rafmacalaba/datause-encoder-data huggingface dataset:
Relative growth (Ei,t-3 - Ei,t-3)) / Ei,t-3) is biased towards small firms, as small units are much more likely to exhibit high rates of proportional growth than large firms.

Birch (1981 1987) and Schreyer (2000) suggested a synthetic indicator – in the following called a ‘Birch’ indicator: (Ei,t-3 - Ei,t-3) * (Ei,t-3 / Ei,t-3) ). The purpose is to control for the bias of indictors towards larger or smaller firms.

Only growth of existing firms will be considered. Mergers and acquisitions are not included. Employment can be generated by start-ups or destroyed by firms exiting the market. The INTERIM ES does not contain any information on firm entry and exit, which is why the analysis focuses on existing establishments. Furthermore, it is important to differentiate firm’s expansion strategies. Firms can grow from within, or through the acquisition of another business. It is important to differentiate between these two. While internal growth is achieved by creating new employment, growth by acquisition is achieved by adding existing employment. This paper solely considers internal growth.

Furthermore, firm age is defined as t-(t-3); dummy variables will be used for urban areas –defined as localities with more than one million inhabitants, and for conflict regions, defined as Afghanistan and Pakistan.

# **Descriptive employment growth diagnostics**

## **Employment growth at the industry level**

**Employment growth differed greatly across industries.** The expansion patterns of the economies of South Asia have been quite different. The annual average relative growth rate of the entire INTERIM ES dataset is approximately 9.9%, which is high by international standards. The median growth rate is nil, which is a common finding in growth literature (see for instance Coad and Holzl, 2009 for an overview of distribution of growth rates). There is great variance of growth rates, both across industries and across countries. This reflects different business cycles, variance in the investment climate that asymmetrically affects industries, and points at differences in the availability of industry specific conditions such as technological capabilities or management skills.

**There is great variance across industries.** We find that the highest mean of the annual relative growth rate was in real estate renting and business activities (27.3%), construction (22.5%), fabricated metal products and machinery and equipment (15.7%). While the median is driven by some few firms that expanded rapidly – sometimes referred to as ‘gazelles’ – there is also evidence of a broader expansion of these industries. The median of the relative for both construction, and real estate and business renting is 4%, and for fabricated metal products and machinery and equipment it is 3%. The slowest employment expansion occurred in hotels and restaurants expanded (3.5%), the food industry (3.8%). Similarly, the median firm of those two industries did not grow. Notably, there was no significant difference between services and manufacturing in the relative growth rate (see figure 1).

6
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Benchmarking air transport indicators for Ghana and other countries**

|Country|Ghana|Nigeria|Côte<br>d’Ivoire|Senegal|Kenya|Tanzania|
|---|---|---|---|---|---|---|
|**TRAFFIC (2007)**|||||||
|Domestic seats (millions per year)|0.14|9.30|0|0.13|2.09|1.87|
|Seats for international travel within Africa (millions per<br>year)|0.91|1.37|0.85|1.26|3.14|1.27|
|Seats for intercontinental travel (millions per year)|0.83|2.44|0.30|1.23|2.76|0.59|
|Seats available per capita|0.08|0.09|0.06|0.23|0.28|0.12|
|Herfindahl index – domestic market (%)|100.0|18.0|-|100.0|60.5|31.0|
|Herfindahl index – international market (%)|6.4|6.4|9.8|10.3|34.1|13.0|
|**QUALITY**|||||||
|Percentage of seat km in medium or smaller aircraft|15.7|29.6|52.3|39.3|23.3|48.6|
|Percentage of seat km in newer aircraft|96.8|71.4|90.8|98.3|80.2|79.3|
|Registered carriers on EU blacklist|0|0|0|0|0|0|
|FAA/IASA Audit Status|Fail|No audit|Fail|No audit|No audit|No audit|
|Percent of carriers passing IATA/IOSA Audit|0|28.6|0|50.0|11.1|33.3|

Source: Bofinger 2008. Derived from AICD national database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data

## **Challenges**

Like many African countries, Ghana continues to face significant safety and security issues in air transport. Ghana failed the FAA/IASA Audit and none of its carriers has passed the IATA/IOSA audit.

More broadly, West Africa lacks a clear air transportation hub to play the role that airports such as Addis Ababa, Johannesburg, and Nairobi are playing on the other side of the continent. Abidjan, Accra, Dakar, and Lagos each play a significant role with respect to their immediate neighbors, but connectivity between each of these zones is much more limited and as a result connections can be much more complicated.

# Water supply and sanitation

## **Achievements**

Ghana is one of only five African countries that have already achieved the MDG target for water supply. According to DHS survey evidence, the percentage of households with access to an improved drinking water source rose from 69 percent in the 2003 survey to 84 percent in the 2008 survey, exceeding the MDG target of 76 percent.

Compared with other low-income African countries, Ghana has a relatively large share of the population relying on utility water (private or public taps). In the mid-2000s, 25 percent of Ghanaian households reported access to utility water of some kind, well ahead of the 26 percent in the low-income peer group, but still far behind the 76 percent in the middle-income peer group. By 2008, that figure had exceeded 40 percent (table 6). During the period 2003-08, coverage of stand posts in Ghana rose by 1.8 percent of the population per year and reliance on surface water declined by 1.4 percent of the population 16
medium density, but are weak in districts with low density. The medium-density findings may suggest that GQ upgrades helped activate intermediate cities, where some observers believe India’s development has underperformed compared to China.

Our project contributes to the literature on the economic impacts of transportation networks in developing economies, which is unfortunately quite small relative to its policy importance. The closest related study is Datta (2011), who evaluates the impact of GQ upgrades using inventory management questions contained in the World Bank’s Enterprise Surveys for India in the years 2002 and 2005. Even with the short time window of three years, Datta (2011) finds that firms located in non-nodal districts along the GQ network witnessed a larger decline in the average input inventory (measured in terms of the number of days of production for which the inventory held was sufficient) relative to those located on other highways. He also finds that firms in districts closer to the GQ network were more likely to switch their primary input suppliers vis-à-vis firms farther away. These results suggest improved efficiency and sourcing for establishments on the GQ network after its upgrade.

Beyond India, several recent studies find positive economic effects in non-nodal locations due to transportation infrastructure in China (e.g., Banerjee et al. 2012, Roberts et al. 2012, Baum-Snow et al. 2012). These studies complement the larger literature on the United States (e.g., Fernald 1998, Chandra and Thompson 2000, Michaels 2008, Duranton and Turner 2012, Baum‐Snow 2007, Lahr et al. 2005)<sup>1</sup> , those undertaken in historical settings (e.g., Donaldson 2010, Donaldson and Hornbeck 2012), and those focusing on other developing or emerging economies (e.g., Brown et al. 2008, Ulimwengu et al. 2009). A related literature considers non-transportation infrastructure investments in developing economies (e.g., Duflo and Pande 2007, Dinkelman 2011).<sup>2</sup> Recent papers in these literatures have grappled with concerns regarding the endogenous placement of infrastructure that prevents a causal interpretation of infrastructure’s role. As Duranton and Turner (2011) highlight, the endogenous placement could bias findings in either direction. Infrastructure investments may be made to encourage development of regions with high growth potential, which would upwardly

> 1 ‐ The impact of highways has been studied for other developed countries as well. For example, Holl and Viladecans Marsal (2011) study the impact of highways on Spanish cities while Hsu and Zhang (2011) work with Japanese data. 2 More broadly, a number of studies find high elasticities of private output with respect to public capital, often greater than 0.3, but some more disaggregated studies cast some doubt on these elasticities by observing that infrastructure has not been necessarily related to productivity in sectors that should have benefited the most. See, for example, Aschauer (1989), Munnell (1990), and Otto and Voss (1994).

5
# **Table T1.R: SUMMARY STATISTICS (RURAL SAMPLES)**

||**Fu**|**ll**|**Sons**|**Daughters**|
|---|---|---|---|---|
||**Mean**|**SD**|**Mean**<br>**SD**|**Mean**<br>**SD**|
||||**CHINA**||
|CFPS2016 (18-35)|N=8|040|N=3955|N=4085|
|Father's Sch.|5.9|4.27|5.93<br>4.29|5.88<br>4.26|
|Children's Sch.|8.96|4.08|9.2<br>3.91|8.74<br>4.23|
|CFPS2016 (18-30)|N=5|647|N=2833|N=2814|
|Father's Sch.|6.01|4.16|6.11<br>4.17|5.91<br>4.15|
|Children's Sch.|9.4|3.93|9.53<br>3.75|9.26<br>4.1|
|CFPS2010 (6-22)|N=4|326|N=2224|N=2102|
|Father's Sch.|6.44|3.88|6.45<br>3.9|6.42<br>3.87|
|Educ. Exp.|1301.35|2386.63|1328.64<br>2535.81|1272.48<br>2218.14|
|No. of Children|1.63|0.93|1.57<br>0.9|1.7<br>0.97|
|CFPS2010 (8-23)|N=3|632|N=1843|N=1789|
|Father's Sch.|6.4|3.9|6.43<br>3.88|6.37<br>3.91|
|Educ. Exp.|1432.85|2593.57|1449.28<br>2710.91|1415.92<br>2467.5|
|No. of Children|1.62|0.96|1.52<br>0.9|1.72<br>1|
||||**INDIA**||
|IHDS2012(18-35)|N=3|3979|N=16957|N=17022|
|Father's Sch.|3.87|4.41|3.9<br>4.32|3.83<br>4.49|
|Children's Sch.|7.18|4.71|8.13<br>4.36|6.24<br>4.86|
|IHDS2012(18-30)|N=2|5903|N=12946|N=12957|
|Father's Sch.|4.08|4.46|4.12<br>4.37|4.04<br>4.54|
|Children's Sch.|7.61|4.59|8.43<br>4.21|6.8<br>4.8|
|IHDS2005 (6-22)|N=2|3058|N=12631|N=10427|
|Father's Sch.|5.58|4.58|5.47<br>4.61|5.71<br>4.55|
|Educ. Exp.|1489.73|3110.61|1613.94<br>3440.39|1339.27<br>2649.23|
||287|153|279<br>151|297<br>155|
|No. of Children|.|.|.<br>.|.<br>.|
|IHDS2005 (8-23)|N=1|9002|N=10514|N=8488|
|Father's Sch.|5.59|4.56|5.47<br>4.6|5.74<br>4.52|
|Educ. Exp.|1667.11|3469.9|1802.6<br>3836.22|1499.27<br>2945.42|
|<br>No. of Children|2.91|1.55|2.8<br>1.53|3.03<br>1.55|
|NSS1995 (6-13)|N=2|6668|N=15819|N=10849|
|Father's Sch.|3.31|2.08|3.18<br>2.06|3.50<br>2.10|
|Educ. Exp.|386.44|477.52|398.38<br>484.29|369.03<br>466.95|
|No. of Children|2.47|1.06|2.37<br>1.04|2.61<br>1.07|
|NSS1995 (6-18)|N=3|7155|N=23172|N=13983|
|Father's Sch.|3.36|2.10|3.21<br>2.08|3.59<br>2.12|
|Educ. Exp.|543.89|655.29|568.55<br>677.05|503.02<br>615.41|
|No. ofChildren|2.45|1.10|2.35<br>1.08|2.63<br>1.11|

Notes: CFPS stands for China Family Panel Survey, IHDS stands for Indian Human Development Survey, and NSS stands for National Sample Survey.
How might an analyst use the GTA database to construct answers for the four key questions regarding the expected impact of a new trade barrier? To address this question, we go through the exercise of examining a specific example of a GTA announcement. Consider the Brazilian government’s decision in August 2009 to increase its ad-valorem import duty from 2% to 14% for industrial fatty alcohols.<sup>21</sup> First, the GTA takes care to note that the average bound tariff of the products concerned is 35% and that the products were on the list of exceptions of Mercosur’s common external tariff, and they are now back to their original rate. Thus a simple examination of this information suggests that while Brazil may have imposed a new import restriction with this applied tariff increase, there is no obvious WTO violation – e.g., such as when a country increases its applied tariff above the binding rate. Regarding the first question of whether such a measure is likely be legally challengeable at the WTO, since there is no evidence to suggest a violation, the analyst might stop there and move on to assessing the next measure on the GTA list.

However, suppose just for the sake of our hypothetical exercise that the Brazilian tariff increase on industrial fatty alcohols _did_ contain a potential WTO violation that satisfied question (1). How could an analyst begin to use the data provided by the GTA to address the _other_ three questions to which answers are needed for an exporter or its advocate to make an informed cost–benefit assessment of whether to engage in WTO dispute settlement? Because the GTA also explicitly identifies the HS codes at issue under the measure, an outside analyst could take this information and answer question (2)

> 21 See Global Trade Alert ‘Brazil: tariff increase from 2% to 14% on industrial fatty alcohols,’ Measure #1035, Published 17 Dec 2009. The tariff lines that the GTA reports as being raised are for products

26
Low-income countries must address two opposing challenges when crafting health policy: many patients do not seek care when they are ill (Whitehead, Dahlgren and Evans, 2001; Sautmann, Brown and Dean, 2017), but those who do often consume a great deal of unnecessary treatment (Das and Hammer, 2014; Li et al., 2012). One solution could be to subsidize critical treatments in the public sector, while staffing facilities with trained healthcare providers to act as gatekeepers to subsidized care.

Yet providers often fail to live up to their gatekeeping mandate. For example, in an audit study Das et al. (2016) find that 74 percent of public sector providers in India dispensed at least one unnecessary treatment; the Centers for Disease Control and Prevention estimate that 30 percent of antibiotic prescriptions in the United States are unnecessary (Fleming-Dutra et al., 2016); and studies across Sub-Saharan Africa document large shares (36-81 percent) of malaria-negative patients receiving antimalarials at public health centers (Reyburn et al., 2004; Hamer et al., 2007; Bisoffi et al., 2009; Ansah et al., 2010). Providers’ gatekeeping ability is key for healthcare policies that increase demand, such as subsidies: only if healthcare providers are good gatekeepers will such policies expand access to those who need treatment while limiting overuse among those who do not need treatment.

Why are so many providers such poor gatekeepers? Most existing literature studies _doctor-driven_ reasons for overtreatment. These include misaligned financial incentives (e.g. Iizuka, 2012; Currie, Lin and Meng, 2014; Das et al., 2016) and knowledge gaps, compounded by low motivation (e.g. Banerjee, Duflo and Glennerster, 2008; Das, Hammer and Leonard, 2008).<sup>1</sup> Doctors and other healthcare providers can overprescribe to patients because health care is a credence good: since patients cannot verify the true cause of their illness, they must rely on the provider’s recommendation. But by a similar argument, there is scope for _patient-driven_ demand for unnecessary treatment: if the provider cannot persuade the patient that a medication is not needed, she may write a prescription just to keep the patient satisfied. Anecdotal evidence suggests patient demand matters: for example, in 2016 the U.S. pharmaceutical industry spent $6 billion on direct-to-consumer marketing of prescription drugs, 22 percent of total marketing spending (Schwartz and Woloshin, 2019), and explicit or perceived patient demand is often cited by providers as a reason for overtreatment (Kotwani et al., 2010; Linder et al., 2014). Yet there has been much less rigorous research on this channel.

> 1For a detailed review of this literature, see Dupas and Miguel (2017).

2
yields correlate poorly with the rainfall index. In addition, while index insurance is in part designed to minimize transaction costs, these costs may still be significant relative to the modest value of insurance purchased by an average policyholder, making the product expensive, at least by comparison with insurance in the developed world. Finally, even if the insurance product is well designed, other frictions may prevent households from purchasing it. For example, households may be liquidity constrained, may not have a sufficient level of financial literacy to properly evaluate the product, or may not fully trust the insurance provider.

As part of this chapter, we describe the basic structure of rainfall insurance contracts commonly sold in India and present some stylized facts on the distribution of returns on the insurance. While aggregate data on market size and growth are difficult to come by, we do document changes in product demand over time, summarizing data generously provided by the microfinance institution BASIX.

We also describe stylized facts regarding the types of households that buy insurance and factors that inhibit demand for insurance, summarizing results of academic research conducted by three of us (Giné et al. 2008). Among our findings, we show that product demand is quite price sensitive, suggesting that increased economies of scale and competition could lead via lower prices to significant increases in insurance take-up. Our previous research shows, however, that other frictions—such as financial constraints and the level of trust of the household in the insurance provider—are also important for take-up. We conclude with a discussion of the future of rainfall insurance and other related index insurance products.

# 2. Monsoon Variation and Production Risk

In 2007, agriculture in India accounted for 18.6% of GDP, employed more than 60% of the country’s population, and used 43% of its arable land (Hohl and Kannan, 2007). The country
# **Figure 14. Debt Ratios and Gross Financing Needs**

<!-- Start of picture text --> 180<br>Debt-to-GDP Ratio (in percent)<br>160<br>Baseline<br>Alternative scenario (country-specific shock)<br>140<br>Second most extreme stress test<br>Most extreme stress test<br>120<br>100<br>80<br>60<br>40<br>20<br>2005 2006 2007 2008 2009 2010 2011<br>450<br>Gross External Financing Need (in billions of U.S. dollars)<br>400 Baseline<br>Alternative scenario (country-specific shock)<br>350 Second most extreme stress test<br>Most extreme stress test<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>2005 2006 2007 2008 2009 2010 2011<br>Source: Staff calculations.<br><!-- End of picture text --> 49
Table 1: Contagion of Debt Distress with Asymmetric Shocks

||Initial Equilibrium|Moderate Shock|Bigger Shock|
|---|---|---|---|
|_A_, _A_<sup>_∗_</sup>|4.0, 4.0|4.0, 2.0|4.0, 1.0|
||_Inco_|_mplete Integration_||
|_z_<sup>_N_</sup>, _z_<sup>_∗N_</sup>|0.738, 0.639|0.738, 0.633|0.738, 0.626|
|_z_<sup>_N_</sup>, _z_<sup>_∗D_</sup>|0.737, 0.620|0.737, 0.601|0.737, 0.582|
|_z_<sup>_D_</sup>, _z_<sup>_∗N_</sup>|0.705, 0.639|0.704, 0.632|0.738, 0.625|
|_z_<sup>_D_</sup>, _z_<sup>_∗D_</sup>|0.704, 0.618|0.703, 0.600|0.738, 0.626|
|_U_(_z_<sup>_N_</sup>,_z_<br>_∗N_)_≥U_(_zD_,_z_<br>_∗N_)|Yes|Yes|Yes|
|_U_(_z_<sup>_N_</sup>,_z_<br>_∗D_)_≥U_(_zD_,_z_<br>_∗D_)|Yes|Yes|No|
|_U_<sup>_∗_</sup>(_z_<sup>_N_</sup>,_z_<br>_∗N_)_≥U∗_(_zN_,_z_<br>_∗D_)|Yes|No|No|
|_U_<sup>_∗_</sup>(_z_<sup>_D_</sup>,_z_<br>_∗N_)_≥U∗_(_zD_,_z_<br>_∗D_)|Yes|No|No|
||_Co_|_mplete Integration_||
|_z_<sup>_N_</sup>, _z_<sup>_∗N_</sup>|0.845, 0.808|0.775, 0.775|0.772, 0.819|
|_z_<sup>_D_</sup>, _z_<sup>_∗D_</sup>|0.831, 0.788|0.751, 0.751|0.738, 0.792|
|_U_<sup>_CG_</sup>(_z_<sup>_N_</sup>,_z_<sup>_∗N_</sup>)_> U_<sup>_CG_</sup>(_z_,<sup>_D _</sup>_z_<sup>_∗D_</sup>)|Yes|Yes|Yes|

Parameter Values: _β_ = 0.5, Ω<sup>(</sup><sup>_∗_)</sup> = 1, _μ_ = 3.0, _θ_ = 0.8, _A_ = 2.0, _R_ = 1.04, _ξ_ = 0.9, _<u>y</u>_ 1 = 1.2, _<u>y</u>_ 1<sup>_∗_= 1.0,</sup><sup>_δ_= 0.05,</sup><sup>_<u>η</u>_= 0.1,</sup><sup>_b_=</sup><sup>_b∗_= 0.13,</sup><sup>_bCG_= 0.26</sup>

# Notes:

Asymmetric regions. Member-2 (denoted by asterisks) receives negative productivity shocks. In the case of a moderate shock ( _A_<sup>_∗_</sup> = 2.0) member-2 defaults, member-1 does not. With a bigger shock ( _A_<sup>_∗_</sup> = 1.0), there is contagion: as member-2 defaults, this leads to a default by member-1 as well. In the case of a complete integration, with the same level of total foreign borrowing, the union chooses not to default when it faces the same negative productivity shocks.

22
that the average share of labor income in the GDP corresponds to<sup><u>2</u></sup> 3<sup>ofthereturnstoscale.</sup> The returns to scale obtained is 0.62, very close to the calibration in Gourio and Kashyap (2007) of the returns to scale for Chile.<sup>15</sup> We set the interest rate to be 17.6 percent which is the average discount rate for African and Latin American economies from the IMF International Financial Statistics (IFS) dataset. Using this information, the parameter _β_ is set to 0.850. We choose the depreciation rate of capital to be _δ_ = 0 _._ 06 which is the calibrated value from Gourio and Kashyap (2007). Since there are no studies on the exit rate parameter in developing countries, we set the annual exit rate to the same value as previously, i.e., _λ_ = 0 _._ 1. The other parameters of the model remain the same as previously. Table 6 below summarizes the parameters in the alternative calibration.

Table 6. Alternative calibration

|Parameter|Value|Description|Source|
|---|---|---|---|
|_τ _<sup>_d_</sup>|[0; 1]|Distortionary infrastructures|WBES|
|_A_|[0; 1]|Public policies efectiveness|WGI|
|_p_|[0; 1]|Detection probability|WGI|
|_β_|0.850|Real rate of return|IMF International|
||||Financial Statistics (IFS)|
|_η_|0.207|Capital income share|ILOSTAT and Literature|
|_γ_|0.414|Labor income share|ILOSTAT|
|_δ_|0.06|Investment to output ratio|Gourio and Kashyap (2007)|
|_λ_|0.1|Annual exit rate|Literature|
|_θ_|3|Evasion cost parameter|Assumption|
|_ce_|1|Entry costs|Literature|
|_cf_|0|Fixed costs|Literature|
|_z_|[1; 3.98]|Distribution of productivity|Relative labor demand|

Notes. See Restuccia and Rogerson (2008) for parameter values from the literature.

> 15Gourio and Kashyap (2007) calibrated the returns to scale to 0.6 in Chile. Our findings remain the same using this returns to scale and<sup><u>1</u></sup> 3<sup>and</sup><sup><u>2</u></sup> 3<sup>astheshareofcapitalandlaborrespectively.</sup>

36
**Figure 5. Income per capita (change w.r.t baseline scenario)**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>CAM CHI MDE NAF ROW SAM SAS SEA SSA<br>Trade adjustment assistance Institutionalreform<br>Technical assistance & capacity building Economic infrastructure<br><!-- End of picture text -->

**Source: Authors' Modeling Results**

Table 5 reports the most preferred scenario per indicator for every recipient countries or regions. A combination of the adjustment trade assistance and technical assistance and capacity building scenarios would guarantee the effectiveness more than the other scenarios for all the countries Institutional reform will be more effective in China compared with other scenarios to alleviate income inequalities.

Both trade adjustment assistance (TAA) and technical assistance and capacity building (TA & CB) scenarios are also enforced at international level. In both these scenarios, the international income inequalities, which computed by the coefficient variation and the Atkinson’s index decrease, implying that AfT effectiveness occurs in redistributing income from the richest to the low income countries. The opposite effect occurs for the institutional reform and infrastructure scenarios (Figure 6).

**20**