rafmacalaba/datause-encoder-data huggingface dataset:
rejected when they applied. Non-credit constrained firms are those that either had no need for credit or whose demand for credit was satisfied.<sup>4</sup>

# _Firm covariates_

Firm-level control variables include the log of firm age (and its square), percentage of employees with a university degree, and dummy variables for whether the firm is publicly listed, a sole proprietorship, an exporter, and whether an external auditor reviews its financial statements. In the main paper, we present estimates without firm covariates; estimates with firm covariates included can be found in the Appendix.

# _Local credit market conditions controls_

To control for local credit market conditions, we use detailed data about the banking sectors in our sample countries. We connect the firm and branch data by drawing circles with a radius of 15km around the coordinates of each firm and then linking the firm to all branches inside that circle. For each firm, we first measure the number of bank branches within a 15km radius. Next, we calculate the branch-weighted average asset size of banks with branches within this radius. This allows us to control for the number and the size of banks that make up the local credit market around each firm.

# Descriptive statistics

Table 1 presents summary statistics (mean, median, standard deviation, minimum, maximum) for the explanatory variables (Figures 7 and 8 show the relative frequencies for the outcome variables). It shows that almost a quarter of firms are credit constrained (24.6%). The standardized green management variable is by construction close to zero on average, but varies between -1.91 and 6.98. Close to a fifth of the firms in our sample are exporters, and 6% of them are publicly listed. Among the firms, 19.7% are sole proprietorships and 39.4% had their annual financial statements checked and certified by an external auditor. On average, 30.9% of the firms’ employees had a university degree. The table also shows that all firms in our sample have at least one bank branch within a 15km radius. The change in local branchweighted average Tier 1 ratio between 2007 and 2014, one of our instrumental variables, was on average 1.7 percentage points.

> 4 We start by using the question: “Did the establishment apply for any loans or lines of credit in the last fiscal year?” For firms that answered “No”, we move to the question: “What was the main reason the establishment did not apply for any line of credit or loan in the last fiscal year?” Firms that answered “Yes”, were asked: “In the last fiscal year, did this establishment apply for any new loans or new credit lines that were rejected?” We classify firms that applied for credit and received a loan as unconstrained while we classify firms as credit constrained if they were either rejected or discouraged from applying due to “Interest rates are not favorable”; “Collateral requirements are too high”; “Size of loan and maturity are insufficient”; or “Did not think it would be approved”.

13
20 Because misallocation is potentially related to distortions arising from the limited competition in the domestic economy, we juxtapose the results with Malaysia’s global competitiveness ranking in areas related to domestic competition. The World Economic Forum’s countries ranking for goods market efficiency and intensity of competition in local markets provide some anecdotal evidence of the business conditions in which firms operate (WEF 2009 and 2010). The modest improvement in the intensity of local competition in 2010 (to a ranking of 38, from a ranking of 42 in 2009) is negated by the deterioration of the efficiency of the goods market (from 20 in 2009 to 27 in 2010). Anecdotal evidence suggests that the subsequent recovery from the economic downturn in 2009 was not induced by any discernable improvements in resource allocation efficiencies.

A comparison among the upper-middle-income countries shows that Malaysia has similar levels of resource misallocation as China (figure 12, panel a).<sup>21</sup> The upper-middle income countries are estimated to have higher allocation efficiencies than India, where TFP gains are estimated to be around 128%. This means that Turkey, China, and Malaysia are closer to the frontier of resource allocation efficiency than India. In the case of China, Hsieh and Klenow (2009) find evidence of a more rapid reallocation as less efficient state-owned enterprises are being weeded out of the economy, while the reallocation process appears to be slower in India.

**Figure 12:** TFP Gains

Allocation of Resources of the United States in 1997 <!-- Start of picture text --> Percent TFP gains<br><!-- End of picture text --> <!-- Start of picture text --> Percent TFP gains<br><!-- End of picture text --> Source: Authors’ calculations; Ciera, Fattal Jaef, and Maemir (2017); Nguyen, Taskin, and Yilmaz; and Hsieh and Klenow (2009). Note: CHN = China; IND = ; KEN = Kenya; MYS = Malaysia; TUR = Turkey; US = United States.

# **5.2 Productivity Dispersion**

Figure 13, panel a, shows the distributions of log TFPQ, Asi for 2000, 2005, and 2010. log( A̅s ) TFPQ represents the physical productivity of firms. The panel shows that distributions have a longer left tail, and over time, the dispersions appear to widen. The standard deviations (SD)

> 21 Note that the comparisons of TFP gains are made across different time periods. China’s gains are estimated for 2005, while India’s are estimated for 1994.  See Hsieh and Klenow (2009) for explanations on the differences in TFP gains for China and India.
agriculture.'<sup>9</sup> Nevertheless, the very fact that our results show a weak relationship between urbanization and traditionally accepted migration factors may indicate that, in Africa at least, we are omitting part of the urbanization story. The fact that the informal sector provides a significant source of income to urban migrants, coupled with the apparent overlap of rural and urban activities, may shed light on the nature of urbanization in Africa.

> '<sup>9</sup> Nicaragua draft PRSP (http://www.mipres.gob.ni/grupoconsultivo)

27
# health sector variables:

_Health Status, =f(Incomej, Female Education,, Income Distribution;, Z,)_ where Z refers to a vector of additional country specific non-health related variables.

At this stage we resolve the problem of attribution of effects in a multivariate regression by assuming that our non-health factors may cause better health policy but that better health policy does not independently affect the included factors. That is, we assume more female education might lead to better health policy but not vice versa. This means we attribute to female education, for example, both whatever health improvements it may cause directly through behavioral changes _and_ whatever indirect impact it may have through better health policy. This assumption about the causal ordering of health policy and non-health factors is seems reasonable for nearly all of the variables and we return to the only problematic case of the joint determination of health status and income below.5 The child mortality figures used are from a UNICEF publication (UNICEF, 1992).6 While there are difficulties with measuring child mortality, it is arguably superior to alternative measures. Life expectancy is not reliably measured in many countries and many of the figures reported in official sources are not based actual data, but are extrapolations from child mortality and assumed life tables. More comprehensive measures of health status that go beyond mortality 5 While there are a number of difficult details about the data and the empirical estimation of this equation we will not deal in detail with them here, as its main purpose is to place a (possibly quite strict) upper bound on the magnitude of the health variation across countries that can be attributed to differences in health care strategies (see appendix **1** for the details on the data).

> 6 Based on background work done by demographer Ken Hill (see United Nations, 1992).

S
**Algorithm 1** Standard PMT <u>(forward</u> selection)

**Input:** Training data ( **X** _,_ **y** ) **X** _sel ←_ **1** _f_ () _←_ `fit` ( **X** _sel,_ **y** ) _▷_ Predictive model **y** ˆ _sel ← f_ ( **X** _sel_ ) ( **X** _tmp,_ ˆ **y** _tmp_ ) _←_ ( **X** _sel,_ ˆ **y** _sel_ ) **while** _∃_ **X** : _,j ∈_ **X** _, /∈_ **X** _sel_ **do** : **for each X** : _,j ∈_ **X** _, /∈_ **X** _sel_ **do** : **X** _cand ←_ `append` ( **X** _sel,_ **X** : _,j_ ) _f_ () _←_ `fit` ( **X** _cand,_ **y** ) **y** ˆ _cand ← f_ ( **X** _cand_ ) **if** _AIC_ ( **y** _,_ ˆ **y** _cand_ ) _< AIC_ ( **y** _,_ ˆ **y** _tmp_ ) **then** : _▷_ Selection criterion ( **X** _tmp,_ ˆ **y** _tmp_ ) _←_ ( **X** _cand,_ ˆ **y** _cand_ ) **end if end for if** _AIC_ ( **y** _,_ ˆ **y** _tmp_ ) _>_ = _AIC_ ( **y** _,_ ˆ **y** _sel_ ) **then** : _▷_ Stopping criterion **break end if** ( **X** _sel,_ ˆ **y** _sel_ ) _←_ ( **X** _tmp,_ ˆ **y** _tmp_ ) **end while**

comprise 46%, indicating that about 75% of the population live on incomes below middle- and upper-class levels. The consumption distribution in fig. 2 shows how tightly clustered the consumption level of a large part of the population is, in turn highlighting the challenge of targeting social assistance programs to the poorest.

The country’s PMT was revised in 2015 to consist of 514 distinct districtlevel models, to account for the differences within a nation that spans geographically and economically diverse regions. Since the introduction of the Unified Database for Targeting (now referred to as DTKS), improved targeting outcomes were seen for social protection programmes that adopted it [World Bank, 2017]. The country’s first unconditional cash transfer in 2005 covered more than a third of the population with the aim to protect them from a reduction in the fuel subsidy. Beneficiary incidence – the share of total beneficiaries found in a welfare grouping – in the poorest 20% of the population comprised 36%. When an unconditional cash transfer was launched for a third time in 2013, its coverage was about 40 percent, though beneficiary incidence increased to 40%. Similarly, a conditional cash transfer for families launched in 2007 saw an increase in beneficiary incidence in the poorest 20% from 39% to 44% between 2010 and 2018<sup>3</sup> . Its targeting

> 3Based on Holmemo et al. [2020] and World Bank staff calculations from SUSENAS 2010/2018.

10
|**TABLE 14: WORLD HEAL**|**TH ORGANIZATION**<br>|**WELL-BEING IN**<br>|**DEX†**<br>|||
|---|---|---|---|---|---|
||(1)<br>Happy and in a<br>good mood in<br>the last 2 weeks|(2)<br>Calm and<br>relaxed in the<br>last 2 weeks|(3)<br>Active and<br>energetic in the<br>last 2 weeks|(4)<br>Refreshed and<br>rested in the<br>morning in the<br>last 2 weeks|(5)<br>Days filled with<br>interesting<br>things in the last<br>2 weeks|
|Post indicator|-0.025|-0.128|0.055|-0.069|0.081|
||[0.057]|[0.084]|[0.063]|[0.090]|[0.086]|
|PBF/Post interact|0.044|0.016|-0.117|-0.053|-0.157|
||[0.082]|[0.108]|[0.074]|[0.108]|[0.112]|
|Control 1/Post interact|-0.009|0.094|0.022|0.134|0.062|
||[0.087]|[0.114]|[0.079]|[0.113]|[0.116]|
|Control 2/Post interact|-0.039|0.037|-0.123|0.058|-0.096|
||[0.080]|[0.113]|[0.076]|[0.120]|[0.106]|
|p-value PBF vs. C1|0.569|0.451|0.025|0.037|0.047|
|p-value PBF vs. C2|0.332|0.835|0.906|0.254|0.528|
|p-value PBF vs. C3|0.592|0.885|0.117|0.624|0.163|
|Baseline mean C3|0.816|0.684|0.776|0.643|0.582|
|N|991|991|990|991|991|

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

<mark>† Results from difference-in-differences regression models examining the effect of PBF on</mark> WHO well-being index items reported by health worker <mark>s. Regression models adjusted for individual (age, sex, marital status, education level) and facilitylevel control variables (type of health facility public/private/religious, urban/rural). Standard errors were clustered at the health facility level.</mark> 47
Policy Research Working Paper 7595

# **Abstract**

- This paper studies welfare dynamics, especially changes asso ciated with middle-class status in countries in the Middle East and North Africa, before and after the Arab Spring transitions, using objective and subjective welfare measures. Absent panel data, the analysis employs state-of-the-art synthetic panel techniques using repeated cross sections of expenditure data from household surveys and subjective well-being data from value surveys, which were conducted during the 2000s and the Arab Spring period. The objective welfare dynamics indicate mixed trends. About half the

poor in the 2000s moved out of poverty by the end of the decade, but chronic poverty remained high; upward mobility was strong in Syria and Tunisia, but downward mobility was pronounced in Yemen and Egypt. Subjective well-being dynamics suggest negative developments in most countries during the Arab Spring transitions. Low education achievement, informal worker status, and rural residency are positively associated with lower than average chances for upward mobility, and greater than average chances for downward mobility according to both types of welfare measures.

This paper is a joint product of the Poverty and Inequality Team, Development Research Group and the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hdang@worldbank.org or eianchovichina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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36
countries will result in differences that are small relative to the confidence intervals. A useful rule of thumb is that the observed difference in estimates of governance between two countries is not statistically significant if the confidence intervals overlap. For example, comparing Guatemala with Zambia on Government Effectiveness (ge), it is clear from the graph that the 90 percent confidence intervals overlap. This means that although Zambia has a higher estimate of governance than Guatemala, the difference between the two countries should not be considered as statistically significant. In contrast, Mauritius has a much higher estimate of Government Effectiveness (ge) than either Guatemala or Zambia. For these comparisons, 90 percent confidence intervals do not overlap and the difference in estimates of governance can be considered statistically significant.

Looking more systematically across all six aggregate indicators and all years since 1996, 61 percent of all pairwise comparisons of countries within an indicator and year will result in estimates of governance that are significantly different in the sense that the 90 percent confidence intervals do not overlap. There are some differences across indicators and over time. For example, looking across the six aggregate indicators, the share of significant pairwise comparisons ranges from 52 percent (for Political Stability and Absence of Violence/Terrorism) to 70 percent (for Voice and Accountability), in part reflecting the considerably smaller number of data sources that feed into the former versus the latter (23 vs 10, see Annex 3). Looking over time and averaging across all six indicators, the share of significant pairwise comparisons increased from 52 percent in 1996 to 62 percent in 2005, again in part reflecting a steady increase in the number of WGI data sources over this period from 12 to 31 over this period (see Table 1). From 2005 onwards, the share of significant pairwise comparisons has fluctuated between 61 and 65 percent. This indicates that while margins of error are both substantial and important to consider when comparing countries, it also is possible to use the WGI data to identify many significant pairwise differences in estimates of governance across countries.

# 4.2 Over Time Comparisons

Figure 2 illustrates changes over time in estimates of governance, for Voice and Accountability and Rule of Law over the period 1996-2023. We plot the estimate of governance for 2023 on the vertical axis, and for 1996 on the horizontal axis. Points above (below) the 45-degree line correspond to improvements (declines) in estimates of governance over this period. As with comparisons across countries, considering confidence intervals is important when making comparisons over time. As we discuss in detail in Kaufmann, Kraay and Mastruzzi (2005), the same rule of thumb of considering whether confidence intervals overlap when making comparisons across countries also applies when 17
thus will induce governments to be more transparent and accountable to the populace thereby improving the quality of governance (Khazaeli and Stockemer, 2013).

For investors, quality of governance is an important driver of investment decisions, as it largely influences the cost of doing business and also reduces uncertainty in the business environment. To this end, we test the association between high-speed connectivity via the SMC arrivals and quality of governance using metrics such as regulatory quality, government effectiveness, control of corruption, political stability, and rule of law from the World Bank’s World Governance Indicator database.<sup>27</sup> Specifically, we run the following specification at the country-level regression where _Yct_ is a placeholder for the measure of the quality of governance in country _c_ at year _t_ ; 1 ( _Submar inect_ ) an indicator variable set equal to 1 if the country is connected to at least one submarine Internet cable and 0 if otherwise. We also control for represent baseline controls such as whether the country is anglophone or otherwise interacted with time trends ( _Xc × T_ ).<sup>28</sup> Standard errors are clustered at the country level.

The results in Table 6 show a positive relationship between the arrival of the SMCs and governance measures albeit, the relationship is statistically significant only with respect to regulatory quality, and government effectiveness. In column 2, for instance, high-speed Internet connectivity is associated with a 0.072 standard deviation (sd) increase in the regulatory quality index. This result is particularly important to the findings of the paper since the regulatory quality index captures "perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development". Hence, access to the Internet is associated with policies that support private sector development. Similarly, high-speed Internet connectivity is associated with a 0.12 sd increase in the government effectiveness index. Overall, the results herein provide suggestive evidence that Internet connectivity is associated with an improvement in the quality of governance, which could plausibly incentivize foreign investments.

> 27https://info.worldbank.org/governance/wgi/

> 28The anglophone/francophone/Lusophone classification is used as a proxy for the legal origins of the country, i.e., common law vs civil law, as the current legal framework in many African countries was inherited from their colonial rulers (Anderson, 2018).

18