rafmacalaba/datause-encoder-data huggingface dataset:
income on loans separately, and they merely report total interest income, including interest from interbank operations and interest-earning securities, such as government and corporate bonds. Using total interest income, although more widely available, is not a realistic indicator of the return on the lending operations of each bank, and would create an upward bias of the estimated lending rate if referred to total loans. In addition, we need data on non-performing loans for each bank, since we are interested in the interest rates charged by the bank, not the interest rates earned by the bank.

To enhance comparability across countries, we compute bank-level interest rate spreads for the ten largest banks in each country, if available, and construct country-level interest rate spreads by averaging across these ten banks by country. Again, we calculate the spreads for the year 2000, the most recent year for which data are available.

In order to construct variables that measure the return on interest-earning assets and the cost of funding of those operations, we need data on interest income on the loan portfolio, interest expense, total loans, problem loans, total deposits, money market funds, and other funds. Since Bankscope does not have such an extensive coverage for the interest income on the loan portfolio or for the amount of problem loans, we had to restrict our sample to the 35 countries that offer this level of information. After selecting the ten largest banks in each country we also collected the data on problem loans and interest income on the loan portfolio from the banks’ annual reports in order to crosscheck the data reported by Bankscope and insure their reliability. For a few cases (12 banks in seven countries to be exact), we found a discrepancy between the data reported by Bankscope and the data contained in the audited financial statements. For these banks, we have replaced the Bankscope data derived from the audited annual reports.

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# **4.3 Firm-level panel data**

We construct a firm-level panel dataset using the annual ASI data. We use firm identifiers to track firms over the years 2010-2018 and limit the sectors to manufacturing- related sectors and omit agriculture and other primary activities. As mentioned earlier, the ASI survey does not cover all firms every year and chooses a sample of small firms to be covered. Our study, however, focuses on all firms with at least 100 workers. As the ASI survey is supposed cover all such large firms in every round (as part of the “census” section of the ASI), we are less likely to lose out on firm observations because of sampling. In addition, we do not lose out on a lot of manufacturing activity because as discussed, this subset of large firms accounts for most of formal manufacturing activity.

The dataset includes information on the 3-digit industry group and the state in which the firm is located. It also includes information on output, inputs and employment. On employment, it covers labor involved in production as well as the split of workers into contract and permanent categories. For each of the labor categories, there is also information on the total wages paid out. With regard to inputs, the dataset covers expenditure on the fixed capital stock as well as variable domestic and directly imported inputs (that is, materials) sourced by the firm in the last fiscal year. We sum up the latter two expenditures to get a measure of the firm’s total expenditure on variable material inputs.

Similar to the case of the state- level panel, this dataset also drops all small states and union territories. In addition, we have to drop firms that were in the new state of Telangana as it was not possible to track the firms back in the period before the split. The dataset does not consider those firm observations that do not report any labor information. All outcomes are winsorized at the 1/99 level by year.

# **4.4 Firm-level descriptive statistics**

Table 12 describes the outcome variables used in the firm (factory) level panel analysis. The panel analysis is limited to firms with 100+ workers at baseline, with the treatment group covering firms with 100-300 workers and the control group covering firms with more than 300 workers. Given that the treatment and control groups are defined based on size, one would expect them to be different on this dimension. As the table suggests, the typical firm in the treatment group has fewer employees compared to a typical control firm and this difference is statistically significant 17
inequality by adversely affecting TFP in the source economy and increasing it in the host economy. Our empirical analysis provided evidence which is consistent with this hypothesis. Using a data set on the bilateral emigration of skilled workers, our results suggested that an increase in high-skilled emigration rates tends to slightly increases TFP differences and therefore (albeit also slightly) wage income for skilled workers in destination countries relative to source countries in a causal way. None of our estimations suggested that skilled workers in the destination country lose from skilled migration relative to the source country. Finally, skilled workers in the receiving countries unambiguously gain from low-skilled migration.

# **<<table A.1 about here>>**

# _REFERENCES_

Acemoglu, Daron, and Joshua Angrist. 2000. “How Large are Human-Capital Externalities? Evidence from Compulsory Schooling Laws.” _NBER_ _Macroeconomics Annual_ 15: 9–59.

Beine, Michel, Frédéric Docquier, and Hillel Rapoport. 2001. “Brain Drain and Economic Growth: Theory And Evidence.” _Journal of Development Economics_ 64: 275–89.

Beine, Michel, Fréderic Docquier, and Hillel Rapoport. 2008. “Brain Drain and Human Capital Formation in Developing Countries: Winners and Losers.” _Economic Journal_ 118: 631–52.

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_+ γXijc + dj + dc + εijc_ (4) In equation (4) a significant coefficient for _λ_ will indicate that the effect of corruption on productivity depends on the country’s level of institutional quality, as represented by the diffusion of corruption and the efficiency of the legal system. In particular, a positive (negative) coefficient of _λ_ will indicate that high _institutional quality_ will lessen (strengthen) the negative effect of _corruption at the firm level_ on productivity.

# **4 Econometric Analysis**

We now turn to the results of econometric analysis in the three stages outlined above. Namely, we examine the effects of corruption on productivity; of the interaction between corruption and the time tax; and of the relevance of country characteristics for firm level outcomes.

As discussed, the determinants of productivity are estimated using a one-step augmented production function. In order to address the potential endogeneity of firm level bribe tax and time tax, equation (1) is estimated with an instrumental variable (IV) approach. Implementation of the IV method requires the utilization of adequate instruments that must fulfil two conditions, namely being correlated with the endogenous variable and being uncorrelated with the error term in equation (1).

While it is difficult to find fully excludable instruments, the BEEPS data set offers a number of potential candidates. For instance, firms are asked whether or not the owner is female. There is evidence in the literature that women are more risk averse than men (Barsky et al., 1998). If that is the case, then they may also be less likely to engage in corrupt behaviour, which can be seen as a risky activity. While risk aversion may also play a role for the productivity of females, it is not a priori obvious that gender should have a direct influence on productivity that is not related to the indirect channel of risk aversion. Also, the data relate to the owner of the firm, not the manager. While the owner may have more influence on fundamental decision such as whether or not a firm should engage in illegal activities such as corruption, the owner may matter less for 20
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Maintaining East Africa’s regional backbones, once complete, would cost a significant $418 million a year. The corresponding total for EAC is $193 million. Although the bulk of regional infrastructure spending needs relate to new investment, there is also a significant ongoing need for maintenance. Most of the associated maintenance costs are in the power sector—$263 million a year for East Africa and $113 million for EAC.

Completing the regional backbone would cost around 3 percent of East Africa’s collective gross domestic product (GDP), but only around 1 percent of EAC’s GDP. Overall, then, the cost of completing regional infrastructure backbones looks quite challenging for East Africa as a whole but much more manageable for EAC alone.

**Figure 5.1 . Spending for regional infrastructure as a share of GDP**

<!-- Start of picture text --> 10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Percentage of GDP<br>EAC Plus EAC Ethiopia Sudan Rwanda Uganda Tanzania Burundi Kenya<br><!-- End of picture text --> Source: AICD calculations.

However, the burden of regional spending as a percentage of GDP varies enormously across countries and is quite daunting in some cases (figure 5.1). For example, although Ethiopia’s and Sudan’s spending needs are roughly the same—approximately $1.2 billion a year—this total represents only 4 percent of Sudan’s GDP compared with 9 percent for Ethiopia. Most of the other EAC countries would need to spend around 2 percent of their GDP to achieve

**Figure 5.2 . Spending for regional infrastructure as a percentage of national infrastructure spending**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>infrastructure spending<br>Ethiopia Rwanda Uganda Tanzania Kenya<br>Spepnding Needs as a % of annual<br><!-- End of picture text --> Source: AICD calculations.

regional spending targets, which is still quite challenging. Kenya is the only country in the region for which regional infrastructure spending requirements appear modest in relation to the size of the national economy.

59
# **1. Introduction**

Standard welfare measures are based on household consumption and assume an equal and equitable distribution of resources among family members. This presumes that households either distribute resources equally among all family members (equal) or according to the respective dietary or nutrition requirements of all respective family members (equitable). However, existing literature has demonstrated that neither equal nor equitable distributions are observed for most households (Udry, 1996; Brown et al., 2017) and the inferior outcomes of many, particularly women, have been documented (Oster, 2009; van de Walle, 2013). However, disparate intrahousehold distributions may extend further, to differences in the quality and nutritional value of foods consumed between family members. This may ultimately affect nutrition and health outcomes (Coates et al., 2018; Harris-Fry et al., 2018; D’Souza & Tandon, 2020; Brown et al., 2021; De Vreyer & Lambert, 2021).

Due to the potential biases associated with these household-level aggregations, welfare metrics and measures of food consumption and diet quality which are based on individuals may depict a more accurate picture of the conditions of these individuals within households. This has the potential to improve policy recommendations which may otherwise be skewed if everyone in a household is assumed to be allocated equal or equitable resources. There is a growing literature that indicates that the cost of nutritious diets is quite high, suggesting that we may gain richer insight from assessing inequalities in diet quality as opposed to only inequalities in food expenditure (Dizon et al., 2019; SOFI 2020).

In this paper, we assess the extent of disparity between household and individual measurement of food expenditure and diet quality in the Philippines. We use the National Nutrition Survey (NNS) from the Philippines in 2013, which includes an extensive accounting of consumption for 18,472 individuals, residing in 4,213 households. To better understand intra-household food expenditure and diet quality in the Philippines, we estimate individual- and household-level food expenditure and food consumption, and then compare these across different individuals within a household. We seek to answer two questions. First, do individuals with low food expenditure live in households with low food expenditure? Second, do individuals with poor quality diets live in households with poor quality diets? In this paper, we categorize food expenditure into five quintiles. We also categorize individuals as having above or below the median food expenditure.

2
transport infrastructure investment on multiple development outcomes (such as income, equity, and environmental quality) together.<sup>7</sup> Likewise, extending the single-equation RF regressions to a system of RF equations such as vector auto-regressions (VARs) can help analyze general equilibrium effects in a reduced-form manner. For instance, new economic geography theory could be used to motivate the vector of endogenous variables to be included in the VAR without imposing the possibly problematic structural restrictions (functional form, cross-equation coefficient restrictions) implied by the theory. One example of an effort in this direction is the work of Revoltella et al. (2016) on transport connectivity and economic resilience.

To date, RF regressions have typically focused on the effects near transport corridors and not those further away. While using macroeconomic data and simulations lacks locational specifics compared to spatial data, it could be a good complement to capture more system-wide (general equilibrium) effects when using RF econometrics.

# **4. Structural (General Equilibrium) Econometrics**

The second most popular econometrics for appraising the impact of transport connectivity on development outcomes is structural econometrics, including structural general equilibrium (GE), using calibrated or estimated parameters (Bosker, Deichmann, and Roberts 2015; Burgess and Donaldson 2012). It helps analyze several development outcomes jointly, while identifying clear transmission channels, linking model parameters to micro-founded behaviors, and considering feedback effects and counterfactuals. The potential of these models is great. But the current applications are rather far from exploiting it.

> 7 See Bougna et al. forthcoming for application of that idea in the context of a meta-regression analysis.

11
# **_1. Introduction_**

A major stylized fact uncovered by the empirical industry evolution literature in developed as well as developing countries is the enormous degree of heterogeneity in productivity across firms even within narrowly defined manufacturing industries (Bartelsman and Doms, 2000; Tybout, 2000). It has also been shown that long-term growth and development across countries is driven to a large extent by productivity growth (Easterly and Levine, 2001). From a policy perspective, it is therefore crucial to understand which factors underlie the heterogeneity in productivity across firms and which are associated with higher productivity.

The literature has proposed various potential determinants of firm productivity. A strand of research has focused on the role of openness and international integration for firm total factor productivity (TFP), following the theoretical insights from the endogenous growth literature. A large number of studies show a beneficial effect of exports on firm TFP (e.g., Kraay, 1999; Blalock and Gertler, 2004; Fernandes and Isgut, 2006).<sup>1</sup> Another set of studies investigates the effects of foreign ownership on firm TFP (e.g., Arnold and Javorcik, 2005; Kee, 2005). A different literature examines the importance of human capital and training for firm TFP (e.g., Tan and Lopez-Acevedo, 2002; Aw et al., 2005). Yet another literature assesses the impact of research and development (R&D) activities on firm TFP (e.g., Griliches, 1998). Finally, a recent literature focuses on the role of the business environment for firm TFP (e.g., HallwardDriemeier, et al., 2003; Dollar, et al., 2005).<sup>2</sup> Our paper contributes to the literature by integrating into a single analytical framework various TFP determinants that have been analyzed separately in previous studies: human capital, integration into world markets, technology, finance, business environment, as well as firm size and age. Using data from a recent survey of manufacturing firms in Bangladesh, we estimate consistent TFP measures for the period

> 1 See also Wagner (2006) for a thorough review of the literature on exports and productivity.

> 2 Similar types of factors have been considered in the macro literature to explain differences in growth across countries.

2
lack of market discipline as the government can cover potential losses; (3) managerial corruption, illegal behavior and rent extraction by politicians; and (4) conflicting objectives and impacts of short-term political goals. Such inefficiencies might also affect the innovation behavior of public firms. Bortolotti et al. (2019) investigate the impact of state ownership on the innovativeness of firms, measured by the number, quality and value of patents produced within a sample of roughly 5,000 listed European firms in 1999-2016. They find less innovativeness under government control (fewer patents per dollar invested) and no difference in patent quality (measured by citations in other patent filings).

Part of the literature also focuses on whether developing countries actively invest in green innovations and what are the alternative options to foster a worldwide diffusion of green technologies. Based on international patent data, many studies find that frontier green innovations are concentrated in high-income countries (Dechezleprêtre et al., 2011, Dutz and Sharma, 2012, and Napolitano et al., 2022). While Dechezleprêtre et al. (2011) and Dutz and Sharma (2012) explore historical patterns and difference across countries and regions, Napolitano et al. (2022) investigate the relationship between those patterns and income inequality. However, the three studies find the existence of limitations in the capacity and incentives for the developing countries to undertake frontier and relatively complex green innovation. This may also open opportunities to develop those green technologies in countries that have intermediate levels of per capita income (Napolitano et al., 2022).

As an alternative to frontier green innovations, many studies explore different ways of bringing green innovation to local and poor communities (Dutz and Sharma, 2012). Some studies focus on making the green technologies meet the needs of poor consumers with a dramatic reduction of prices. This strand of literature uses different name such as "bottom-of-the-pyramid (BoP)"(Prahalad, 2012), "inclusive innovation" (Foster and Heeks, 2013), "below-the-radar innovation" (Kaplinsky, 2011), "catch-up innovation" (Yuan et al., 2021) and "pro-poor innovation" (Tawney et al., 2015). The other strand of literature considers how to adapt innovation to local contexts to shape the technology diffusion. Different examples are discussed to support the importance of local innovation adaptation, for instance residential air conditioning or home food freezers (Ormrod, 1990). Dutz and Sharma (2012) highlight the adaptation of non-electric air conditioning technology from developed countries to the Chinese context by using the waste heat from buildings to power its machines, which 6
significantly when we focus on a smaller firm-level panel data where we can control for firmfixed effects or when we explicitly instrument for the introduction of CB reforms.

Our paper is related to an extensive literature that has studied the relationship between information sharing, bank lending volumes, perceived financial constraints, and default rates. One set of papers uses country-level data to compare the amount of bank lending in countries with and without information sharing systems or with less developed systems (Jappelli and Pagano, 2002; Detragiache, Gupta, and Tressel, 2005; Djankov, McLiesh, and Shleifer, 2007). Analyses of the relationship between credit information sharing schemes and measures of aggregate credit at the country level have at least two important drawbacks. First, correlations suggested by regressions at the aggregate country level are more likely to suffer from omitted variable bias. Second, such aggregate regressions are not instructive to answer questions about the differential impact of firm characteristics on the overall effect of credit information on access to finance.

Another set of papers uses firm-level data to examine the relationships between countries’ information sharing systems and firm’s access to bank lending. Galindo and Miller (2001) use firm-level balance sheet data from 23 countries to estimate an investment equation that measures the degree of credit constraint as the sensitivity of investment to cash flow. Paired with a self-collected online survey of CBs and CRs, they find that scope and quality of credit information schemes are correlated with lower financing constraints. Love and Mylenko (2003) study the impact of the existence of CB/CR on more directly obtained firm level dependent variables - perceived financing constraint, a subjective ranking provided by the World Bank Environment Survey respondents, and the reliance of firms on bank financing. They find 4
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