rafmacalaba/data-use-mentions-extended huggingface dataset:
). In fact, such discrepancy is one of the most contentious issues dividing those who support adopting indicators of social and economic progress that go beyond the narrow measure of economic production (GDP per capita), and those who dismiss these as “rather silly analyses” (Hamermesh, 2004). The Easterlin paradox originating from his 1974 article – which was asking why well-being – does not seem to go up in line with economic growth is often cited as a catalyst for the attention of the discipline to subjective well-being and for policy makers to devise broad indicators of social and economic progress. By the mid 2010s, the number of papers con– – cerned with happiness economics some published in top journals had increased from less than a few to more than 60 per year (MacKerron, 2012). Across contributors to this literature the consensus is that while monetary income is a strong predictor of subjective well-being, the latter is influenced by many other factors including economic security, access to economic opportunities, health status, education, marriage status, relative income vis-à-vis a reference group, among others. These academic discussions influenced the debate in policy circles, more prominently but not exclusively, in the development policy area. Some clear examples include the Human Development Index introduced by 2
|Yes|Yes|
|Initial productivity|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|8886|6809|9119|6902|8888|6810|9121|6902|

**N** otes: All continuous variables are winsorized at the 1% and 99%. Robust standard errors in parentheses

* p<0.10, ** p<0.05, ** p<0.01

-Parasites exhibit a significantly lower probability of adjustments at the 1% level in Panel B, cols. 3, 7, and 8. Both the Soto and parasite firms adjust less at the 5% level of significance in Panel B, col. 4.

-The coefficients of PCA 1 and PCA 2 are statistically different at the 1% level in Panel C, cols. 1, 2, 5, and 6. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
12 between the sample of small firms and the sample of all public limited companies does not indicate such a relationship.

5) A much larger share of the smaller firms' borrowing is from banks (62%) rather than from the development finance institutions (11%). Since the special financial institutions provide only term loans, while banks in India largely provide short term finance, this also suggests that access to term finance may be harder for the small firms.

# IV. **Empirical Evidence on Maturity using Panel Data.**

In this section we discuss the empirical evidence on the maturity structure of debt using firm level panel data. We first present some descriptive statistics and simple bivariate correlations and then discuss the econometric evidence on the determinants of maturity.

# **IV.I Descriptive Statistics for the Panel and Simple Correlations**

Table 3 presents descriptive statistics for the panel of firms. Looking at the (weighted) mean values of the two measures of the maturity composition of debt (mat 1 and mat2), we see that they are virtually identical to those presented in Table 2 using the aggregate data for public limited companies. Approximately 39% of the total stock of debt (inclusive of trade debt) is represented by long term debt and 37% of this long term debt comes from development finance institutions. Bank finance accounts for 37% of all debt and approximately 27% of all debt is arm's length debt. The time series of debt maturity for the balanced panel (not reported in the table) indicates an increase during the period in the share of long term debt held by firms in the sample. There is also evidence of a decline in the share of bank finance and an increase in the share of finances provided by DFIs.
in Jordan <!-- Start of picture text --> a. Syrians pre‐crisis b. Syrian refugees in Jordan<br>75‐79 75‐79<br>60‐64 60‐64<br>45‐49 45‐49<br>30‐34 30‐34<br>15‐19 15‐19<br>0‐4<br>0‐4<br>20 10 0 10 20<br>20 10 0 10 20<br>% Male % Female<br>% Male % Female<br><!-- End of picture text --> _<mark>Source:</mark>_ <mark>Own calculations based on United Nations, Department of Economic and Social Affairs, Population Division</mark> 2017 and ProGres data.

<mark>The sex‐ratio of Syrian refugees in Jordan differs from that of the pre‐crisis population in Syria. Women account for 52 percent of the refugees in Jordan, whereas in Syria in 2010 they represented 49 percent of the population. This is mainly explained by the increase in the share of women under 20 from 46.2 percent in 2010 to 53.9 percent in the female refugee population. In contrast, the proportion of women in all the</mark> 7
to changes due to the implementation of rules of origin.

We complement the general equilibrium analysis with a simple global microeconomic model to obtain impacts on poverty and income distribution. The initial global distribution of per capita consumption/income was constructed with household-based data. Country-specific growth rates in real per capita household consumption from the CGE are fully transmitted to households assuming distribution-neutrality. To calculate the number of poor, the total population in each country is adjusted using United Nations population projections. There are 163 countries represented in the microeconomic model with 146 harmonized, nationally representative household surveys obtained from the World Bank’s Global Micro Database. Additional per capita consumption/income distributions for 17 countries were obtained from the PovcalNet website. The analysis on poverty omits Brunei Darussalam, Cambodia, and Singapore due to lack of data.<sup>13</sup>

> 12 There is extensive empirical evidence that finds that trade liberalization leads to an increase in productivity, mainly in developing countries, as reviewed by Shu and Steinwender (2018). CGE models with constant returns to scale usually underestimate the gains from liberalization (USITC 1997; Tarr 2012), and for that reason, including a productivity shifter is empirically valid (Partdrige and Rickman 2010). The elasticities of productivity to trade liberalization vary. Following Topalova & Khandelwal (2011), we assume that a 10 percentage fall in tariffs leads to a 4.8 percentage increase in labor productivity in the economy. Similar results are found by Fernandes (2007) for Colombia.

> 13 For further details, see <u>http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx.</u>

9
(2019) find that in Colombia, an increase in internally displaced persons (IDPs) adversely affected the total output of the formal sector firms. Their suggested interpretation is that an increase in IDPs entails an expansion of the informal sector, which in turn adversely affects the total output of firms in the formal sector. Loayza (1996) suggests that a large informal economy may lower tax revenues and increase public service congestion, which in turn, can negatively affect all firms in the economy. In Loayza’s (1996) study however, these negative effects of informality do not explicitly result from the informal competition faced by the formal sector firms.

The present paper contributes to the above literature in several ways. First, it analyzes the impact of informal competition on the growth rate of employment among formal manufacturing SMEs. To the best of our knowledge, this is a first such attempt. The informal sector is often credited with providing employment especially to the poor and in the developing countries. Thus, it is important to properly understand the impact that the informal sector has on employment growth in the formal sector.

Second, informal firms are known to be very small and therefore unlikely to pose any problems for the large firms. Thus, a proper analysis requires that we distinguish between the effects of informality on SMEs and large firms. Studies using macro-level data cannot capture this heterogeneity. Our analysis based on firm-level data confirms that informal competition affects employment growth in SMEs but not the large firms.

Third, we pay due attention to endogeneity concerns. We do so in several ways. These are using the proportion of all other firms (that is, other than the firm in question) that compete against informal firms as a proxy for the level of informal competition experienced by the firm in question (cell average method); using a large number of firm- and country-level controls; showing that our main results follow several predications of the parasite view of informality which is unlikely if our 5
provisions on FDI over the period 1990-2011. Each row corresponds to a separate econometric model based on the specication from column (3) of Table 2 after replacing the dummy variable for DTAs with investment provisions ( _INVij,t_ ) with corresponding indicator variables for each of the other seventeen types of provisions from DCRTA. The dependent variable is the value of FDI stock. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including origin-time, destination-time, directional pair xed eects, and all other control variables from column (3) of Table 2. The estimates of all xed eects and controls are omitted for brevity. The standard errors in all specications are clustered by country pair. See text for further details.

The following results stand out from our analysis. First, due to (near) perfect collinearity with the DTAs dummy, we could not identify the eects of agreements with provisions covering `antidumping duties', `countervailing duties', and `competition policy'. Our indicator for agreements including `competition policy' provisions is perfectly collinear with the DTA dummy, while the correlations between the indicators for agreements with provisions 28
90% of the observed consultations. Measurement is higher in the PBF group by 8 percentage points (p = 0.107).

For the child consultations, we find some significant differences in the conduct of physical examinations. Overall, weight and height of the children was measured in 85 percent of consultations. In the PBF facilities, these measurements were more likely to be conducted by 12 percentage points (p = 0.037). For the 13 core physical examination procedures, the share of completed procedures were higher by eleven percentage points in the PBF group. PBF providers were statistically significantly more likely to observe the children’s ears, perform skin pinch tests for dehydration, check for lethargy, and count radial pulse. We do not find significant impacts on duration of these consultations, whether vaccination history was checked, and whether providers washed their hands prior to conducting the physical examinations.

Results from the analysis of the data collected at the health facilities show that the PBF project had a significant impact on the quality of care. As a result of the program, availability of equipment and medical supplies increased. We detect improvements in facility infrastructure and procedures related to infection prevention and control. We find positive impacts on provider competence and even some improvements in the content of curative care of children under-5. Although we find these positive impacts, substantive gaps in quality of care remain. For example, many providers could not correctly diagnose the clinical vignette cases and many providers did not properly wash their hands before conducting physical exams. There may be further improvements in quality with time, but additional intervention targeting quality of health may be needed. Next, we turn to analysis of the household data to evaluate whether the project was successful in increasing utilization of health services.

# Health outcomes and health-related behaviors

In Table 7, we present results on the impact of the PBF intervention on health outcomes and health behaviors. As seen in Panel A, we do not find impacts on anthropometric measures of children. We also do not find
_Panel B: Onl_|5460<br>_y LSMS _|-0.0137**<br>(0.0063)<br>_databases_|0.0215<br>(0.0437)|-0.0072<br>(0.0319)|
|_Hours per week (total)_<br>34.39|4530|0.1056|-0.2895|-1.1751|
|||(0.4501)|(2.8443)|(2.0713)|
|_Hours per week (of-farm)_<br>18.60|4530|-1.609***<br>(0.426)|-4.3926<br>(2.8683)|-0.8306<br>(2.1766)|
|_Hours per week (on-farm)_<br>9.15|4530|2.744***|2.3598|0.5399|
|||(0.390)|(2.2198)|(1.6274)|
|_Hours per week (self)_<br>7.65|4530|-0.741**|2.7154|-0.0305|
|||(0.331)|(2.3503)|(1.7875)|
|_Second occupation_<br>0.058|4530|0.008*<br>(0.004)|0.0321<br>(0.0289)|0.0406*<br>(0.0230)|
|_Log(Monthly Labor Income)_<br>193092.2|4530|-0.3816***<br>(0.1144)|-0.6700<br>(0.7176)|-0.0883<br>(0.5305)|
intensities for new Chinese manufacturing exporters between 1999 and 2005. In 2005, 58% of domestic new exporters reported an export intensity of between 0 and 10%, while this percentage was 36% for foreign new exporters. Although these percentages were somewhat lower in the preceding years, at least a third of domestic new exporters and at least a quarter of foreign new exporters export 10% or less in their first year. We assume that a similar distribution holds for our sample. Given that the average export intensity of all direct exporters in our sample is 48.3%, this reflects that old exporters usually export a larger share of their sales.

(ii) Firms that exported in the previous period and stop exporting now (exiting exporters) have an export intensity below threshold _ζ_ . Using Colombian customs data from 1996 to 2005, Eaton, Eslava, Kugler, and Tybout (2007) find that almost half of all exporters did not export in the previous year, but their contribution to export revenues is extremely small and most new exporters leave the export market in the following year. While firms exiting the export market do not necessarily have to be new exporters, it is much more likely that firms with a smaller export intensity stop exporting as opposed to firms with a high export share.

(iii)We relax the assumption that new exporters have to carry the whole burden of sunk costs upon entry into the export market. This assumption is in line with Eaton et al. (2007) who suggest that new exporters and their buyers undergo a period of learning about one another before locking in major exporting contracts. Surviving exporters typically start to export into a single foreign market and gradually expand into additional markets, suggesting that “the costs of “testing the waters” may be substantially less than the cost of locking in major exporting contracts” (Eaton et al., p. 17).

We will attribute an export dummy to firms only that exceed a certain threshold _ζ_ of export intensity. Assumptions (i
**Table 3. Investment rates and returns on fixed capital in eight countries**

||Investment rate|Average return on fixed<br>capital|
|---|---|---|
|Cameroon|0.11|1.36|
|Ghana|0.13|3.63|
|Kenya|0.11|1.82|
|Uganda|0.12|0.75|
|Zimbabwe|0.12|0.85|
|Belgium|0.13|0.18|
|France|0.11|0.12|
|Germany|0.12|0.16|
|UK|0.12|0.13|

Source: The data on all countries except Uganda were reported in Bigsten et al. (1999a). The Ugandan data were reported in Reinikka and Svensson (2001).

Using the same data set as in their 1999 study with Zambia added, Bigsten et al. (2005) examine more closely if investment is affected by irreversibility and fixed adjustment costs. In such models firms will not invest anything unless the marginal return on capital exceeds some threshold level, and so one would expect to see a significant share of zero investments in the data if the theory is right. Table 4, taken from Bigsten et al. (2005), summarizes the proportions of non-zero investments in this data set by country and firm size. With the exception of Zimbabwe, in all countries and size categories the proportion of positive investment is lower than 0.5. That is, the majority of the firms in these categories do not invest anything during a whole year. There is a weak positive relationship between firm size and the propensity to invest, although among the largest firms in Ghana only one in five firms invest in a representative year. In Zimbabwe investment activity is generally higher than in the other countries. With a third of the firms in the whole sample refraining from investing
Country-level data**

At the country level, we control the quality of regulation, financial development, bank concentration, foreign aid, inflation, economic growth, and the level of wealth. Indeed, Agostino et al. (2020) establish strong evidence that better local institutions (the rule of law and government efficiency) help small and mediumsized firms become more productive in Europe over the period 2010-2014. We also have evidence of a positive effect of foreign aid on firm sales growth (Chauvet and Ehrhart, 2018). Furthermore, financial development and greater competition between banks (strong bank concentration) favor the performance of firms ( Chauvet and Jacolin, 2017; Lee et al., 2020). Finally, Bambe et al. (2022) show that inflation targeting increases the growth and productivity of firms in targeted countries compared to non-targeted countries. Indeed, inflation targeting improves the performance of developing countries by reducing the level and volatility of inflation (Lin and Ye, 2009).

Further details on the country-level variables can be found in Appendix Table (16).

11
advantage captures how far is a given product from the comparative advantage of a given country. Building upon the notion of “product chain of comparative advantage” within the Heckscher-Ohlin theoretical framework (see Online Appendix A2 as well as Jones 1956-57; Bhagwati 1972; Deardorff 1979) and previous

> 11 The idea of using data on publicly listed US companies to compute a generic industrial measure of dependence on external finance relies on the argument that these companies face relatively frictionless financial markets. Consequently, the need for external finance of a median US public company captures the true technologically determined need for external finance at the industry level that is unaffected by various financial and contractual frictions facing smaller firms or firms in less financially developed countries. See Rajan and Zingales (1998) and Braun (2003) for a more detailed discussion.

8
for each individual or household. Such data are not available in most countries (and surely not in Africa). Lacking this information, some inferences can still be made about the degree of seasonality from nationally representative integrated household consumption surveys, such as the Living Standard Measurement Studies.<sup>9</sup> At a minimum, repeated cross sectional surveys are needed that are each time conducted throughout the year, so that a representative sample of households is interviewed in each month of the year.

Let _mhw_ be the month number (January = 1, February = 2, etc.) in which household _h_ was interviewed in wave _w_ and _chw_ food consumption per equivalent adult of household _h_ in wave _w._ The seasonal factors of consumption can then be estimated as: With κ _w_ a wave‐specific intercept (to capture the wave‐specific effects), _xhw_ a vector of time varying household and environmental characteristics, η _h_ a household‐specific constant and ε _hw_ a disturbance. The variables of interest are the eleven seasonal variables _zhwj_ ( _m_ = 1,...,11) defined by The vector of seasonal factors _s_ derives from the estimated δ coefficients using equation (3) and the seasonal gap follows from equation (5).

The seasonal factors can in principle be obtained by estimating (8) through pooled OLS (i.e. setting the household‐specific constants η to zero). Two concerns deserve attention. First, if the interview months are not random (for example if poorer and more remote areas tend to be surveyed towards the end of the wave) and heterogeneity across households is not fully controlled for by the vector of control variables x, then pooled OLS estimates of the seasonal factors may be biased. Household fixed effects estimation can mitigate these concerns (by controlling for all time invariant variables affecting both the month of interview and

> 9 <u>www.worldbank.org/lsms</u>

10
(5.691)|20|49|
|Age at frst birth<br>7480|23.22|(3.489)|14|41|
|Years of education<br>7404|10.47|(3.135)|0|21|
|Rural<br>7480|0.482|(0.499)|0|1|
|Household size<br>7480|5.188|(1.478)|1|16|
|Extended family<br>7480|0.381|(0.485)|0|1|
|Marital status (married)<br>7480|0.980|(0.137)|0|1|
|Muslims<br>7480|0.816|(0.387)|0|1|
|Catholics<br>7480|0.081|(0.273)|0|1|
|Orthodox<br>7480|0.067|(0.251)|0|1|
|Coastal region<br>7480|0.264|(0.440)|0|1|
|Central region<br>7480|0.282|(0.450)|0|1|
|Mountain region<br>7480|0.291|(0.454)|0|1|
|Tirana region<br>7480|0.161|(0.367)|0|1|

_Notes:_ The data used are the 2002 LSMS, the 2005 LSMS, the 2008-09 DHS and the 2012 LSMS 35
# 2.1 _Main variables – description and motivation_

Summary statistics of all the variables used are provided in table A2. The outcome or dependent variable is (log of) labor productivity of the firm. Labor productivity equals the total sales (in USD) of the firm in a typical month over the last year divided by the total number of workers (paid plus unpaid and including the owner if applicable) in a regular month over the last year. Total sales are converted to USD using official exchange rate taken from World Development Indicators (WDI), World Bank.

For our main explanatory variable, we define a female-owned firm as a firm where the largest owner is female. All other firms are male-owned. For over 96 percent of the firms in our sample, the largest owner is also the main decision maker. Thus, there is almost no distinction in our sample between the largest owner and the manager (main decision maker).<sup>7</sup> Table A1 provides the percentage of female-owned firms by country in the sample. Ghana has the highest percentage of female-owned firms (64 percent) followed by Guatemala (57 percent). At the other end of the spectrum are Mali (18 percent), and DRC (23 percent). The average for the whole sample is 44.9 percent.

We account for several factors that are found important for the productivity of businesses. We account for firm size using (log of) the total number of employees in a regular month. Informal firms of different sizes may show different levels of productivity. Large informal firms may have more resources, but they may face transformation and evasion costs that smaller informal firms are not burdened with (Amin and Islam, 2015). It is also well-established that formal firms are more productive than informal business (La Porta and Shleifer, 2014). Thus, the degree of

> 7 Restricting the sample to only those firms where the largest owner is also the main decision maker makes no noticeable difference