rafmacalaba/datause-encoder-data huggingface dataset:
Mobile phones have been used to collect data using three approaches: (1) _Face-to-face interview using phone, laptop, or tablet computer with electronic form._ The mobile phone essentially takes the place of paper in a traditional household survey, with enumerators conducting a “live” interview but capturing data provided by the respondent using an electronic device.<sup>2</sup> (2) _Direct response to phone using SMS, USSD, IVR, or electronic form._ Respondents interact with their own devices to answer questions digitally using the mobile’s keypad. The interface and data transfer technology employed could be SMS, USSD, or electronic forms. An alternative approach is to link the respondent to an automated interactive voice response system (IVR) in which the respondent dials a number and is prompted by a computer to record answers using the keypad.

(3) _Interview with enumerator via phone._ Respondents receive a call from an operator at a call-center, are asked questions and dictate their answers which are then recorded by the operator on a computer.

Few systematic studies are available that compare the data quality and data collection efficiency of these alternative approaches. In one India-based study evaluating the accuracy of data collection using electronic forms, voice, and SMS through mobile phones, MIT researchers found that a voice interface (in a setting where respondents dictated answers through the mobile phone to an operator in real time) provided the highest level of accuracy, leading them to favor the voice-operator combination for the implementation of a tuberculosis program (Patnaik et al. 2008). In Tanzania, a research team from Twaweza, a citizen-centered initiative in East Africa, favored a voice-operator interface to collect weekly data on provision of public services, due to implementation challenges associated with USSD, electronic forms, and IVR (MobileDataGathering 2011). For reasons explained below, SSEPS also employed a voice-operator approach.

> 2 The face-to-face, device assisted approach has also been used with devices other than mobile phones, such as tablet and notebook computers and is more broadly classified as Computer-Assisted Personal Interviewing (CAPI). See IRIS Center (2011) for a detailed review of CAPI software options and Caeyers et al. (2012) for an analysis of the performance of CAPI vs. traditional Pen-and-Paper Interviewing (PAPI).

5
<!-- Start of picture text --> ln(GDP) ln(lights)<br>23 9<br>8.8<br>22.8<br>8.6<br>22.6<br>8.4<br>22.4 border conflict<br>8.2<br>22.2<br>8<br>22<br>7.8<br>21.8<br>7.6<br>21.6 7.4<br>Ln(GDP) Ln(lights)<br><!-- End of picture text --> Source: WDI (2014) and NGDC (2014). Authors’ calculations. Vertical lines demarcate the border war period.

Overall, the average annual growth in GDP for the 46 countries in our sample amounted to 4.4 percent, a little lower than the annual growth in lights (4.8 percent). The three countries that recorded the slowest GDP growth between 1992 and 2012 (Zimbabwe, Burundi, and DRC) are also among the countries with the slowest growth in night lights (Table 1). The countries with the fastest-growing economies (Cape Verde, Liberia, and Equatorial Guinea) also experienced fast growth in night lights as measured from space. Overall, there is a strong positive relationship between changes in lights and changes in GDP in the sample of Sub-Saharan African countries (Figure 4).<sup>3</sup> Table 1: Annual GDP growth and night lights for fast-and slow-growing countries

||Annual GDP growth<br>(1992-2012)<br>Ann<br>|ual lights growth<br>(1992-2012)|
|---|---|---|
||_Countries with slowest growth_||
|Zimbabwe|-0.5|-0.3|
|Burundi|0.7|2.5|
|DRC|1.1|2.2|
||_Countries with fastest growth_||
|Cape Verde|8.2|5.5|
|Liberia|9.0|12.1|
|Equatorial Guinea|21.5|37.0|

> 3 This is net of country and year effects.

6
difference between offer and bid prices divided by the bid price. This mean spread data is based on 48 separate price quotations, with about 2 week intervals, during the period from March 11, 1986 to June 9, 1988. Discarding the fluke of a mean spread of 290 per cent for claims on Sudan, our computations show that the mean spread ranges from 1 per cent for negotiable Turkish loand to 55 per cent for rather shaky Nicaraguan loans. Mean spreads are 2 or 3 per cent for the major Latin borrowers of Argentina, Brazil, Chile and Mexico.

To explain movements in secondary market prices, Sachs and Huizinga regress the secondary market price on the following explanatory variables: debt relative to GNP, the growth rate of real GNP, dwnmy information reflecting a country's interest payment record and information on whether or not its loans have been judged 'value-impaired' by U.S. bank regulators. Using the growing evidence on secondary market price, it is now possible to test for the role of an additional factor: country-specific uncertainty as reflected in the volatility of the secondary market prices themselves. As in indicator or price volatity, we take the standard error of auxiliary regressions of the bid price on a linear time trend for each country.

The results of a regression which includes this standard error, denoted SE, as an explanatory variable are reported in Table 6. Other included explanatory variables are ARR, which is a dummy variable set equal to 1 if the country has commercial bank arrears as of July 1988, public debt relative to GNP, and again the growth rate of real GNP. Public debt rather than total debt relative to GNP is included, as secondary market quotations in fact primarily reflect the value of public sector obligations. The coefficient on the SE variable is negative and statististically 6
important to test for. If the investment climate affects the relative productivity of exiting firms, this has implications on the efficiency of the allocation of resources in a country and thus on its overall level of productivity.

# _Firm size_

The literature consistently finds that small firms are more likely to exit than larger firms (Roberts and Tybout (1996); Haltiwanger et al. (2004)). Small firms are more likely to lack the scale needed to be more efficient and compete with larger competitors. As many small firms are also new, they do not have the same experience or proven track record of succeeding in business. The question here is whether investment climate conditions exacerbate these trends – or whether there are dimensions of the investment climate that affect them less. Costs, particularly fixed costs that will be relatively higher for small firms, are likely to hurt smaller firms more. Access to credit as well as property rights are touted as the benefits of firms formalizing and thus should be relatively more beneficial for SMEs compared to microfirms.

One issue when looking at the effects of size is that the data source can matter. Studies that rely on census data can have truncated samples. Many censuses only target firms of a certain size, typically 10 employees or more. What is then recorded as ‘exit’ is a mixture between those firms that truly end operations and those that dip down below 10 employees but who remain in business. The surveys used here also have a cutoff – but it is at 2 employees. Thus, all the firms in the original sample in 2002 have 2 employees or more. In 2005, those with only a single employee are still recorded as surviving, although they were not re-interviewed. This makes it possible to look at the whole range of sizes of firms, with a 12
common trend for all primate cities, the interaction between the _primate_ indicator and _year_ . It enters negatively and highly significantly, implying that holding constant the transport cost effects, the overall growth trend for primate cities is slower than that for the rest of the sample. This is consistent with Henderson, Storeygard and Weil (2011), who show that summing across Sub-Saharan Africa as a whole, primate cities grew more slowly than other areas during the same time period. The main coefficient of interest is more strongly negative. In Column 3, the primate trend is replaced by one proportional to distance to the primate. The coefficient on _distance_ ( _Primate_ ) _∗ Poil_ is virtually identical to that in column 4, the baseline specification, suggesting that trends proportional to distance to the primate account for much of the variation in city growth net of transport costs.

Column 4 adds city fixed effects and city-specific time trends. The negative coefficient of -0.68 on _distance_ ( _Primate_ ) _∗ Poil_ implies that if the price of oil increased from $25 to $97 per barrel (as it did between 2002 and 2008), if city A is 465 kilometers (1 standard deviation) farther away from the primate than initially identical city B, its lights are 23 percent smaller than city B’s at the end of the period. Applying the light-income growth elasticity _εGDP,light_ = 0 _._ 277 from Henderson, Storeygard and Weil (2012), this implies a city product differential of 6.1 percent. This is consistent with the model above. Far cities see their transport costs increase more than near cities, so their income falls more. In column 5, 5.5 is added before the DN is logged and an OLS specification is used instead of a tobit.<sup>37</sup> Results are very similar.

Figure 7 shows that there were two broad oil price regimes during this period: a relatively flat stretch followed by a steeply rising one. In order to ensure that the results above are not simply driven by the difference between these two regimes, column 6 reports the results of a specification in which two linear splines were fit for each city. The “knot” year, the transition between the two regimes, along with both slopes, were estimated separately for each city to minimize the variance in city-specific residuals. The magnitude of the coefficient of interest is slightly smaller than in the baseline case, but it is still negative, despite the fact that even more temporal variation has been removed.<sup>38</sup> The coefficient in column 4 can be interpreted as a semi-elasticity in the context of the model above. An elasticity of city product with respect to transport costs is in some respects a more intuitive measure, but since _ln_ ( _ptxi_ ) is equal to _ln_ ( _pt_ ) + _ln_ ( _xi_ ), 37This specification uses 5.5 because of the integer nature of the DN data in combination with the censoring at 6 described above.

> 38Note that the standard errors have not been corrected for the first stage estimation of the splines. The key point is that the point estimate is very similar to that in the baseline specification.

16
|**Table 1: Africa Sam**|**ple summar**<br>**Benchm**|**y statistics**<br>**ark year**|||
|---|---|---|---|---|
|**1993**|**1998**<br>|**2003**<br>|**2008**|**Total**|
|**Number of surveys**<br>22|20|30|34|106|
|**Years between survey year and benchmark year**|**(%, by bench**|**mark year)**|||
|‐2<br>0.00|20.00|6.67|26.47||
|‐1<br>27.27|5.00|13.33|14.71||
|0<br>27.27|35.00|36.67|14.71||
|1<br>31.82|5.00|16.67|20.59||
|2<br>13.64|35.00|23.33|23.53||
|3<br>0.00|0.00|3.33|0.00||
|Within +/‐ 1 of benchmark<br>86.36|45.00|66.67|50.01||
|**Income vs. Consumption surveys (%, by benchm**|**ark year)**||||
|**Consumption**<br>100|100|100|100|100|
|**Income**<br>0|0|0|0|0|
|**GDP (in 2011 PPP‐adjusted USD) (% of regional G**|**DP represen**|**ted in the dat**|**abase)**||
|**AFR**<br>77.51|78.18|78.95|86.80|80.36|
|**Resource rich**<br>66.46|66.07|73.34|92.32|74.54|
|**Non‐resource rich**<br>85.65|86.89|83.33|81.22|84.27|
|**Fragile**<br>35.84|29.65|38.74|87.25|47.87|
|**Non‐fragile**<br>86.27|88.61|86.73|86.72|87.08|
|**Low income**<br>63.49|61.64|69.28|55.68|62.52|
|**Lower middle income**<br>80.54|80.52|78.94|96.05|84.01|
|**Upper middle and high income**<br>83.55|86.74|85.32|93.78|87.35|
|**Population (% of regional population represente**|**d in the data**|**base)**|||
|**AFR**<br>68.32|71.20|70.23|76.25|71.50|
|**Resource rich**<br>57.46|54.52|63.91|93.41|67.33|
|**Non‐resource rich**<br>77.49|84.55|76.16|65.17|75.84|
|**Fragile**<br>29.82|21.47|33.52|79.93|41.19|
|**Non‐fragile**<br>82.74|89.70|83.84|74.88|82.79|
|**Low income**<br>61.57|62.99|65.05|61.07|62.67|
|**Lower middle income**<br>77.58|78.61|76.96|94.46|81.90|
|**Upper middle and high income**<br>71.50<br>_Notes: The last column is the(unweighted) average ov_|89.72<br>_er the benchma_|74.87<br>_rkyears 1993_|97.16<br>_to 2008._|83.31|

6
<!-- Start of picture text --> Share of economies (%)<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>World Bank Group Regional development banks Bilateral agencies<br><!-- End of picture text --> _Source: Reform Committee data Note: Share of economies with reform committees_ As a part of their work program, reform committees also engage in peer-to-peer learning initiatives, such as meetings with states or other departments that have successfully implemented reforms. These initiatives capture knowledge sharing across states and can play a role in reforming, as studies show countries can learn from their neighbors’ efforts to establish reform committees. Sub-Saharan Africa followed by East Asia and the Pacific and high-income OECD are the regions with the highest number of peer-to-peer learning initiatives organized by and within the reform committee/council.

# **5. Case Studies**

In this section, we present the following four case studies: Djibouti, Moldova, Mexico, and Malaysia.

# **5.1. Djibouti**

The Republic of Djibouti launched the Djibouti Economic Development Council (Conseil de Développement Economique or CDED) in 2016 to organize and closely monitor the implementation of national reforms, for the purpose of promoting sustainable economic growth and overall prosperity of the country. To ensure the soundness and appropriate handling of the State’s ventures and projects, CDED oversees and maintains harmony between the various domestic organizations and ministries involved in the development of economic policies. In this regard, the reform committee aims at stimulating the commercial and industrial growth of Djibouti, along with generating employment (Décret No 2016-242/PRE, 2018).

The Council, which has a permanent mandate, meets at least once a month; if judged necessary the President may call a meeting at any time (Décret No 2016-242/PRE and Reform Committee Survey). The President of the Republic also acts as the President of the Council, with six additional members including ministers of Economy and Finance, Investments and Foreign Affairs, the Secretary General, the President’s Economic Affairs Advisor and the Ports and Free Zones Authority’s President. The President can appoint at least two other members but cannot exceed five and they each serve a term of three years. The President holds the right to revoke appointments and members can be reappointed following the end of their term (Décret No 2016-242/PRE, 2018). In their absence, the President may also be represented at the reform council meeting by another duly mandated member. The minutes are taken at each council which must be 19
# **5 Mechanisms**

In this section we aim at gaining further insights into the mechanisms that drive the baseline results, with an emphasis on disentangling banks’ soft budget constraints from their longtermism. The literature predicts that the occurrence of soft budget constraint problems in the banking sector is related to the degree of independence of banks from the influence of the government and of related parties and to the quality of monitoring and supervision of banks. Exploiting this prediction, in Section 5.1 we take a step towards disentangling a possible role of soft budget constraints by supplementing information from our main databases with country-level data on the independence, monitoring and supervisory quality of banks.

As for the more benign explanation of the baseline findings (banks buffer exporters from unforeseen shocks), banks’ ability to renegotiate contracts and insulate firms from unforeseen contingencies should especially kick in when ex ante it is hard to gather information about shocks that can hit export projects and when it is difficult to write detailed contracts that account for such unexpected contingencies (Allen and Gale, 2001). Based on these arguments, we conjecture that if the baseline results especially reflect domestic banks’ buffering and long-termism (rather than soft budget constraints) the effects should be stronger in the event of negative shocks, such as crises or recessions. We test this hypothesis in Section 5.2. Moreover, we would expect the effects to be relatively stronger for products that are informationally more complex and for which detailed, state-contingent contracts are harder to write. We test this hypothesis in Section 5.3.

## **5.1 Banks’ soft budget constraints**

As it is typically the case in the empirical literature, a direct test of soft budget constraints is not feasible. However, following the banking literature, we can capture the exposure of countries to a bank soft budget constraint problem by exploiting information on banks’ independence and on the regulation and monitoring to which banks are subject. Banks’ soft budget constraint attitudes can be more pronounced in settings where banks are less independent in their loan-granting decisions and more susceptible to the influence of external parties such as the government, which could put pressure on banks to rollover loans to exporters (Allen and Gale, 2001). Soft budget constraint problems may also be more severe in countries in which bank managers are less subject to close supervisory scrutiny on their decisions, implying more freedom of bank managers to evergreen loans to borrowers (Arestis, de Paula and de Paula, 2008; Lardy, 1998). Bank managers may indeed have the incentive to conceal the status of loan portfolios by evergreening loans (Niinimaki, 2007).

To perform the tests of this section, we integrate the data sources used in the baseline analysis with data from the World Bank Banking Supervision Survey, which collects 15
countries shown in the first column of Table 1 and the average share of central government expenditures from our panel dataset. In the absence of better government finance data on fiscal decentralization, we use central government expenditures to get a feel for the weight of central government in economic activity.

We also use a non-financial measure of decentralization, which is derived from the World Bank’s Database of Political Institutions (DPI). This decentralization indicator is created by taking the sum of three other indicator variables from DPI for local elections in municipal governments (Muni), local elections in state/province governments (State) and authority over taxing, spending or legislating in state/provinces (Author).<sup>9</sup> Hence, higher numbers in our decentralization indicator mean greater degree of decentralization.

There is considerable literature on the effects of fiscal decentralization on economic growth. Determinants of fiscal decentralization received less attention. Two recent papers provide theory and empirical evidence on the determinants of the decentralization process in a wide variety of countries. Panizza (1999) builds on the work by Alesina and Spolaore (1997) on the number and size of nations by introducing different levels of government. Panizza’s theoretical model shows negative correlations between fiscal centralization and country size, income per capita, taste differentiation and level of democracy. He finds empirical evidence supporting these theoretical correlations. Panizza also compares his results to two other cross-sectional or panel studies by Oates (1972) and Wallis and Oates (1988) and finds similarities particularly in regards to country size and income per capita. Arzaghi and Henderson (2005) examine the determinants of fiscal decentralization using panel data on 48 countries over the period

> 9 Muni, State and Author are the names used in DPI under the sub-heading “Federalism.” See Beck _et al._ (2001) and Keefer (2007) for detailed descriptions of these variables. Higher numbers indicate greater degree of federalism, and thus decentralization of central government authority.

13
employment. Descriptive analysis of the KMS data during this period revealed that among adults participating in the labor force, international migrants from Kerala were drawn from the middle of the education distribution, while domestic migrants were mainly selected from the upper range of the educational spectrum.<sup>7</sup> This fact, coupled with the empirical findings, suggests that for the less educated Keralite migrants moving overseas, there is a wage premium over non-migrants that generates asset gains to the origin household comparable to the gains that the more highly educated individuals moving for employment within India are able to produce. In order to maximize the gains from migration, households in Kerala appear to be behaving strategically when deciding who, and where to send their family members for employment.

In the next section, I present background on migration from Kerala. Section 3 lays out several theoretical considerations. In Section 4, I describe the data and estimation strategy. Section 5 presents the results. I discuss those results in Section 6, with the aim of explaining the key finding that asset gains to households from the two migration pathways are actually similar. Section 7 provides a brief conclusion.

# **2. Background: An Overview of Labor Migration in Kerala**

India’s experience with migration is noteworthy, making it an excellent setting for a study of the welfare gains generated by labor migration. With the remittances sent back to the country from abroad reaching nearly US$78.6 billion in 2018, India receives more money through this channel than any other nation in the world.<sup>8</sup> According to the Reserve Bank of India, the state of Kerala received an estimated 19 percent of India’s total overseas remittances in 2017.

> 7 One sees similar selection patterns with Mexican migrants. Kaestner and Malamud (2014), and the literature they review in that paper, find that Mexican migrants to the United States are drawn from the middle of the Mexican education distribution. Aguayo-Téllez and Martínez-Navarro (2013) show that relatively more educated Mexicans moved within Mexico.

> 8 Migration and Development Brief 31, World Bank (2018).

4