rafmacalaba/data-use-mentions-extended huggingface dataset:
We run the analysis at several cut-off levels for both methods to discover whether there is an embedded tipping point in the data that minimizes cutting data and maximizes information (Figure C.1). Figure C.1 suggests the higher the cut-off, the more variables we keep, which is expected. However, there does not seem to be a unilaterally optimal cut-off point at the aggregate level. This would have occurred if each increasing the cut-off point did not significantly alter the aggregate number of variables retained by the analysis.

|50<br>60<br>70||Cut-off<br>**0.5**|Survival<br>method<br>28|Average<br>method<br>23|
|---|---|---|---|---|
|20<br>30<br>40||**0.6**<br>**0.7**|46<br>55|29<br>29|
|10||**0.8**|63|30|
|0<br>0.45|0.5<br>0.55<br>0.6<br>0.65<br>0.7<br>0.75<br>0.8<br>0.85<br>0.9|**0.9**|66|30|

Figure C.1: Number of variables/groups/headings remaining (y-axis), by cut-off point (xaxis), survival method (dashed), average method (solid) _Source: ESMAP RISE dataset and authors' elaboration based on dataset described in this paper._ The survival method retains fewer independent variables than the average method. While averaging retains more information than the survival method, it may obscure underlying disparities in the averaged variables. Specifically, using group
|24|1.51|45|59|0.75|
|gio|Benishangul-Gumuz|16|19|0.84|23|34|0.68|
|Re|SNNP|22|24|0.95|31|45|0.68|
||Gambella|24|17|1.40|30|41|0.73|
||Harari|13|15|0.90|20|28|0.72|
||Addis Ababa|3|6|0.50|4|9|0.48|
||Dire Dawa|20|16|1.23|27|35|0.78|
||Moisture-reliable<br>highlands|16|20|0.82|22|35|0.64|
|ogical<br>nes|Drought-prone<br>highlands|13|19|0.66|19|30|0.63|
|Ecol<br>zo|Moisture-reliable<br>lowlands|17|21|0.82|25|37|0.67|
||Drought-prone<br>lowlands|34|23|1.54|41|55|0.74|
|Urban -|Urban|7|10|0.69|9|16|0.59|
|Rural|Rural|21|22|0.92|28|42|0.67|
|National||18|20|0.90|24|37|0.66|

Source: Author calculations based on HCES/WMS, 2016.

18
Yet, since the standard error around the true poverty rate is 2.3 percent for Sample 1 and 2.6 percent for Sample 2, all these differences are in fact still within one standard error of the true poverty estimates. As such, statistically speaking, the differences between the three specifications and the true poverty rates for both samples are negligible.

The alternative imputation model based on the empirical distribution of the error terms (Table 1, row 2) performs even better than those based on the normal linear regression, although both methods provide estimates within the 95 percent CI of the true poverty rates. Finally, since the HV data set is originally a non-random subsample of the PG database, we also re-run Table 1 using only variables that are available in the HV data set. The estimation results, shown in Table 2.2 in Appendix 2, are very similar to those in Table 1.

In summary, the set of variables available in the PG registration data seems sufficiently powerful to predict the true poverty rate with a 95% accuracy level. This is very encouraging considering that these variables were not selected for this purpose when the registration system was designed.

# **III.3. Robustness Checks and Extensions**

This section provides some simple robustness tests for the results presented on the Jordan case in Table 1. We test robustness to the poverty line, more disaggregated population groups, and alternative estimation methods. In the next section, we also consider the question of sample size.

_Sensitivity to the poverty line_

> larger inconsistency (De Luca, Magnus, and Peracchi, 2018). Also note that the standard errors around the true poverty estimates are larger than those for the imputation-based estimates, since the latter are model-based; see Dang _et al._ (2019) for more discussion.

13
# **Appendix 1: The Case of Argentina**

This appendix studies in detail the case of Argentina. This case is worth studying due to a number of reasons. First, Argentina underwent a sharp process of financial liberalization during the early 1990s. Second, Argentina is under a currency board system since **1991,** with assets and liabilities legally **held** both in peso and **U.S.** dollars. Dollar liabilities irepresent a very large proportion of total liabilities, implying a high degree of dollarization. Third, some Argentine firms became rapidly integrated with world financial markets. Fourth, Argentina suffered the spillover effects of the Mexican, Asian, and Russian crises. Fifth, the Argentine financial system consolidated during the mid-1990s with a strong participation of foreign banks. Sixth, microeconomic data on Argentine corporations was not studied before in the literature. Seventh, unlike the other countries in our sample, there is information on debt currency denomination of Argentine firms. To study Argentina, we follow the same methodology used for the rest of the paper. The results for Argentina are presented in the appendix tables. They can be summarized as **follows.**

# **_Firm Specific Characteristics and Financial structure:_**

The evidence suggests that larger firms increase their leverage. This contrasts with the East Asian experience, which suggests that larger firms increase long-term debt and reduce short-term financing. The data also show that within a given firm, changes in size are positively correlated with increases in short-term financing. Both in East Asia and Latin America, the data suggest that increases in firms' assets reduce short-term debt. Finally, larger firms extend the maturity structure of their liabilities. The experience of

**35**
business representatives in Indonesian claim that severance pay costs are high and constrain the workforce decisions that firms make.

One way to evaluate these claims is to examine the trends in various indicators of labor market rigidity. Using unemployment data and work experience data from the annual Sakernas labor force surveys from 1999 to 2008 for turnover in the labor market,

> 4 The hiring tax measures the discounted expected cost, at a time a worker is hired, of dismissing him/her in the future or if the worker quits.  It shows the severance payment an employer expects to pay in the future for hiring a new worker.  The calculation is based on the probability a new worker leaves in a particular year based on the reason for the separation.  Different rates apply depending on the specific reason for the separation (quit, dismissal for minor violations or economic cause).  See Alihsjahbana (2007) and Padjadjaran University (2004).
</sup> A set of recent papers revisited the issue using nationally representative data (e.g., <mark>Aguilar et al.. 2015; Ali et al. 2016;</mark> Backiny-Yetna and McGee 2015; <mark>Carletto, Savastano and Zezza 2013; de la O Campos, Covarrubias and Patron 2016; Kilic, Palacios-Lopez and Goldstein 2015; Oseni et al. 2014; and Slavchevska 2015). These studies relied on the Oaxaca-Blinder decomposition method (O</mark> axaca 1973; Blinder 1973) <mark>to decompose the gender gap in agricultural productivity into two portions: a component explained by differences in observable attributes (endowment effect) and an unexplained component of differences in the returns to endowment (structural effect). Compared to earlier estimates in the literature, these studies found a larger magnitude of the fraction of the (unexplained) returns to endowment, implying that even if female farmers had access to the same inputs as male counterparts, a productivity gender gap would still remain. That is, simply equalizing access to productive resources may</mark>

> 1 For instance, using nationally representative data from Burkina Faso, Akresh (2005) was able to replicate the Pareto inefficient intrahousehold resource allocations found by Udry (1996) in the areas examined in the paper but not in other parts of the country.

2
We then use plausibly exogenous variation exposure of Tunisian local labor markets to foreign demand shocks and show that the empirical results are consistent with the theoretical prediction. In Tunisia, a country with a high degree of gender segmentation in labor markets, foreign-demand shocks have been relatively larger in male-intensive sectors. This induced a decrease in the female-tomale employment ratio. Since male-intensive sectors had relatively more favorable foreign demand shocks, the equilibrium response is that households likely substituted female for male labor supply. Estimates using data from 2006 to 2016 confirm the theoretical mechanism postulated in this paper.

One important policy implication of our study is that less gender segmentation in labor markets will dampen the effect of foreign demand shocks on gender inequality. Taking foreign demand shocks as exogenous, the policymaker can only induce changes in the institutions that generate domestic gender segmentation in labor markets. If every sector has no gender segmentation, however, any foreign shocks would be distributionally neutral across genders – that is, males and females would be equally affected.

Thus, as countries develop their trade policies, they might want to consider policies that reduce gender segmentation in labor markets. Policies that promote gender equity have the immediate benefit of more gender equity in the present – but they can also have the unintended benefit of inducing more equitable effects in the future, whenever economic shifters affect the local economy.

16
), and 26 percent correctly in English (just over 4 questions).<sup>22</sup> While the goal was originally to administer the tests in a way that would allow estimation of valueadded models (i.e. by comparing growth in performance from Round 1 to Round 2), this is likely not advisable. The main reason is that the time between the two rounds was compressed due to delays in the implementation of Round 1. For some students, the gap between rounds was as little as just over 1 month— meaning that value added estimates would be largely meaningless. We nevertheless exploit the two rounds of data by averaging student scores across both rounds—thereby increasing the signal-to-noise ratio.

# **4. Basic descriptive statistics**

> 19 Round 2 included 3 additional questions for mathematics, which we ignore in this analysis.

> 20 - Section 2 of Appendix 2 (available online via a link on https://sites.google.com/site/decrgdeonfilmer/working <u>papers) details the psychometric analysis conducted on the student test score data. We make the determination of</u> “poor item functioning” based on analyzing the results of correlation between item responses, poor properties of item characteristic curves and item information function curves.

> 21 Properties are similar in Round 2: the Cronbach alphas for the three subjects was 0.85, 0.71, and 0.63 for mathematics, Kiswahili, and English respectively.

> 22 Further details on the test questions and the item analysis are available in Appendix 2 (available online via a link on https://sites.google.com/site/decrgdeonfilmer/working-papers).

14
where υir is the rth random draw for each individual. We use Halton draws because the literature suggests that these are superior to standard draws in this context.<sup>10</sup> Clearly, eliminating the stochastic component of β yields a standard ordered probit with random effects. Likewise, if T = 1, then the simulated log likelihood collapses to a simulated estimation of the pooled sample or cross section.

Though we also present fixed effects estimates following Ferrer-i Carbonell and Frijters (2004) for reference (See Annex A), in general this estimator faces two problems in the present as in other non linear contexts (See Greene and Hensher (2010a) for a more exhaustive literature review). First, maximum likelihood estimation generates often severe bias when T is small (the incidental parameter problem). In the binary logit model, and with T = 2, Abrevaya (1997) has shown analytically that the full MLE estimate converges to 2β. The Monte Carlo results in Greene (2004) suggest that biases comparable to those in binary choice models also jaundice fixed effects ordered choice models. Second, no appropriate procedure has been proposed in order to estimate a fixed effect ordered choice model with random parameters.

# 3 Data and Descriptive Statistics

## 3.1 The Ghana Urban Household Panel Survey

The Ghana Urban Household Panel Survey (GUHPS) conducted by the Centre for the Study of African Economies provides the basis for our empirical analysis. It collects information on inter alia, incomes, education, labor market experience, household characteristics and subjective wellbeing of labor force participants (ages 15 to 60) in the four largest urban centers

> 10Bhat (2001)’s Monte Carlo analysis found the error measures of the estimated parameters was smaller using 100 Halton Halton (1960) draws than 1000 random numbers in mixed logit models.

9
Figure 3: Expenditure Shares of Subsidized Energy Products across Countries and Quintiles <!-- Start of picture text --> Liquefied Petroleum Gas Electricity<br>2.5 4<br>3.5<br>2<br>3<br>1.5 2.5<br>2<br>1<br>1.5<br>0.5 1<br>0.5<br>0<br>Morocco Tunisia Libya Iran, Islamic Djibouti 0<br>Rep. Morocco Tunisia Libya Iran, Islamic Rep.<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Gasoline Diesel<br>3.5 1.2<br>3 1<br>2.5<br>0.8<br>2<br>0.6<br>1.5<br>0.4<br>1<br>0.5 0.2<br>0 0<br>Tunisia Djibouti Iran, Islamic Libya Morocco Morocco Tunisia Libya Iran, Islamic Djibouti<br>Rep. Rep.<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Expenditure share (in %)<br>Expenditure share (in %)<br>Expenditure share (in %) Expenditure share (in %)<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

For food products (figure 4), the situation is much simpler. For all products and in all countries, the household budget shares of expenditure on subsidized products is higher for poorer households and progressively lower for richer households, as we should expect. The decrease between quintiles is also very steep in general, particularly for flour and sugar in Djibouti and bread in the Islamic Republic
GTAP7 and Argentina’s INDEC. Since each of these sources uses different classification schemes, we first merged all databases together through the concordances described in Table 2, which also describes the five input service sectors and the nine manufacturing sectors used in our analysis. The final sample consists of 132 developing countries over the period 2002-2008. The OECD CRS database includes detailed information about the donor and recipient country, purpose of the aid disbursed, and the amount disbursed. We use this information to classify data on aid flows targeting each service sector as well as aid directly addressed to each manufacturing sector. Figure 2 illustrates total aid disbursements to service sectors between 2002 and 2008. Our sample only starts in 2002, due to the quality of data on aid disbursements. Indeed, OECD affirms that the annual coverage of the CRS data on disbursed aid is below 60% before 2002, whereas it is over 90% since 2002 and reached nearly 100% starting with 2007 flows.<sup>7</sup> The key ingredient in our analysis is the link between inputs and outputs across sectors in an economy, as described in the input-output matrix. Extracting balanced input-output tables from the GTAP dataset requires several steps: the essential reference is McDonald and Thierfelder (2004). We first extracted all the social accounting matrix (SAM) components that are part of an input-output table, and then calculate the total input requirements for each sector.<sup>8</sup> Out of the 132 countries in the sample, only 60 have country specific input-output tables in GTAP7; the remaining 72 countries are matched to 18 regions for which regional input-output tables are available.

> 7 http://www.oecd.org/document/50/0,3746,en_2649_34447_14987506_1_1_1_1,00.html 8 Input-output data extracted from the GTAP data set are the result of the GTAP fitting procedure. Original data are not provided in the GTAP data set.

11
% for private investment as a share of GDP (averages for 2006‐2010 and 2011‐2016 respectively) and from 10% to 7% for public investment as a share of GDP. In our simulations, we use the World Bank estimates for our central projections, assuming I<sup>�</sup> ⁄Y and I<sup>�</sup> ⁄Y remain stable at 4% and 1% respectively over 2016‐2018. We then consider the ESCWA estimates as an upper estimate to our projections.

A deterioration in the efficiency of public investment is a likely concern. No Syria‐specific value of the Infrastructure Efficiency Index (IEI) (Devadas and Pennings 2019) is available. As a proxy for Syria’s pre‐conflict efficiency of new public capital, we use the LMI average of 0.734, and build in a decline in the efficiency of public investment to the low‐income (LI) group average of 0.570 by 2017 (at which point the World Bank’s income classification of Syria switches from LMI to LI).

# _3.1.2 Demographics and the labor force_

In terms of Syria’s total population, UN population statistics (United Nations 2017)<sup>23</sup> indicate negative average annual growth of ‐1.7% over 2011‐2018, with the population declining from 21 million in 2010 to 18.3 million in 2018. UN statistics also indicate for the working age‐to‐population share, negative average annual growth of ‐0.09% over 2011‐2018. The projections capture developments during the conflict. From mid‐2010 to mid‐2020, estimated deaths are higher at 1,036,445 compared with 657,131 the previous decade<sup>24</sup> and reflect the rise in the proportion of male deaths (68% versus 32% for women). Further, net migration abroad is also higher at 5,
conducted in 2019 served as the frame for the Malawi HFPS. The sixth round of the Uganda National Panel Survey (UNPS) fielded in 2019/20 served as the frame for the Uganda HFPS. Lastly, the fourth round of the General Household Survey – Panel (GHS-Panel) in 2018/19 served as the frame for the Nigeria HFPS.

The four LSMS-ISA surveys that served as the frames for the HFPS are panel surveys that follow the same households over time. To facilitate recontact and tracking in the subsequent wave of the survey, interviewers collected phone numbers of up to 4 household members and 2 non-household member reference persons. It is this contact information that enabled seamless implementation of the HFPS surveys in each country. However, the availability of contact details varies between the countries, largely due to much higher mobile phone penetration in Nigeria than in Ethiopia, Malawi, and Uganda. Figure 3.1 summarizes the LSMS-ISA samples, contact information availability, and selection process for the HFPS in each country. The share of LSMS-ISA households with phone numbers (i.e. the coverage rate in Figure 3.1) ranges from 73 percent in Malawi and Uganda up to 99 percent in Nigeria. This at least partially reflects the much higher mobile phone penetration in Nigeria compared with the other three countries.<sup>7</sup> Figure 3.2 presents estimates of phone ownership in the four countries. Nigeria has the highest share of households who own a mobile phone at 76 percent, followed by Uganda (69 percent), Malawi (59 percent), and Ethiopia (48 percent). In all four countries, the LSMS-ISA sample households with contact information (row B in Figure 3.1) served as the frame for the HFPS.

**Figure 3.1 Survey sample and selection process for HFPS**

|Survey Sample and<br>Selection Processs|Ethiopia|Malawi|Nigeria|Uganda|
|---|---|---|---
destruction process and, by exploiting the different dimensions of our data, we make the first attempt at understanding the sources of the observed variations across countries and industries. Our country dataset includes 24 economies over a period covering most of the past decade; ten industrial countries, five Central and Eastern European countries in transition, and nine emerging economies in Latin America and East Asia. These countries differ significantly along different dimensions including the underlying economic conditions and the policy and institutional settings.

The remainder of the paper is organized as follows. In Section 2, we briefly review the recent theory on the reasons behind firms’ heterogeneity and the importance of experimentation and learning by doing. In this section, we also discuss how policy and institutional settings may influence firm heterogeneity. We argue that different policy settings may influence firm behavior in multiple ways and that several firm-level indicators are needed to assess how the different policy choices ultimately affect economic efficiency. In Section 3, we provide a brief description of the data for 24 industrial, transition and emerging economies. We then turn to the empirical evidence. In Section 4 we first present the distribution of firms by size; we then document the magnitude and key features of firm dynamics (entry and exit of firms) and, finally, we study post entry performance of different cohorts of new firms. In Section 5 we analyze the effectiveness of creative destruction for productivity growth. We distinguish between the productivity contribution coming from the process of creative destruction (entry and exit of firms) to that stemming from within-firm efficiency improvements and reallocation of resources across incumbents. In the final section, we draw some preliminary conclusions and propose a research agenda to start exploring the links between policy and firm dynamics.

# **2. Firm heterogeneity, market structure and institutions**

## **_Stylized Facts_**

Over the past two decades, evidence has mounted suggesting sizable heterogeneity of firms across different interrelated dimensions, size, growth, market shares, life cycle etc. In particular, some regularities have
Fifth, misclassification of goods may create huge revenue losses. Low-tax headings should therefore be closely monitored.

Against this backdrop, a new multipronged strategy is being implemented in Madagascar, with the aim not only of curtailing fraud, but also of facilitating legal trade. A reform strategy for the 2015–2018 period was adopted in September 2014.

Several significant reform measures have recently been adopted or are in the process of being adopted:

- A system has been instituted whereby the performance of each customs office will be monitored based on monthly indicators. This should enable better scrutiny of the Customs Authority and make monitoring more effective, thus facilitating trade.

- The post-clearance audit unit has been reinforced with the recruitment of additional auditors, and several major importers have since been audited and have been found to have committed fraud.

- A new circular has been signed by the Minister of Finance revising the criteria under which companies are eligible for fast-track customs clearance.

- Moreover, in the context of the “quick results” approach adopted, the Customs Authority is making every effort to scale back physical inspections, given their negligible impact.

- Finally, the risk analysis and statistics departments have been reinforced to provide more targeted monitoring.

# **VII. Concluding Remarks**

This paper proposes a simple methodology aiming to identify some risky import operations. The mirror analysis first helps to identify and target products or sectors with the greatest risk. Analysis of declaration patterns (that is, mining of customs data) is then performed to identify and target the most high-risk companies. In the specific case of Madagascar, and within the current context of customs reform, this paper suggests that there was sizeable fraud in the areas of customs valuation and tariff classification in 2014.

Recent studies (Yang (2008), Carrillo et al. (2014)) have highlighted that many tax evaders respond to the implementation of corrective measures (such as increased monitoring, establishment of cross-checking systems, etc.) not by changing their behavior, but by changing their tax