rafmacalaba/datause-encoder-data huggingface dataset:
scheme. Our results thus indicate that employment subsidies are also effective in increasing formal employment among smaller firms, which are also presumably more prone to hire workers informally in the absence of employment subsidies compared to larger firms.

# 6.3. Distinguishing between job creation and formalization effects

A question of interest for policy purposes is whether the observed increase in formal employment originates from new job creation in treated firms or, instead, from the formalization of already existing jobs in the same firm. Our social security dataset, by design, does not capture workers who are employed informally, i.e. not registered with social security institutions. To provide some indicative evidence on whether the increase in employment was driven by formalization or new job creation, we use data from the nationally representative Household Labor Force Survey in Turkey, which asks each working individual in the household whether they are registered with social security institutions. To provide indicative evidence of whether the rise in formal employment was mostly driven by the formalization of existing informal jobs, we run similar regression as our firm-level estimation, but at the worker level. We run two separate regressions: one in which the dependent variable is a dummy variable for being wage employed, and a second specification where the dependent variable is a dummy variable for being a formal wage employee. Tables 9 and 10 show that while the likelihood of being a wage employee did not increase after the policy change, there was a statistically significant increase in the likelihood of being formally employed after the policy change. This indicates that the increase in formal employment originated mainly from the conversion of existing jobs into formal jobs, rather than from new job creation by firms.

In Figure 5, we follow Betcherman et al. (2010) by looking at changes in firm electricity consumption before and after the policy change in treated and control firms, as a proxy for economic activity. The rationale behind this corroborative analysis is that if firms actually hired new workers in treated provinces thanks to the subsidy, one should observe an increase in economic activity, proxied by electricity consumption, in those provinces after the policy change. In contrast, if the subsidy resulted in the pure conversion of existing informal workers into formal workers through social security registration, one should not observe any noticeable break in the trend in corporate electricity consumption post-2016 in treated provinces. Figure 5 does not show any break in trend in treated provinces after the subsidy implementation, in contrast with the control provinces where one observes an uptick in electricity consumption. This evidence is further indicative of the positive effects on formal employment being largely driven by the formalization of existing workers, rather than by new job creation.

17
database,<sup>11</sup> data on oil prices from BP energy statistics,<sup>12</sup> and data on the connectivity index and GDP from the World Bank database. Table 3 displays the descriptive statistics for variables used in the maritime transport costs and gravity estimations.

Data on annual container throughput, total terminal area, total length of berths, the number of mobile cranes with container-handling capacity, and the number of ship-to-shore gantry cranes come from various editions of the _Containerization International Yearbooks_ (2002–09).

Table 4 presents descriptive statistics by region. The average port in the sample moves about 900,000 TEUs annually. It has a terminal area of about 475,000 square meters, a total berth length of 1,501 meters, 7 gantry cranes, and 4 mobile cranes. The summary statistics show that East Asian and Pacific ports handle considerably more containers (by employing larger facilities) than African and South Asian ports.

**Table 4 Descriptive statistics of ports in database, by region**

|_Region_|_Number_<br>_of ports_|_Statistic_|_Annual_<br>_throughput_<br>_(TEUs)_|_Terminal area_<br>_(square meters)_|_Berth_<br>_length_<br>_(meters)_|_Number of_<br>_mobile_<br>_cranes_|_Number of ship-_<br>_to-shore gantry_<br>_cranes_|
|---|---|---|---|---|---|---|---|
|Africa|7|Average|531,338|457,002|1,254|0.31|5.84|
|||Standard<br>deviation|570,609|457,779|757|0.83|5.42|
|||Maximum|2,511,704|1,940,000|2,854|4|25|
|||Minimum|26,225|22,000|549|0|0|
|East Asia|14|Average|1,339,226|716,762|2,156|6.78|10.52|
|and<br>||Standard|1,696,850|693,046|2,143|10.57|13.48|
|Pacific||deviation||||||
|||Maximum|7,118,714|2,061,530|8,382|47|52|
|||Minimum|26,303|7,118,714|342|0|0|
|South<br>|14|Average|638,236|243,865|979|2.98|5.06|
|Asia||Standard<br>deviation|820,798|212,101|756|5.91|7.04|
|||Maximum|4,059,843|1,208,400|3,176|23|26|
|||Minimum|17,890|3,200|168|0|0|
|Total|35|Average|900,964|476,658|1,501|3.99|7.42|
|||Standard<br>deviation|1,273,409|544,606|1,576|8.07|10.25|
|||Maximum|7,118,714|2,061,530|8382|47|52|
|||Minimum|17,890|3,200|100|0|0|

_Note:_ TEU = 20-foot equivalent unit. Figures are averages for 2000–07 **.**

> 11 https://comtrade.un.org/.

> 12 http://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html.

13
# **Tables**

Table 1: Impact of Heat Exposure on Standardized Scores

|||Standardize|d Score||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|T_>_33C|_−_0_._009<sup>_∗∗∗_</sup>|_−_0_._009<sup>_∗∗∗_</sup>|_−_0_._007<sup>_∗∗_</sup>|_−_0_._007<sup>_∗∗_</sup>|
||(0_._002)|(0_._002)|(0_._003)|(0_._003)|
|T 30-33C|_−_0_._003<sup>_∗∗_</sup>|_−_0_._004<sup>_∗∗∗_</sup>|_−_0_._002|_−_0_._002|
||(0_._001)|(0_._001)|(0_._002)|(0_._002)|
|T 27-30C|_−_0_._00004|_−_0_._001|0_._002|0_._001|
||(0_._001)|(0_._001)|(0_._002)|(0_._002)|
|T 24-27C|_−_0_._002<sup>_∗∗_</sup>|_−_0_._002<sup>_∗∗∗_</sup>|_−_0_._003<sup>_∗_</sup>|_−_0_._003<sup>_∗_</sup>|
||(0_._001)|(0_._001)|(0_._001)|(0_._001)|
|T 21-24C|_−_0_._002<sup>_∗∗_</sup>|_−_0_._002<sup>_∗∗∗_</sup>|_−_0_._002<sup>_∗∗_</sup>|_−_0_._002<sup>_∗∗_</sup>|
||(0_._001)|(0_._001)|(0_._001)|(0_._001)|
|T_<_18C|_−_0_._003<sup>_∗∗_</sup>|_−_0_._003<sup>_∗∗_</sup>|0_._0003|0_._0004|
||(0_._001)|(0_._001)|(0_._002)|(0_._002)|
|Female Control||X|X|X|
|Avg T in First Half of June Control|||X|X|
|<br>Age Control||||X|
|<br>**Fixed Effects**|||||
|Year|X|X|X|X|
|School|X|X|X|X|
|Stream|X|X|X|X|
|Observations|2,132,635|2,132,635|1,082,496|1,082,496|
|R<sup>2</sup>|0.189|0.247|0.332|0.339|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variable is the standardized test scores of students taking the 12th grade exams and is constructed to have mean of (or close to) zero. All regressors include the number of days in the temperature bin for the school-year cycle: June-May. All regressions control for the number of days in the precipitation bins. Errors are clustered at the school level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

20
8 will be **the** degree to which<sup>these</sup><sup>**are**shifted upward by introductiori</sup> of **an** AL program.

The norm is derived in this<sup>study through regression analysis</sup> The fixed-effect econometric<sup>methodology used here Dermi%-s si.multaneous</sup> estimation of the normal response<sup>to the external environment (;he slope of</sup> C(T) in Figure 1), the country-specific<sup>economic performance (the OA and OB)</sup> and the average contribution<sup>to recipient countries of AL programs.</sup> III. Estimation of the Impact<sup>of Adjustment Lending.</sup> I have assembled data for 76 countries<sup>from the 1989 World Tables and</sup> World Debt Tables of the World Bank;<sup>the characteristics and precise</sup> definitions of these data and<sup>countries are given in the appendix. Economic</sup> growth (YGR) and the rate<sup>of domestic inflation (DINF) are indicators of</sup> internal balance: success<sup>in structural adjustment occurs when resources are</sup> being allocated efficiently<sup>and with little excess demand. The current</sup> account surplus or deficit (CAR)<sup>mearures performance in attaining external</sup> balance. Structural adjustment<sup>has most recently required adjustment to</sup> limited access to international<sup>credit, and this variable measures relative</sup> success in that. The investment<sup>ratio (IR) is a measure of intertemporal</sup> balance: even though policies<sup>and external '.vents may stimulate growth and</sup> adjustment, this will not be<sup>sustainable without a concomitant expansion in</sup> productive capacity.

I have collected as well comparable<sup>data on economic policy choices in</sup> these countries. The ratio of government<sup>current expenditure to GNP (GOV) is</sup> a measure of fiscal policy<sup>stance. The ratio of money, broadly defined, to</sup> nominal GNP (MON) is an indicator<sup>of the monetization and financial deepening</sup>
higher order thinking and life skills are still a big challenge and have been objectives of the latest two education reforms. The major goal is to bring the level up and reduce the percentage of students at the lowest international benchmarks.

Figure 3: Jordan’s Performance in the Program for International Student Assessment (PISA 2006) <!-- Start of picture text --> 100%<br>80%<br>Level 6<br>Level 5<br>60%<br>Level 4<br>Level 3<br>Level 2<br>40%<br>Level 1<br>Below Level 1<br>20%<br>0%<br>Reading Math Science<br><!-- End of picture text --> 7
|**Table 9: Earnings Regressi**<br>Dependent variable:Log of m|**ons - Lee Selecti**<br> onthly earnings<br>(1)|**on Correctio**<br>(2)|**ns**<br>(3)|(4)|
|---|---|---|---|---|
|Days per month (log)|0.519***|-0.263|0.518***|-0.265|
||(0.132)|(0.28)|(0.11)|(0.28)|
|CasualDummie|<br>-0.385***|<br>-0.409|<br>-0.386***|<br>-0.405|
||(0.069)|(0.26)|(0.09)|(0.26)|
|Age|0.002|-0.019|0.005|-0.007|
||(0.022)|(0.03)|(0.02)|(0.03)|
|AgeSquared|0.010<br>|0.037<br>|0.005<br>|0.021<br>|
||(0.031)|(0.04)|(0.03)|(0.04)|
|Sex|<br>0.398***|<br>0.366***|<br>0.401***|<br>0.371***|
||(0.069)|(0.11)|(0.09)|(0.11)|
|Schooling|-0.020|0.015|-0.013|0.013|
||(0.043)|(0.05)|(0.05)|(0.07)|
|Schooling2|<br>0.004|<br>0.003|<br>0.004|<br>0.002|
||(0.003)|(0.00)|(0.00)|(0.00)|
|Prog*Schooling|0.022|-0.077|0.034|-0.057|
||(0.081)|(0.09)|(0.07)|(0.09)|
|Prog*Schooling2|-0.003|0.0009|-0.004|-0.0003|
||(0.004)|(0.01)|(0.00)|(0.01)|
|Prior-Casualwork|0.213**|0.305***|0.21**|0.299***|
||(0.102)|(0.12)|(0.09)|(0.12)|
|Prior-employee firm|<br>0.216***|<br>0.153|<br>0.214***|<br>0.146|
||(0.068)|(0.09)|(0.07)|(0.10)|
|ApprenticeshipCompleted|0.084|0.142|0.086|0.15*|
||(0.065)|(0.09)|(0.07)|(0.09)|
|Contractor|0.347|0.452*|0.676***|0.469*|
||(0.382)|(0.26)|(0.17)|(0.26)|
|Program-Contractor|0.681***|0.499|-0.130|0.396|
||(0.163)|(0.48)|(0.36)|(0.49)|
|Program-Constructor|-0.077|0.781*|0.316|0.719|
||(0.428)|(0.45)|(0.32)|(0.46)|
|Workers||0.115**||0.11**|
|||(0.06)||(0.06)|
|Capital-Intensity||-0.002||-0.002|
|||(0.02)||(0.02)|
|Input-Intensity||<br>0.045||<br>0.044|
|||(0.03)||(0.03)|
|Lee|0.005|0.265|-0.032|0.216|
||(0.144)|(0.20)|(0.15)|(0.21)|
|V|||<br>-0.003|<br>0.026|
||||(0.04)|(0.05)|
|V2|||-0.003|-0.003|
||||(0.01)|(0.01)|
|VLee|||0.009|-0.006|
||||(0.03)|(0.04)|
|V2Lee|||<br>0.005|<br>0.005|
||||(0.01)|(0.01)|
|Constant|3026***|4994***|3001***|4937***|
||.<br>(0.611)|.<br>(1.04)|.<br>(0.58)|.<br>(1.06)|
|N|<br>0.357|<br>345|<br>550|<br>345|
|R2|550|0.272|0.359|0.275|
|Adjusted R2|0.325|0.205|0.322|0.198|

Note:

- Subcity dummies included but not reported

- V is the residual from the education model

- Bootstrapped standard errors

- *, **, *** indicate significance at the 10%, 5% and 1% level respectively

-19-
- [45] Swidler, Ann and Susan Cotts Watkins (2007), “Ties of Dependence: AIDS and Transactional Sex in Rural Malawi." Studies in Family Planning 38 (3): 147-162.

- [46] Townsend, Robert (1994). “Risk and Insurance in Village India.” Econometrica 62 (3): 539-591.

- [47] UNAIDS (2002), Sex Work and HIV/AIDS: UNAIDS Technical Update. Geneva: UNAIDS.

- [48] UNAIDS (2004), Epidemiological Fact Sheet –2004 Update (Kenya). Geneva: UNAIDS.

- [49] UNAIDS (2007). AIDS epidemic update: December 2007, Geneva: UNAIDS.

- [50] Wojcicki, Janet Maia (2002a), “Commercial Sex Work or Ukuphanda? Sex-for-Money Exchange in Soweto and Hammanskraal Area, South Africa.” Culture, Medicine, and Psychiatry 26 (3): 339-370.

- [51] Wojcicki, Janet Maia (2002b), “‘She Drank His Money:’ Survival Sex and the Problem of Violence in Taverns in Gauteng Province, South Africa.” Medical Anthropology Quarterly 16 (3): 267-293.

31
27 frequently used (and, we would argue, most sensible) recall period for inpatient care. Concretely, when a survey reported pap-smear (mammogram) utilization data for a recall period other than 5 (2) years, we transform the reported utilization rates to a 5 (2) year recall using the formula for the probability of an event over multiple trials, 1 ��1 �x�<sup>�/�</sup> , where _x_ is the percentage of women obtaining pap smears (mammograms) over the survey’s reported recall period _z_ (in years), and _y_ is our preferred recall period of 5 (2) years.<sup>30</sup> To obtain the adjustment factors for inpatient care data with less than 12 months recall, we exploit the availability of both 4-week and 12-month recall inpatient utilization data in the MCSS. Using the observed 4-week and 12-month inpatient utilization rates across 54 MCSS countries, and assuming zero utilization at time zero, we fit a nonlinear model of the relationship between time and utilization.<sup>31</sup> We then use the model to estimate hypothetical utilization rates for 2-week, 3-month, and 6-month recall periods. The adjustment factors are obtained by dividing the observed 12-month utilization rate by the respective estimated rates (and the observed 4-week rate).<sup>32</sup> Finally, the observed rate for the respective shorter recall period is multiplied with its adjustment factor to estimate the 12-month utilization rate.

# <u>Data processing process</u>

Figure 6 summarizes the data-processing steps. Whenever possible, we first generate from the raw household survey microdata. In these microdata sets, we generate a standardized or harmonized microdata set that contains our standardized indicators. The rates for each survey are then compared to the rates reported elsewhere in a quality-control exercise (see below for further details). Rejected datapoints are dropped. The remaining datapoints are consolidated into a ‘meso data set’, which has one row per survey-indicator combination, e.g. Armenia/2010/DHS/cervical

> 30 For surveys where the recall period is unspecified (‘Have you _ever_ had a pap smear/mammogram?’), we assume z� ������ � β, where α and β are the upper and lower bounds, respectively, of the age-group for which the survey question is asked (e.g. 49 and 30 for pap smear, and 69 and 50 for mammograms).

> 31 The fitted model takes the form y = -4E-05x2 + 0.0044x.

> 32 The adjustment factors to a 12-month recall utilization rate are 14.82 for 2-week, 7.58 for 4-week, 2.54 for 3-month and 1.47 for 6-month recall.
In order to dig deeper into these findings, we ask if the average impact of water shortages on firm output across our sample differs for water-intensive industries. More water-intensive industries may react differently to water shortages. For instance, water-intensive industries may on the one hand be more vulnerable to water shortages than non-water-intensive industries, or, on the other hand, may be more likely to have adapted to water infrastructure deficits by, for example, securing access to alternative water sources to those supplied by public utilities. Thus, the sample is split into two – one with water-intensive manufacturing industries and the other with other manufacturing industries that are not water-intensive. The water-intensive manufacturing sectors are chemicals, basic metals, paper, food, and refined petroleum.<sup>8</sup> Results are presented in columns 1 and 2 of table 5. Water shortages have a negative effect on sales regardless of the water intensity of the manufacturing sectors, statistically significant at the 10% percent level. However, the magnitude is almost twice as large for water-intensive sectors as non-water intensive sectors. This finding highlights the vulnerability of water-intensive sectors to poor water infrastructure.

Magnitude of the effects In table 6 we present the magnitude of the effects. An additional increase in water outage incidents in a day experienced by a firm can lead to annual average losses of around US$1,052,645. This represents about 8.7% of the average annual sales of manufacturing firms in the sample. For firms in low and lower-middle income economies, such an increase in the frequency of water outages could reduce sales by US$1,082,400 (an 8.2% loss based on average firm sales in these economies). As Table 6 shows, the elasticity between water shortages and sales is 0.002; that is, a 1% increase in water shortages per day results in an approximately 0.002% decline in sales. A one standard deviation increase in incidents of water shortages per day (equivalent to 0.19 extra shortage) results in a loss of US$268,699 in revenues for the average firm in our sample, which is about 1.8% of the average revenues. For firms in low and lower-middle income economies, a one standard deviation increase in outages is associated with US$271,628 in lost revenues.

> 8 Water intensity can differ for the same industry across economies. Economies can use different amounts of water to manufacture the same product. Data on water intensity for specific manufacturing industries in developing economies are sparse, thus the categorization of water-intensive industries is based on data from European economies. Sources: <u>http://ec.europa.eu/eurostat/statisticsexplained/index.php/Water_use_in_industry#Water_use_in_industry, http://www.wrap.org.uk/sites/files/wrap/PAD101-201%20-%20Manufacturing%20sector%20water%20report%20-</u> - <u>%20FINAL%20APPROVED%20for%20publication%20 %2012,03,12.pdf</u>

14
the same. But this assumption may not be tenable if temperatures increase across the board and inter-regional labor migration is high. Since such migration flows may respond very little to a nationwide warming that does not alter relative wages across regions, local labor supplies in the future may be less elastic than they have been in the past. Consequently, the present cross-sectional climate-wage differential could understate the wage response to a generalized warming. On the other hand, since labor mobility within India has historically been quite low (see, e.g., World Bank, 2010), this underestimation may not be too serious.

# **3.1 Irrigation in the Ricardian framework**

Within the Ricardian literature, the treatment of irrigation has generated considerable debate (see Cline, 2007, for an overview). Should one control for irrigation in a regression of farm profits or land values on climate, and how? Schlenker et al. (2005) answer the first question in the affirmative; irrigated land is not only more productive than rainfed land, but also differs in its responsiveness to climate variation. However, the coefficient on _θ_ in a Ricardian regression in which irrigation infrastructure is also included as a control asks a specific question: What is the impact of climate change on farm productivity holding the stock of irrigation fixed? This question is relevant only if the stock of irrigation is indeed expected to remain fixed. Otherwise, an expansion of irrigation infrastructure must be taken into account in forecasting the impact of climate change on future agricultural productivity.<sup>11</sup> Since our objective is merely to estimate the separate climate sensitivities of irrigated and unirrigated land, there is no need to model the future trajectory of irrigation. To see why this is so, while laying out the empirical methodology, consider a regression based on a large sample of individual plots of land _p_ located in different cross-sectional units (e.g., districts) _i_ of the form where _Ipi_ = 1 if plot _p_ is irrigated (0 otherwise), and _μi_ and _ζpi_ are error terms representing, respectively, common geographic-level and idiosyncratic plot-level unobservables. Local climate, _θi_ , affects land productivity directly and interactively with irrigation; for simplicity, other agronomic factors, _Zi_ , are assumed to enter only directly. Notice that the climate sensitivity of overall land productivity, _E_ [ _d_ log( _π_ ) _/dθ_ ] = _α_ + _β_ Pr( _I_ = 1)+( _βθ_ + _γ_ ) _d_ Pr( _I_ = 1) _/dθ_ ,

> 11Kurukulasuriya and Mendelsohn (2007) treat irrigation as an endogenous variable in a Ricardian framework, using irrigation potential (water flow) as an instrument. If the goal is simply to estimate the overall effect of climate on farm productivity, however, structural modeling has no advantage over a reduced-form that controls for irrigation potential directly.

8
needs. These costs are organized according to a selected list of poverty gap correlates derived from the previous regression analysis.

<!-- Start of picture text --> Daily costs of basic needs per one refugee to be covered in FCFA across different characteristics<br>Figure 9: Daily cost of basic needs assistance per Figure 10: Refugee characteristics<br>refugee<br>50.0<br>317<br>45.0 43.0<br>300<br>40.0<br>34.3<br>35.0<br>200<br>30.0<br>355<br>321 325<br>301 25.0<br>282<br>267<br>20.0<br>100<br>15.0<br>10.7<br>10.0<br>0 5.0<br>At least Less than Some No More than Less than<br>primary primary connection connection half half<br>level members member are 0.0<br>are dependent Head completed at Social connection Less than half<br>dependent least primary outside of the camp member are<br>education dependent<br>Head's education Social connection outside Share of dependent<br>camp<br>Cost to cover Cost to cover under current scenario<br>Source: EHCVM 2018, Authors’ calculations<br>FCFA Percent<br><!-- End of picture text --> The descriptive analysis presented in Figure 9 indicates that the dependency ratio of households emerges as the most important factor in reducing the cost of basic needs aid for the Sudanese refugee population under the current scenario. For instance, refugee households with more than half of their members being dependent fall short some 88 FCFA per day and per person compared with those where the majority of the members are active (aged between 15 to 64). Hence, the active individuals are the most likely to contribute to a refugee household generating income source. Besides, human capital also emerges as a significant factor correlated with a decrease in the costs of basic needs under the current scenario. Notably, achieving at least a primary level of education for a Sudanese refugee household head is associated with an important decrease of more than 10 percent. For instance, the cost of basic needs drops from 321 FCFA per day and per person for refugee households with a household head having less than a primary level of education to 282 FCFA.

In addition to human capital, the social capital of Sudanese refugee households is a somewhat equally important factor correlated with a decrease in the costs of basic needs under the current scenario. Specifically, the costs of basic needs are more than 24 FCFA per day and per person for refugee households with some connection outside the camp, compared to those with no connection.

Figure 10 reveals that the characteristics associated with a significant decrease in the cost of basic needs for Sudanese refugees are unfortunately underrepresented in their population in Chad. Notably, the majority of these refugees belong to households with relatively more dependents, no connections outside the camp, and with a head that has limited educational attainment. For example, only 11 percent of the 14