rafmacalaba/datause-encoder-data huggingface dataset:
_26_

# **VI. Conclusion**

Using matched climate and firm data covering 154 countries, we documented that the effects of rising temperature are non-linear and uneven across climate zones. Firms in hotter zones experience steeper losses in productivity with increases in temperature compared to firms in relatively colder zones, which tend to register productivity gains. A unit (1<sup>0</sup> _C_ ) increase in WBT in the hottest climate zone of 25.7<sup>0</sup> _C_ and above results in a productivity decline of about 20.8% compared to firms in the coldest climate zone. There is a change in the direction of impact or sign of the main coefficient, indicating a potential inflection point beyond which an increase in temperature has a detrimental impact on firm productivity, which was estimated to be 25 _._ 7<sup>0</sup> _C_ in our sample. In addition, the effects vary not only based on the temperature categories within which firms are located, but also on other factors such as firm size, industry classification, income group and region. For example, large firms, firms in manufacturing and those in low-income countries and hotter climate zones tend to experience the biggest productivity losses due to climate change. Given that many low income countries are in hotter climate zones, the climate change impacts due to higher temperature are further exacerbated by limited capabilities to invest in adaptation. Firms in poorer regions experience the highest productivity losses due to climate change especially those in the hottest temperature categories. This reinforces what we know about climate change reinforcing existing vulnerabilities in the regions of the world that are least capable of responding and adapting to climate change.

This study builds on and makes important contributions to the emerging literature. First, using a globally comparable and standardized survey of firms rather than an individual country presents opportunities for better understanding the potentially heterogeneous and uneven impacts of temperature changes on firm outcomes. Most earlier studies that examined climate change impacts using micro-data relied on individual country observations due to limitations of data comparability across countries and regions especially at the micro level. The survey data also allows for estimation of impacts across regions, climate zones, industries, and incomes. This structure enables estimation of the impact at the global level. Comparison of these impacts is essential given the global nature of climate change and the potentially distinct types and magnitudes of the impacts. This may be the only study that estimates the impact of temperature changes on productivity at the widest geographical coverage using a representative sample of firms in more than 150 countries, providing the most comprehensive study in the literature. In addition, despite the relatively rich literature on the impacts of climate change on agriculture, studies on the non-agriculture sector are relatively scarce. This study contributes to understanding the impact on the nonagriculture sector, using firm-level data.
**Table A7.** Decomposition results of the global emissions changes and contributions of various driving forces in the **LMI countries** over the early period (1980-1990), late <u>period (2005-2015), and entire period (1980-2015).</u>

|**Period**|**1980-1990**|**2005-2015**|**1980-2015**|
|---|---|---|---|
|**Emissions change (Mt)**||||
|Overall change|456.4|1,318.1|3,012.3|
|_EC_|-15.6|-123.2|82.8|
|_ECS_|132.1|330.7|800.2|
|_EI_|-5.7|-667.0|-767.3|
|_PCG_|167.4|1,360.3|1,881.5|
|_P_|178.2|417.3|1,015.2|
|**Contribution (%)**||||
|Growth rate|82.1|58.6|542.1|
|_EC_|-2.8|-5.5|14.9|
|_ECS_|23.8|14.7|144.0|
|_EI_|-1.0|-29.6|-138.1|
|_PCG_|30.1|60.5|338.6|
|_P_|32.1|18.5|182.7|

**_Note:_** _EC_ , _ECS_ , _EI_ , _PCG_ , and _P_ refer to emission coefficient, energy mix, energy intensity, income, and population, respectively.

35
development and migration in Bolivia is constructed using data from several different sources. Table 1 lists the variables, their definitions, and the sources of the information.

**Table 1: Variables in the municipal level data base for Bolivia**

|**Variable**|**Definition**|**Unit**|**Source**|
|---|---|---|---|
|Total population|The number of inhabitants in the<br>municipality.|10.000<br>persons|2001 Census|
|Urbanization rate|The share of the population who<br>lives in urban areas, where urban<br>is defined as human settlements<br>that have more than 2000<br>inhabitants.|-|2001 Census|
|Years of education|Average number of years of<br>education of working age<br>population in 2001.|Years|PNUD (2004)|
|Life expectancy|Life expectancy at birth for each<br>municipalityin 2001.|Years|PNUD (2004)|
|Per capita<br>consumption|Average household consumption<br>per capita in purchasing power<br>adjusted international dollars of<br>2001.|USD/month|PNUD (2004)|
|Elevation|Elevation of the main city in the<br>municipality.|Kilometers<br>above sea<br>level|Conservation<br>International’s GIS<br>database|
|Normal average<br>annual temperature|The average annual temperature<br>in the main city of the<br>municipality as measured over a<br>reference period (typically 1961-<br>1990).|oC|Conservation<br>International’s GIS<br>database|
|Normal annual<br>precipitation|The average annual precipitation<br>in the main city of the<br>municipality as measured over a<br>reference period (usually 1961-<br>1990).|meters|Conservation<br>International’s GIS<br>database|

In order to assess the climate change trends in the different parts of Bolivia, we obtained monthly temperature and precipitation data from 1948 to 2008 from the Monthly Climatic Data for the World (MCDW) publication of the US National Climatic Data Center.<sup>1</sup> The original data are organized in 61 printed volumes with 12 issues in each (one for each month of the year), totaling 721 months. All data have been quality-checked and were published by the NCDC about 3 months after the raw data were collected. From each of these monthly issues, we extracted average monthly temperature and total monthly for each of 31 Bolivian stations, in order to create time series for each station.

The “normal” temperature for each station-month is calculated as the average temperature observed for the reference period 1960-90. Some stations have so few and scattered

> 1 This data is available for free at <u>http://www7.ncdc.noaa.gov/IPS/mcdw/mcdw.html.</u>

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# **3 – Formal derivation of poverty effects in the micro module**

The indirect utility function of household h is a function of the income yh and of a vector of prices of goods, **p** .<sup>**31**</sup> That is: Totally differentiate (1): Dividing both sides by the marginal utility of income ( dv h<sup>dy</sup> h ) and using Roy’s identity, we obtain: where dwh is the monetary value of the change in indirect utility, ch,g is the consumption of good g by household and dpg is the change in price of good g. Income of household h is given by the sum of labor income, remittances (which are a function of wages), profits associated with production of a particular good and income obtained through government transfers (partly tariff revenue):

> 31 The following formalization is just an adaptation of that found in Nicita and Olarreaga (2004) “Trade, Trade reforms and Poverty in Ethiopia”.

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# **Figure 17. Origin of methodological weakness in studies done in key populations, by country. Andean Region; 2009**

<!-- Start of picture text --> Defines objective<br>Inclusion criteria<br>Sample size<br>Sampling technique<br>Technique for reducing data collection errors<br>Analytical technique<br>Describes limitations<br>Calculation of statistical errors and/or p<br>value<br>0 10 20 30 40 50 60 70 80 90 100<br>Percentage of studies<br>Peru n= 32 Ecuador n= 25 Bolivia n= 10<br><!-- End of picture text -->

**Source:** Authors’ calculation using information from _Database and References for an Evidence-based Strategy_ in Peru, Ecuador and Bolivia.

34
Hausman and Taylor (1981), taking the mean over all plots owned by the household and subtracting this from the original equation produces which is an equation that will produce unbiased estimates of the,8's, which are the main parameters of interest in the above equation. Elements included in the vector Z are exogenously given physical land characteristics such as the topography, land attached investments, the type of land use, area and length of possession, and the titling status which, as discussed above, can also be considered to be exogenous.

# 4. **Empirical results**

Applying this analytical framework to the Nicaraguan data at hand, we obtain three main results. First, the propensity to undertake largely labor-intensive investments is increased significantly by the receipt of land title and the nature of the data allows to virtually exclude the possibility of reverse causality. Second, comparison of returns across asset classes points towards significantly higher returns (28%) for assets attached to land as compared to moveable assets (3%) and livestock (11%). This suggests that award of title would not only increase investment but also help producers to move towards an asset portfolio that would be socially more optimal. Finally, we find that values for land that is registered (but not merely titled) are almost 30% higher than for land that is not. This implies not only that programs to improve tenure security can have a significant impact but also that the legal nature of the documents being awarded clearly matters. Simple award of a document is thus not equivalent to greater tenure security (Wachter, 1992) and producers are well aware of the much lower tenure security conveyed by nonregistered titles and adapt their behavior accordingly.

# **4.1 Investment impact of increased tenure security**

Results from the probit as well as tobit regression equations for net investment are presented in table 5. Since the main form of intervention was to either award agrarian reform title or full registration, we use a probit equation with a zero-one dummy for whether an investment had taken place, and a tobit with the actual amount of investment. The Huber-White heteroskedasticity-consistent estimator is used throughout. The probit equation illustrates that full registration of a plot after 1990 had a very significant investment-enhancing impact; according to the regression results it increased the propensity to invest by between 8% and 9% (figures in column 1 of table 5 are marginal probabilities). It also highlights that award of agrarian reform title per se, without accompanying registration, had only minimal impact on investment. The coefficient on this variable is small (increasing the marginal propensity of undertaking _15_
<!-- Start of picture text --> Figure 4.1: Income per Capita by Decile, 2001 (gourdes)<br>80,000<br>Metro<br>70,000<br>Urban<br>60,000<br>Rural<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Source: Own calculations based on HLCS 2001.<br>Gourdes per capita<br><!-- End of picture text --> The poorest in rural areas get a marginally higher share of total income than in urban areas. The share of income going to the lowest 40 percent of the income pyramid amounts to 8, 7, and 6 percent in the rural, metropolitan and urban area respectively. This compares to the income share going to the top 10 percent of the income distribution that receive 43, 43, and 54 percent in the rural, metropolitan, and urban areas respectively.

Table 4.3: Income Source for Households per Capita 2001 (percent)

|Decile|Self-<br>employed|Salary|Transfer|Property|Self-<br>consumption|Barter|Other|
|---|---|---|---|---|---|---|---|
|1|46.7|5.0|26.4|3.8|4.1|0.0|14.1|
|2|48.4|5.9|22.6|3.0|10.8|0.0|9.4|
|3|43.2|5.4|22.1|2.8|17.9|0.1|8.5|
|4|39.5|8.7|22.6|2.7|21.2|0.3|5.2|
|5|40.9|5.7|20.8|3.2|23.9|0.4|5.1|
|6|38.2|7.5|22.4|2.6|24.1|0.0|5.1|
|7|40.2|10.4|23.8|2.4|19.5|0.1|3.5|
|8|38.6|14.2|25.0|2.2|16.5|0.2|3.3|
|9|37.2|19.3|28.8|2.6|9.2|0.2|2.8|
|10|30.9|30.7|29.8|3.0|1.3|0.1|4.3|

_Source:_ Own calculations based on HLCS 2001.

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following instalment cancelled. Students who missed a term or dropped out of school without an acceptable justification would be dropped from the program. However, before the implementation it was decided that conditions become soft, as it would have been impossible for the implementing agency to monitor school attendance on a quarterly basis.

The soft conditionalities were, however, a main focus of the program. Children were not required but rather highly encouraged to be enrolled and attend school, and parents were reminded at each payment of these soft conditionalities. Other soft conditions included attending accompanying measures sessions and taking children to a medical center for a general check-up.

The schedule of payments, illustrated in Figure 3, did not follow the initial plan, due to the following reasons. Logistical problems in the implementation, administrative school closures related to Ebola and teacher’s strikes, and security issues related to the elections (see above for the school disruptions) all had an effect on the expected timing of transfers. One of the complaints from the feedback received by the implementing agency was that parents would have benefitted from more regular, foreseeable transfers.

The program was implemented in 148 villages, distributed in 16 sous-prefectures, located in four distinct and non-contiguous prefectures. The 148 villages were randomly assigned into 75 treated and 73 control villages. Figure 1 presents a diagram with the different treatment arms, and the number of villages, children and households in treated and control groups.

# 4. Geographical targeting

The selection of regions, districts and villages was jointly made with the National Statistical Institute of Guinea (INS). The INS released in 2012 a national household survey called the Enquête Légère pour l’Evaluation de la Pauvreté (ELEP). Guinea has 8 administrative regions, and a total of 33 prefectures. The ELEP was used to target four different prefectures among the poorest ones in the country, and to determine the cash transfer amounts. The selection of the four different prefectures was due to the will to cover distinct climates and seasons, as is shown in Figure 2.

The four prefectures selected were Mali, Telimele, Siguiri and Kerouane, and are shown in Figure 2. The distribution of households between the four different prefectures is presented in Table 1. In each of these prefectures, four sous-prefectures were chosen. In each sous-prefecture, between 9 and 10 villages were randomly selected to be either control or treatment.

# 5. Timing of the cash transfers and the Ebola epidemic

The Ebola epidemic started in March 2014 in Guinea. Figure 3 presents the timing of the epidemic, the school closures and the cash transfers. As can be seen, in 2014, the Ebola epidemic was ongoing and the schools were closed, not only in Guinea but in most of the neighboring countries as well. The baseline survey was conducted in this context of the ongoing Ebola crisis. Schools could not open as expected in September 2014, so the school year started instead in 6
Population, Residence and Birth Places

## Table 2.1.

# 1970

||Total|population|||Of whi|ch, born in|other pr|ovinves||
|---|---|---|---|---|---|---|---|---|---|
|||000s|||OOOs|||percent|s|
|Provinces|Total|M|F|Total|M|F|Total|M|F|
|Turkey, total|35,605|18,007|17,598|5,789|3,338|2,451|16.3|18.5|13.9|
|Istanbul<br>Ankara<br>|3,019<br>2,042<br>|1,625<br>1,067<br>|1,394<br>974<br>|1,670<br>826<br>|<br>954<br>464<br>|716<br>362|_55.3_<br>40.5|58.7<br>43.5|51.4<br>37.2|
|Izmir|1,427|739|688|431|242|189|30.2|32.7|27.5|
|Big cities|6,488|3,431|3,056|2,927|1,660|1,267|_45.1_|48.4|41.5|

Source: Statistical Yearbook of Turkey, 1981, Table 28.

||Population<br>Popustio<br>_<br>OOOs|1970.75<br>did not<br>aangd<br>residence<br>percent|T<br>Populati<br>Came<br>Another<br>province<br>center|able 2.2.<br>on<br>and Mi<br>Mi<br>to a provin<br>District<br>c<br>Same<br>province|grants, 19<br>grants,<br>per<br>ve<br>center<br>enter<br>in<br>Another<br>province|75<br>cent<br>from<br>A village<br>Same<br>province|<br>in<br>Another<br>province|Other<br>and<br>unknown|Total<br>migrants|
|---|---|---|---|---|---|---|---|---|---|
|Total|40,197|76.2|3.3|0.5|0.6|0.5|0.4|4.8|10.0|
|Male<br>|20,417<br>|74.8<br>|3.8|0.5|0.8|0.5|0.5|5.3|11.4|
|Female|19,780|77.7|2.8|0.4|_0.5_|_0.5_|0.3|4.2|8.7|

c Table 29.
# _3.2 Data from Other Sources_

A number of other databases are used to augment the information on U.S. firms in the BEA data. In order to obtain information on parent firm R&D expenditures in years in which this item was not captured in BEA surveys, the BEA data on publicly traded parents is linked to COMPUSTAT using employee identification numbers. Parent firm data is also linked to data on patenting activity captured in the NBER patent citation database.<sup>24</sup> This comprehensive database covers all patents granted by the U.S. Patent and Trademark Office (U.S. PTO) throughout the 1982-1999 sample period. These data provide a rich picture of the evolving technological trajectories of parent firms and are used to test if patent reforms have larger effects for firms that make more extensive use of the U.S. patent system prior to the reforms.

Data on patent filings in the countries that underwent patent reform provide additional information on the level of technology being transferred. Data on patent applications, broken down by nationality of applicant (foreign vs. domestic), are available from the World Intellectual Property Rights Organization (WIPO). If patents provide little or no protection in a country, there is little incentive to patent. If multinationals respond to an IPR reform that substantially strengthens patent rights by increasing the volume of technology being transferred (rather than merely increasing the price of technology that has already been introduced to the market in question), then this should be reflected in an increase in both the level and the growth rate of total patent

> 24 This comprehensive data set on U.S. patents is documented in Hall et. al. (2001).  Linking these data to BEA data requires the use of a mapping between the assignee codes used by the U.S. PTO to identify patent applicants and the CUSIP identifier codes used by the COMPUSTAT database created for all firms in the 1989 COMPUSTAT data.

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not include self-proprietorships (the self-employed). We further exclude business cooperatives from the analysis. New cooperatives are founded usually in construction and are relatively few in number (less than 2% of the number of new firms in 2014). Furthermore, no information is given on whether cooperatives are foreign-owned. The available data encompassed the period between 2009 and 2014. The number of new firms reported by the Chamber of Commerce (57,710) seems to match the World Bank data on new business registrations (57,760) for Turkey in 2014. The World Bank data places Turkey below even the lower middle income country average on a per capita new business density scale, implying considerable room to improve entrepreneurship and firm entry (World Bank, 2017). The data on foreign-owned firms begins one year later, in 2010. While the data provided are relatively basic, they allow us to make the distinction between new firms and new foreign firms. We take the natural logarithm of the firm entry variables for a more parsimonious estimate and easier interpretation.<sup>8</sup> Without the log transformation of the outcome variables, the estimated effects remain positive but have larger standard errors especially in the DD models.

The data on total gross profits and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key difference from the Chamber of Commerce data is that the sales and profits data include all businesses including selfproprietorships.<sup>9</sup> Data were provided for the years between 2010 and 2014 and are reported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by definition and they are likely to be less accurate and complete for smaller firms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and profit figures.<sup>10</sup> We scale the variables according to province size by dividing sales and profits by the population of the provinces. If we use sales and profits in absolute terms, we get qualitatively similar results.

The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12
The changes over time in aggregate productivity volatility can result mainly in two forces: first, the composition of firms of different characteristics; and second, the changes in the effect of firm characteristics on productivity volatility. In this section, we will examine these two driving forces using a development of the Blinder-Oaxaca decomposition to analyze the change in productivity volatility for the firms that survived at least for five consecutive years during the period 2001 to 2005 (Blinder, 1973; Oaxaca, 1973; Smith and Welch, 1989; The World Bank, 2007).

Mathematically, starting from two productivity volatility equations for each firm, one for 2001 and one for 2005: _J_ where<sup>_y_</sup> _i_<sup>represents the productivity volatility of firm</sup><sup>_i_; and</sup> _X i_ = { _x j_ , _i_ } _j_ = 1 are the independent variables, we estimate: We can decompose the influence of various attributes into (i) main (characteristic or endowment) effects that occur because of changes in firm characteristics and (ii) year (price or coefficient) effects which are due to changes in return to the specific characteristics. This decomposition allows one to assess the sources of volatility variation during the period studied (2001-2005).

The change in productivity volatility during the period 2001-2005 can be decomposed as follows: ∆ _y_ = _~~y~~_ 05 − _~~y~~_ 01 = β ˆ 01( _X_ 05 − _X_ 01) + ( β ˆ 05 − β ˆ 01) _X_ 05 = β ˆ 01 ⋅ ∆ _X_ + ∆β ˆ ⋅ _X_ 01 (8) where _~~y~~_ 01 and _~~y~~_ 05 are arithmetic mean volatility in 2001 and 2005, respectively. β ˆ 01( _X_ 05 − _X_ 01) = β ˆ 01 ⋅ ∆ _X_ denotes the main effect of the independent variables, that is, endowment or characteristic effects, and ( β ˆ 05 − β ˆ 01) _X_ 05 = ∆β ˆ ⋅ _X_ 05 the year effect, which represents changes in returns to specific characteristics.

12