rafmacalaba/datause-encoder-data huggingface dataset:
4

# **II. The Role of External Factors on the Regional Slowdown during 1998-1999**

A noticeable feature of regional output growth in 1998 and 1999 was the magnitude and persistence of the slowdown. Casual examination of quarterly GDP growth rates (Fig. 1) shows that the recent slowdown is more persistent than that experienced during the Tequila episode<sup>3</sup> . Regional GDP annual growth rates fell from 5.4% in 1997 to 2.3% in 1998 and to an expected -0.3% in 1999 (Table 1). In 1998 and 1999 the eight largest Latin American economies experienced decelerations ranging from -13.5 to -2.8 percentage points. The 5.7% regional slowdown experienced between 1997 and 1999 is only comparable, in magnitude, to the initial deceleration registered in 1982.

<!-- Start of picture text --> Figure 1<br><!-- End of picture text --> <!-- Start of picture text --> Latin America Big 8*: Quarterly Gross Domestic Product<br>(Neighted y-o-y growth rate)<br>-2 a<br>911 913 921 923 931 933 941 943 951 953 961 963 971 973 981 983 991<br>Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Pe- and Venezuela.<br><!-- End of picture text --> The widespread nature of the slowdown strongly suggests that common factors were at play, besides the particular domestic circumstances of each country. In this section we seek to quantify the relative importance of external factors vis-a vis the internal ones accounting for the regional slowdown during 1998-1999.

> 3Comparisons with the eighties using quarterly GDP data was not possible due to non-existent data. A more thorough examination of business cycles in Latin America during the eighties and the nineties, probably using monthly industrial output could be an interesting task, but falls beyond the scope of this paper.
The program received approximately 6,000 applicants. Interested applicants had to submit a one‐page written proposal for a new business idea. The document had to be written in English, although communication materials were also provided in Juba Arabic. In addition, the applications required proof of their South Sudanese nationality and documents needed to open a bank account. This application process was designed to incentivize positive self‐selection into the sample. In this sense, the program participants may be better equipped to use the cash grant successfully to improve their business or employment situation than the average population. From the received 8,240 applications, 4,699 were found to be eligible.<sup>2</sup> From these eligible applicants, 1,200 were randomly selected to receive the grant and 1,200 were randomly selected for study in the control group, with equal proportions per state and by gender. The baseline survey was conducted between January to March 2015 and data were collected from 1,144 treatment participants and 1,148 control participants. Approximately 4.5 percent of initially identified study participants could not be tracked and did not participate in either the baseline survey or the program. The baseline survey was concluded prior to the commencement of the one‐week training that was held across the 6 states between April and May 2015.

_<mark>Figure 1 Treatment streams of original and new intervention.</mark>_ <!-- Start of picture text --> Business Startup Competition<br>(Participants interviewed at<br>baseline, Feb‐Mar 2015,<br>N=2,292)<br>Program cancellation<br>(Participants reached in endline<br>survey, Sep‐Dec 2017, N=1,507)<br><!-- End of picture text --> <!-- Start of picture text --> Randomly<br>selected Eligible<br>applicants<br>(N=2,292)<br>Treatment ‐<br>Control offered training<br>and grant<br>(N=1,148)<br>(N=1,144)<br>Non‐ compliers<br>Training but no Training and ‐ Did not attend<br>Control<br>grant grant training or open<br>(N=757) (N=408) (N=210) bank account<br>(N=132)<br><!-- End of picture text --> The intensification of violence between 2015 to 2017 forced many study participants to migrate reducing the number of participants that could be located for the endline survey. About a quarter of the population of South Sudan was displaced during the study period, which made it difficult to locate all participants of the original control and treatment group. Before the endline survey, the World Bank

> 2 Of those deemed ineligible, the desired purchase of land was the main reason. Other explanations included blank or unrealistic business ideas, age listed outside target range (18‐35 years), no identification attached, or not being South Sudanese.

7
<!-- Start of picture text --> Figure 2 : Migration and Fertility in MENAcountries<br>(at the time of maximum variation in fertility)<br>8<br>Gulf Migration System Yemen<br>7 Gulf<br>Palestine Syria<br>6 Jordan<br>Egypt<br>5<br>Algeria<br>Morocco<br>4 Tunisia<br>Lebanon Turkey<br>3<br>2<br>Europe<br>Western Migration System<br>1<br>-100% -80% -60% -40% -20% 0% 20% 40% 60% 80% 100%<br>Cumulated Migration Rate 1950-90<br>Source: see Table 5<br>Total Fertility Rate 1985-90<br><!-- End of picture text --> As soon as a surprising pattern of Mashreq-Maghreb demographic differentials became clearly revealed by large fertility surveys in the late 1980s-early 1990s, the idea was advanced that “cultural models encountered through international migration to a certain extent reinforce the geography of demographic transition : the Maghreb, which has a foothold in Europe through its émigrés, has experienced a marked decline in its birth rate whereas the Egypt of the _Infitâh_ , strengthening its Arab exchanges by a million and a half expatriates in the Gulf, saw it rise again temporarily from 1974 to 1985. [...] Migration itself contributed to the renewed rise in the birth rate, because of the Egyptians’ contact with the large family standard common in the Gulf—footnote : For the same reason, one could assume that migration to Europe has accelerated the decline of the birth rate in the Maghreb” (Fargues 1993: 164). Later on, a comparison between Morocco and Egypt reiterated the hypothesis that emigration contributes to the diffusion in emigrants’ home countries of ideas and models 16
# **3. Results**

Before turning to the estimates of equation (1) we motivate our results by showing school attendance for applicants as a function of the ranking based on the dropout-risk score, relative to the cutoff. We do this showing both the raw average attendance at each value of the relative rank, as well as a quartic in the relative rank.<sup>11,12</sup> Figure 1 has two panels, corresponding to differences between children offered $45 scholarships and those offered no scholarship (left-hand panel), and those offered $60 scholarships compared with those offered $45 scholarships (right-hand panel). In each case, distinct “jumps” at the cut-off would suggest that the program affected the behavior of applicants.

The left-hand panel of Figure 1 clearly suggests that children who were offered the $45 scholarships were more likely to be attending school on the day of the visit than those who were not offered a scholarship; the difference in attendance rates is large, around 25 percentage points. The righthand side of the panel suggests very little (if any) difference between children who were offered a $60 scholarship and those who were offered a $45 scholarship.

Our main set of results is presented in Table 2. For every specification, we present three panels. In the top panel, the sample is limited to children who were offered a $45 scholarship and those who were offered no scholarship (excluding children who were offered a $60 scholarship); in the middle panel, the sample is limited to children who were offered a $45 or a $60 scholarship (excluding children who were not offered any scholarship); the bottom panel compares the magnitude of the program effects from these two specifications by reporting the average percentage point increase in attendance per dollar of scholarship transfer.

11 Because the cut-off falls at different values of the underlying score in different schools, depending on the number of applications, the mean characteristics of applicants, and whether a school was defined as “large” or “small”, it is not informative to graph outcomes as a function of the score. Rather, for these figures we redefine an applicant’s score in terms of the distance to the school-specific cut-off, so that (for example), a value of -1 represents the “nextto-last” applicant to be offered a $45 (or $60) scholarship within a school, 0 the “last” applicant offered a scholarship, and a value of +1 represents the “first” applicant within a school who was turned down for a scholarship of that amount. The figures then graph outcomes as a function of this relative rank.

12 As in our main analysis, these figures pool data from the four visits. The regression lines in these figures are based on a single quartic in the relative rank, rather than the school-specific parametrization of the dropout-risk score; also, they do not include the vector of school fixed effects or indicator variables for school visit.

9
# High school students

<!-- Start of picture text --> 14% Attend 4-year college<br>27%<br>Attend vocational college<br>25%<br>Work after graduation<br>34%<br>It does not matter<br><!-- End of picture text --> _Source:_ Authors.

Figure 4.7 Students’ opinions on how well completing middle school and high school would prepare themselves for the labor market <!-- Start of picture text --> 100%<br>90% 21%<br>80%<br>49%<br>70%<br>60%<br>50% 60%<br>40%<br>35%<br>30%<br>20%<br>10% 10% 12%<br>0% 2% 4% 3% 4%<br>Middle school High school<br>Completely sufficient Sufficient Almost sufficient<br>Insufficient Completely insufficient<br><!-- End of picture text --> _Source_ : Authors.

Correlation analysis suggests that preference for school tracks is closely associated with perceived chance of entering the academic track. According to correlation analysis, household preference for academic high school versus vocational school is not significantly correlated with parents’ education level and family wealth. Rather, the choice for school track is strongly correlated with students’ school performance (self-evaluated), which is a proxy for the chances to entering academic track versus vocational track. In other words, the household choice for the type of high school is strongly affected by the number of slots available in academic schools versus vocational schools and their chances of obtaining the admissions to the academic schools based on entrance exams.

Pursuit of tertiary education through academic high schools is widely recognized among parents and students while less so for secondary vocational school graduates. Based on the feedback from the interviews, the common perception for the academic track is that it is key to greater economic opportunities and beneficial for personal development and a high-quality academic secondary school is widely perceived as a pathway to first-tier universities, higher education degrees, and better economic opportunities outside of the province.

51
# **3.1 Sample Size and Sampling Strategy**

Originally, a stratified random sampling from FICCI‘s member database of over 3,000 firms was considered for the Employer Satisfaction Survey. Sample size was calculated based on the following formula.

where _n_ is the sample size. A 90% confidence interval with margin of error 0.05 was applied. _z_ is the abscissa of the normal curve that cuts off at a given significance level, i.e., 1.65, _p_ (in this case 0.6) is the estimated proportion of an attribute that is present in the population, _q_ is 1- _p_ , and _e_ is the desired level of precision, i.e., 0.05. Using this formula, the originally estimated sample size was about 260, and it was further proportionately allocated to FICCI‘s classification of 17 economic sectors.

There were several difficulties in sampling. First, although the sampling method was originally a stratified random sampling, some member companies were directly contacted to participate in the survey, due to a low response rate (convenience sampling). This may have introduced a bias in the representativeness of the sample. Second, the web-based survey was opened in the last few weeks of the survey to all companies that registered. This slightly increased the sample size. This self-selection could also have caused a selection bias. Third, the sample size was not large enough to meet a 90% confidence interval. Due to time constraints, it was reduced from 260 to 157 companies, i.e., an 80% confidence interval with a margin of error of 0.05.

Despite these shortcomings, the study brings value since it is the first of its kind in India. Further, the participation number of 157 is comparable to other employer satisfaction surveys. The employer satisfaction survey is expected to be conducted every two years. Therefore, the sampling methodologies and the survey design are expected to improve in future rounds, and the quality of the data will be further enhanced over time.

# **3.2 Survey Design (Preparation and Implementation of the Survey)**

NPIU, FICCI and World Bank held a series of interviews with employers. Suggestions provided by employers were incorporated into the questionnaire. As a result, the overall survey design and instrument were improved over the course of pilot surveys.

During the pilot surveys, the employers were asked four specific questions to improve the survey questionnaires and implementation. The four questions were: First, ― _Who will evaluate employer’s satisfaction”?_ (Human resource department, supervisors of newly hired, or a third person). Presumably, employers are in the best position to identify appropriate evaluator of the fresh engineering hires. Therefore, the survey invitation was sent to the human resource department which decided the responsibility to complete the survey.

7
_33_ 3.2.2. Export crop production versus food crop production'<sup>9</sup> "Higher aggregate crop prices and lower agricultural costs increase the profitability of crop production, thus encouraging an aggregate expansion of agricultural production through either agricultural intensification or extensification; the impact of agricultural pricing on the relative returns to agricultural production can influence long-run decisions to invest in sustainable land management and conservation; changes in the relative prices of crops (and crop inputs) can influence the substitution of more environmentally benign cropping and farm production systems for systems that are more environmentally damaging; the variability of crop prices and crop price inputs can affect the farmers' choice of the method and type of crops grown, and decisions to invest in sustainable land management, by affectincg the risk associated with alternative agricultural investments and production systems" (Barbier and Burgess, 1992, p.1).

This quote emphasizes arnong other things that pricing policy does dictate the choice of crop to be grown, under certain conditions. Indeed, linited evidence from Indonesia and Malaysia corroborate this fact (Barbier, 1989, 1991). This naturally has a bearing on the environment. Note that although erosion rates vary greatly according to types of crops and farming production (cultivation practices), the type of crop grown is the key influence on the erosion rate.

In this context, two tendencies clearly emerge. On the one hand, there are a number of authors who believe that cash crop or export crop production is detrimental to the environment. Maxwell and Fernando (1989, p.1689) summarize well the position of the protagonists (see references in that paper): (a) the introduction of new export crops and generally unsuitable practices lead to soil erosion, desertification, water pollution and salination; (b) "cash cropping adds to the value of land and leads to land mining or the incorporation of previously uncultivated land, which is often unsuited to cash cropping"; (c) the use of pesticide in cash cropping causes problems for the environment, workers

> **19** While the qualitative impact of prices and other factors on the environment is somehow substantial, the quantitative evidence is really meager. There is an urgent need for quality data to undertake serious quantitative studies.
Table 10: Product Classi...cation Frauds: Second Stage IV Regressions

|Dependent variables|Quant|ity Discrep|ancies|Valu|e Discrepa|ncies|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|AVEborder|1.868***|1.977***||1.965***|2.051***||
||(0.223)|(0.239)||(0.213)|(0.227)||
|Tari¤||0.0973**|||-0.0479||
|||(0.0471)|||(0.0487)||
|AVEborder + Tari¤|||0.260***|||0.134**|
||||(0.0511)|||(0.0550)|
|SPS/TBT NTMs|||-3.561***|||-2.668**|
||||(1.223)|||(1.190)|
|Other NTMs|||-3.105***|||-3.063***|
||||(1.178)|||(1.159)|
|Observations|256,565|250,285|152,919|251,785|245,466|150,418|
|Product FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-Exporter FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Robust standard errors in parentheses are clustered by importer-product; *, ** and *** indicate that coe¢ cients are signi...cant at 90%, 95% and 99%, respectively.

Table 11: Product Classi...cation Frauds: First Stage Instrumental Variable Regressions

|Dependent variables|AV<br>(3)|E<br>|AVE+Tari¤|(6)|AVE|AVE+Tari¤|
|---|---|---|---|---|---|---|
|Average AVE of exporter|0.636***|0.610***||0.650***|0.625***||
||(0.030)|(0.031)||(0.030)|(0.031)||
|Tari¤||0.023***|||0.023***||
|||(0.005)|||(0.005)||
|Average AVE of exporter|||0.990***|||0.984***|
|+Tari¤|||(0.021)|||(0.022)|
|SPS NTMs|||6.877***|||6.828***|
||||(0.370)|||(0.369)|
|Other NTMs|||-0.285|||-0.235|
||||(0.461)|||(0.464)|
|Product FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-Exporter FE|Yes|Yes|Yes|Yes|Yes|Yes|
|F-Statistics|445.38|396.27|2147.43|472.86|419.99|2088.07|
|Observations|256,565|250,285|152,919|251,785|245,466|150,418|

Notes: Standard errors are clustered by importer-product , ** and *** indicate that coe¢ cients are signi...cant at 90%, 95% and 99%, respectively 45
expenditure per capita and the location of refugee settlement sites. I estimate a household fixed effects variant of equation (1) to eliminate the concern from such characteristics and present the result in column (3). To partially circumvent the remaining concern from time-varying factors, I additionally control for many (12) household- or zone-level time-varying variables in the household fixed effects specification and report the result in column (4). The estimates in these last two specifications remain consistent with the estimate in column (2), confirming that hosting refugees has a significant negative effect on household consumption expenditure per capita. Specifically, on average, the estimated elasticity of household consumption expenditure per capita to refugee intensity is about -0.19.

This finding is not in line with the existing empirical evidence on the short-term household consumption impact of hosting refugees (Kreibaum, 2016; Rozo and Sviastchi, 2018). Kreibaum (2016) shows that hosting Congolese refugees in Uganda is beneficial and Rozo and Sviastchi (2018) find that hosting Syrian refugees in Jordan is immaterial. Unlike the case in my paper, it is important to note that these studies employ repeated cross-section data and the overwhelming majority of refugees considered in Rozo and Sviastchi (2018) are out-of-camp refugees.

Additionally, the documented adverse consumption effect of hosting refugees is not consistent with 80%-90% of the existing evidence if the broader literature that examines the shortor long-term welfare impacts of hosting refugees, expellees, or internally displaced persons is considered, as per the survey in Verme and Schuettler (2021).

As there are differences between rural and urban areas in, among others, the economic structure, the skill levels and skill mix of individuals, and access to and type of financial services, which may be useful in smoothing household consumption, the aggregate estimate may mask important difference in the consumption impact of hosting refugees between these areas. Thus, I next examine if there is heterogeneity in the estimated impact according to urbanity. Columns (5) and (6) in Table 4 present separate results for rural and urban households, respectively. For brevity, I only report estimates from a household fixed effects specification, controlling for numerous (12) time-varying characteristics. The findings show that hosting refugees adversely affects household consumption expenditure per capita in rural areas while it has no statistically significant effect in urban centers.

Decomposing household consumption expenditure per capita into food, education, and other non-food components, I next explore whether hosting refugees alters the composition of 20
_89_ Table 7.3 shows estimated loss in consumer surplus for households disaggregated on the basis of total household expenditures. The estimates are made using a "worst case price elasticity of 0, giving an upper boundL Welfire loss is calculated for the electricity price change from 1987 to 1982 and is expressed as a percentage of total expenditures for the relevant group of households.

_Table 73: lItrke Household Monthly Income and Estfmated Welfare Loss due to Domestfic Electfldty Price Changes, 198748 (fncome thousand ll;_

|Incomegroup|Urban (%/l)|Ruraln%)|Alle/()|
|---|---|---|---|
|0- 49.9<br>|2.4<br>|1.8<br>|1.9<br>|
|50- 99.9<br>|_1.5_<br>|1.0<br>|1.2<br>|
|100- 149.9<br>|1.2<br>|0.8<br>|1.0<br>|
|150-199.9<br>|1.1<br>|0.7<br>|0.9<br>|
|200 -249.9<br>|1.0<br>|0.6<br>|0.7<br>|
|250 -299.9<br>|0.9<br>|_0.5_<br>|0.7<br>|
|300-349.9<br>|0.8<br>|_OA_<br>|0.7<br>|
|350-399.9<br>|0.7<br>|0.5<br>|0.6<br>|
|400 -449.9<br>|0.7<br>|0.4<br>|0.6<br>|
|450 -499.9|0|0|0.6|

_Now._ **Los** in **consunm** surplus **exp** d **as** a peceta of median houshold **iucomt** The **iucome groups** Wren aoount **for nerly** 82p d 89 pece offte ua dn housedhlds respectively. The figues are based on an asumpton thatpc elaticity of dman is equal to zeo and thus represent a"wost case _sa._ As the estimates above show, welfare effects vaiy according to the household group. In genora, low-income households suffer a greater welfare loss as compared to the higher-income households. This is due to the relaively igher share of electricity in total expenditures of the low-icme groups. Also, utan houeholds loss of welfare is slightly above that of the rural households as heir w endie sares for electricity are generally higher. In tms ofelecr subsidies, the average price of electricity during 1986-88 ws m n at around 6 US cents per kilowatt hour. Ths was relatively close to dte long-nm iementa cost of power system expansion in Turkey. In this respect Turkey was one of the few developing coumtries with no subsidy for domestic electricity price during the period under study."' s5 To other dveloping cubis in his group weo Ediopia, Gambia, Ivkey Coat, ea, M adagacr, Mal, Mozabre Fji _Kr.,_ Malaysif , Pip _Now_ Guinea, P pne, Cyprus, Jordan, Morocco, Yeoe AR and Ym **PD. Seo** Wodd Bank (1990), **Reviow** of **Electicit** Tariffs in **Doveping** Couties During **th 1980s,** Ee **Seri** Papr No. **32, E** **_aul_** Industy Deput_et Vorld.g **Pwper,** Wasington, DC.