rafmacalaba/datause-encoder-data huggingface dataset:
address the different biases resulting from the standard OLS cross-country growth regression, a fourth approach has used disaggregated firm and, more recently, household level data to assess the impact of access to financial services on firm growth and household welfare. The advantage of using micro-level data is that it allows more clearly the disentangling and testing of the mechanisms and channels through which financial development enhances economic growth. A disadvantage is that it focuses on the direct effect of finance on firm growth and household welfare but commonly does not consider spill-over effects on other firms and households and therefore does not allow for individual effects to be added up to an aggregate growth effect.<sup>33</sup> Further, as in the case of cross-country regressions, biases due to omitted variables, measurement error and reverse causation have to be addressed. This section discusses several studies using micro-data that assess whether easier access to finance is associated with faster firm growth and higher household welfare. Unlike the previous section, this section does not introduce new methodologies, but rather discusses methodological challenges stemming from the use of micro, as opposed to country-level, data.

# **6.1 Firm-level approaches**

The different approaches discussed in this section consist of relating firm-level growth or investment to country-level financial development measures. As in the case of crosscountry regressions, however, this implies controlling for biases stemming from reverse causation and omitted variables. A first approach, suggested by Demirguc-Kunt and Maksimovic (1998), compares firm growth to an exogenously given benchmark. Specifically, they calculate for each firm in an economy the rate at which it can grow, using (i) only its internal funds or (ii) using its internal funds and short-term borrowing, based on the standard “percentage of sales” financial planning model (Higgins 1977).

> 33 Indirect effects of financial development can be very important, as shown by Beck, Levine and Levkov (2007), who find that the main channel through which branch deregulation across U.S states led to lower income inequality was through labor market effects rather than through providing increased access to finance.

26
growth and were more likely to find employment after the cyclone.<sup>12</sup> Mottaleb et al. (2013) compare paddy-farming households in moderately and severely devastated districts during Cyclone Aila in Bangladesh and find that the latter spent more on food and health but less on children’s education after the cyclone. Kirchberger (2017) finds that the Yogyakarta earthquake in Indonesia increased the incomes of agricultural workers because their wages increased as labor moved from agriculture to the construction sector.

A number of important papers from the early 1990s show that rural farming households in developing countries mitigate idiosyncratic income shocks and smooth consumption using community-based credit arrangements and dis-saving/saving as coping mechanisms.<sup>13</sup> Sawada and Shimizutani (2008) find that only households with enough collateralizable assets could maintain their consumption by borrowing after the Kobe earthquake of 1995 in Japan, whereas others were subject to a binding borrowing constraint that adversely affected their consumption. Gallagher and Hartley (2017) show that Hurricane Katrina in the United States reduced household debt in most flooded blocks in New Orleans as homeowners used flood insurance claims to lower their mortgage debts. Patnaik, Sane, and Shah (2019) find that the 2015 Chennai flood in India increased household consumption in the months immediately after the event and argue that households financed it by reducing asset purchases and savings. Acconcia, Corsetti, and Simonelli (2020) also find evidence of a temporary increase in homeowners’ consumption after three major earthquakes in Italy, which they attribute to increased liquidity positions of homeowners due to receipt of public funds for reconstruction. Yao, Xu, and Zhang (2019) argue that natural disasters may have different psychological effects on households depending on the severity of suffering; they show that the 2008 Wenchuan earthquake in China had differential impacts on time preference and consequently on household savings in moderately and severely affected counties.

> 12Akter and Mallick (2013) conducted a survey in 12 of the worst hit villages of coastal Bangladesh one year after the cyclone and construct pre- and post-cyclone scenarios.

> 13These include Paxson (1992), Townsend (1994), Udry (1994), and Udry (1995). Shocks include rainfall variability and farm damages caused by various factors, such as wind, animal invasion, weeds, and poor germination caused by flooding.

9
(b) demographic factors – these include dispersion, urbanization, social composition and age structure; and (c) environmental factors including physical and economic factors (see Table 4).

**Table 4. Costs/Need Factors Used in the Australian Expenditure Need Calculations**

|**_Factors_**|**_Justification _**|
|---|---|
|**Indigenous influences**|Higher cost of services|
|**Socio-demographic**<br>**composition**|Effects of age, sex, cultural and linguistic diversity,<br>income and cross-border use of services|
|**Wage levels**|Wage differentials across states|
|**Other input costs**|Costs of office rent and electricity, interest costs,<br>isolation from major sources of supply, retirement<br>benefits|
|**Administrative scale of**<br>**service provision**|Effects of sub-optimal service area and population|
|**Urban influences**|Effects of urban complexity, urban traffic<br>management, urban transit capital and pricing<br>subsidies|
|**Population dispersion**|Net<br>effects<br>of<br>population<br>dispersion<br>and<br>geographical location on hospital costs and patient<br>transport|
|**Economic Environment**|Effects of structure and nature of state economies,<br>road use, availability of private medical services,<br>national capital region etc.|
|**Physical Environment**|Effects of climate, natural hazards, conservation<br>tasks, water availability, physical environment,<br>road and bridge length|
|**Expenses - other**|Miscellaneous factors, and interaction among<br>factors|

Source: Spasojevic, 2008 State-local transfers in Australia follow the CGC methodology and are faced with similar measurement issues although the degree of difficulty may be considerably less as local governments in Australia have extremely limited expenditure responsibilities i.e. mainly roads and rubbish.

# _Denmark_

Denmark uses a variant of the representative expenditure system approach where statistical analysis aids in identifying need factors that are further scrutinized for their incentive effects. Demographic factors (population by age groups with the highest weight for school age population and commuting time) with are assigned 70% of the weight whereas socio-economic factors receive 30% weight (see Table 5) ( Mau, 2008).

14
substantial time-series variation. This variation will allow us to identify the effect of changes in transport costs on changes in geographic concentration.

To summarize, the descriptive evidence points to a significant decrease in the geographic concentration of manufacturing industries in Canada over the last 20 years, no matter whether that geographic concentration is measured in terms of plant counts, employment, or sales. The pace of decline, however, likely differs across industries in systematic ways. Understanding which factors drive that decrease to what extent and for which industries – with a special focus on transport costs – is the key objective of the remainder of this paper.

# **.2 3 Transport costs**

The second key ingredient of our analysis is an industry-specific measure of transport costs. Contrary to most existing studies, we use _direct measures_ constructed from detailed micro-data files on shipments within Canada.

To estimate ad valorem rates, we first use a model to predict trucking firm (carrier) revenues for a 500 kilometers trip by commodity for the average tonnage using shipment (waybill) data from Statistics Canada’s Trucking Commodity Origin-Destination Survey (see Brown, 2015, for details). We estimate the ‘prices’ charged by trucking firms as a function of distance shipped, tonnage, and a set of commodity and firm fixed effects. To begin, we assume firms set prices such that both fixed and variable (linehaul) costs are just covered. Firms are assumed to set prices based on a fixed component and kilometers shipped: _Rm_ , _kc_ = _α_ + _βdk_ , where _Rm_ , _kc_ is the revenue earned by carrier _m_ for shipment _k_ composed of commodity _c_ , _α_ is the fixed price component, _β_ is rate per kilometer, and _dk_ is the distance shipped. Of course, firms may also price on a per tonne-km basis and this needs to be taken into account. Assuming firms set prices based on an unknown average tonnage _t_<sup>_∗_</sup> shipped implies that the rate per tonne-km is _Rm_ , _kc_ = _α_ + _β_ ( _dk_ / _t_<sup>_∗_</sup> ) _t_<sup>_∗_</sup> . For loads less (greater) than _t_<sup>_∗_</sup> the implicit price per tonne-km will be scaled upward (downward) to ensure that the price on a per-km basis is maintained, which is captured by the following function: where _tk_ is the actual tonnage shipped and _φ_ ( _t_<sup>_∗_</sup> _− tk_ ) is the scaling factor. Factoring out the known tonnage results in a flexible function that allows firms to price using either rule (or some hybrid of the two). Equation (2) can be estimated using a simple quadratic form The model is further augmented with commodity and carrier fixed effects and a series of controls to account for the quarter the shipment was made, the effect of empty backhauls on 9
# **Appendix C2: Robustness Analysis: Heterogeneity within Developing MENA**

The developing MENA region encompasses a wide variety of Arab countries. Hence, the correlates of dissatisfaction with life might differ across countries. In our robustness analysis, we distinguish between (1) North Africa, (2) Middle East, (3) Levant (including and excluding Iraq), and (4) Iraq.

**Table C2: Determinants of Life Satisfaction in Developing MENA by Subregion (OLS)**

|VARIABLES|(1)<br>North<br>Africa<sup>A</sup>|(2)<br>Middle<br>East<sup>B</sup>|(3)<br>Levant Area 1<sup>C</sup>|(4)<br>Levant Area 2<sup>D</sup>|(5)<br>Iraq|
|---|---|---|---|---|---|
|Dissatisfied with freedom to|0.017|-0.077*|-0.115**|-0.078*|-0.052|
|choose life: Yes|(0.041)|(0.043)|(0.055)|(0.044)|(0.079)|
|Dissatisfaction with standard of|-1.211***|-1.246***|-1.295***|-1.204***|-0.657***|
|living: Yes|(0.042)|(0.040)|(0.052)|(0.043)|(0.081)|
|Income (1,000s)|0.023***<br>(0.002)|0.022***<br>(0.003)|0.018***<br>(0.003)|0.021***<br>(0.003)|0.064***<br>(0.015)|
|Dissatisfied with efforts to increase<br>with high quality jobs: Yes|-0.117***<br>(0.039)|-0.192***<br>(0.048)|-0.096<br>(0.061)|-0.190***<br>(0.050)|-0.334***<br>(0.098)|
|<br>Dissatisfied with the educational|<br>-0.218***|<br>-0.144***|<br>-0.148***|<br>-0.181***|<br>-0.285***|
|system or the schools: Yes|(0.039)|(0.041)|(0.054)|(0.043)|(0.080)|
|Corruption widespread within<br>Y|-0.079*|-0.078|0.029|-0.082|-0.472***|
|government: es||||||
||(0.044)|(0.052)|(0.062)|(0.052)|(0.114)|
|People cannot get ahead by<br>ki hd Y|-0.209***|-0.233***|-0.283***|-0.245***|-0.172**|
|worng ar: es||||||
||(0.060)|(0.048)|(0.061)|(0.049)|(0.084)|
|Self-employed|0.380***<br>(0.085)|-0.143*<br>(0.083)|-0.209*<br>(0.107)|-0.115<br>(0.087)|0.105<br>(0.145)|
|Unemployed|-0.168|-0.437***|-0.576***|-0.368***|-0.220|
||(0.106)|(0.111)|(0.156)|(0.114)|(0.184)|
|Out of workforce|-0.011|-0.048|0.017|-0.009|-0.193|
||(0.061)|(0.071)|(0.087)|(0.073)|(0.154)|
|Underemployed|-0.098|-0.167|-0.185|-0.158|-0.148|
||(0.131)|(0.102)|(0.148)|(0.116)|(0.175)|
|Other|0.170***|0.269***|0.226**|0.202**|0.114|
||(0.065)|(0.088)|(0.103)|(0.081)|(0.181)|
|Individual characteristics|YES|YES|YES|YES|YES|
|Country fixed effects|YES|YES|YES|YES|YES|
|Month and year of interview|YES|YES|YES|YES|YES|
|<br>Constant|6.142***|5.956***|6.226***|5.561***|5.291***|
||(0.223)|(0.243)|(0.297)|(0.246)|(0.911)|
|Observations|10,444|14,800|9,184|13,244|2,432|
|R-squared|0.249|0.188|0.206|0.180|0.174|

_Note:_ Robust standard errors are in parentheses. ***p < .01; **p < .05; *p < .10. A North Africa includes Morocco, Algeria, Tunisia, Libya, and Egypt. B Middle East includes Syria, Palestine, Jordan, Lebanon, the Republic of Yemen, and Iraq. C Levant 1 includes Syria, Palestine, Jordan, and Lebanon. D Levant 2 includes Syria, Palestine, Jordan, Lebanon, Egypt, and Iraq.

Table C2 shows the results of the subsample analyses, where three findings stand out. First, the socioeconomic correlates of satisfaction with life are fairly consistent across different groupings of countries in the developing MENA region. Second, satisfaction with freedom to choose life is not 36
**Table A - 4 : Number of Documents and Signatures Requirements for Exporting**

|||<br>**Air Fr**|<br>**eight**|||<br>**Sea F**|<br>**reight**|||**Road Tr**<br>|**ansport**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Selected PAFTA Countries**|**No. of D**|**ocuments**|**No. of S**|**ignatures**|**No. of D**|**ocuments**|**No. of**|**ignatures**|**No. of D**|**ocuments**|**No. of**|**ignatures**|
||**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*) **</sup>|**Average**<sup>**(*) **</sup>|**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*) **</sup>|**Average**<sup>**(*)**</sup>|
|**Egypt**|4 - 6|4|5 - 10|8|5 - 7|6|7 - 20|10|6 - 8|7|9 - 20|12|
|**Jordan**|3 - 4|4|3 - 4|4|4 - 5|5|4 - 5|5|3 - 4|4|3 - 4|4|
|**Lebanon**|4 - 5|4|4 - 5|4|4 - 6|5|4 - 5|4|4 - 5|5|3 - 5|4|
|**Morocco**|6 - 7|6|5 - 7|6|6 - 7|6|6 - 9|8|3 - 6|5|2 - 4|2|
|**Saudi Arabia**|4 - 5|4|4 - 5|4|4 - 5|4|4 - 5|4|4 - 5|4|4 - 5|4|
|**Syria**|5 - 7|7|10 - 25|13|6 - 8|7|11 - 21|15|5 - 8|7|10 - 16|15|
|**Tunisia**|3 - 4|3|3 - 4|3|3 - 4|3|3 - 4|3|3 - 4|3|3 - 4|3|
|**UAE**|3 - 4|4|2 - 4|2|3 - 4|4|2 - 3|3|3 - 4|4|2 - 4|3|
|**Yemen**|7 - 8|7|16 - 20|20|5 - 7|7|9 - 22|21|5 - 7|6|20 - 21|21|
|**Total Countries (2008 Survey)**|**3 - 5**|**4**|**3 - 7**|**4**|**3 - 7**|**5**|**3 - 11**|**5**|**3 - 6**|**4**|**3 - 12**|**4**|
|**Total Countries (2001 Survey)**|**-**|**5**|**8 - 10**|**-**|**-**|**6**|**8 - 10**|**-**|**-**|**5**|**11 - 15**|**-**|

(*) See Footnote in Table A-1.

24
level panel data, find that the PRC’s capital market became more fragmented between 1980s and 2001, and the government tended to systematically reallocate capital from more productive regions to less productive ones. Zhang and Tan (2007), using the data of 1978–2001 with four sectors (urban industry, urban services, agriculture, and rural enterprise) in each region, find that estimates of marginal productivity of capital were much higher and rising for the rural nonfarm sector compared to the other three sectors, and the variability of marginal product of capital rose through 1990s.

There is evidence that the stock market in the PRC did not function well either. The PRC’s stock market was established in 1990, after which the stock market capitalization grew quickly, to 54% of GDP in 2000. However, there is no evidence that it contributed to better allocation of capital. Wang, Xu, and Zhu (2004) examine the effect of public listing on firm performance. They find that operating performance in listed firms did not necessarily improve, and some actually declined. This could be due to either moral hazard associated with lower managerial ownership stake, or financial packaging (i.e., window-dressing a firm’s performance) before listing. Firm performance tends to be better when top shareholders have more equal ownership stakes and could monitor one another more effectively. The benefits of public listing are mostly enjoyed only by large firms with good access to finance, and they tend to be state-owned or state-controlled with poor corporate governance. Thus, the stock market has not facilitated small firms’ access to financing, despite their higher returns of capital.

But there are also signs of progresses in the financial sector. The return to capital has been high despite high investment rates (at 45% of GDP in 2008). It is highest in the coastal area, followed by the central region, then the west region. The dispersion of returns to capital across provinces has decreased over time. <mark>Bai, Hsieh and Qian</mark> **<mark>(2006)</mark>** find that the rate of return to capital was around 25% during 1978–1993, fell during 1993–1998, and stayed flat around 20% since. In the early years of the reform (1978–1982), Shanghai had a much higher return than all other provinces, and this disparity has reduced over time.

In the prereform period, SOEs mainly obtained investment funding through government budgetary allocation, but now bank financing is the key channel for external financing, and is responsive to firm performance (Cull and Xu 2000 and 2003).<sup>3</sup> There were instances where credits allocated by state-owned banks to SOEs were rechanneled to the private sector. Cull, Xu, and Zhu (2009) find that poorly performing SOEs redistributed loans to other firms via trade credit. Profitable

> 3However, there is also evidence that bank financing became more inefficient—that is, the link between bank finance and firm performance became weaker when banks took on more bailout responsibility in the early 1990s.

11
academics, our view is that the potential advantages are too significant to ignore. In fact, the result discussed earlier that there is a lot of heterogeneity across firms in Africa suggests that the case for panel data is particularly strong for this region. Panel data are costly to collect and the “returns” come in the medium and long term, when sufficiently long time series have been constructed. Nevertheless, without panel data the range of policy questions that analysts can hope to find answers to is inherently limited.

# **3. What Have We Learned?**

We begin this section with a review of what we have learned from the data about the business environment in which African manufacturing firms operate. We focus on market size, risk, access to credit, labor, and infrastructure. We then turn to issues to do with how firms choose to organize themselves and how firms do business. We end the section by looking in some detail at what we have learned about three key aspects of firm performance, namely firm growth, investment and technology acquisition, and exports.

## **3.1 The Business Environment**

The business environment has emerged as the prime suspect as to why firm performance in Africa is poor, and improving the investment climate is seen as a policy priority for the continent (e.g. World Bank, 2004b, 2005). Batra, Kaufmann and Stone (2003) analyze the obstacles to business based on the WBES data. The leading constraint cited by the company managers in Africa is financing, followed by corruption, infrastructure and inflation. Pooling data across all regions, the authors find a negative and statistically significant relationship between taxes, regulations and financing and the growth in sales and investment.<sup>6</sup> One implication of a poor business environment is that the costs for certain services important to manufacturers will be high. Eifert, Gelb and Ramachandran

> 6 Because Batra et al. (2003) include country fixed effects in the regressions, the interpretation of the results is that firms that rate the constraints as relatively severe compared to other firms _in the same country_ tend to have relatively low growth rates. Thus the results indicate that a good local investment climate is good for local business. The results do not tell us whether firms in countries with poor average business environment conditions have lower growth rates of sales and investment than firms in countries with good average conditions.

8
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**Table 2A. Baseline balance tests of individual characteristics** (cont.)

||<br>|**Panel 6: Family**||||
|---|---|---|---|---|---|
||Control|Treatment|Difference|P Value|Observations|
|Number of brothers and sisters|6.182|6.017|-0.165|0.330|1598|
|Is father alive|0.714|0.675|-0.039|0.104|1601|
|Is mother alive|0.872|0.863|-0.009|0.622|1601|
|Ever displaced during the war|0.781|0.773|-0.008|0.723|1601|
|Any living children|0.665|0.715|0.050|0.037**|1601|
|If yes, how many?|1.570|1.537|-0.033|0.510|1115|
|Are you pregnant?|0.097|0.063|-0.034|0.014**|1601|
|Desired number of children|3.501|3.470|-0.031|0.618|1588|
||**Panel**<br>|**7: Sexual Behavio**|**rs**<br>|||
||Control|Treatment|Difference|P-Value|Observations|
|Have you ever had sex?|0.976|0.976|0.000|0.988|1601|
|If yes, age at sexual initiation||||||
|(conditional on ever having sex)<br>|17.198|16.981|-0.217|0.022**|1530|
|Have you ever had a regular partner?|0.939|0.947|0.008|0.485|1563|
|If yes, number of regular partners|0.993|1.009|0.016|0.418|1561|
|Have you ever had a casual partner?|0.331|0.336|0.005|0.850|1563|
|If yes, number of casual partners|0.539|0.542|0.003|0.961|1560|
|Have you ever used a condom?|0.701|0.716|0.015|0.524|1601|
|If yes, did you use a condom last time<br>you had sex with your regular<br>partner?|0.547|0.556|0.010|0.762|1087|
|Wereyou ever forced to have sex?|0.092|0.114|0.022|0.175|1563|

Notes: The Self-Regulation Score is based on a set of eleven questions about self-reported behavioral patterns, adapted from the Adolescent Self-Regulatory Inventory (Mollanen 2006). Responses were coded as follows: 1=strongly disagree, 2=somewhat disagree, 3=somewhat agree, 4=strongly agree. The score is equal to the proportion of responses that reflect positive behaviors or attitudes, of the items that the respondent reported on, conditional on having responded to at least 6 items. The Entrepreneurial Ability Score is based on a set of questions that asked the respondent to rate how well she had been able to perform the following activities in the past year: (i) find information about job opportunities in your area, run her own business, (ii) save in order to invest in future business opportunities, (iii) manage business finances effectively, (iv) bargain with a supplier to obtain good prices when purchasing, (v) collect money from someone who owed her for purchases who are not repaying on time. The response categories were: very well, somewhat well, not very well, and not at all well. Numerical values were assigned to each response. The score is based on the proportion of items to which the respondent answered “very well” and “somewhat well” as opposed to “not very well” and “not at all well,” conditional on having responded to at least three of the activities.

31