rafmacalaba/datause-encoder-data huggingface dataset:
their destructive capacity. For example, while a drought may reduce savings and investment, a flood can destroy all savings and capital, damage infrastructure such as roadways and contribute to disease such as cholera. All told, these impacts may have longer term effects on economic activities than a drought.

**Table 6b: Lagged GDP per Capita Growth with Country FE’s**

Dependent variable: GDP per Capita Growth

||(Precip)|(Temp)|(P,T)|(W+1)|(T,W+1)|(W-1)|(T,W-1)<br>|(T,W-1,W+1)|(W-1,W+1)|
|---|---|---|---|---|---|---|---|---|---|
|Precip_1|-0.00<br>(0.00)||-0.00<br>(0.00)|||||||
|Temp_1||0.04|0.04||0.05||0.01||-0.01|
|||(0.05)|(0.05)||(0.07)||(0.07)||(0.10)|
|WASP_minus1|(lag)|||||0.35<br>(0.47)|0.34<br>(0.48)|-2.20<br>(1.01)*|-2.19<br>* (1.01)**|
|WASP_plus1(l|ag)|||-0.23<br>(0.54)|-0.22<br>(0.55)|||-2.64<br>(1.12)*|-2.63<br>* (1.12)**|
|Observations|4897|4897|4897|2782|2782|2955|2955|1677|1677|
|Countries|181|181|181|178|178|180|180|137|137|
|R-squared|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.005|0.005|

Notes: Columns represent model specifications. Standard errors are clustered at the country level. Standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. P = Precipitation; T = Temperature; W+1 = WASP(+1); W-1 = WASP(-1). The WASP_plus1_1, WASP_minus1_1 and Temp_1 variables are lagged by one year.

The lagged regression results for agricultural value added (Table 7) were also generally consistent with the concurrent regressions. Here, however, temperature was weakly significant (90% level) while no other variables were significant. The sign of the regression coefficient for agriculture was negative, implying higher temperatures coincide with lower agricultural value added. It is difficult to explain how a temperature effect would have a lagged impact but it may be a result of a trend present in the temperature variable. For example, it is well known that global temperatures are increasing measured globally, and the same is true regionally and for most countries. With this trend in place, any trend in agricultural value added will appear correlated with the temperature trend, although it may be caused by other factors such as the decreasing role of agriculture in the economies of many industrialized countries.

19
<!-- Start of picture text --> Figure 1e: Bangladesh<br>Firm Count & Employment by Firm Size<br>Number of Firms Share of Firms<br>1995 22,830 1995 0.79<br>10-49 Workers 2005 20,444 10-49 Workers 2005 0.74<br>2012 25,974 2012 0.62<br>1995 3,532 1995 0.12<br>50-199 Workers 2005 3,270 50-199 Workers 2005 0.12<br>2012 10,251 2012 0.25<br>1995 1,640 1995 0.06<br>200-499 Workers 2005 1,416 200-499 Workers 2005 0.05<br>2012 3,791 2012 0.09<br>1995 767 1995 0.03<br>> 500 Workers 2005 2,333 > 500 Workers 2005 0.08<br>2012 1,700 2012 0.04<br>Source: Census of Manufacturing Industries, Source: Census of Manufacturing Industries, 1995, 2005, & 2012. Shares may not<br>1995, 2005, & 2012. sum to one due to rounding errors.<br>Total Employment Employment Share<br>1995 384,460 1995 0.19<br>10-49 Workers 2005 419,730 10-49 Workers 2005 0.18<br>2012 573,713 2012 0.12<br>1995 361,069 1995 0.18<br>50-199 Workers 2005 297,136 50-199 Workers 2005 0.13<br>2012 1,043,740 2012 0.21<br>1995 520,058 1995 0.26<br>200-499 Workers 2005 462,905 200-499 Workers 2005 0.20<br>2012 1,100,902 2012 0.22<br>1995 751,051 1995 0.37<br>> 500 Workers 2005 1,168,044 > 500 Workers 2005 0.50<br>2012 2,264,091 2012 0.45<br>Source: Census of Manufacturing Industries, Source: Census of Manufacturing Industries, 1995, 2005, & 2012. Shares may not<br>1995, 2005, & 2012. sum to one due to rounding errors.<br><!-- End of picture text --> 38
# **Figure 12**

<!-- Start of picture text --> Forecasted nuclear energy share in t+30 according to the year of publication Forecasted share -0.71 x Publication Year + 1428.6Forecasted share =<br>Robust Tukey smoother R 2 = 0.63<br>Linear regression line<br>50<br>45<br>40<br>35<br>30<br>x 100)<br>25<br>20<br>N uclear share (%<br>15<br>10<br>5<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->

**Forecasted nuclear energy share in t+30 according to the year of publication**

20
|**Table 5: Ge**<br>Dependent variable:SBM Efficiency|**ndered effec**<br>**(1)**|**ts of volatility**<br>**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Volatility in Occupancy*Women's|-0.079|0.299**|0.420**|0.456**|
|ownership (proportion)|(0.093)|(0.137)|(0.155)|(0.193)|
|Volatility in Occupancy|0.071|0.510***|0.475***|0.540***|
||(0.059)|(0.114)|(0.117)|(0.146)|
|Women's ownership (proportion)|-0.105|-0.411*|-0.465**|-0.521*|
||(0.179)|(0.206)|(0.186)|(0.294)|
|Volatility in Occupancy*Number of||-0.199***|-0.186***|-0.203***|
|workers (logs)||(0.053)|(0.051)|(0.048)|
|Number of workers (logs)||0.133**|0.158**|0.135*|
|||(0.061)|(0.064)|(0.066)|
|Central region Y:1 N:0||0.004|-0.059|0.005|
|||(0.022)|(0.044)|(0.084)|
|Eastern region Y:1 N:0||0.215***|0.217***|0.224***|
|||(0.040)|(0.053)|(0.073)|
|Northern region Y:1 N:0||0.019|0.080|0.024|
|||(0.039)|(0.056)|(0.121)|
|Sabah & Sarawak Y:1 N:0||0.160*|0.116|0.136*|
|||(0.078)|(0.068)|(0.072)|
|Age of hotel (logs)|||0.032|0.078*|
||||(0.046)|(0.040)|
|Part of a chain Y:1 N:0|||-0.195*|-0.157**|
||||(0.105)|(0.061)|
|Finance major obstacle Y:1 N:0|||0.007|-0.010|
||||(0.123)|(0.097)|
|Occupancy rate|||0.086|0.103|
||||(0.184)|(0.192)|
|Number of power outages|||0.003|-0.009|
||||(0.022)|(0.023)|
|Largest owner is manager Y:1 N:0|||-0.032|0.022|
||||(0.058)|(0.051)|
|Website Y:1 N:0|||-0.150*|-0.080|
||||(0.076)|(0.094)|
|Low competition Y:1 N:0|||0.007|0.014|
||||(0.037)|(0.058)|
|Intermediate competition Y:1 N:0|||-0.008|0.054|
||||(0.096)|(0.105)|
|Two-star hotel Y:1 N:0||||-0.106|
|||||(0.062)|

45
**Table** 5.1: **Qoth** **_d_ diniaim s ,wk 1991-97** (nial u in tilicns **oUS** %dolas)

||<br>|**hInz**<br>|**uTwftrdWon**<br>|**echroes**<br>||**Oar-**<br> *****|**tmjounW**<br>|**IrnI~**<br>||
|---|---|---|---|---|---|---|---|---|---|
||**te**<br>**rat**|**e irteret**<br>**re**|**tjiwy **|**Sock nwIrd**|**Tctb**|**Ir&tre**|**lrtl rae**|**QJ1WY**|**Td;**|
||**ft*ue**|**c4iq**|**ii&Xu**|**J&~a**<br>**krs**<br>6r&6|**d**<br>fiq||eUessn<br>ax|<br>9sr6~|**Cit**|
|91|2157|<br>**1,07**|**3**<br>**81**|<br>109|351|_577_|<br>305|807|<br>4,449|
|S|2Z913|<br>1,38|5<br>93|<br>23B|4,6|65|U351|<br>83|5,346|
|93|4,959|Z3E|2<br>110|<br>340|7,77|1<br>1,39|8<br>6,177|<br>90)|8,475|
|94|<br>5,77|8Z62|4<br>**6**|**5**<br>E|86|1,57|3 8,81|6<br>915|11,303|
|95|<br>50,3|<br>274|2<br>82|<br>5C2|**918**|<br>2,70|5 1281|1 1,197|17,713|
|93|<br>5,93|1<br>,27|8<br>97|<br>574|<br>9,88|0 4723|19,17|1 1,5E|2;453|
|97|<br>7,48|9 264|0<br>85|<br>993|1207|<br>!,0|22116|<br>1,55|2B733|

9L EIa r **kf** rIr a _: mart5~ nI **_lW_** at (i s **-)** Developing countries were not forgotten. The net flow of foreign capital to developing countries rose from $98 billion in 1990 to $335 billion in 1997, a major part of it originating from foreign direct investment, portfolio investments in domestic stocks and bonds, commercial bank lending, and the issues of equity and bonds in offshore markets (Table 5.2). Flows of foreign direct investment to developing countries increased more than six times from 1990 to 1998, and their share of global FDI flows has risen from 25 percent in 1991 to an estimated 42 percent in 1998, compared with 18 percent in the mid-1980s (World Bank, GDF, 1998).

**Table** 5.2:

Net **long-term resource flows to developing countries (US$ billion)**

|Type of flows:|1990|1991|1992|1993|1994|1995|1996|1997|1998a|
|---|---|---|---|---|---|---|---|---|---|
|Net long-term resource|100.8|123.1|152.3|220.2|223.6|254.9|308.1|338.1|275.0|
|flows||||||||||
|Official flows|56.9|62.6|54.0|53.3|45.5|53.4|32.2|39.1|47.9|
|Private flows|43.9|60.5|98.3|167.0|178.1|201.5|275.9|299.0|227.1|
|From international capital|19.4|26.2|52.2|100.0|89.6|96.1|149.5|_135.5_|72.1|
|Markets||||||||||
|Private debt flows|15.7|18.6|38.1|49.0|54.4|60.0|100.3|105.3|58.0|
|Commercial bank loans|3.2|4.8|16.3|3.3|13.9|32.4|43.7|60.1|25.1|
|Bonds|1.2|10.8|11.1|37.0|36.7|26.6|53.5|42.6|30.2|
|Others|11.4|3.0|10.7|8.6|3.7|1.0|3.0|2.6|2.7|
|Portfolio equity flows|3.7|7.6|14.1|51.0|35.2|36.1|49.2|30.2|14.1|
|Foreign direct investment|24.5|34.4|46.1|67.0|88.5|105.4|126.4|163.4|155.0|

a. Preliminary Source: World Bank, Debtor Reporting System 6
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that have attempted to quantify the effect of climate on economic growth have almost entirely focused on changes in mean climate conditions (e.g., Nordhaus, 2006; Tol, 2002; Nordhaus and Boyer, 2000). As Nordhaus describes “ ... current theories and empirical studies of economic growth give short thrift to climate as the basis for differences in the wealth of nations.” Other studies, while acknowledging the potential impact of climate and variability, have classified it as a subcomponent of “geography” (Sachs, 2001). Typically approached using a cross-country analysis method, the results were generally unable to specify significant geography effects that could be distinguished from other institutional factors (Rodrik et al., 2004; Sachs, 2003).

A small number of studies have begun to highlight the importance of year to year variability or changes in climate on economic growth. Brown and Lall (2006) used statistics of rainfall and temperature variability in a cross-country analysis of economic level, finding that poor countries tended to have higher levels of precipitation variability. Dell et al. (2009) evaluated the effects of annual variations in precipitation and rainfall over the previous 50 years as a way to estimate potential economic impacts of climate change. Using national level economic and climate data in a global assessment, their results indicated that higher temperatures had negative consequences in poor countries, while there were no climate effects in wealthy countries. The effects in poor countries were not limited to agricultural production, as they found industrial output, investment growth and political stability all to be impacted. Several recent studies have investigated the effects of climate variability on economic growth in Sub-Saharan Africa (SSA), where the largely agrarian based economies may be particularly vulnerable due to low levels of infrastructure and lack of large scale insurance mechanisms. In a review of a wide range of economic data, Christiaensen et al. (2002) found that rainfall variations and ill health have profound effects on poverty, resulting in a need to social provision of protections against such shocks. Barrios et al. (2008) provide a broad overview of the ways in which rainfall affects economic activities in Africa. Their empirical investigations of agricultural production and GDP growth found that the decline in rainfall that occurred between the 1960s and 1990s in much of SSA was a major contributor to the reduced agricultural 6
Other variables of interest include residence in a rural region, ethnicity dummies and the controls for education of parents. Living in a rural area increases chances that an individual completed at least nine years of schooling, while rural residence is negatively related to a chance of completing 11 grades of schooling. Taken as a group, the estimated coefficients on the dummies for the educational attainment by parents of women are statistically significant at 1% level for the completion of basic or more and secondary or more levels of schooling (Table 1, Col. 6 and 8). For men, the education of their parents has a significant impact on the completion of at least secondary schooling (Table 1, Col. 4). The ethnicity dummies taken as a group have a significant impact on the completion of basic and secondary levels by men (Col. 1 and 3), and secondary level by women (Col. 7). However, the effect of ethnic group is robust to the inclusion of parental education dummies only in the regressions for women (Col. 8).

I also estimated the same regression models for the larger sample, adding to the control group these born in the 1958-1965. The results (not reported) are very similar to those shown in Table 1. The estimated coefficients on the interaction terms are slightly larger in absolute value (significant at the 5% level) in the regressions for women, suggesting that the results reported in Table 1 provide us with a conservative estimate of the effect of this conflict on education.

To test whether the individuals from the affected cohort were able to catch-up on the years of schooling between 2003 and 2007, we should estimate the same base specification for the pooled samples of the 2003 and 2007 data with the same dependent variable and add to the regressions a dummy for a survey year. Note that the 2003 TLSS survey did not include questions on the respondent’s ethnicity or education of his/her parents. Therefore the pooled regressions include only variables that are found in both datasets such as “war cohort”, war cohort interacted with residence in conflict area and rural residence. All regressions include fixed effects at the raion level (69 groups). Tables 2.1 and 2.2 report results of the OLS regressions based on the pooled samples of 2003 and 2007 TLSS data for men and women, respectively.

The results from regressions on the pooled sample of 2003 and 2007 data for men (Table 2.1, Col. 3) indicate that there was some catch-up in the completion of basic education by individuals born in 197619
regions in the top 50 percent in terms of relative foreign market access. In order to insure consistency with the dependent measure of regional inequality which treats each region as one observation, the coincidence ratios are also computed disregarding regional population sizes.

The question is of course how to determine relative market access positions. In the absence of adequate and comparable datasets of regional transport costs to an equivalent selection of international trade points in each country, the method used consists in first identifying the trade entry points accountable for at least 70% of the country’s total trade, as well as the top quarter or half of the regions in terms of border or coast location in closest proximity to the main trade routes. In the cases where two regions were close in terms of border/coast accessibility to the main trade routes, the region with the higher number of international ports or border crossings was chosen.

Beyond a mere response to limited data availability, this geography based construction of the coincidence measures also addresses a potential endogeneity issue. Assuming that perfect data about each region’s foreign market access in terms of actual transport cost weighted market potential is available, it is highly likely that high degrees of regional inequality are associated to higher degrees of coincidence, because regional prosperity tends to be a driver of market access when measured in terms of human-built infrastructure. Relying on physical proximity and border or coast location instead is not subject to this potential endogeneity issue. As in the case of the previous structural conditioning variables, the coincidence measures are averaged across periods for each country.

The data sources for each of the variables are presented in Table A2 in the Appendix.

# Finally ε represents the error term.

In order to assess the original questions of whether trade and the remaining variables included under equation (4) affect regional inequalities and whether this relationship changes over time, both static OLS with country and time fixed effects, as well as dynamic panels are run. The static analysis aims at discovering the association (or lack of it) between trade and the evolution of regional disparities. In the case of the 18
the-envelope calculation on cost-effectiveness suggests that when including the differences in probability of survey completion, rather than doubling the implementation costs, phone-based surveys are 25 percent less costly than self-completion surveys.

Second, survey methods can change the reasons for non-response. Using reports of causes for nonresponse provided by enumerators, we show that amenability was lower among phone-based survey experiment participants in comparison to those with connectivity issues relative to the same proportion of non-respondents in the self-completion survey mode. This result is important for contexts with weak connectivity settings, such as low internet speed or bandwidth, and for surveys that use self-completion questionnaires with high respondent fatigue and therefore greater refusal rates (Savage and Waldman, 2008). This result also highlights that phone surveys may ease the experience of survey completion for participants, thereby raising response rates.

Third, given the magnitude of the effect, we perform several additional analyses to better understand the implications of our results. First, we analyze impact heterogeneity across groups and identify a set of interesting patterns. Relative to men, women are 15 percentage points more likely to answer the survey when contacted by phone. The magnitude of this estimate is substantial; it implies that in the case of women, completion rates can increase from 30 to 80 percent in the context of phone-based surveys. Furthermore, we detect that the magnitude of the effect increases with age. Older populations are much more likely to complete the survey when reached by phone. In particular, relative to respondents who are younger than 40 years old, response rates increase by 41 percentage points among those who are 40 years old or older. Importantly, contrary to the initial hypothesis, we do not detect significant responses based on whether the respondent shares the cellphone with relatives or access to digital resources. Altogether, these results show that response rates can increase substantially when targeting specific groups such as women or older people.

In addition to differences in completion rates, as a fourth result, we also examine the extent to which the survey method altered the group composition of respondents. In principle, the survey method can affect an observational outcome by altering the group composition of respondents or—alternatively—the willingness to report a particular outcome among respondents. This can be relevant for surveys on sensitive issues such 4
endogenously and will reflect differences in investment risks between formal and informal sectors. As risk on informal housing investment decreases, informal land becomes a closer substitute to formal land, and the price differential between formal and informal land will decrease.

# **6 Conclusions**

This work was motivated by the simple evidence that in many developing countries construction of informal dwellings, rather than being an exception, is a viable alternative to constructing in the formal sector. Our contribution can be summarized as follows. We show that appropriate data on rents and prices of dwelling units in the formal and informal sectors can be used to decompose the premiums for the formal sector in prices and rents in two parts: the difference in rates of return on housing investment, which reflects the risks borne only by investors, and the formal premium on rents, which reflects conditions prevailing in rental markets and tastes of households. Those two quantities have different economic meanings, and should be analyzed separately. When risk on informal investment is modeled as an exogenous annual destruction rate, estimates of the destruction rates can be obtained very simply from the observed rate premium on informal investment and the investor’s opportunity cost of capital.

On the empirical side, we show that rates of return on housing investment are indeed higher in the informal sector than in the formal sector in the city of Pune. The implied risk as perceived by investors in the housing sector is estimated to be equivalent to an annual destruction rate ranging between 1 and 2 percent. The two distributions of rates of returns present highly idiosyncratic components, and traditional explanatory variables proxying either the strength of informal property rights or lower perceived risk of eviction have little power to explain the differences between individual rates of return.

Our approach, by separating (temporal) risks faced by investors from (instantaneous) risks faced by renters and other factors affecting the rental market differently in the for26
15

- old low-rise units saw the greatest increase in privatization (Table 1.9);

- tenants of standard modem panel buildings still showed no eagemess to privatize (Table 1.9);

- potentially mobile households (e.g., couples with children) displayed more interest in privatizing (Tabie 1.1 1);

- the main distinction from previous years was that the high-end income categories were now more active than the others (Fig. 1.2). The greatest changes in the attitudes towards housing privatization occurred in the 20% highest-income group. The share of apartment owners among the middle-income group fell (this can be explained, in part, by redistribution of incomes among social groups).

The trends described here have positive implications for the process of housing privatization, and consequently, for the fledgling housing market, as the entrance of younger, more mobile and wealthier families into the housing market strengthens its development. As the market develops, it in turn affects the tenants' inclination to privatize their apartments. That is, the two processes of housing privatization and the development of the housing market exert reciprocal, mutually beneficial influences on each other.

**Fig.** 1.2. Moscow households who privatized their dwellings By deciles based on total household income

<!-- Start of picture text --> 70 1<br>50 e<br>1047 1<br>60 ~ ~ ~ ~~ 61.1 3. 29<br>1 2 3 ~ 403 6 B 7 8 S 140.3<br>40 3.2-37.7 -<br>30 - 31.2<br>27.5<br>20 -<br>1 2 3 4 5 6 7 8 9 10<br>1-01994 1 6<br><!-- End of picture text -->

**Source: The Moscow LDngitudinal Household Survey in Dec-1994 and Jan-1996. Note:** 1 **- lowest** income **decile, 10 - highest** incmne decile

Note in the above figure that the lowest income group (decile 1) is also the group that exhibits by far the highest rates of privatization in both 1994 and 1996, which is consistent with the finding that pensioners, who generally have quite low incomes, are the demographic group most likely to have privatized their apartments.