rafmacalaba/datause-encoder-data huggingface dataset:
financial private sector. Because under financial liberalization transactions take place both domestically and abroad, we also evaluate the use of foreign capital markets. Second, we characterize which firms obtain financing from capital markets. Third, we analyze whether the use of capital market financing is associated with changes in firm performance around the capital raising activity. Fourth, we study the implications of the changes in firm size and growth for the firm size distribution (FSD) of listed firms.

To conduct the analysis, we assemble a unique and comprehensive data set on domestic and international capital raising activity and performance by Chinese and Indian firms. We particularly focus on the recurrent use of equity and bond markets among publicly listed firms (after their Initial Public Offering or IPO).<sup>5</sup> To do so, we compile transaction-level information on new capital raising issues of common and preferred equity from 1990 to 2011 and on corporate bond issues from 2000 to 2011 from Thomson Reuters’ Security Data Corporation (SDC) Platinum database. We then match the SDC Platinum data on the use of capital markets with the Bureau van Dijk Orbis data on annual firm-level balance sheet information for publicly listed companies from 2003 to 2011.<sup>6</sup> Our matched data set comprises 2,458 firms from China and 4,305 firms from India, out of which 1,915 and 3,428 firms, respectively, did not have any equity or bond issues between 2003 and 2011.

Two main broad features emerge from our analysis. First, our results suggest that the expansion of financing to the private sector in China and India has been much more subdued than what the aggregate numbers suggest. Although capital raising activity in equity and bond markets expanded substantially in 2005-2010, it remained small as a percentage of GDP.

> 5 Our focus on publicly listed firms provides us with a more homogeneous group of firms that, vis-à-vis non-listed firms, are large, have already met the costly listing requirements, and are formal corporations that can raise capital. 6 We also use the Thomson Reuters’ Worldscope database to obtain information on capital expenditures and to check the robustness of the results.

3
<!-- Start of picture text --> 100%<br>7<br>90% 14<br>22<br>80%<br>28<br>70%<br>35<br>60%<br>45<br>50%<br>40%<br>64<br>30%<br>50<br>20%<br>31<br>10%<br>0%<br>1995/96 2003/04 2010/11<br>Poor Vulnerable Middle Class Upper<br><!-- End of picture text --> _Source_ : World Bank staff estimates based on data from three rounds of NLSS data for 1995/96, 2003/04 and 2010/11 _Notes_ : The consumption aggregate used in this analysis is the 30-day recall version which is not the version used for official poverty estimates for 2010/11.

**Figure 6: Geographic Characteristics of the Poor, Vulnerable and the Middle Class, 2011**

<!-- Start of picture text --> 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Rural 35 48 17<br>Urban 13 35 47<br>0<br>Eastern 30 51 18<br>Central 22 44 31<br>Western 29 46 24<br>Mid Western 45 45 10<br>Far Western 55 33 11<br>0<br>Mountains 39 46 15<br>Hils 30 42 25<br>Terai 30 48 20<br>Poor Vulnerable Middle class Upper class<br><!-- End of picture text --> Source: World Bank staff estimates based on data from three rounds of NLSS data for 1995/96, 2003/04 and 2010/11 Looking at education and employment characteristics, it is clear that the likelihood of being in the middle class is significantly higher for households headed by those with more than 11 years of education.

12
37 Table 8. Income transfiers to rural consumers from subsidized food by expenditure quintile group, 1997

|Subsidized food|Mean per c<br>Lowest<br>Quintile<br>[1|apita incom<br>by: (in LE<br>Second<br>[2]|e transfers<br>a per capita<br>Third<br>[3]|from subsid<br>per week)<br>Fourth<br>[4]|ized food re<br>Highest<br>Quintile<br>[5]|ceived<br>_All_|
|---|---|---|---|---|---|---|
|Baladi bread|0.59|0.62|0.68|0.77|0.84*|0.70|
|Baladi wheat flour|0.64|0.57|0.45|0.65|0.46*|0.55|
|Edible oil|0.17|0.20|0.20|0.:23|0.26|0.21|
|Sugarb|0.22|0.17|0.24|0.:22|0.30|0.23|
|Total income transfers from<br>subsidized food|1.62|1.57|1.58|1.87|1.86|1.70|
|Total income transfers from<br>subsidized food as pearcent olf<br>total per capita expernditures|10.43|7.68|6.17|5.77|3.39|5.26|
|Mean total per capita<br>expenditures per week (on all<br>food and nonfood iterns)|15.51|20.47|25.60|32.39|54.85|32.29|

Source: IFPRI Egypt Integnrated Household Survey, 1997.

Notes: N = 1,274. Rural population is divided into quintile groups o0 the basis of total per capita fitted expenditures.

a In 1997, 1 Egyptian pound (LE) = US$0.295. All expenditure figures are in nominal terms. b Includes expenditures on fully and partially subsidized sugar.

Difference between lowest and highest quintile groups is significant at the 0.05 level.
The new solidarity pillar serves as an alternative to improve the coverage of the pension system and improve the retirement benefits of low-income individuals with limited capacity to contribute to the pension system. Since the support of the solidarity pillar is linked to the amount of contributions to the pension system, low-income individuals always have incentives to contribute to the funded pillar. The old PASIS was replaced by a noncontributory Basic Solidarity Pension (PBS) of approximately USD 150 (CLP 75,000) per month in 2009. It will be paid to all aged people with no pension of their own. The benefit initially covered 40 percent of the poorest individuals and it will be gradually extended to 60 percent of the poorest population by 2011 (Table 5). A pension solidarity supplement (APS) was also introduced. This is paid to all aged persons who are in the lowest 3 quintiles (60 percent) of the income distribution and have a pension from the private system. The APS will be equal to the PBS but subject to a clawback provision of eventually 29.4 percent of the private pension (not the total income) of eligible pensioners. The APS will be exhausted when the private pension equals 3.4 times the PBS in steady state. This level is known as the maximum pension with solidarity support (PMAS). Between 2010 and 2011, the PMAS will increase gradually from approximately USD 300 to approximately USD 500 per month (Figure 5).<sup>16</sup> After 2012, these benefits will be indexed to price inflation.

**Table 5: Benefits Included in the 2008 Amendments to the Pension Law**

|Date|PBS|PMAS|Coverage|
|---|---|---|---|
||(CLP)|(CLP)|Target|
|Jul-09|75,000|120,000|45%|
|Sep-09|75,000|150,000|50%|
|Jul-10<br>|75,000|200,000|55%|
|Jul-11<sup>1</sup>|75,000|255,000|60%|

PBS: Basic Solidarity Pension; PMAS: Maximum Pension with Solidarity Support 1 Initially these changes were expected to take pace in 2012, but were modified by law in 2009.

Source: SP

> 16 Conversion into US dollars uses an exchange rate of CLP 500 per dollar

9
food insecurity increased to 59 percent in the aftermath of the onset of the pandemic, largely driven by the sharp increase in food insecurity among non-PSNP households. The average household reported a food gap of 1.3 months in the August 2019 survey, but this grew to 1.6 months in June 2020. Non-PSNP households reported a 0.5 month increase in food gap while the corresponding increase for PSNP households was 0.1 month. Compared to August 2019, the food gap increased in three of the four surveyed regions. The largest increases were in SNNP (from 1.8 to 2.6 months) and Tigray (from 0.5 to 0.9 month). While the mean food gap was highest in Oromia, it changed little between the two rounds. PSNP beneficiaries report smaller increases in mean food gap (0.2 versus 0.5 month).

We asked respondents about their perception of their food security status in the last three months preceding the June 2020 survey compared to similar months the previous year. The food security question is framed in terms of households’ ability to satisfy their food needs. As shown in Figure 3, almost half of the respondents (49 percent) report that their ability to satisfy their food needs worsened, and the rest report that it remained about the same (48 percent) or improved (3 percent). The worsening of the problem of satisfying food needs appears to have been greater in areas that are more severely affected by the pandemic. In addition to the household survey, we collected zone-level (sub-regional) data on spread of COVID-19 until June 2020. March 13 marked the first case of COVID-19 in Ethiopia and the pandemic begun to take off in June where cumulative new cases reached 5,846 by June 30 (EPHI, 2020). We construct an indicator variable of zone level status of COVID-19 spread using the number of cases reported as of end of June 2020. The indicator variable takes value 1 for zones in the top tercile of COVID-19 cases and 0 for zones in the bottom two terciles. Households reporting that their food security situation has worsened (34 percent vs 15 percent) or about the same (35 percent vs 13 percent) are concentrated in zones with high number of COVID-19 cases.

We obtained comparable data on maternal and child diets in both the 2019 face-to-face and 2020 phone surveys. Mothers were asked about their and the index child’s food consumption in the 24 hours prior to the interview using a listing of food items. Mothers’ food items are grouped into 10 food categories: all starchy staple foods, beans and peas, nuts and seeds, dairy, flesh foods, eggs, vitamin A-rich dark green leafy vegetables, other vitamin A-rich vegetables and fruits, other vegetables, and other fruits (FAO and FHI-360, 2016). Thus, mothers’ dietary diversity score ranges from 0 to 10 food items. Children’s food items are grouped into 7 categories: grains, roots 7
# Table 8: Wage **equation [LMFS N=38871**

Dependent variable: dW93 (average wage end 1993/end 1992) <!-- Start of picture text --> Variable B T BE T<br>dRevenues .1367 10.8 .1376 10.9<br>Bankruptcy 4.4983 -5.5 -4.51 -5.5<br>Bad debt .7779 2.1 .8044 2.2<br>Investment 1.6115 4.6 1.6242 4.7<br>dU .0095 -2.8<br>U (93.1) .0438 2.1<br>Size 500+ 2.2516 4.8 2.3114 4.9<br>Size <50 -3.1939 -9.2 -3.2381 -9.3<br>(Constant) 99.2246 70.8 97.3369 70.8<br>aR2 .1223 .1212<br>F 60.67 60.03<br><!-- End of picture text --> Variables: dRevenues= revenues end 1993/end 1992; Bankruptcy= in process June-December 1993; Investment- new capacity installed; Bad Debt= debt to other firms; U level= January 1993, rates for 176 regions; dU= change in unemployment, September 1993/January 1993; Size **a10** - 5 **_nf%l ----I_** ; **_Oi 'Cf_ ia t Cf ornnlnnnoc. -)** **_Ju_ ciuVpiuyc , 'JA ~-ov- At'O** **_t"m_** .. n.a Source: HEill Table 9: Average wage and bonus change in 1992 by size and financial status

||||**---**|Wage--|----<br>Bonuses--|
|---|---|---|---|---|---|
|Size|Wage|**cv**|Profit|Loss|Profit<br>Loss|
|**<10**|18.2|_.55_|23.5|12.3|22.2<br>9.9|
|11-20|19.4|.37|15.8|23.6|12.0<br>10.5|
|21-50|20.3|.33|20.7|19.6|13.1<br>8.6|
|51-200|22.7|.22|22.8|22.7|7.0<br>4.6|
|201-300|20.5|_.15_|25.8|17.8|4.0<br>4.6|
|>300|18.2|.15|22.2|14.7|3.6<br>4.0|
|All firms|||21.8|18.3|13.5<br>6.9|

Source: Ministry of Finance and World Bank 22
- Matsa D.A., A. R. Miller. 2013. “A female style in corporate leadership? Evidence from quotas.” _American Economic Journal: Applied Economics_ 5(3), 136-169.

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21
High-performing firms in clusters in the quality-improvement phase require finances to move from their original industrial clusters to more spacious and better-equipped industrial zones. These firms are worthy of being granted low-interest loans, in view of financial market failures evidenced by a host of empirical studies (e.g., Beck _et al_ ., 2009; Karlan and Morduch 2009). The advantage of offering cheap credit to assist the industrial relocation at this stage, over the earlier stage, is that it is relatively easy to distinguish between innovative and, hence, promising firms and non-promising firms. It is desirable to link the management training programs with the provision of credit and space in the industrial zone so that firms with improved managerial and technical capabilities and, hence, higher ability to repay loans, tend to receive loans and relocate their production bases.

# **V. Industrial Development without Government Failure**

In the previous section, we discussed that the provision of training programs, followed by the provision of adequate infrastructure and low-interest loans, which would address the major market failures that hinder industrial development, particularly the transition from the quantity-expansion phase to the quality-improvement phase.<sup>22</sup> However, there is always the danger that government failure outweighs market failure or even aggravates the market failure. Although corruption and bribery may not be a part of government failure, they tend to be associated with the distortive government policies (Shleifer and Vishny 1993; Mauro 1995; World Bank 1997). The question is

> 22 We believe that any development strategy should specify right sequential of interventions since financial as well as human resources are limited, especially in low-income countries.  Big-ban approaches, such as the big push theory and the balanced growth theory advocated by Rosenstein-Rodan (1943) and Nurkse (1953), among others, and discussed more recently by Murphy, Shleifer, and Vishny (1989), are likely to misfire.

23
is constant across countries, the latter increases for countries which are more distant from the high-income OECD sample in terms of their independent variables.<sup>3</sup> As a result, out-of-sample countries might exhibit higher Financial structure gaps by construction which can lead to spurious results. We therefore standardize the absolute value of the financial structure deviation by the prediction error because we were concerned that countries that the high-income OECD countries might be relatively weak benchmarks for much poorer countries. If this is the case, the estimated financial structure will be measured poorly, yielding a large estimated gap. Thus, we normalize to reduce this potential bias. For similar reasons, we also conduct the analyses omitting Log Real GDP from the variables used to construct the estimated financial structure ratio, obtaining similar results as we show below.

The Financial structure gap is our estimate of deviations of financial structure from the estimated or optimal level for a country at a particular stage of economic development. The Financial structure gap can potentially take on values between negative and positive infinity, where smaller values signify smaller deviations of actual financial structure from the estimated optimum. While the Financial structure gap is clearly measured with error, there seems little reason for believing that these errors bias the results in a particular manner. Table 5-Panel B provides descriptive statistics on the Financial structure gap for different groups of countries. The average Financial structure gap is smallest among the OECD countries, largest for the group of low-income economies, and the group of high-income, non-OECD countries falls in the middle.

# **3.2 Relationship between the financial structure gap and economic development**

Figure 2 presents the estimated coefficients from quantile regressions of Log Real GDP per capita on the Financial structure gap. We again graph the coefficients from the 5<sup>th</sup> through the 95<sup>th</sup> percentile of Log Real GDP per capita. We provide results both from quantile regressions without controls and from quantile analyses using the “standard controls” defined above.

> 3 The true structure ratio y0 we estimate for a particular country-year in the (out-of-sample) non-high-income OECD sample is: y<sup>0</sup> = x<sup>0</sup> β+ ε<sup>0</sup> , where x<sup>0</sup> represents the vector of independent variables associated with the out-ofsample observation. Our estimate, based on the high-income OECD countries regression, is:y�<sup>0</sup> = x<sup>0</sup> b. Therefore, the variance of the prediction error is: Var[e<sup>0</sup> |X, x<sup>0</sup> ] = σ<sup>2</sup> (1 + x<sup>0′</sup> [(X<sup>′</sup> X)<sup>−1</sup> ]x<sup>0</sup> ).

14
Table 1. FDI vs. FAI - Stylized Comparison

||UP|to H|UP to IP|IP|to H|Total|
|---|---|---|---|---|---|---|
|FDI|2||1|1||4|
|FAI|3||0|0||3|

of equity directly into the affiliate in the host country, and $1 of equity into it indirectly through the affiliate in IP, possibly through a loan.

Total inward FDI in country H is then $3, comprising $1 FDI from country IP and $2 FDI from country UP. But there is also another $1 of FDI recorded as a flow into country IP from country UP, even though those funds only pass through to H, without any real activities taking place in IP. Aggregate FDI is thus $4, double counting all the cross-border funds that pass through intermediaries.

In contrast, the total amount of inward FAI in H will be between $0 and $3, depending on how much these funds are invested in physical capital goods—and it is that invested amount which is recorded in the FAI data. All the FAI would be recorded in country H, with FAI in country IP being zero. Supposing, for instance, that all the funds go to real investment, FAI will be recorded as $3 in H and zero elsewhere.

The two sets of statistics diverge further if there is local financing of real investment since, as noted, real investment supported by a local injection of funds is included in FAI but not in FDI. Local borrowing in H to finance real investment of $0.5, for example, increases FAI into H by $3.5 but leaves measured FDI unaffected. On the other hand, any changes in the amount of retained earnings in the host affiliate that is not invested in real assets would change FDI, but not FAI: with uninvested retained earnings in H of 0.8, for instance, FAI in H remains at 3 _._ 5, while FDI into H increases to 3 _._ 8 and global FDI to 4 _._ 8.

# **3.2 The FAI Dataset**

The FAI dataset used here has been constructed by combining information on inward real investment from the _Foreign Affiliate Statistics_ (FAS) provided by Statistics Canada, the 14
- 4 -

**Figure 1. Existing road network and districts covered by our sample**

<!-- Start of picture text --> Nepal Roads<br>Districts covered by our sample<br>Nepal DISTRICTS<br>/<br>Source: Benamghar and Iimi (2011).<br><!-- End of picture text --> The public contracts in the sample are relatively low-value, but the contract performance has not been satisfactory. Many delays and cost overruns have been experienced. In the sample, the projects were delayed on average about 100 days. This is significant compared to an average contract duration of 185 days. About 10 percent of the projects delayed more than one year. Cost overruns are also prevailing. Cost underruns occurred in some cases, but half the contracts underwent cost overruns. The average cost overrun rate is about 4 percent with some cost overruns offset by cost underruns.

Ex post adjustments depend on internal and external factors.<sup>2</sup> Weather is among the most important factors in Nepal. For instance, it rains a lot particularly during summer. In the sample, the district average precipitation is about 200 mm per month (Figure 2).<sup>3</sup> Heavy rains are likely to interrupt civil works and cause project delays, because contractors may not be able to convey the necessary road work equipment, such as pavers, and other materials on time.

Road procurement often has a cyclical pattern every year. In the case of Nepal, road works are started throughout the year but tend to be launched from April to July (Figure 3). This

> 2 See Benamghar and Iimi (2011) for further detailed discussion.

> 3 The precipitation data are based on only 20 weather stations that are close to our surveyed roads. There may be a certain measurement error, since the data are district-specific. In each district, there may be a variation in precipitation, depending on location. From the empirical point of view, however, it is worth noting that our precipitation variable is time-variant, not fixed to districts.