rafmacalaba/datause-encoder-data huggingface dataset:
16 _Table 3: Descriptive statistics, by survey and wave_

|||CH|NS||GS|CF|H8BS|
|---|---|---|---|---|---|---|---|
||1991|1993|1997|2000|2000|2004|1998|
|High out-of-pocket payments|3.3%|1.9%|2.9%|3.4%|21.6%|15.0%|56.5%|
|Catastrophic out-of-pocket payments|2.7%|1.6%|2.5%|2.9%|19.0%|14.1%|35.2%|
|Out-of-pocket payment share|2.0%|1.6%|2.2%|7.4%|15.9%|13.9%|12.9%|
|Health insurance|27.0%|23.1%|23.8%|20.0%|0.3%|18.2%|4.0%|
|Public insurance|11.2%|10.6%|8.2%|7.1%||||
|Worker insurance|6.7%|4.9%|3.8%|3.7%||||
|Dependents’ insurance|1.7%|1.2%|0.5%|0.2%||||
|Work unit insurance|3.7%|0.5%|8.2%|5.0%||||
|Cooperative medical insurance|0.2%|2.4%|0.7%|0.4%||||
|MCH insurance|0.1%|0.0%|0.1%|0.1%||||
|Planned immunization insurance|2.8%|1.8%|0.8%|0.8%||||
|Other health insurance|0.6%|0.8%|0.8%|0.4%||||
|Insured but type unknown|0.1%|0.8%|0.8%|2.3%||||
|Cooperative medical scheme|||||||2.5%|
|GIS, LIS, etc.|||||||1.4%|
|SAH excellent|13%|13%|14%|14%|49.5%|36.9%||
|SAH good|63%|64%|61%|50%|22.8%|33.1%||
|SAH fair|21%|21%|22%|31%|21.0%|21.9%||
|SAH poor|3%|3%|3%|5%|5.1%|6.2%||
|SAH very poor|||||1.5%|1.8%||
|Chronic illness|||||||3.5%|
category can be found in the Data Appendix. The overall take is that the higher _parreg_ is, the better the influence of interest groups on government policies is regulated. The disadvantage of using this measure is that it is not as well-known and used as _ICRG_ , therefore it is difficult to compare with other studies. Nonetheless it captures better the degree of influence that powerful groups have over government, which will be a key feature in the model presented in Section 3. Although I cannot rule out the potential endogeneity problem of using regulation of participation, that is, when default crisis cause government structure and rules to change, I argue that institutions are very persistent and thus evolve slowly.<sup>9</sup> To calculate default probability, my main data source is Reinhart and Rogoff (2009).<sup>10</sup> I supplement it with a dataset constructed by Laeven and Valencia (2008), De Paoli et al. (2006) and Levy-Yeyati and Panizza (2011). Default probability for each country is calculated as the number of sovereign default episodes that occurred between 1960 and 2008 divided by the number of years since 1960 (or year of independence). As a proxy for countries’ default history, I follow Reinhart and Savastano (2003) and use the percent of years in a state of default or restructuring between 1800 (or year of independence) and 1959.<sup>11</sup> As control variables, I include the log of average real GDP per capita in constant 2000 dollars from the WDI database and debt as a percent of gross national income (GNI), the 1970 to 2008 average, from the Global Development Finance database.

Table 1 provides some basic descriptive statistics of the sample of countries and Table 2 reports the pairwise correlation among the determinants of default crisis.

# **2.2 Stylized Facts**

The main objective of the paper is to explain why some countries default on their sovereign debt more often than other countries. The key findings using the dataset described above are:

## **Fact 1: Institutional quality and sovereign default probability are negatively correlated.**

> 9 Acemoglu, Johnson and Robinson (2005).

> 10 The definition of sovereign default crisis can be found in the appendix.

> 11 Results are similar if we use number of default episodes over years (1800-1959) as proxy for default history, or exclude countries that are independent after 1959 since they have no default history by construction.

8
calories and dietary diversity.<sup>8</sup> Our inference from this finding is that experiential measures contain an important signal about food insecurity status that is not conveyed in measures of access and availability (such as calories and dietary diversity). Another inference from our finding is that rising food prices, _regardless of whether they have direct adverse effects on caloric intake or diet quality_ , lead to increased anxiety about meeting food needs. The next section of this paper proceeds by discussing the panel data used in this analysis and a descriptive profile of food security status in Malawi. Section 3 discusses the regression model used to examine the connection between rising food prices and the experiential measures of food security status, and section 4 provides some concluding discussion.

# 2. **Data and Country Context**

# _2.1. Data_

This paper makes use of two waves of panel survey data, representative at national and subnational (rural/urban) levels. The first wave was carried out from March to November of 2010. This wave is a sub-sample of the Third Integrated Household Survey (IHS3), which is part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMSISA) project in Malawi. The second wave was fielded from March to November of 2013 as part of the Integrated Household Panel Survey (IHPS). The panel element of the survey was designed such that individuals (not households) were followed between waves. Therefore, the IHPS sample includes 4,000 households that can be traced back to 3,104 baseline households

> 8 This finding aligns with the literature on armed conflict, in which it is well recognized that various measures of safety and security do not correlate well with perceptions of safety and security, and are sensitive to the finding that statistical improvements in safety and security may not directly improve perceptions of safety and security (Rigterink, 2015).

4
# **Share of sectoral exports in total merchandise exports: several generations of flying geese**

**Figures 2.6a Shares of Textile exports in total merchandise exports:  Five generations: Japan, Korea,** **<u>China, Indonesia and Vietnam.</u>**

<!-- Start of picture text --> 0.05<br>Korea China<br>0.04 China<br>Hkong<br>0.03<br>Japan Indonesia Indonesia<br>0.02<br>Vietnam Japan<br>0.01 Thailand<br>0 Korea<br>Singapore<br>Figure2.6b Share of Clothing exports in total merchandise exports, two generations<br>0.4<br>cloth_export_chn<br>0.3 Turkey cloth_export_hkg<br>0.2 cloth_export_ind<br>cloth_export_idn<br>0.1<br>cloth_export_tha<br>0<br>cloth_export_tur<br>cloth_export_kor<br>Figure 2.6c Shares of Footwear exports in total merchandise exports, three generations<br>0.08<br>footwear_export_chn<br>0.06 Korea<br>footwear_export_hkg<br>China<br>0.04<br>footwear_export_idn<br>IDN<br>0.02<br>HongKong footwear_export_jpn<br>0 footwear_export_tha<br>footwear_export_kor<br>Figure 2.6d Shares of toys exports in total merchandise exports, two generations<br>0.08<br>HongKong toy_export_hkg<br>0.06<br>toy_export_idn<br>0.04<br>toy_export_jpn<br>China<br>0.02 toy_export_tha<br>Japan<br>0 toy_export_kor<br>toy_export_sgp<br>Source: calculated based on COMTRADE data, SITC Rev 1, 3-4 digits, extracted from WITS .<br>1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2010 2010<br>1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010<br>1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010<br>1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010<br><!-- End of picture text --> 26
5 World Bank 2010a; AEI 2012). Several policy issues arise in trying to strike the appropriate balance.

Speaking broadly, tariffs must be high enough to allow the utility to recover its costs and finance new investment, but not so high as to frustrate demand and deny access to poor households that consume small amounts of electricity (“subsistence consumers”). That dilemma is resolved through graduated tariff structures in which unit costs rise at higher levels of consumption. Similarly, connection charges must be high enough to reimburse utilities for the connection service, but not so high as to discourage potential new customers from joining the network.

The pricing of electricity—and particularly the advantages and disadvantages of different tariff structures—has been the subject of a vast amount of research. But the connection charges have received much less attention, despite the fact that high connection fees are a significant deterrent to consumers desiring to connect to a network. The next section explores how connection charges levied on new consumers have affected electrification rates in Sub-Saharan Africa.

# **Customer Connection Charges in Sub-Saharan Africa**

Most households deciding whether to access electricity from the national grid find themselves in one of two situations. In the first scenario, electricity may not be physically available in the local area. In this case, no household in the community receives electricity from the grid. The choices available to households are decentralized options such as minigrids, solar home systems, and batteries. Organizations and small businesses may rely on diesel-powered generators. The second scenario is that once an electricity network has been extended to a given community, households must decide whether to obtain the service. Of the factors influencing that decision, and the most researched, is the price of electricity.

High electricity prices can discourage low-income households that may not be able to afford the monthly bills. However, in practice, high prices are not such an obstacle, because the cost of alternatives generally used, such as kerosene oil, candles and batteries, are comparable to most grid-supplied electricity tariffs for small consumers. The main obstacle is the high up-front charges the consumer has to pay for a grid connection. These include the cost of house wiring and the connection charge payable to the utility. The connection charges faced by small residential consumers vary considerably across countries. The − charges can range from very modest (often subsidized) sums of $10 $20 to close to $200 and more in some countries (see appendix 2). In many cases, to obtain service the consumer is expected to reimburse the utility for the entire 20- or 30-year capital cost of an electricity service that often has been designed not for subsistence consumers, but for users of larger amounts of electricity. High connection charges have a dramatic dampening effect on electrification rates because they significantly deter poor households from electing to obtain available service. Not only are households deprived of the benefits of electricity, but utilities
# **Table 2: Results with Total Public Expenditures (Dynamic Panel - GMM)**

## **_Dependent variable: Growth rate of GDP per capita_**

|Total fiscal revenue|-0.214|
|---|---|
|(% of GDP)|(-0.521)|
|Total fiscal expenditures (-1)<br>(% of GDP)|0.387<br>(1.774)*|
|Budget surplus<br>(% of GDP)|0.014<br>(0.062)|
|Inflation - consumer price index|-0.327<br>(-3.511)***|
|Growth rate of GDP per capita (-1)|0.167<br>(0.899)|
|No. of observations<br>J-statistics|94<br>7.542|

Note: The estimation method is a dynamic GMM. Annual data are used. (-1) indicates variables lagged one period. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients. J-test is for overidentification problem where H0: there is no overidentification problem. We fail to reject in each case.

29
NASA Earth Observing System Terra satellite. The data are available at the 8 km spatial level on an annual basis for the period 1981 to 2000 and are given as grams of carbon per square meter (gC/m<sup>2</sup> /year). We use the annual average of NPP to proxy cropland productivity. Although the Moderate-resolution Imaging Spectroradiometer data are at a greater resolution than the GLC 2000 data, this generally did not pose a problem in terms of masking out cropland given that most cropland cells were agglomerated at a much larger scale than 8 km.

The use of the Moderate-resolution Imaging Spectroradiometer data as a proxy for actual cropland productivity has been validated in a number of studies (Zhang et al. 2008; Turner et al. 2006). To roughly verify that this measure can be related to standard agricultural data, we aggregated its value for all of South Africa and correlated it with FAOSTAT (2011) data-derived measures of cropland productivity. The resultant Pearson correlation coefficient was 0.74 and was significant at the 1 percent level, providing evidence that these measures are substantially positively related. We also investigated whether the relationship between NPP and data-derived cropland production might be nonlinear by using a Kernel-weighted local polynomial smoothing estimator, but we found no evidence of this. To provide a “monetary” feel for the rough equivalent of a unit of NPP, we also regressed the total value of agricultural cropland production in South Africa on NPP, additionally controlling for time dummies.

23
with respect to GDP.

# **5.0.1 Elasticities of Substitution**

We estimated equation 19 for each HS-6 product and obtained 4,537 elasticities of substitution (sigmas henceforth) on average for all the 28 countries. Although it is impossible to report all sigmas,<sup>7</sup> Table 2 presents the descriptive statistics of sigmas for each country. By examining the table, we can obtain a sense of the degree of substitutability among varieties. If sigma is high, say above 5 or 10, then this suggests that the potential for gains from variety are small. This is intuitive. When _σg_ is high, since new and disappearing products are close substitutes to existing varieties, they will only have a minor influence on the price index and hence the gains from variety.<sup>8</sup> On the other hand, if sigma is low, then this suggests that goods are highly differentiated by country, meaning the potential for gains from variety is high.

Table 2 shows that the median elasticity is 4.1 with no country in the sample exceeding 5. The average elasticity of substitution is 13.0 for the 28-country sample.<sup>9</sup> Based on these results, we can expect the potential gains to be relatively high, especially for countries like Malawi, the Philippines, Fiji, among others, as both median and mean are relatively low.

# **5.0.2 Change in Varieties**

The second step is to calculate the changes in variety over time (i.e., the lambda ratio). The calculation of lambdas requires the existence of common varieties in the beginning and at the end of the period.<sup>10</sup> This is one of the major obstacles we face when implementing the

> 7The estimated sigmas for each country are published by the World Bank Data Development Hub https: //datacatalog.worldbank.org/data/dataset/0066672/elasticities_of_substitution_for_28_countries_.

> 8If we look at equations (8) and (11), it is clear that if _σg_ is high, _σg_ <u>1</u> _−_ 1<sup>isclosetozeroandtheadditional</sup> <u>1</u> term � _λλgtg−t_ 1 � _σg −_ 1 is closer to unity. Hence the variety change has small influence on the price index when _σg_ is high.

> 9As a reference, the results of Broda and Weinstein (2006) are as follows, mean is 17.3 in HS9, 7.5 in SITC-5, and median is 3.7 in HS9, 2.8 in SITC-5 in period 1972-1988 in the United States.

> 10The reason why we need common varieties is that we cannot value the creation and destruction of a variety without knowing something about how this affects the consumption of other varieties (Broda and Weinstein, 2006).

16
achieved just with the business registration certificate. A BRC is required, and sufficient, for firms wishing to open a business bank account or to take a business loan from a formal bank. In addition, it is required for registration at the Malawian Chamber of Commerce, for registering land, and to access business development services provided by the government. The TPIN requires a BRC to be issued. The main additional benefits it offers on top of the BRC are that: (i) firms cannot be paid for a successful government tender without a TPIN; (ii) avoiding fines or harassment for failing to pay taxes (although enforcement is infrequent); and (iii) firms may be able to use their history of paying taxes to document their financial history to financial institutions when applying for loans.

# **3. Data and Impact Evaluation Design**

We discuss first the process of obtaining a sample of informal firms, before providing details on the randomization process and the interventions.

# **3.1 Obtaining a Sample of Informal Firms**

In this study we target the informal micro and small enterprises that are likely to be able to benefit the most from business registration, and that the government has said would be their first group of interest for a future roadshow on business registration. We target firms in urban Lilongwe and Blantyre, the major commercial cities in Malawi. At the end of 2011, we listed over 100 business centers – that is, concentrations of firms including industrial parks, markets, streets with shops, sets of workshops, etc. – and randomly sampled 46 of these business centers (23 in each city) to list all businesses operating within these areas. Through this process we listed 7,603 enterprises, 85 percent of which were not registered at the DRG. With this process, we excluded from the sample household-based enterprises. Surveys in Africa have shown that household-based enterprises tend to be smaller on average than those operating in business centers (see for example, Bossuroy et al, 2013). Similar proportions of unregistered firms were identified in Blantyre and Lilongwe, despite the DRG being located in Blantyre.

Only one-quarter of the firms listed were female-owned. We therefore oversampled female-owned enterprises in order to be able to examine how these interventions differ by gender. Given that female-owned enterprises tend to be smaller and have a lower capital base than male-owned 8
19 Table 2: Coefficient of correlation between regional NRA and international price, 12 key covered farm products,<sup>a</sup> various regions, 1965 to 2007

||Africa|South<br>Asia|South East<br>Asia and<br>China|Latin<br>America|High-<br>income<br>countries|All focus<br>countries<sup>b</sup>|
|---|---|---|---|---|---|---|
|**Grains, oils, sugar**|||||||
|Rice|-0.19|-0.58|-0.51|-0.52|-0.10|-0.16<br>(0.99)|
|Wheat|0.01|-0.81|0.09|-0.12|-0.28|-0.41<br>(0.85)|
|Maize|-0.20|-0.70|-0.55|-0.04|-0.29|-0.57<br>(0.71)|
|Soybean|-0.15|-0.42|0.16|-0.27|-0.07|-0.18<br>(0.30)|
|Sugar|-0.57|-0.74|-0.57|-0.40|-0.69|-0.70<br>(0.99)|
|**Tropical cash crops**|||||||
|Cotton|0.28|-0.33|-0.16|-0.29|-0.74|-0.57<br>(0.96)|
|Coconut|na|-0.16|-0.14|na|na|-0.12<br>(0.99)|
|Coffee|-0.35|na|0.02|-0.30|na|-0.28<br>(0.99)|
|**Livestock products**|||||||
|Milk|0.19|-0.57|-0.70|0.33|-0.10|-0.31<br>(0.98)|
|Beef|0.20|na|0.05|0.55|0.29|0.32<br>(0.97)|
|Pigmeat|na|na|-0.53|-0.47|-0.60|-0.76<br>(0.98)|
|Poultry|0.59|na|-0.52|-0.78|-0.22|-0.34<br>(0.87)|

a Computed using the weighted average regional NRAs and a common international reference price for each product, from World Bank (2008).

b Numbers in parantheses are the coefficient of correlation between the unweighted average regional NRAs and CTEs for individual covered products. For all covered products the coefficient is 0.93.

Source: Source: Anderson et al. (2010).
Some projects were completed quickly, such as livestock projects, while road and building construction took up to one year to complete. All projects were completed by October 2015, with a mean completion of June 2015. We conducted the project quality assessment from December 2015 to early February 2016. From this assessment we constructed the project quality information scorecard and randomized communities to receive the scorecard intervention in February. We then distributed the scorecards from February to March 2016. Six months after the assessment, in June to July 2016, we completed the final household survey. The final household data collection was done on a rolling basis to coincide with the timing of the project assessment and ensure that communities were visited on a consistent timeline.

The design and number of projects by type and treatment status for the social accountability training intervention are presented in Table 1. A total of 940 projects were included in the sample. However, our main outcomes are not easily comparable across each of the project types. In the project types with the smallest number of communities, we were unable to create a reliable index of outcomes from the first project assessment, and so we focus on the most common project types: enterprise development, fencing, livestock, road and housing construction, and tree planting. This reduces our sample to 895 projects.<sup>8</sup> For household-level welfare outcomes, we include all project types.

In Table 2 we present the information scorecard design. As described previously, we developed and delivered the project quality information scorecard only to communities with livestock projects to improve comparability. Due to operational difficulties, we did not include the northeast part of the country, the Karamoja region, with 61 communities. A total of 574 communities are thus included in the sample. The end design is a 2x2 that includes both social accountability training treatment and control communities.

The NUSAF2 program and the social accountability training were implemented across the broad north of Uganda. We present a map of training treatment intensity in Figure 4. The figure shows the number of NUSAF2 communities that received training by parish across the

> 8 Because we had limited information on project type before selecting communities for the social accountability training treatment, we were not able to pre-drop project types that were implemented in numbers too small to allow for reasonable comparison of project quality, as is commonly done in similar experiments. We instead drop them in our analysis here. As the number of such projects is small (less than 5% of the sample in total), and given that we target all the projects delivered by NUSAF2 in two funding tranches, this post-dropping should not affect our internal or external validity.

12
costs. Following the _training-of-trainers_ , RICEM master trainers provided regular follow-up support and supervision via onsite visits and telephone calls to ensure the timeliness, consistency, and quality of program delivery.

# **3. Study Design, Timeline, and Data**

# **3.1. Study Design and Timeline**

The research design is based on a randomized control trial methodology and a pre-analysis plan recorded with the AEA RCT registry. Randomization was done by computer at the SACCO level, stratified by province. Rwanda has one _Umurenge_ SACCO in each of its 416 sectors, across thirty districts and five provinces.<sup>6</sup> Of these 416 SACCOs, 179 had recently participated in a financial education program prior to our study and another 5 were included in the piloting of the _Financial Education through SACCOs_ program. Hence, these SACCOs were excluded from the study sample. Of the remaining 232 SACCOs, we randomly selected 200 to be part of our study sample.

The research design comprises two treatment arms and one comparison arm. The two treatment arms differ in the amount of local community control over trainer selection. Specifically, the three groups are as follows: T1: SACCO community members received the full financial education program. The delivery of financial education at the SACCO level was led by three representatives from each SACCO, who were selected by SACCOs themselves and trained to teach the program. There were 65 SACCOs in this autonomous trainer selection group.

T2: SACCO community members received the full financial education program. The delivery of financial education at the SACCO level was led by the SACCO manager, a loan officer, and a board member, who were all trained to teach the program. There were 65 SACCOs in this fixed trainer selection group.

> 6 Rwanda has both _Umurenge_ SACCOS (those established by the GoR beginning in 2009) and non- _Umurenge_ SACCOs. The program and evaluation studied in this paper cover only the _Umurenge_ SACCOs (which far outnumber non- _Umurenge_ SACCOs). Note that we use the terms “SACCO” and “ _Umurenge_ SACCO” interchangeably.

9