rafmacalaba/datause-encoder-data huggingface dataset:
imprecisely (see Deaton, 1997, pp. 100-01). Simultaneity bias may arise because FONCODES encourages beneficiaries to participate in the execution of a project, and may favor areas in which there is a large predisposition for volunteering. The causality would then run, at least in part, from volunteer levels to FONCODES expenditures, and including the level of FONCODES expenditures as a regressor in the volunteer equation would produce biased and inconsistent results. The usual solution for this problem is instrumental variables (IV). I attempted two IV specifications to address the possible endogeneity of FONCODES investments. FONCODES makes al locations to districts on the basis of a formula which includes the population of the district, the fraction rural, and a composite index which is a weighted average of eight variablesthe rate of chronic malnutrition, illiteracy, school-aged children not in school, overcrowded housing, inadequate roofing, and the proportion of the population without access to water, sewerage, and electricity. If district-level allocations are uncorrelated with the error term in the volunteer equation, conditional on the log of mean per capita income and the log of population, then the district-level allocation would be a valid instrument for FONCODES expenditures (Paxson and Sclhady, 1999). Unfortunately, the 2SLS regression which uses allocations as an instrument is quite sensitive to exactly how FONCODES expenditures are specified (in levels, logs, or a per capita basis). In a different approach, I use results reported by Schady (2000), which show that FONCODES expenditures were affected, in part, by the voting record of a province. Specifically, after 1993, FONCODES funded a disproportionate number of projects in provinces which had voted for Fujimori in the 1990 presidential election, but against Fujimori in the 1993 referendum on the new constitution. If the change in the province-level vote is uncorrelated with the error term in the volunteer equation, conditional on the log of mean per capita income and the log of population, then the change in the vote would be a valid instrument for FONCODES expenditures. Unfortunately, the first-stage regression shows that the change in the vote is not a significant predictor of expenditures in the sub-sample of provinces in the rural sample of the 1994 LSMS. Since neither instrument is fully satisfactory, it is not clear to what extent the coefficient on FONCODES expenditures in specification (ii) is biased by endogeneity.

# 6. Conclusion

This paper makes a contribution to the analysis of volunteering and other forms of unpaid "participation" in community affairs. To the best of the author's knowledge, there is no other _quantitative_ analysis of the determinants of volunteering in an LDC. I show that volunteering is widespread in rural Peru, and that there are large, significant differences in the probability of volunteering. Volunteers in rural Peru have a high opportunity cost of time: they are better educated, more likely to head a household, and more likely to be married. Moreover, I find no 11
are interviewed two times: once in the “post-planting” period, from August to November, and once in the “post-harvesting” period, from February to April. Data are deflated using 2010 as the base year. The poverty line is the national poverty line in 2010 used for calculating the most recent (unofficial) poverty figures (NER, 2014).

In 2001, the Senegalese national statistics office (ANSD) implemented the second Enquête Sénégalaise Auprès des Ménages (ESAM2), and in 2005, the first Enquête de Suivi de la Pauvreté au Sénégal (ESPS1). ESAM2 consists of about 4,562 households while in ESPS1 10,780 households were interviewed. These surveys were conducted at different times of the year but for the purpose of our analysis they are comparable. One has to just bear in mind that the 2005-06 survey, conducted just after the harvest, may have underestimated the level of poverty. In Niger, we used the 2007 round of Enquête Nationale de sur le Budget et la Consommation des Ménages au Niger (ENBC). Due to security concerns, the rural part of Agadez region was not covered.

# **5.2 Demographic and Health Surveys**

The data on child health come from various rounds of the Demographic and Health Surveys (DHS) that are financed by the United States Agency for International Development (USAID) and implemented by ICF<sup>2</sup> . The DHS collects information on different topics such as fertility, family planning, reproductive health, child health, and HIV/AIDS, reflecting the situation of the country analyzed. The surveys are nationally representative. Child height and weight are converted into standardized Z-scores using WHO methodology. For each child, the Z-score expresses anthropometric values in terms of standard deviations below or above the median of an international reference group.

Table 1 presents some summary statistics on the prevalence of malnutrition in the region over the last decade. We dropped Mali 2001 because we could not link the household data to rainfall in the year prior to the survey. In contrast, all other countries have two rounds. As in the case of consumption, we restricted our sample to Northern Nigeria and Northern Ghana. With the notable exception of Senegal and Northern Ghana in 2008, about one-third of sampled children are underweight (column 3) and more than 10% are severely underweight (column 4). Percentages are similar for stunting (column 7 and 8) but lower for wasting. Burkina Faso and Northern Nigeria show particularly high rates over all the measures. In the analysis we used -2 standard deviations as the minimum threshold for permanent welfare.

# **5.3 Rainfall**

> 2  ICF recently acquired MACRO internationational which used to be the main implementator of DHS surveys on behalf of USAID.

13
equity and venture capital markets, which are much less developed than those in developed countries.

A large part of this literature has focused on how small firms are more severely constrained by access to finance. In this section we also present new statistics across 99 developing countries on the percentage of small and medium enterprises (SMEs) with access to bank accounts, overdraft facility, and lines of credit. We find that the percentage of SMEs with access to these financial instruments increases monotonically from low to high income countries.

Sections 6 and 7 address the prevalence of different financial systems across the world and their relative merits. We begin with a review of the evidence on bank versus market based systems in Section 6. The evidence overwhelmingly suggests that banks and market based systems are complementary and co-evolve as countries become richer and more developed. New research also shows that <mark>different financial structures may be better at promoting economic activity at different stages of a country‟s economic development. Section 7 compares the role of formal versus informal financial systems and shows that they serve different segments of firm population and are not substitutes. Some studies have argued that countries like C</mark> hina are unique in their large reliance on informal and alternative financing channels compared to other countries. However, others have shown that even in China, the formal financing channel, specifically bank finance, is positively associated with higher growth and reinvestment while informal financing systems are not.

In Section 8, we highlight areas that need additional research and conclude with policy implications of the existing body of theoretical and empirical evidence on financing in developing countries.

# **2. Stylized Facts about Firms in Developing Countries**

Much of our intuition about corporate finance comes from research conducted on firms in the U.S. or other developed countries. Firms in developing countries differ from firms in developed countries along several dimensions such as size distributions, ownership, financing patterns, and institutional constraints. In this section, we outline nine distinctive features about firms in 4
**Table 4.4b (continued): Overall bias in Ethiopia HFPS by Urban-Rural domain**

||**E**<br>|**thiopia- Urb**<br>|**an**<br>|**E**<br>|**thiopia- Ru**<br>|**ral**<br>|
|---|---|---|---|---|---|---|
||**ESS**<br>**Urban**|**H**<br>**Inter**|**FPS**<br>**viewed**|**ESS**<br>**Rural**|**H**<br>**Inter**|**FPS**<br>**viewed**|
||||_Overall_|||_Overall_|
||_Mean_|_Mean_|<br>_bias†_<br> _(2) -(1)_|_Mean_|_Mean_|<br>_bias†_<br> _(5) -(4)_|
|**Characteristic**|(1)|(2)|(3)|(4)|(5)|(6)|
|**_Housing_**|||||||
|Improved roof|0.954|0.976|0.022|0.562|0.643|0.081*|
|Improved floor|0.482|0.513|0.032|0.030|0.048|0.018|
|Improved toilet|0.593|0.625|0.032|0.179|0.201|0.022|
|Improved water source|0.976|0.988|0.011|0.637|0.689|0.053|
|Access to electricity|0.905|0.944|0.039*|0.393|0.502|0.108**|
|**_Asset ownership_**|||||||
|Solar|0.051|0.046|-0.005|0.202|0.247|0.045|
|Generator|||||||
|TV|0.505|0.578|0.073*|0.021|0.042|0.021*|
|Refrigerator|0.213|0.245|0.032|0.004|0.008|0.004|
|Bicycle|0.032|0.044|0.012|0.002|0.003|0.001|
|Motorbike|0.019|0.020|0.001|0.004|0.004|0.000|
|Car or other vehicle|0.028|0.029|0.001|0.000|0.000|0.000|
|Mobile Phone|0.866|0.952|0.086***|0.403|0.662|0.258***|
|**_Other activities_**|||||||
|Owns land||||0.962|0.947|-0.015|
|Cropfarming||||0.876|0.904|0.028|
|Operates nonfarm enterprise|0.380|0.383|0.003|0.157|0.191|0.034|
|Anymember has financial account|0.773|0.850|0.076***|0.305|0.429|0.124***|
|Income from remittance(int'l & domestic)|0.215|0.198|-0.017|0.111|0.111|-0.001|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

Looking at the results in columns 3, 8, 13, and 18 which present the difference between the F2F sample and the HFPS sample using the HFPS weight _with_ the bias adjustment, there is a substantial improvement in the bias. For Nigeria (column 13) there are zero indicators which are significantly different from the GHS-Panel sample, suggesting that the bias correction using the calibration approach has been very effective at reducing the bias. In Ethiopia, Malawi, and Uganda, there are still some indications of bias, however the bias has been substantially reduced following the weighting adjustments. Only a handful of the characteristics that were significantly biased when using the unadjusted weights are also biased when using the adjusted weights. For those characteristics that do still exhibit bias after applying the adjustment, the bias is reduced in all cases. This illustrates the effectiveness of the bias reduction methods employed in the HFPS in all four countries, but also highlights that these adjustments do not fully counteract the bias, particularly in the Ethiopia, Malawi, and Uganda HFPS.

31
# **Annex 2.  Data Sources, Availability, and Definitions**

The following sections outline data availability, sources, and definitions of variable by sector. The full data set supporting this paper can be accessed at _http://ecadata-worldbank.org._

## **The Energy Sector**

In the energy (power and natural gas) sector data have proved to be generally available for twenty-two countries in the ECA Region through external data sets particularly where these data relate to energy production, consumption, import, export, and loss statistics and to a slightly lesser degree tariffs. Loss statistics have been generally available in the power sector but have been patchy at best for the natural gas sector.

Key data sets accessed include ENERDATA and ERRANET; however there have been no externally available data sets for cross-country data on normative losses, collection rates, and cost-recovery prices. For the purpose of this exercise these data have been sourced through World Bank Group sector specialists that are working in these countries. While efforts have been made to verify and assure the quality of data provided and to check for consistency and outliers in data sets, there has been no fieldbased data collection effort or audit in the Region.

A summary of data sources follows:

- Values for domestic energy production, imports, exports, and change in stocks—which together account for the total domestic supply—have been obtained from ENERDATA (http://www.enerdata.fr/) through subscription.

- Energy tariff rates have been obtained from ERRANET through subscription. (http://www.erranet.org/Products/TariffDatabase/TariffIndex)

- The Bank’s World Development Indicators Database (2004) is the source for GDP, Exchange Rate, and Price Deflators.

- Sector specialists—based at the World Bank office in Washington, DC, or in World Bank country offices, have provided supplementary data on the energy sector, or have suggested corrections in the values reported in ENERDATA or ERRANET. Such expert data, where available, have been used to substitute primary data.

## _Data Availability_

For the analysis, countries included in the data set have been classified into groups based on a combination of geographic and economic criteria. The data set covers the four-year period 2000-03.
testing the effect of each individual source of innovation on final output, it is found that the coefficient of external innovation is 0.021, which demonstrates that external innovation contributes most to the overall effect on output productivity. These results are also documented in previous studies on Tunisian firms using a different data set, such as the innovation survey. Hence, it was more efficient for Tunisian manufacturing firms simply to acquire foreign technology created externally to enhance their output productivity during the period of data coverage.

Third, external innovation’s effect on final output is larger for those firms that invest in only external sources of innovation than for firms that invest in both sources of innovation. When the interaction terms for internal and external innovation investment are added, the coefficient of external innovation becomes larger while the coefficient of internal innovation remains insignificant.

Fourth, there is no synergy from using both sources of innovation simultaneously. The coefficient of the interaction term is close to zero and statistically insignificant. This result might suggest that internal innovation does not play a role in firm absorptive capacity in Tunisia. The results further support my argument that for a developing country like Tunisia, where firms face resource constraints and have limited internal skills and limited capability to innovate, it is better to invest more in only external innovation, rather than dividing resources between the two sources of innovation.

Fifth, the Tobit model estimation result supports the assumption that a minimum innovation investment is required. When considering the censoring issue of innovation variables, using Tobit methods to analyze determinants of innovation investment, the direction and statistical significance of the coefficients are consistent with the main specification, but the magnitude of the coefficients becomes larger. It appears that the Tobit model fits the data considerably better given that a large number of observations have been left censored (zero innovation investment) and the significant sigma values of Tobit regression. In fact, sizable thresholds exist, which are systematically related to determinants of innovation, that is, demand and technological factors.

54
<!-- Start of picture text --> 7000<br>6000<br>‘ 5000<br>3<br>§ 4000<br>a<br>v<br>3000<br>g<br><4 2000<br>1000> - me O £ | U i |<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>@ All Indirect Taxes Personal Income Tax<br><!-- End of picture text -->

<!-- Start of picture text --> 800<br>700<br>o 600<br>8 500<br>a<br>co 400<br>2<br>3 300<br>“ 200 | |<br>; | ie<br>1 2 3 4 5 6 7 8 9 10<br>Decil<br>mSEED School feed program<br><!-- End of picture text -->
**Figure 3.1: Ratio between Argentina’s and Australia’s per capita GDP**

<!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>1870 1877 1884 1891 1898 1905 1912 1919 1926 1933 1940 1947 1954 1961 1968 1975 1982 1989 1996 2003<br><!-- End of picture text -->

Source: Author’s elaboration based on Maddison (2007).

In the 1990s the government in power sought to implement an open competitive market with marked reduction of governmental intervention (Nogués and Baracat, 2006). Major sectors of the economy that were dominated by public enterprises were privatized and foreign investment was put on an equal footing with domestic investment. Tariff rates that had averaged 40 percent in the mid-1980s fell to an average of 14 percent by the mid-1990s. On imports from Mercosur partners, most rates fell to zero. This liberalization was accompanied by the introduction of disciplined mechanisms for managing pressures for protection: GATT/WTO-sanctioned antidumping and safeguard mechanisms.

This liberalization was however short-lived, and starting with the “corralito”<sup>12</sup> in late 2001, it has been reversed by the governments in power to policy profile that has been dominant over the past 8 or 9 decades:

- An announced return to an import substitution strategy,

- Reversion to instruments of trade control that lack the transparency and accountability the WTO system attempts to foster. The procedural/governance regulations introduced in the 1990s have been displaced by non-transparent and discretionally implemented restrictions mechanisms.

- Widespread import controls; ultimately (early 2012) as the peso became increasingly overvalued, the requirement of approval by the government of each request for the purchase of foreign exchange

- Export-balancing or countertrade requirements as a condition for importing,

- Quantitative export restrictions and high export taxes on cereal and bovine meat exports,

- Targeted industry support measures.

> 12 The term _corralito_ refers to emergency measures put in place near the end of 2001 to stop a bank run. The government severely limited withdrawals from Argentine peso accounts and completely prohibited withdrawals from U.S. dollar–denominated accounts.

11
**Table 4:** Effect on parental involvement

|Organized<br>school activities<br>& events|Met with<br>teachers to discuss<br>student performance|Involved in<br>school<br>decision making|Percent of parents<br>regularly attending<br>meetings|
|---|---|---|---|
|(1)|(2)|(3)|(4)|
|_Double grant experiment_||||
|Treatment (1 year)<br>0.076*|0.034|0.153**|-0.726|
|(0.044)|(0.042)|(0.060)|(2.383)|
|WY _p_-value<br>0.160|0.660|0.020|0.780|
|Treatment (2 year)<br>0.084**|0.003|0.046|0.034|
|(0.042)|(0.042)|(0.062)|(1.901)|
|WY _p_-value<br>0.160|1.000|0.840|1.000|
|Treatment (3 year)<br>0.067*|-0.021|0.016|-1.073|
|(0.040)|(0.045)|(0.064)|(1.666)|
|WY _p_-value<br>0.320|0.900|0.900|0.840|
|Control mean<br>0.824|0.840|0.584|86.776|
|Observations<br>244|244|244|244|
|_Information experiment_||||
|Treatment (1 year)<br>0.150***|0.129**|0.037|3.300|
|(0.055)|(0.054)|(0.075)|(2.078)|
|WY _p_-value<br>0.020|0.080|0.560|0.280|
|Control mean<br>0.730|0.750|0.550|87.770|
|Observations<br>174|174|174|174|
|_Single grant observation_||||
|Treatment (1 year)<br>-0.056|-0.084|0.013|-1.839|
|(0.055)|(0.053)|(0.085)|(2.123)|
|WY _p_-value<br>0.740|0.340|0.900|0.740|
|Control mean<br>0.910|0.910|0.615|91.231|
|Observations<br>151|151|151|151|

Notes: Robust standard errors in parentheses. Westfall and Young (WY) p-values reported. Dependent variables in columns (1)-(3) are binary (where 1=Yes). Dependent variable in column (4) is continuous. Data source: Parent association surveys. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01
where _dj_ is an indicator function that equals 1 if the household is poor and equals −1 if the household is non-poor in period _j, j_ = _2017, 2022_ , and 1 2 .<sup>5</sup> As can be noted, the application of (2) requires that ε _i,_ 2017 and ε _i,_ 2022 have a bivariate normal distribution with ff ρρ� = dd dd ρρ� zero mean, (the partial) correlation coefficient ρ, and standard deviations σε,2017 and σε,2022, respectively. This assumption is standard for most analysis of household consumption data. Furthermore, we exploit a logarithmic transformation to normalize the dependent variable. We are also implicitly assuming that no cohort-specific effects exist; neither do we explicitly assume individual level heterogeneity. While these could result in more restrictive analysis, we are here forced by the limited availability of data for Bhutan. A final crucial step in this framework is represented by the estimation of the correlation coefficient . Given equation (1) and the aforementioned assumptions, and further assuming that the number of cohorts (C) constructed from the survey data is large enough, ρρ� the partial correlation coefficient ρ is estimated from a modified version of equation (1) where all the variables are aggregated at the cohort level: where the error term εcj includes now a cohort fixed effect τc and the error νcj.<sup>6</sup> All predictions from model (1) are obtained by applying the estimated parameters from the 2017 and 2022 survey rounds to 2022 data. Following Appendix 1 in Dang and Lanjouw (2023), standard errors are obtained adjusting for sampling errors and model errors � according to V 2) , assuming that these two errors are uncorrelated with each other. The variance for the sampling errors ss mm has been estimated using the bootstrap VV(ΦΦ<sup>�</sup> = ΣΣ<sup>�</sup> + ΣΣ<sup>�</sup> method while the Delta method has been exploited to estimate ss . ΣΣ<sup>�</sup> mm ΣΣ<sup>�</sup>

> 5 We consider household sampling weights to obtain population-level numbers.

> 6 The formula used to obtain the correlation coefficient is ~~�~~ ccffcc(εεcc1, εεcc2) ) ~~,~~ where and are defined as in equation (3). ρρ= ccv (εεcc1) ccv (εεcc2 εεcc1 εεcc2

11
**Table 7: Evolution of financial constraints: Transition matrix 2005 to 2008/9**

|||2008/9<br>|Status<br>||
|---|---|---|---|---|
|2005 Status|Has Loan|Loan<br>Application<br>Rejected|Discouraged<br>from<br>Applying for<br>Loan|No Need for<br>Loan|
|**_Full Sample_**|||||
|Had Loan (n=78; 40.8%)|56.4%|12.8%|12.8%|17.9%|
|Discouraged (n=45; 23.6%)|15.6%|4.4%|40.0%|40.0%|
|No Need(n=68;35.6%)|14.7%|4.4%|13.2%|67.6%|
|**_Small Firms_**|||||
|Had Loan (n=29; 30.2%)|37.9%|6.9%|27.6%|27.6%|
|Discouraged (n=30; 31.3%)|16.7%|6.7%|36.7%|40.0%|
|No Need(n=37;38.5%)|8.1%|5.4%|21.6%|64.9%|
|**_Large Firms_**|||||
|Had Loan (n=21; 58.3%)|76.2%|14.3%|0.0%|9.5%|
|Discouraged (n=3; 8.3%)|0.0%|0.0%|0.0%|100.0%|
|No Need(n=12;33.3%)|41.7%|8.3%|0.0%|50.0%|

46