rafmacalaba/datause-encoder-data huggingface dataset:
Gregorio Impavido, Alberto R. Musalem, and Thierry Tressel contractual savings institutions are competitors to banks, the latter may indeed respond to competitive pressures by concentrating on their core comparative advantage (associated to narrow banking), -that is their superior ability to monitor firms - and increase shortterm loans. Banks will therefore be able to avoid losses caused either by unexpected increase in short-term interest rates or by sudden withdrawals. On the other hand, if contractual savings institutions provide resources to the banking system (either in the form of loans, deposits, or by buying securities issued by banks), banks will be less subject to liquidity risks for a given level of long-term assets. They may therefore increase the supply of long-term loans 12, Finally, the recent emerging economies' crisis have stressed the risks associated to short-term capital flows. Excessively volatile capital movements have exacerbated the structural weaknesses of these economies, that were magnified by fragile banking systems 1<sup>3</sup> . This financial risk is associated to the difficulty (or impossibility) for these economies to borrow abroad in their own currencies, and long-term. One of the solution is to deepen domestic financial system in order to develop domestic sources of long-term finance (see for instance Caballero (2001)), which is the comparative advantage of contractual savings.

The development of contractual savings institutions, given adequate regulation and supervision'<sup>4</sup> , may therefore enhance the stability and efficiency of the banking system.

## 11 DATA AND EMPIRICAL STRATEGY

# _Il.A_ **_Data_**

We use balance sheet and income statement of commercial banks, aggregted by country each year, from the Bankscope data base provided by IBCA. We use an unbalanced sample of banks over the period 1991-2000, in order to maximize the time-series dimension for each country.

The database on contractual savings is taken from Inpavido and Musalem (2000), extended for several countries (Argentina, Brazil, India and Mexico). It includes information from different sources (including the OECD institutional investors data 2000, and national sources) on total assets, financial assets, and allocation of assets for pension funds and insurance companies.

All other macroeconomic variables are obtained from various sources: Datastream, the World Development Indicators, the IMF International Financial Statistics and the Bank for Internationa I Settlement for bond markets data.

> 12 Our results are consistent with this assertion.

> 13 See the recent literature, such as Aghion, Banerjee and Bachetta (2000), Caballero and Krishnamurthy (2000), Chang and Velasco (1999), Rodrik and Velasco (1999) and Deckle and Kletzer (2001). For empirical analysis, see Demirguc-Kunt and Detriagache (1997, 1998, 2000) and Harwood et al. eds. (1999). 14 See Barth et al. (2001 ) for an analysis of the prudential regulation of banks (see also Dewatripont and Tirole (1994)). Generally, La Porta et al. (1997, 1998) claim that laws are important determinants of crosscountry differences in firms' external finance choices.
<!-- Start of picture text --> 4- lg 4 100<br>8<br>3.5 4 + 35 E35 a0<br>3 ao se<br>—y 37 Female sex work ;3 2 3 70 =<br>= = o<br>0<br>8<25 +25|982=25 >8<br>ao 2 2 BSoo *Ss= z 50 =)3<br>zB 15 (18 E& g 18 oo<br>| + 1 Ss 1 ce<br>as ro 2 <<br>j O85 Fa 0.5 Fomale sex workers 10<br>0 = ao a "PH : lo<br>1990 1995 2000 2005 2010 2000 2002 2004 2005 2008 2010 2n12<br>“Horizontal error bars denote time period in which prevalence or incidence was measured. Value is at midpoint of time period.<br><!-- End of picture text -->

es Modelled incidence (%), adults 15-49, from Spectrum software Prevalence (%), females 15-24 (source: national DHS/AIS reports) wee= Prevalence (1), young people, UNAIDS Indicator 1.6 (source: AIDSinfo database) == Incidence rate (per 100 person years), adults (source: published studies, see Annex) sees== ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2072, 2013
16 household to account for intra-household optimization. This is particularly important for households where women do not work or for multi-generational or multi-family households; and (vi) Keep samples with at least 100 observations per experience bin.

Additionally, we focus on workers, which causes a selection problem in samples with skewed unemployment/non-labor participation (NLP) rates. In the context of low unemployment/NLP, non-workers may have specific unobservables. That is also true in contexts with high unemployment/NLP where competition for few jobs selects the “best”. That is only an issue for us if unemployment/NLP is correlated with development. However, we find no such correlation and results hold if we (Web Appx. Section A.4 and Web Appx. Table A.14): (i) Add controls for selection, i.e. sector fixed effects, occupation fixed effects, an informal sector dummy, an urban dummy, and the square of education, to compare workers that are similar except in their experience level; (ii) Keep or drop samples with unemployment or NLP rates below the 25th percentile (7% and 35%, respectively) or 50th percentile (10% and 35%, respectively) in the sample; and (iii) Use the Heckman correction with the selection equation also including marital status and the number of children in the household.

**Developing vs. Developed.** The developed-developing gap holds after conducting the following checks (Web Appx. Section A.5 and Web Appx. Table A.15): (i) Add seven World Bank region fixed effects; (ii) Drop each developing region one by one or ex-Communist countries; (iii) Drop the top and bottom 10% outliers in the returns, i.e. countries that may have abnormally high or low returns, either because these returns are real or because of measurement error; (iv) Add country-level controls for factors generating cohort effects: whether a country had a Communist regime,<sup>27</sup> past child and adult mortality rates (including for HIV),<sup>28</sup> and past per capita GDP or population growth rates<sup>29</sup> for each country-decade from 1950 to 2010 (source: United Nations (2019)).

In addition, if agriculture exhibits lower returns to experience and given that developing countries have a higher share of their workers in agriculture (37% vs. 5%

> 27These include a dummy equal to one if the country ever had a Communist regime, the number of years the country has had a Communist regime, and the year the country transitioned to capitalism (source: Wikipedia (2019)). Since we are controlling for the country-specific mean year in the data, this also controls for how many years ago the country transitioned to capitalism.

> 28These include average child mortality rates (for 0-5 year-olds) and adult mortality rates (15-45) for each country-decade from 1950 to 2010 (source: United Nations (2019)) and average HIV infection rates for each country-decade from 1990 to 2010 (sources: World Bank (2018a); data unavailable before 1990). 29These include average annual per capita GDP growth rates (source: see footnote 15).
di¢ cult to save at home, as individuals are often asked for money by friends and family members and ...nd it di¢ cult to refuse these requests if they have cash on hand. Indeed, mounting evidence suggests that informal savings mechanisms are ine¤ective, and that the provision of more formal savings services increase savings balances and make individuals less vulnerable to unexpected income shocks (Dupas and Robinson, 2009).

Consequently, individuals in poor countries are often unable to save as much as they would like and are unable to hold savings balances su¢ cient to deal with unexpected shocks. This in turn will make it more likely that the 1-period budget constraint binds, and make it more likely that a woman may adjust her accepted health risk in response to even small, short-term shocks.<sup>6</sup> The implication of this simple model - that labor supply may adjust to short-term shocks in the absence of perfect consumption smoothing - is not new, but the importance of our study is that the increase in labor supply comes at great individual cost in terms of increased risk of contracting HIV or other STIs. The increased health risk will also likely impose an external cost on society, as HIV is transmitted between sex workers and clients and then on to other partners. In addition, quite apart from any e¢ ciency calculation, the tests in this paper are of signi...cant importance in terms of designing interventions to limit the spread of HIV-related mortality and morbidity.

# 3 Research Design

## 3.1 Background on Busia, Kenya

This study takes place in Busia District, a rural area in Western Province, Kenya with a semiurban center, Busia Town. The estimated HIV prevalence in Busia District was 9.8% in 2004, compared to the national average of 6.7% (Central Bureau of Statistics, 2004). Busia Town has a population of 44,196 (Central Bureau of Statistics, 2001) and is located on the Ugandan border, along one of two major trucking routes from the port city of Mombasa (on the Indian Ocean) to Kampala, via Nairobi.

> 6 Few women in our sample have formal savings accounts (see Table 1) or access to bank credit, but most save money informally at their home and have access to gifts and loans from friends and family.

6
34 are used as a benchmark In the calculation. Tables on undernutrLtion indices by country and region are presented in the annex.

uillQo;2L;f2D Two sets of data are used to assess prevalence of malnutrition: child anthropometric measurements on weight for age, weight for height, and height for age and data on dietary deficiencies of micronutrients. The anthropometric measurements are used as indicators of overall malnutrition (underweight), acute malnutrition (wasting) and chronic malnutrition (stunting), respectively. Data on underweight population tends to be more readily available than that on wasting and stunting-. In addition, data on weight at birth is increasingly being collected, largely, however, as an indicator of maternal, rather than child, malnutrition. In general, the number of national surveys of undernutrition as measured by anthropometry has increased from 6 in 1974 to over 50, at present. For a review of the available data and assessment of child malnutrition in 76 countries refer to a recent UNICEF publication: "A Global, Regional and Country Assessment of Child Malnutrition."30 Availability of data on essential nutrient intake is mixed. Per capita protein intake in grams is readily available from BESD. Availability of national data on the prevalence of iodine deficiency in school children is relatively good particularly for countries in Latin America. On the other 30Beverly Carlson and Teresa Wardlaw, "A Global, Regional and Country Assessment of Child Malnutrition," UNICEF Staff Working Paper No. 7 (New York: UNICEF, 1990).
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considered as strong evidence showing why firms agglomerate in the same place, despite the expected intensification of local competition among themselves. Firms can share the same input market, such as skilled labor, and more business services would likely become available in the agglomerated area. As a result, firm operating costs decline and competitiveness increases.

## **Table** **<u>8. Estimated economies of agglomeration</u>**

||SUR<br>Elasticity|Std. Err.||SFA<br>Elasticity|Std. Err.||
|---|---|---|---|---|---|---|
|Localization economies|-0.043|(0.008)|***|-0.033|(0.008)|***|
|Urbanization economies|-0.001|(0.081)||0.064|(0.106)||

# **VII. CONCLUSION**

Infrastructure is an important driving force for economic growth. It reduces trade and transaction costs and stimulates the productivity of the economy. Africa has been lagging behind in the global manufacturing market despite its strong growth in recent years, possibly boosted by high international commodity prices. Among others, absence of quality infrastructure is an important constraint in many African countries.

Using firm-level data for East Africa, the paper recast light on the relationship between firm performance and infrastructure. It is shown that labor costs are important to stimulate firm production. Among the infrastructure sectors, electricity costs are found to have the highest elasticity of firm output. Transport costs are also found to be important. In addition, firm production increases with the quality of transport infrastructure measured by firm inventory. All the indications are that the provision of reliable infrastructure services at low costs is essential to improve firm competitiveness and increase firm production.

The paper also found that agglomeration economies are important to increase firm production. Unlike the traditional firm location literature, our evidence is direct to show that
**Figure 3.4. Predictive margins for sovereign risk**

<!-- Start of picture text --> A. CDS spread, log<br><!-- End of picture text --> <!-- Start of picture text --> B. Combined numeric sovereign rating<br><!-- End of picture text --> _Source:_ Authors’ estimates.

_Notes:_ Predictive margins for CDS spreads (log) in Panel A and combined numeric sovereign ratings in Panel B at different levels of PIQ-F and public investment as a share of GDP, based on models 1 and 2. The color scale reflects model-predicted sovereign risk levels ranging from blue (low risk) to red (high risk).

**Figure 3.5. Marginal effects for estimations with region and country fixed effects**

_Source:_ Authors’ estimates.

_Note:_ The figure shows the marginal effects of public investment on sovereign risk at different levels of the PIQ indexes. The vertical axis indicates the marginal effect of public investment on sovereign risk. Vertical bars indicate 90-percent confidence bands.

29
gains are reduced or eliminated due to such leakage. Furthermore, if open grazing elimination were to move cattle to nearby common lands, there would also be leakage. Understanding the likely extent of leakage is therefore critical for understanding the degree to which PES schemes, such as REDD+ actually achieve stated goals. In this paper, we are not able to conclusively address the possibility for leakage, but we do present descriptive results that suggest more stringent fuelwood collection limits would not cause households to primarily rely on alternative common forests.

To estimate carbon supply functions, we use marginal effects derived from a choice experiment conducted in Nepal during April – June 2013. Alpizar et al. (2003), Hanley et al. (2001), Hensher et al. (2005), and Hoyos (2010) provide reviews of the choice experiment methodology. The purpose of this experiment is to examine preferences for REDD+ contracts in 1,300 households in 130 formal and informal forest user groups across Nepal.<sup>7</sup> The sample was developed based on a nationally representative sample of 137 forests analyzed during an evaluation of the Nepal Community Forestry Program (MoFSC, 2013). We randomly chose 65 official community forests (CFs) from this sample and then matched it with 65 communities outside the program so our samples of CFs and non-CFs could be compared; half of respondents are in CFs and half in non-CFs. We limited our sample to communities in the middle hills and Terai regions of Nepal and omitted the high mountain region, which offers limited carbon sequestration potential. Figure 1 shows the sample distributed across Nepal.

> 7 Comprehensive results from the analysis can be found in Dissanayake et al. (2015), but this paper did not explicitly use the results to analyze carbon supply.  We therefore extend our earlier paper here.

9
three factors made roughly equal contributions. In (i) differential changes in health service coverage stand out as the single largest contributory factor to the faster mortality decline among the better-off, though differential changes in mother's education and access to safe water were also important. In (ii) health services were also a major factor, reflecting _inter alia_ the lower impact of antenatal visit coverage among the poor. But other coefficient differences also left their mark, including the smaller effect of drinking water among the poor. In (iii) the lower levels of consumption and health service coverage among the poor meant that the increases in the impacts of these factors on survival resulted in smaller reductions in mortality aniong the poor. These increased impacts were not, however, significant at conventional levels.

# **6. Child survival prospects to 2015**

The next question we address is how child survival prospects are likely to evolve over the next 15 years. Vietnam is currently preparing a PRSP-Vietnam's Comprehensive Poverty Reduction and Growth Strategy (CPRGS)-to qualify for continued support through IDA. Like other countries preparing PRSPs, Vietnam is setting out in its CPRGS broad poverty-reduction strategies as well as targets for poverty and human development indicators. In other countries these targets have included rates of infant and under-five mortality, since these are among the seven key international development goals (IDGs). The latter involve _inter alia_ a reduction by all countries of infant and under-five mortality rates by two thirds between 1990 and 2015. In this section, we address the issue of whether such a reduction is possible for Vietnam, and whether commitment to such a path would be realistic for Vietnam's CPRGS.

## **_Methods and assumptions_**

Using the Oaxaca decomposition methodology, we can write the hazard rate for the year 2015 for the "poore and "nonpoor" separately as follows: (7) -In 4 5 = -lnA- 8 +(x 1' -x5 8 )pi8 **+15** - /8) 1 5<sup>_'=_</sup> poor, non-poor where the notation is self-evident. So, the (negative of the log of the) hazard in 2015 will depend on the hazard in 1998, the change in the means of the deterninants of survival during the period 1998-2015, and the changes in the _impacts_ of these determinants over the same period. We know the first term on the RHS. The other terms are unknown, and some assumptions are required to operationalize the method.

We need to make some assumptions about the changes in the x's and the ,B's. For the nonpoor, we assume with respect to the x's that the annual average rate of growth (or 25
# **5. Results**

## **Selection model**

As indicated in the previous section, the first step of the semi-parametric methodology employed is estimating the probability of a household diversifying. The probability is modeled through a logit model (Maddala, 1983). Figures 2 (2010/11 round) and 3 (2012/13 round) present the propensity scores for diversifying and non-diversifying households. As is evident from the graphs there is almost full common support in our sample. The results for this model are presented in Table 5, with columns labeled accordingly for each round of the GHS-Panel survey. The values reported are the marginal effects from the logit model.

The characteristics of the head of the household, such as marital status and years of completed education, are significantly related to the diversification choice. The head’s years of education is positive and significantly related to the probability of a household diversifying in both years. This is possibly related to education allowing households to overcome barriers to entry, and possibly have access to a broader range of options on which to diversify. The gender of the head is significant (1%) only for 2012/13, while the head’s age does not appear to be significant in both models.

As expected, higher infrastructural constraints limit the opportunities to diversify for farming households (e.g. if the household is very distant from a bus stop or a market center, the opportunity cost of diversifying may be too high for them to do so). Thus, it seems to be the case that improved infrastructure may “pull” households towards diversification, or at least provide more options. The excluded instrument used for the participation equation is statistically significant. An increase in the average of the mean yearly precipitation during the rainy season from 2001 up to 3 years before the survey interview for each household is negative and significantly related to diversification. Households who experienced higher than average rainfall during the rainy season in 2001-2007 (round 1) and 2001-2009 (round 2) were less likely to choose to diversify. Rainfall shocks potentially “push” households into diversification in order to cope with the shocks itself and to ensure smooth consumption patterns. In this instance, a positive rainfall shock seems to make households more likely to rely solely on agriculture, a finding which is consistent with the hypothesized effect of the 2011 drought previously discussed.

As mentioned previously, one of the potential reasons of households’ decision to diversify is due to decreasing marginal labor productivity in agriculture. Supporting this argument, we 17
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**<u>Table 5. TOBIT estimation results for market sales</u>**

||ln_S_||ln_S_||
|---|---|---|---|---|
|ln_COSTCITY_|-0.283|(0.129) **|||
|ln_MAI_|||0.660|(0.988)|
|ln_L_|-0.292|(0.237)|<sup>-0.231</sup>|(0.239)|
|_SEED_|-0.078|(0.246)|<sup>-0.065</sup>|(0.252)|
|_FERT_|0.133|(0.283)|0.192|(0.285)|
|_COOP_|3.471|(0.371) ***|3.399|(0.379) ***|
|_MKT_|3.545|(0.194) ***|3.578|(0.194) ***|
|_TRAD_|3.727|(0.352) ***|3.750|(0.362) ***|
|_MALE_|-0.149|(0.223)|<sup>-0.115</sup>|(0.225)|
|_SIZE_|0.526|(0.223) **|0.492|(0.223) **|
|_AGE_|0.330|(0.285)|0.340|(0.288)|
|_CAR_|-0.845|(0.417) **|-0.805|(0.410) **|
|_MOTR_|0.209|(0.545)|0.206|(0.546)|
|_BICY_|-0.810|(0.857)|<sup>-0.932</sup>|(0.822)|
|Constant|1.964|(1.215)|2.251|(1.240)*|
|Obs.|2587||2587||
|Obs. Censored|1247||1247||
|F stat|18.57||18.44||
|Dummy variabl|es:||||
|Crops|Yes||Yes||
|Prefectures|Yes||Yes||

33. The IV regression on market sales shares shows similar results ( **Table 6** ). Transport connectivity to cities is important. Of particular note, the coefficients of _COSTCITY_ and _MAI_ are statistically significant in this case. Market accessibility is no doubt essential to promote farmers’ output market participation. In addition, it is confirmed once again that not only transport infrastructure but also transport and trade services are equally important: _COOP_ , _MKT_ and _TRAD_ systematically have positive and significant coefficients. Other household characteristics do not seem to play an important role in determining households’ decision to be involved in market transactions, which is also broadly consistent with the above.
educational attainment for the years 1990 and 2000. The authors count as emigrants all foreign-born individuals at least 25 years old who live in an OECD country and class them by educational attainment and country of origin. Thereby, emigration into OECD countries is captured, representing approximately 90 percent of educated migrants in the world.<sup>9</sup> Because we are interested in bilateral migration patterns, we employ an extended dataset by Docquier et al. (2007). We construct the high-skilled emigration rate from country _i_ to _j_ in year _t_ , denoted by _SMigij,t_ , as the stock of skilled emigrants from country _i_ living in (OECD) country _j_ divided by the stock of skilled residents in (source) country _i_ . In some regressions, we also control for the (lagged) low-skilled emigration rate, _UMigij,t-1_ , which is constructed analogously.

Denote by<sup>_y_</sup> _i_ , _t_<sup>the outcome measure in country</sup><sup>_i_in year</sup><sup>_t_. We consider GDP per</sup> capita, TFP, and the wage income of skilled workers. For a country pair ( _i_ , _j_ ) , we estimate

> ∗ ~ Equation (23) is theoretically motivated by relationships _wH_ / _wH_ = ω _H_ ( _mS_ , _mU_ )

> ∗ ~ and _A_ / _A_ = α ( _mS_ ,<sup>_m_</sup> _U_ ) ; see equations (14) and (13) derived in section I, respectively.

> According to equation (19), the theoretical model suggests that β 1<sup>></sup> 0 when the log difference in TFP, log( _A_<sup>∗</sup> / _A_ ) , is the dependent variable. When the log difference of wages for skilled workers, log( _wH_<sup>∗</sup> / _wH_ ) , is the dependent variable, then we predict β 1<sup>></sup> 0 if and only if the TFP effects of migration are sufficiently high, according to

15