rafmacalaba/datause-encoder-data huggingface dataset:
**Pap** 22

1967 the system that experienced continued growth during the 19SOs was in need of major investments, both because of unsatisfied demand as well as because of badly maintained equipment.

40. The renegotiation of JTC's license provides a basic insight on the role of licenses as safeguarding institutons: their finite term implies that the company will face substantial uncertainty about its investments unless either the license clearly specifies how the company's assets would be valued at expiraton time.' or the company is able manipulate the political process appropriately. Since expiration time is the main time for govemment to change the nature of the license, the company faces substantally more uncertainty at expiration than at renewal ime. JTCs 1945 license stipulated that if the government does not want to issue a new .Icemrne -o t.io :;censee, then it has to announce it year ahead that the licensee has to sell to the Governmen i s assets at a fair market value plus 10%. The **t** 1945 license, though, was silent for the case when the Govemment does not force a sale but the company does nOt agree to the new license. The failure to specify the :iquidabon terms for that contingency implies that the Courts may have to be called to arbitrate these terms. Meanwhile, the company would be operating without a license, in a precarious legal environment and - with - overwhelming foreign ownership limited political supporL T&GT seemed to have realized this eventuality by a) stopping its investment plans early in the negotiation process, b) agreeing to the new license even though it did not intend to operate under it, and c) looking for another company interested in operating the domestic network under the new conditions.

41. The new license, which was issued in 1966. made the JPUC the regulator of JTC. The JPUC was granted wide powers and responsibilities, both in terns of service supervision, planning and rate setting. The JPUC was created as a statutory corporaton, its decisions subject to review only by te 3' We have lo differentiste between renewal and expirntion. In a renewr4 the govewment may decide not to renew, and the lime vil usually detil ways to dispose of the company's assm (ther through a sale to anothr company, or through a gwomrmental takeover). Expiration requires a new license to be writtn, and the govemment may grant the now ficnse to another company. The old icerewe thogh, wil usualy stipLiot ways by which the assets have to be disposed at. Ucenses usualy _owed for one renwa period.
# **6. Descriptive Statistics of Time Trends for Savings in Egypt**

# **6.1 ICA Data**

The ICA sample contains three waves of ICA data – one collected in 2003, 2005 and 2007. Each wave allows for calculation of two consecutive years of proxies for operating income to sales, “any investment” dummy and investment to capital ratio. However the retained earnings data are only available for the year of the survey. Thus we have 6 years of data for the first three measures (specifically data for 2002-2007) and three years of data for the last two measures (specifically 2003, 2005 and 2007). Also not all data are available for service firms.<sup>14</sup> Table 1 presents the number of observations for each of the five variables used in the study for industries and services. Some data appear unreliable and thus we exclude the outliers to prevent influential observation from driving our results.<sup>15</sup> Figure 10 shows the trends of three savings-related measures over time. The operating income to sales is slowly increasing over time, starting with about 18% in 2003 and reaching about 30% in 2007. This might indicate that the companies are becoming more profitable and have more cash flows available for investment or savings. Indeed over time a larger proportion of firms are making productive investment: “Any investment” measure is increasing from about 20% to 45% over this period. This pattern suggests that as more cash flow is available to firms they can make more investment.

The size of investment, measured as amount spent on investment relative to sales, has also increased in the past 4 years, rising from about 5% of total sales in 2004 to about 10% of total sales in 2007. However, as companies accumulate more capital, the incremental amount of new investment relative to existing fixed capital has declined (i.e., the investment to capital ratio (IK) has gone down from about 0.2 to 0.12). This is to be expected as companies mature.

The retained earnings data are only available for 3 years in this period. The right panel of Figure 2 shows the proportions of investment and working capital financed with retained

> 14 Service questionnaire does not include book value of capital and hence makes it impossible to calculate investment to capital ratio. In addition there are no service firms in the 2003 survey.

> 15 Specifically we exclude any investment to capital ratio if it is bigger than 2. This means a firm has purchased 200% of the current value of machinery in one year, which is highly unlikely. We also exclude operating income to sales ratio if it is below -0.99 or above 0.99. That means if the firm’s costs are less than 1% of total sales revenues or more than 99% of sales revenues, as we deem these data unreliable.

17
Table 1 continued

|||Mea<br>|sures o<br>|f liquidi|ty needs<br>|
|---|---|---|---|---|---|
|ISIC|DESCRIPTION|I/S|I/A|CCC|wL/S|
|3523|Soap and cleaning prep.|0.15|0.24|1.22|0.10|
|3214|Carpets and rugs|0.15|0.30|0.92|0.11|
|3813|Structural metal products|0.15|0.24|0.96|0.22|
|3521|Paints, varnishes and lacquers|0.15|0.25|0.91|0.12|
|3842|Railroad equipment|0.15|0.25|0.82|0.19|
|3540|Misc. prod. of petroleum|0.15|0.15|0.69|0.09|
|3692|Cement, lime and plaster|0.16|0.13|1.06|0.18|
|3211|Textiles spinning and weaving|0.16|0.27|1.01|0.20|
|3522|Drugs and medicines|0.16|0.11|1.12|0.18|
|3710|Iron and steel basic ind.|0.16|0.22|0.90|0.24|
|3320|Furniture and fxtures|0.16|0.28|1.07|0.26|
|3118|Sugar factories and ref.|0.16|0.23|0.88|0.09|
|3620|Glass and glass prod.|0.16|0.20|0.96|0.24|
|3841|Ship building and repair|0.17|0.24|0.80|0.30|
|3812|Metal furniture and fxtures|0.17|0.22|1.12|0.23|
|3610|Pottery, china and earthenware|0.17|0.21|1.23|0.35|
|3219|Textiles n.e.c.|0.18|0.21|0.93|0.16|
|3833|Electrical appliances|0.18|0.24|1.25|0.17|
|3819|Fabricated metal prods.|0.18|0.25|1.06|0.24|
|3821|Engines and turbines|0.19|0.27|1.27|0.24|
|3691|Structural clay prods|0.19|0.22|1.11|0.27|
|3212|Textile goods exc. apparel|0.19|0.34|1.13|0.23|
|3131|Distilling spirits|0.19|0.22|1.37|0.08|
|3220|Wearing apparel exc. footwear|0.20|0.38|1.23|0.24|
|3825|Ofce and computing mach.|0.20|0.22|1.51|0.23|
|3811|Cutlery and hand tools|0.20|0.28|1.39|0.25|
|3909|Industries n.e.c.|0.20|0.28|1.41|0.23|
|3831|Elect. ind. machinery|0.20|0.27|1.54|0.25|
|3113|Fruits and veg. canning|0.20|0.34|0.86|0.11|
|3822|Agric. mach. and equip.|0.21|0.28|1.45|0.16|
|3832|Radio, TV. and comm. eqp.|0.21|0.24|1.46|0.27|
|3903|Sport and athletic goods|0.21|0.34|1.29|0.19|
|3839|Elect. app. and supp. n.e.c.|0.21|0.26|1.41|0.21|
|3829|Machinery and eqp. n.e.c.|0.21|0.29|1.32|0.25|
|3851|Scientifc equipment|0.21|0.24|1.67|0.27|
|3240|Footwear|0.22|0.39|1.33|0.23|
|3845|Aircraft|0.22|0.29|1.35|0.31|

Continue next page 16
<!-- Start of picture text --> Average Prices, Cheese<br>(December 1991 -- August 1992)<br><!-- End of picture text --> <!-- Start of picture text --> 300<br>IS ......... .. . .... .........................<br>9~~~~~~~~~~~~~~~~~~~~~~~060 ee..600.........<br>,<br>d50rw- .........- ............................................... -- r.. .......<br>o.t I I I I I -- _..,I<br>°eCs91 01/28 02/11 02i25 03i31 04i14 04/28 05i12 06'02' 0616 06/30 07'/14' 07i2 08 11 08i2<br>Date<br>Market Price .... State Price<br><!-- End of picture text -->

**45**
- b. Firm age.

- c. Foreign ownership (=1 if 50% of the firm’s stake is owned by foreigners).

4. Country fixed effects: to capture unobservable countries’ characteristics.

The summary statistics are at Table A7 in the Appendix. The correlations between credit and demand proxies are at Table A8.

We first run OLS regressions with country fixed effects. The regressions are adjusted to take into account the weight of stratified random sample. We focus first on the change in sales. The results are reported in Table 1.

**Table 1: Demand and credit sensitivity on firms’ sales**

|||||Change in s|ales||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Demand Sensitivity|-115.708***||||-144.950***|-139.320***|-156.735***|-189.888***|
||(31.68)||||(33.37)|(39.53)|(42.13)|(44.71)|
|Dependence on External Finance||-1.094|||37.767**|||-5.582|
|||(9.12)|||(18.73)|||(24.72)|
|Cash Conversion Cycle|||-10.493*|||2.941||-2.299|
||||(6.28)|||(7.12)||(10.65)|
|% of investment from own fund 2007||||0.002|||-0.025|0.050|
|||||(0.04)|||(0.05)|(0.05)|
|Firm age||||||||-0.037<br>(0.21)|
|Small firm||||||||-7.674<br>(5.02)|
|Foreign owned||||||||-9.298<br>(10.04)|
|Constant|-31.984***|-32.862***|-24.368**|* -11.294***|-21.744***|-17.920**|0.234|-10.830|
||(4.50)<br>|(5.17)<br>|(8.32)<br>|(3.59)<br>|(5.83)<br>|(7.27)<br>|(5.88)<br>|(10.47)<br>|
|Country fixed effects|√|√|√|√|√|√|√|√|
|Observations|1034|882|659|972|462|486|646|243|
|R-squared|0.11|0.04|0.04|0.08|0.13|0.11|0.17|0.27|

Overall the relationship between firms’ demand sensitivity and the change in sales is negative, strongly significant and robust. On average, within a country, if a sector saw a 1 percent decline in the stock index after Sep 11, 2001, firms in that sector saw a 1.15 percent decline of sales in the 2008-2009 crisis. Recall that the demand sensitivity is log(indexSEP10)-log(indexSEP21), hence the coefficient of -115.7 translates to a decline of 1.15 percent. If we control for other variables, the percentage decline is even higher, 1.89 percent. This result complements the findings of previous studies on demand channels (e.g., Tong and 11
Berliant, M., Peng, S., Wang, P. 2002. Production externalities and urban configuration. Journal of Economic Theory 104: 275–303.

Berliant, Marcus & Wang, Ping, 2008. "Urban growth and subcenter formation: A trolley ride from the Staples Center to Disneyland and the Rose Bowl," Journal of Urban Economics, Elsevier, 63(2): 679‐693 Bertaud, A. 2003. The Spatial Organization of Cities: Deliberate Outcome or Unforeseen Consequence?, World Development Report 2003: Dynamic Development in a Sustainable World, World Bank.

Bertaud, A. 2009. Spatial profile of three South African cities. Presented at Spatial Outcomes Workshop South African Reserve Bank Conference Centre Pretoria September 29 ‐ 30, 2009 Bertaud, A. (2011). Mumbai FAR/FSI conundrum. Available at: http://alainbertaud.com/wp‐content/uploads/2013/06/AB‐Mumbai‐FSI‐Conundrun‐Revised_June‐ 2013_kk‐ab1.pdf Bertaud, A., Brueckner, J.K. 2005. Analyzing building‐height restrictions: predicted impacts and welfare costs. Regional Science and Urban Economics 35: 109–125.

Bertaud, A. and S. Malpezzi. 2003. The spatial distribution of population in 48 world cities: Implications for economies in transition, Unpublished manuscript.

Bertaud, A. and S. Malpezzi. 2014. The Spatial Distribution of Population in 57 World Cities: The Role of Markets, Planning, and Topography, Unpublished manuscript.

Bento, A.M., Franco, S., Kaffine, D., 2006. The efficiency and distributional impacts of alternative anti‐ sprawl policies. Journal of Urban Economics 59: 121–141.

Bird, J., 1980, Seaports and seaport terminals (London, Hutchinson University Library) Bleakley, H. and J. Lin. 2012. Portage and path dependence. Quarterly Journal of Economics 127(2):587–644.

Bogart, W.T. and W.C. Ferry. 1999. Employment centers in greater Cleveland: Evidence of evolution in a formerly monocentric city. Urban Studies, 36: 2099–2110.

Borukhov, E., Hochman, O., 1977. Optimum and market equilibrium in a model of a city without a predetermined center. Environment and Planning A 9: 849–856.

Boustan, L.P. 2010. Was postwar suburbanization “white flight”? Evidence from the black migration. Quarterly Journal of Economics 125(1):417–443.

27
between the average lending rate and the average cost of funds, either measured directly at the country level or measured at the level of individual banks but aggregated at the country level. The regression format is the traditional one with controlling variables at the country level and a set of institutional variables intended to capture the effects of judicial reforms.

Focusing on interest rate spreads has several advantages over comparing net interest margins, a variable typically used in cross-country work on bank efficiency. First, net interest margins are typically expressed in terms of total assets, while a correct construction of average spreads should refer interest incomes and payments separately both to the assets and the liabilities sides of the balance sheet. Second, net interest margins typically do not control for the presence of non interest earning assets such as non-performing loans. Third, net interest margins are typically based on total interest income, which also includes interest from non-lending operations, therefore limiting comparability across banks.

We construct country-level interest rate spreads following two distinct approaches in order to address possible concerns about measurement errors and the robustness of our empirical results. First, we use country-level data on average lending rates and deposit rates from the IMF’s International Financial Statistics (IFS) database. The lending rate in IFS (line 60p) is defined as the bank rate that usually meets the short- and medium-term financing needs of the private sector, and the deposit rate (line 60l) is defined as the average of the rates offered to resident customers for demand, time, and savings deposits. We compute the interest rate spread as the difference between the average lending rate (line 60p) and the average deposit rate (line 60l) for the year 2000. For Austria and 5
climatic conditions. These communities have, however, adapted to the conditions over a long period of time, while the predicted climate changes will be relatively more abrupt. In the paper we therefore refer to climate conditions or weather anomalies rather than climate change.

The PEN data set is mostly representative for smallholder-dominated tropical and subtropical rural landscapes with moderate-to-good access to forest resources, and all but very high population densities (Angelsen et al. 2014). The households are in the poorest segments of their countries. Further, in the year of data collection, none of the sites experienced major covariate shocks (Wunder et al. 2014). Our analysis, given the constraint of the dataset, can only provide a partial analysis of the complex relationship between poverty, environmental income, vulnerability and climate change.

# **4 Exposure to climate conditions, weather anomalies and self-reported shocks**

## **4.1 Climate conditions**

Our first research question is: _Are the poor more exposed to extreme climate conditions (temperature and rainfall), including higher climate variability?_ We calculated the mean and variability (standard deviation) of rainfall and temperature for the period 1981-2010, based on data described in section 3.2. We focus first on observed income only, as we controlled for mean precipitation and temperature in the regression model for predicted income. We also note that causality could run both ways: extreme climate conditions can reduce income and assets, and create or deepen poverty, but the least resourceful (asset poor) might also locate themselves in harsher climate conditions, e.g., they cannot afford to buy land in more favorable climates.

The poor households tend to live in dryer and warmer villages in the dry area, and wetter and colder sites in the wet area (defined as rainfall above 1500 mm/year over the 30 year period) (Table 4). The differences in mean temperature and precipitation between the poor and non-poor are statistically significant (except for temperature in dry areas), although the magnitude of the differences is rather small: 5-7% for rainfall, and less for temperature (0.5 degree C for wet areas).

**Table 4: Climate conditions over the period 1981-2010 in the study villages**

||Poor<br>(below median<br>income)|Rich<br>(above median<br>income)|Difference<br>t-value<br>(two sided)|
|---|---|---|---|
|_Dry area_||||
|Precipitation (mm/yr) Mean|1101|1160|14.55***|
|Standard deviation|197|180|11.4***|
|Temperature (degree C) Mean|23.0|22.8|1.08|
|Standard deviation|0.39|0.42|8.19***|
|_Wet area_||||
|Precipitation (mm/yr) Mean|2280|2127|7.80***|
|Standard deviation|325|277|9.94***|
|Temperature (degree C) Mean|24.1|24.6|3.24***|
|Standard deviation|0.36|0.34|4.99***|

*, **, *** Significantly different at 0.1, 0.05 and 0.01 level.

In terms of climate variability, as measured by the standard deviation (SD), the income poor tend to live in villages that experience larger precipitation variability. We observe this pattern in both 15
21 center or agency or website, or looked up advertisements on TV/newspapers, or talked to a company. Only 8.6% of women who want to work had sent their CV to an employer, and 5.2% had interviewed for a job, in the past 6 months.

Women seeking employment underutilize formal channels in KRI. Women registered with Rwanga, and who had found jobs recently had used a few additional formal channels such as job agencies, and websites to look for jobs with a median number of 6 different job agencies, websites, employment offices, and other formal channels compared with 4.5 for non-working women. Conversely, working women had reached out to fewer friends and family. However, in spite of this, more working women had eventually found jobs through friends and family (53.5%) compared to formal channels (46.5%). During follow up calls with staff from Rwanga, most of the total of 1,780 job seekers re-iterated that they did not believe applying through more formal channels would lead to eventual employment. A commonly held belief, that hindered efforts to reach out and work with registered job seekers is that successful employment is only achieved through connections and informal contacts, leading to less effort using formal channels, as well as lower trust in such institutions.

# 4. Discussion

In this paper, we report findings on psychological, social, and cultural barriers to participation from two primary data sets, first a representative sample of women and men, including working women in all the 3 governorates of KRI, and second a sample of job seekers who were registered with a job agency. We find a few positive indicators with respect to female labor force participation and perceptions towards women’s work in the private sector. For example, perceptions towards women working in the private sector appear to be favorable; about 70% of women and men are okay with women working in the private sector, and these numbers are even higher among non-working women, in particular for those who want to work (nearly 84%). Wages are also perceived to be higher on average in the private sector compared to the public sector. In the smaller sample of younger and more educated women who were registered with a job agency, we find that about 75% found work in the private sector, in July and August of 2018.<sup>16</sup> However, both women and men know very few women actually working in the private sector (about 1 out of 10 working women on average) given low female participation rates in the labor market in general. About 58% report not knowing another woman who works in the private sector. Furthermore, private sector work for women is associated with small business engagements such as tailoring, textiles, and baking for example instead of higher-skilled work opportunities in private companies.

16 About 83 job seekers of 1,370, or 6% of job seekers had found jobs in the 2 months.
patterns of heterogenous and asymmetric responses and spillover effects of corrective informational treatment (on plot size) on other, non-land inputs.

**Table 10: Impact of information provision on reporting of early season labor application**

||(1)<br>Log (total labor,<br>person days/acre)|(2)<br>Log (total labor,<br>person days/acre)|(3)<br>Log (household labor,<br>person days /acre)|(4)<br>Log (household labor,<br>person days /acre)|
|---|---|---|---|---|
|**Treatment**(yes = 1, no = 0)|0.022|0.003|-0.006|-0.012|
||(0.052)|(0.052)|(0.058)|(0.057)|
|ln (plot size)|-0.323<sup>***</sup>|-0.317<sup>***</sup>|-0.345<sup>***</sup>|-0.347<sup>***</sup>|
||(0.041)|(0.042)|(0.043)|(0.044)|
|**Treatment**• ln (plot size)|-0.095|-0.075|-0.104<sup>*</sup>|-0.079|
||(0.059)|(0.060)|(0.061)|(0.061)|
|% SR overestimate|0.001<sup>***</sup>|0.001<sup>***</sup>|0.001<sup>***</sup>|0.001<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|
|**Treatment**• % SR overestimate|-0.001<sup>***</sup>|-0.001<sup>***</sup>|-0.001<sup>***</sup>|-0.001<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|
|% SR underestimate|-0.005<sup>**</sup>|-0.005<sup>**</sup>|-0.005<sup>**</sup>|-0.005<sup>**</sup>|
||(0.002)|(0.002)|(0.002)|(0.002)|
|**Treatment**• % SR underestimate|-0.002|-0.001|-0.001|-0.000|
||(0.002)|(0.003)|(0.003)|(0.003)|
|Constant|4.205<sup>***</sup>|4.250<sup>***</sup>|4.106<sup>***</sup>|4.171<sup>***</sup>|
||(0.043)|(0.147)|(0.047)|(0.145)|
|Controls|No|Yes|No|Yes|
|Mean of dependent variable|4.216|4.223|4.099|4.106|
|R-squared|0.269|0.297|0.250|0.278|
|No. observations|1540|1529|1538|1527|

**Notes:** All plot sizes are natural logarithms of GPS measures, demeaned to center the data. SR indicates self-reported data. % over-/under-estimate is computed as percentage difference between self-reported and GPS measures for values above zero and zero otherwise. That is, for each over(under)estimated plot we compute percentage difference between self-reported and objective measures. Labor application includes labor allocations before or during planting (for land preparation, planting, and ridging). Standard errors, clustered at enumeration area level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

# **6. Conclusions**

Leveraging a unique experiment embedded into a national agricultural survey in Malawi, this paper investigates the extent, drivers, and persistence of NCME in farmer-reported plot areas. We also examine how farmers learn and adjust to demonstrably accurate information about plot size, both in terms of correcting pre-existing measurement error in plot size as well as their reporting behavior (responses) on non-land agricultural inputs.

Prior to the information treatment, farmers’ self-reported plot areas exhibit considerable NCME. Measurement error is negatively correlated with true plot size, which accounts for close 29