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- Caballero, Ricardo J. & Eduardo M.R.A. Engel (1999). “Explaining Investment Dynamics in U.S. Manufacturing: A Generalized (S, s) Approach”. _Econometrica_ 67(4) (July): 783–826

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- Chirinko, Robert S. (1993). “Business Fixed Investment Spending: Modeling Strategies, Empirical Results, and Policy Implications”. _Journal of Economic Literature_ 31(4) (December): 1875–1911

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**Table 6. COVID-19 impacts on work hours and earnings**

||Hou<br>|rs<br>|Ear<br>|nings<br>|
|---|---|---|---|---|
||Full-time employment|Weekly work hours|Monthly earnings|Hourly earnings|
||(1)|(2)|(3)|(4)|
|**Panel A: Female**|||||
|COVID|-0.053***|-2.326***|-0.163***|0.361***|
||(0.004)|(0.186)|(0.012)|(0.013)|
||[0.000]|[0.000]|[0.000]|[0.000]|
|Observations|888,854|888,854|483,684|483,684|
|Pre-pandemic average|0.783|34.7|673,100|6,963|
|**Panel B: Male**|||||
|COVID|-0.047***|-2.580***|-0.147***|0.367***|
||(0.003)|(0.197)|(0.009)|(0.008)|
||[0.000]|[0.000]|[0.000]|[0.000]|
|Observations|1,299,580|1,299,580|868,241|868,241|
|Pre-pandemic average|0.884|39.5|1,201,273|11,525|
|Test:βm= βf(p-value)|0.071|0.065|0.121|0.534|

Notes: ***, ** and * denote significance at 1%, 5% and 10%, respectively. Parentheses indicate standard errors, while square brackets indicate the False Discovery Rates (FDR). Full-time employment, work hours, monthly earnings, and hourly earnings are conditional on a respondent’s employment status (i.e., missing for non-employed respondents), and regressions for these variables include only workers who have more than six months of experience in current job/occupation. Earnings regressions exclude the unpaid/family workers. The main variable of interest in the above results is the COVID dummy, which takes on the value of 1 if the time period is August 2020. Pre-pandemic averages are population averages of the employment outcomes throughout 2016–2019 (both February and August rounds). Regressions are of the 15–64-year-old population. All earnings averages are reported in real 2007 rupiah. Control variables include age, squared age term, marital status, number of children under age five, education level dummies, training experience dummy, and urban/rural dummy. All specifications include year, province, month, sector and occupational fixed effects. Results are also robust to excluding sector and occupation fixed effects.

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# **4.1 Impacts on Formalization**

Table 6 reports the impacts of our different treatments on the three key dimensions of formality. These measures are self-reported by business owners from our two follow-up surveys. Although we asked the business owners to show the certificates for each of the dimensions of formality, a significant number of them – including of those that we have delivered business registration certificates - said they had them in a secure place like at home. Hence, reporting only on certificates shown to enumerators would underestimate the impacts on these measures.

We see that obtaining a business registration certificate is rare in the absence of our treatment – only 6.1 percent of the control group firms have a BRC at the time of the first follow-up (4 months post-intervention), and 6.7 percent at the time of the second follow-up (16 months postintervention). All three treatments have large and significant impacts on the likelihood a firm has a BRC, varying from a 54 percentage point increase for the BRC alone assistance to 64 to 68 percentage point increase for the BRC + bank information session treatment. This provides a powerful first stage to enable us to later measure the impact of business registration on firm outcomes.

However, it is notable that the treatment effects are lower than suggested by our take-up numbers, and we can no longer reject equality of effects for the BRC versus BRC+TPIN treatments. One-quarter of the difference in treatment effects compared to the take-up rate can be explained by the counterfactual provided by the control group, which suggests that 6 percent of those treated would have got a BRC without our assistance. In line with that, about 3.5 percent of those in treatment groups that did not take our assistance reported in the survey having registered during the period, which may be associated with people that went on their own, but may also suggest a measurement problem<sup>26</sup> (which could also apply to the control group). This BRC registration of people that had not received our support attenuated the difference between take-up and the treatment effects. The remaining gap is mostly driven by those who our records indicate that they received a BRC with our assistance and reported in the survey that they didn’t have

> respondents without an operating businesses. We get similar results when using the full sample of non-attrition for these surveys, i.e. when we assume a “0” for respondents that do not run a business anymore. 26 In theory, this could be a measurement problem in both administrative and survey data.

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# Figure 18: SSA: Investment needs

# A. Annual SSA infrastructure spending

# B. Logistics Performance Index

needs <!-- Start of picture text --> Percent of GDP<br>10<br>Capital outlays Maintenance<br>8<br>6<br>4<br>2<br>0<br>C. Selected health care indicators<br>Per capita Percent of population capita<br>5,000 120<br>4,000 Range 100<br>SSA 80<br>3,000 AE<br>60<br>2,000 EMDEs<br>40<br>1,000 20<br>0 0<br>Health Improved Improved<br>expenditure sanitation water<br>(LHS) (RHS) source<br>(RHS)<br>Flood Energy Total<br>Irrigation protection Water supply and sanitation Transport<br><!-- End of picture text --> <!-- Start of picture text --> Index Interquartile range<br>3.5 Median<br>3.0<br>2.5<br>2.0<br>EMDEs SSA SAR LAC EAP MNA ECA<br><!-- End of picture text -->

# D. Selected education indicators

<!-- Start of picture text --> Percent of per capita<br>income, ratio<br>50<br>Range<br>SSA<br>40<br>EMDEs<br>30 AE<br>20<br>10<br>0<br>Government Pupil-teacher<br>expenditure ratio<br><!-- End of picture text --> Sources: Haver Analytics; International Monetary Fund; Rozenberg and Fay (2019); World Bank. Note: AEs = advanced economies; EMDEs = emerging market and developing economies; SSA = Sub-Saharan Africa.

A. Average annual cost of investment in the “preferred scenario”, 2015-30 (Rozenberg and Fay 2019). B. Median; bars indicate interquartile range. Logistics Performance Index (LPI) measures the performance of trade logistics and is a weighted average of country scores on six key dimensions: customs performance, infrastructure quality, ease of arranging shipments, logistics services quality, consignments tracking and tracing, and timeliness of shipments. A higher value indicates better performance; for example, Germany’s LPI (top performer) is 4.2. C. Blue bars denote range of unweighted regional averages across EMDE regions. Health expenditure per capita in purchasing power parity terms, unweighted averages of 199 EMDEs, 34 AEs, and 47 SSA economies. Access to improved sanitation facilities (in percent of population), unweighted averages for 150 EMDEs, 33 AEs, and 47 SSA economies. Access to improved water sources (in percent of population), unweighted averages for 148 EMDEs, 34 AEs, and 47 SSA economies. Latest data available during 2011-15.

D. Blue bars denote range of unweighted regional averages across EMDE regions. Government expenditure per primary student (in percent of per capita income), unweighted averages of 87 EMDEs, 32 AEs, and 29 SSA economies. Pupil-teacher ratio in primary education (headcount basis), unweighted averages for 165 EMDEs, 31 AEs, and 44 SSA economies. Latest data available during 2011-15.
# _Independent variables_

Donor‟s ability to benefit from investments in country systems (<sup>_A_</sup> _i_<sup>in the model) is</sup> proxied by donor aid share, defined as the percentage of total aid (inclusive of aid not to the government sector) to recipient _j_ accounted for by donor _i_ . Higher donor aid shares are predicted to increase use of country systems. The sample mean for donor aid share is 7%, with a minimum value of 0.0014%<sup>6</sup> (Switzerland in Egypt in 2010) and a maximum of 83.6% (World Bank in St. Vincent in 2010).<sup>7</sup> Trustworthiness of recipient country systems (  in the model) is measured primarily by the widely used Control of Corruption indicator, from the Worldwide Governance Indicators project.<sup>8</sup> Weaker country systems, as measured by lower scores on Control of Corruption reflect increased risks to donors of corruption-related scandals, diversion of funds to lower priority uses, or inability to account for how funds were spent. The Paris Declaration explicitly asserts that corruption “inhibits donors from relying on partner country systems.” Use of country PFM systems is thus expected to be positively related to corruption scores.

The Freedom House civil liberties indicator is used as a supplementary indicator of trustworthiness of country systems. Civil liberties scores are higher in countries with a free and independent media, freedom of assembly, freedom for NGOs, and greater personal autonomy and individual freedoms.<sup>9</sup> The use of donor and other public funds is likely to be more

> 6 Values of 0 are not present in the sample because the dependent variables are all undefined for donor-recipient pairs with no aid transfers.

> 7 Larger donors at the global level do not always have higher average aid shares at the recipient level, as some donors concentrate their aid in fewer countries.  Portugal‟s average aid share (23.5%) exceeds that of the U.S. (12.4%), although the U.S. provided more than 100 times as much total aid to countries in the sample as Portugal. 8 This indicator is a composite of numerous corruption-related indicators collected from various sources including surveys and risk assessment firms.  It is similar in many respects to Transparency International‟s even more widely known Corruption Perceptions Index, but includes more data sources and covers more countries.

> 9 An index scored from 1 to 7 is available going back to 1972, but we use a more finely-grained version scored from 0-100 that is available for 2003 onward.  The political freedoms indicator produced by Freedom House includes

9
treatment of outliers, the inclusion of benefits beyond food and non-food (i.e., rent, housing, durables), and adjustments for both adult equivalent, and spatial and temporal price differences. For all three designs, the final consumption aggregate is represented by the sum of all its components: i) food consumed inside the home – whether purchased, self-produced, or received in-kind as gift; ii) food consumed outside the home; iii) non-food expenditures of various reference periods – one week, one months, 12 months in our case; iv) education expenditure; v) health expenditure – excluding hospitalization costs; vi) the imputed value of consumer durables; and vii) the imputed value of dwelling benefits.

Construction of the food component was guided by three principles. First, that all possible sources of food consumption were included (i.e., purchases in market, produced by household, received as gift or payment, etc.). Second, non-purchased food that was consumed must be assigned a value. Finally, only food that was consumed – not necessarily the total amount of food purchased – is included. Non-food consumption is slightly more difficult to measure, and we relied on a few widely accepted assumptions to do so. First, that the costs of acquisition of a non-food item is equal to its consumption, and second, that the value of consumption of non-food goods and services from own production or gifts is negligible. Finally, unlike food, non-food expenditures were valued at the purchase or self-reported acquisition cost, as most non-food items are too heterogeneous to collect quantities and calculate prices. Expenditure on some non-food goods and services was excluded from the calculation of the consumption aggregate, including payments of mortgages or debts (financial transactions), losses to theft (neither consumption nor expenditure), expenditure on marriages, dowries, births, and funerals (too irregular). Given that consumption data from household surveys is both extensive and complex, data errors and outliers are expected regardless of data collection method. The following rules were applied to the management of 12
Table 11: Robustness Checks for Job Quality Ratio Regressions

|Model|SEDLAC<br>weights<br>(1)<br>b/se|$1.25/day<br>poverty line<br>(2)<br>b/se|$4.00/day<br>poverty line<br>(3)<br>b/se|Full-time<br>(4)<br>b/se|Satisfed<br>with job<br>(5)<br>b/se|
|---|---|---|---|---|---|
|Age|0.002***<br>(0.000)|0.003***<br>(0.000)|0.003***<br>(0.000)|0.003***<br>(0.000)|0.003***<br>(0.000)|
|Male|0.091***<br>(0.012)|0.079***<br>(0.009)|0.092***<br>(0.008)|0.114***<br>(0.013)|0.085***<br>(0.011)|
|Educ - High School|0.071***<br>(0.003)|0.060***<br>(0.003)|0.081***<br>(0.004)|0.065***<br>(0.003)|0.071***<br>(0.003)|
|Educ - College|-0.007|-0.013**|-0.006|-0.011**|0.004|
||(0.006)|(0.006)|(0.007)|(0.005)|(0.006)|
|Migrant|0.024***<br>(0.002)|0.016***<br>(0.002)|0.027***<br>(0.003)|0.026***<br>(0.003)|0.021***<br>(0.003)|
|Public Firm|0.157***<br>(0.028)|0.158***<br>(0.024)|0.160***<br>(0.018)|0.117***<br>(0.020)|0.174***<br>(0.020)|
|Large Firm|0.141***<br>(0.026)|0.125***<br>(0.028)|0.128***<br>(0.023)|0.109***<br>(0.021)|0.133***<br>(0.025)|
|Formal|0.159***|0.162***|0.169***|0.170***|0.158***|
||(0.026)|(0.025)|(0.018)|(0.024)|(0.020)|
|Self-Employed|0.004|0.025|-0.029|0.033|-0.010|
||(0.036)|(0.035)|(0.030)|(0.040)|(0.036)|
|Employer|-0.105|-0.090*|-0.097**|-0.032|-0.083|
||(0.077)|(0.046)|(0.043)|(0.067)|(0.048)|
|Union|0.118***<br>(0.023)|0.101***<br>(0.010)|0.109***<br>(0.010)|0.070***<br>(0.009)|0.109***<br>(0.011)|
|Urban|0.062***<br>(0.005)|0.041***<br>(0.008)|0.043***<br>(0.008)|0.046***<br>(0.009)|0.033***<br>(0.007)|
|ln(GDP/capita)|0.008**|0.005|0.025*|-0.004|0.031|
||(0.004)|(0.011)|(0.013)|(0.012)|(0.021)|
|Adj. _R_<sup>2</sup>|0.492|0.488|0.488|0.426|0.489|
|Number|6,375,668|6,375,826|6,375,826|6,375,826|6,375,826|

_Notes_ : Authors’ calculations using SEDLAC data. Robust standard errors in parentheses. Significance: *** 1%, ** 5%, * 10%.

in his principal job. In column 4, underemployment ( _Ftim_ ) replaces tenure ( _Ten_ ) as a dimension of job quality. Likewise, having a single job is replaced in column 5 with an alternative measure of satisfaction, ( _Sat_ ). A success is recorded under this satisfaction variable if the worker responded that they did not want to work more hours or find another job. In addition to the regressions shown in the tables, other combinations of the nine possible job quality dimensions were investigated and were found to have similar results.

37
9 biological hazards and to family and health events are far more common than economic and socio‐ political shocks in Laos. Although comparisons across countries and surveys are at best indicative due to differences in the survey modules, recall periods, and so forth, the results are similar to those reported from a risk and vulnerability survey in Guatemala, where agriculture shocks (pests and bad harvests) predominated (Tesliuc and Lindert 2002). Accidents of breadwinner were reported by over 8 percent of households, but the survey does not contain information about other forms of health shocks. Agriculture shocks were also frequent in Tanzania (2007) and Ethiopia (Dercon _et al._ 2005), although many households also report deaths and serious illness. In contrast, Heltberg and Lund (2009) found that health shocks dominated other shocks in Pakistan.

Which shocks disproportionately hit the poor? Getting at this is not straightforward since consumption and income themselves respond to shocks. The same is true of assets, such as TVs, radios, and bicycles, which are sometimes used in asset‐based indices of wealth (cf. e.g. Filmer and Pritchett 2001). We measure wealth for the purpose of Table 2 by applying Principal Components Analysis (PCA) to a vector of housing characteristics, e.g. type of floor, type of roof, etc. These are likely to change relatively slowly, and in response to long‐term trends in incomes rather than to shocks. We use the first principal component; reassuringly, it has the highest mean score in Vietniane and the lowest mean scores in the poorer (largely rural) provinces of Phongsaly and Attapeu. Ranking households by their wealth index and computing the concentration index<sup>3</sup> of the shock variable, we find that the commonest shocks are also disproportionately concentrated among the poor, as reflected in a highly negative concentration index. The other highly unequally distributed shock is “divorce, abandonment, or internal

> 3 The concentration index is analogous to the Gini coefficient except that individuals or households are ranked by (in this case) wealth and inequality in the variable of interest is assessed relative to the wealth ranking. The concentration curve thus graphs the cumulative distribution of the variable of interest on the y‐axis against the cumulative fraction of the population ranked in ascending order of (in this case) wealth.  See e.g. Kakwani et al. Kakwani, N., A. Wagstaff and E. van Doorslaer (1997). "Socioeconomic Inequalities in Health: Measurement, Computation, and Statistical Inference." Journal of Econometrics **77 1** : 87‐103..
and the synergy effect has a negative and significant coefficient, implying that firms belonging to industries with high asset tangibility (essentially capitalintensive industries) are _less_ sensitive to the synergy effect.<sup>17</sup> Given that these interactions have significant explanatory power, we combine them to address a potential omitted variable bias, and to compare there coefficients in a joint multivariate regression. In columns (4) and (5) we combine one regressors capturing each of the three hypothesis that these interactions try to capture: information, financial constraints or capabilities. We find that typically the same signs, magnitudes and statistical significance levels persist as the ones just discussed, and the interpretations from above are valid when we control for all these effects simultaneously.

# **5 Concluding remarks**

In spite of their growing interest for the profession, firm-level datasets are still rare for low-income countries, and virtually inexistent for African countries. Our exploration of African customs data on firm-level exports revealed a set of stylized facts that are consistent with evidence from previous studies analyzing rich or middle-income countries. We showed that exporters in our set of African countries experiment a lot on export markets, at a low scale and with low survival rates, particularly in the first year. That is, they operate in a difficult environment characterized by very high “infant-mortality rates”. Therefore we investigate more in detail what determines if they survive beyond their first year.

The most striking finding coming out of our analysis —and which could not be observed on the product-level data used by previous studies of export survival—is that exporters of similar goods to the same destination exert a _positive_ externality on new entrants. That is, the more they are, the higher the survival probability of new entrants—although the effect is relatively small. This finding is at first sight surprising, as one might expect that exporters of a given product to the same destination may crowd out each other, either through price competition or simply by offering more choice to buy17 Similarly as done for the proxy of external dependence borrowed from Rajan and Zingales, we construct the asset-tangibility variable at the product level, _κp_ , by assigning to that product the corresponding ISIC3 value of Braun’s index.

28