatment of all citizens in the country in the constitution was used as a control; (vii) annual sales (log values) and book value of machinery and equipment (log values) were also controlled for. All our results survive these checks (results not shown) with the only exception that in some of the specifications, the interaction term between _Non-discrimination_ and _Women Population_ was found to be statistically weak (insignificant at 10 percent level).

In summary, the positive relationship between non-discrimination and women’s employment is robust and easily survives a large number of checks and a falsification test. It is also broad in scope, covering both production as well as non-production workers. Our results provide an empirical verification of plausible heterogeneities in the non-discrimination and women’s employment relationship with the relationship being much stronger (more positive) in countries that are richer, have more women in the population and among firms that are relatively smaller. In fact, non-discrimination and women’s employment show no significant relationship among large firms and in countries that are below a critical level of income and women’s population.

# **6. Conclusion**

This study utilizes recently compiled data on whether countries have mandated laws on nondiscrimination along gender lines in hiring practices and how such a law affects the employment of men vs. women in the manufacturing sector of developing countries. We find a strong positive association between the non-discrimination clause and the employment of women relative to men. We also find that this positive association is much stronger among the relatively richer countries, smaller firms and countries that have a larger proportion of women in their total population. A number of issues remain to be explored. For example, from the policy point of 24
**Table 8.  Poverty Rates by Province and by Urban/Rural Area**

|**Province**|**Geogr**<br>**Pro**|**aphic**<br>**file**|**Concen**<br>**Pro**|**tration**<br>**file**|**Uncon**<br>**Profile (**|**ditional**<br>**normal)**|**Uncon**<br>**Pro**|**ditional**<br>**file**|
|---|---|---|---|---|---|---|---|---|
||**Urban**|**Rural**|**Urban**|**Rural**|**Urban**|**Rural**|**Urban**|**Rural**|
|Pichincha|8.2|18.4|8.5|30.1|11.5|36.3|12.7|42.5|
|Azuay|4.7|18.4|8.0|36.8|7.9|41.2|5.0|43.0|
|Bolívar|19.6|33.5|8.0|47.0|19.1|62.3|11.6|65.9|
|Cañar|9.1|14.8|19.4|40.8|20.1|41.5|13.8|41.1|
|Carchi|23.6|48.9|11.7|35.9|25.9|64.2|28.0|64.3|
|Cotopaxi|14.7|21.9|8.5|43.0|16.5|49.9|14.6|48.5|
|Chimborazo|16.1|30.9|6.0|52.0|14.3|64.2|16.0|68.3|
|El Oro|15.5|27.1|11.9|24.9|20.2|41.0|15.3|41.6|
|Esmeraldas|15.8|29.0|15.0|46.0|24.1|53.6|25.9|53.3|
|Guayas|18.6|21.6|11.6|41.5|22.2|46.9|22.4|46.2|
|Imbabura|16.2|37.7|13.2|47.8|21.7|64.2|21.0|68.1|
|Loja|10.8|36.0|6.5|38.6|11.7|58.4|8.4|63.4|
|Los Ríos|26.7|24.9|16.2|33.1|33.0|44.0|33.7|45.5|
|Manabí|25.2|30.0|13.4|42.6|29.0|54.5|31.8|61.2|
|Morona Santiago|7.6|61.7|11.4|59.3|13.4|84.8|17.0|77.2|
|<br>Napo|17.3|42.0|4.6|60.1|12.9|75.4|16.9|71.3|
|Pastaza|12.7|43.8|10.3|68.4|16.9|79.9|11.9|76.0|
|Tungurahua|9.3|23.5|8.4|36.4|12.1|46.2|11.7|46.9|
|Zamora|16.1|38.0|12.5|46.4|21.9|63.5|21.4|68.2|
|Sucumbíos|8.3|31.4|9.7|36.0|12.7|50.8|11.4|50.8|
|Orellana|9.5|40.8|30.2|58.8|28.9|72.6|23.9|66.5|

_Source:_ Own calculations based on ECV Survey.

# 5. Decompositions at the Regional Level

This section expands the analysis from national to regional level. The analysis clearly reveals that for both the Sierra and the Costa, demographics and education account for the largest share in total log welfare ratio (Table 9). In the Costa, demographic factors account for about 28 percent of the differential in log welfare ratio, and education for more than 79 percent. In the Sierra, demographic factors account for about 32 percent of the differential in log welfare ratio, and education for more than 74 percent. In Oriente, the difference in the constant term accounts for the largest share in living standards, which means that a married couple, both illiterate, with male indigenous household head who owns a house and is an employee living in the urban Oriente has a mean log welfare ratio 0.72 higher than a household with the same characteristics located in the rural Oriente.

**Table 9. Contribution of Variables to Average Level of Log Welfare Ratio by Region**

||**Urb**<br>|**an-Rural Differ**<br>|**ence**<br>|
|---|---|---|---|
||**Sierra**|**Costa**|**Oriente**|
|Mean log welfare ratio|0.868|0.516|1.142|
|**Decomposition**||||
|Constant term|-0.090|-0.075|0.720|
|Demographic variables|0.242|0.166|-0.272|
|Education variables|0.682|0.383|0.494|
|House ownership variable|-0.089|-0.024|-0.042|
|Position inoccupationvariable|0.122|0.065|0.242|

_Source:_ Own calculations based on ECV Survey.

13
# **1. Introduction**

One of the enduring legacies of the socialist era in Armenia and other formerly planned economies is that household energy consumption is subsidized and supplied at below market prices. Since the transition to a market economy, one of the priorities for policymakers in the energy sector has been to increase energy prices to cost recovery and economically efficient levels. Such measure would lead to more efficient consumption patterns and improved financial viability of the energy sectors, as well as ease the burden on the state budget. There are also other reasons, often beyond the control of policymakers, that necessitate energy price increases, such as heavy reliance on imported sources of energy, which has been the case of Armenia.

Armenia meets about 75 percent of its energy needs through imports, with natural gas imports from Russia accounting for about 80 percent of total energy imports and 60 percent of total primary energy supply. High dependence on imported energy has made Armenia’s economy and households vulnerable to external energy price shocks. A most recent case in point was the 2010 gas tariff increase. On April 1, 2010, the retail price of gas increased from AMD 96 to 132 per cubic meters (m3) for small consumers (consuming less than 10000 m3 per month) and from $215 to $243 per 1000 m3 for large consumers (consuming more than 10000 m3 per month). This meant that the cost of gas for residential consumers increased by nearly 40 percent and for enterprises and large consumers by 13 percent (Table 1). The change in the retail price of gas was in response to an increase in the import price of the Russian gas from $154 to $184 per 1000 m3, a 17 percent hike.

The increased gas price, the second largest on most residential consumers since 2000, had considerable impact on households, as the residential sector consumes a large share of the imported gas. With households’ heavy dependence on gas for heating, cooking and lighting, the gas price hike may have significant implications for prices of other sources of energy, transportation and consumer goods. Unlike earlier gas price hikes, the 2010 gas tariff increase had come on the heels of the global economic slowdown that hit Armenia’s economy and households hard (see, for example, World Bank, 2010a). The crisis could exacerbate the 2
respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak.<sup>6</sup> On the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, we do not use expert assessments that are explicitly based on other existing data sources.<sup>7</sup> In Section 5.1 we provide further analysis of this issue.

The WGI include 12 expert assessments produced by NGOs. The data provided by these organizations cover a range of specific topics, including press freedoms (Reporters without Borders), political rights and civil liberties (Freedom House), human rights (Human Rights Measurement Initiative), budget transparency (Open Budget Project), and electoral integrity (Africa Electoral Index). A number of these data sources cover a broader range of topics relating to governance, including the Bertelsmann Transformation Index and the Varieties of Democracy Project. The organizations providing the data are geographically diverse, based in Europe (Varieties of Democracy, Reporters with Borders, Bertelsmann Foundation), Africa (Africa Electoral Index and Africa Integrity Indicators), New Zealand (Human Rights Measurement Initiative), in addition to the United States (e.g. Freedom House, Open Budget Project, World Justice Project).

The WGI also include eight data sources provided directly or indirectly by public sector organizations. Most notable among these are four very similar expert assessments of the quality of policies and institutions produced by the African Development Bank (Country Policy and Institutional Assessment), Asian Development Bank (Country Performance Assessment), World Bank (Country Policy and Institutional Assessment), and the International Fund for Agriculture and Development (Rural Sector Performance Assessment). These assessments all cover a similar set of topics relating to policy and institutional performance and are scored by staff of these organizations, often located in the countries they assess, using a structured methodology and clear scoring benchmarks. All four organizations use

> 6 See Kraay and Murrell (2016) for a discussion of respondent reticence and ways to measure it.

> 7 For example, we do not use data from the Transparency International Corruption Perceptions Index because it simply is a compilation of existing data sources, in the same way that the WGI are.  In addition, we have over time dropped specific questions and specific data sources from the WGI when their methodologies have changed to rely explicitly on other data sources.  This includes selected dimensions of the Heritage Foundation’s Index of Economic Freedom, as well as the European Bank for Reconstruction and Development Transition Index.

7
# **Table 5: Estimates of the Fiscal Impact of Aid** **<u>Sample</u>** <u>: 1981-2004</u>

|**Dependent**<br>**Variable**<br>|**D((**<br> <br>|**PUBINV/T**<br>**(PUBINV/**<br>|**OTEXP) (**<br>**TOTEXP))**<br>|**100-**<br>**)**<br>|**D((TAXRE**<br>|**V/GDP)/(10**<br>|**0-(TAXREV**<br>|**/GDP)))**<br>|
|---|---|---|---|---|---|---|---|---|
|**Regression**<br>**Method**|**OLS**|**IV**|**OLS**|**IV**|**OLS**|**IV**|**OLS**|**IV**|
|**Equation No.**|**9**|**10**|**11**|**12**|**13**|**14**|**15**|**16**|
|**Constant**|0.01<br>(0.14)|0.026<br>(0.33)|0.02<br>(0.65)|0.04<br>(0.89)|-0.001<br>(0.42)|-0.001<br>(0.42)|-0.001<br>(0.156)|-0.0004<br>(0.15)|
|**D(EXP/GDP)**|||0.04***<br>(2.65)|0.05***<br>(2.72)|||0.001<br>(1.25)|0.001<br>(1.38)|
|**D(LAB)**|-0.72<br>(1.31)|-0.86<br>(1.47)|||-0.01<br>(0.51)|-0.01<br>(0.55)|||
|**D(ODA/GDP)**|-0.01***<br>(3.17)|-0.01***<br>(4.94)|-0.01***<br>(2.8)|-0.01***<br>(10.09)|-0.001***<br>(28.72)|-0.001***<br>(7.39)|-0.001***<br>(18.18)|-0.001***<br>(6.1)|
|**Dummy(96+)**|0.11<br>(0.64)|0.12<br>(0.71)|-0.06<br>(1.13)|-0.07<br>(1.32)|0.01<br>(1.33)|0.009<br>(1.32)|0.01<br>(1.06)|0.01<br>(1.15)|
|**D(ODA/GDP)***<br>**Dummy(96+)**|0.005<br>(1.001)|0.01**<br>(2.18)|0.01*<br>(1.66)|0.01***<br>(2.72)|-2.66E-05<br>(0.13)|-6.92E-06<br>(0.03)|6.35E-05<br>(0.23)|7.33E-05<br>(0.24))|
|**R-Squared**|0.25|0.13|0.39|0.19|0.68|0.68|0.7|0.7|
|**F-Statistics**|1.62|1.19|5.43|3.77|10.23|4.62|10.98|4.66|
|**D-W **|1.92|2.14|1.81|2.04|2.00|2.01|1.87|1.87|
|**S.E.**|0.25|0.27|0.18|0.20|0.01|0.01|0.01|0.01|

- ***   Significant at 99% confidence level

- **    Significant at 95% confidence level

- Significant at 90% confidence level

Instruments in the IV method are: Constant, D(ODA/GDP)(-1), Dummy(96+), D(ODA/GDP)* Dummy(96+), and either D(LAB) or D(EXP/GDP). Notations: PUBINV_TOTEXP is Public investment to total government expenditure (recurrent public expenditure plus public investment) ratio (multiplied by 100). TAXREV is tax revenue to GDP ratio (multiplied by 100). D(.) denotes first difference. ODA_GDP is ODA to GDP ratio (multiplied by 100). ODA_GDP_Dummy is in fact D(ODA_GDP) times the dummy variable for 1996-2005. PUBINV_T is PUBINV/(100-PUNINV). TAXREV_T is TAXREV(100-TAXREV) 29