rafmacalaba/datause-encoder-data huggingface dataset:
# <u>Table 4- Cross-country regressions using fiscal policy volatility calculated with a panel regression</u>

|Dependent variable: log of<br>volatility of consumption||OL|S|||IV-|1|||IV-2||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Fiscal policy volatility<br>(calculated with a panel<br>regression)|**0.656**|**0.455**|**0.503**|**0.338**|**0.653**|**0.436**|**0.401**|**0.072**|**0.384**|**0.515**|**0.260**|
||(0.000)|(0.000)|(0.000)|(0.002)|(0.000)|(0.000)|(0.056)|(0.803)|(0.001)|(0.000)|(0.048)|
|Government size||**0.266**|**0.233**|**0.189**||**0.312**|**0.278**|**0.128**|**0.118**|**0.551**|**0.235**|
|||(0.047)|(0.070)|(0.161)||(0.027)|(0.042)|(0.515)|(0.600)|(0.021)|(0.242)|
|Trade||**0.0702**|**0.218**|**0.137**||**-0.00607**|**0.169**|**0.125**||||
|||(0.397)|(0.013)|(0.060)||(0.948)|(0.063)|(0.099)||||
|Volatility of income||**0.495**||**0.464**||**0.561**||**0.620**|**0.599**||**0.550**|
|||(0.000)||(0.000)||(0.001)||(0.000)|(0.000)||(0.000)|
|Persistence of income||**0.151**||**0.106**||**0.158**||**0.0809**|**0.168**||**0.124**|
|||(0.118)||(0.272)||(0.112)||(0.453)|(0.094)||(0.170)|
|Financial liabilities|||**0.101**|**0.144**|||**0.0212**|**-0.00250**||**0.0684**|**0.0995**|
||||(0.274)|(0.075)|||(0.890)|(0.986)||(0.633)|(0.347)|
|Volatility of financial liabilities|||**0.341**|**0.239**|||**0.370**|**0.275**||**0.355**|**0.258**|
||||(0.000)|(0.006)|||(0.002)|(0.011)||(0.000)|(0.004)|
|Financial openness|||**-0.189**|**-0.175**|||**-0.240**|**-0.265**||**-0.190**|**-0.183**|
||||(0.044)|(0.030)|||(0.048)|(0.024)||(0.078)|(0.024)|
|Adjusted R-squared|0.64|0.75|0.75|0.80|0.63|0.74|0.74|0.77|0.74|0.73|0.78|
|Test of OID (p-value)|-|-|-|-|0.30|0.39|0.27|0.59|0.76|0.16|0.25|
|Number of countries|84|83|81|80|77|76|75|74|75|75|74|

NB: The p-values in the parentheses are based on heteroscedasticity-robust standard errors. In the IV regressions, the over-identifying test (OID) reports the p- value from the Sargan-Hansen J-test that the instruments are uncorrelated with the residuals. All regressions include an intercept.
unaffected districts, which provide two additional control groups. Reduced-form impacts of the shocks were estimated using the following general empirical relationship between the long-term outcome _Y_ of individual _i_ in district _d_ and at time _t,_ and the shock indicators: ���� = ∑����(��,� ∗Shock�, in utero��� + ��,� ∗Shock� ∗1st year of life���) + District FE + Cohort FE+ ����′�+ ���� (1) Where the parameters of interest are ��,� and ��,� , which measure the effects of either extreme droughts ( _s_ = 1) or extreme floods ( _s_ = 2) on the outcomes of interest, whether they affected the child while in utero (indicated by the sub-index _u_ ) or during the first year of life (indicated by the sub-index _m)_ . The empirical model also includes controls for district and cohort fixed effects, as well as gender and other individual-level covariates (e.g. school attainment), which are captured in the term ����.<sup>1</sup> Finally, the term ���� stands for an individual level zero-mean error term. Standard errors are clustered at the same level of the treatment, namely by district of birth-year. The results of all the empirical models are presented both for the whole sample of adults in the surveys of the census, irrespective of the area of birth (urban or rural), and solely for individuals born in rural districts.

# _Data_

This type of analysis requires extensive data to match indicators of long-run individual wellbeing with rainfall anomalies that occur around the time of birth. For the weather information, we use extensive spatial and temporal gridded monthly precipitation data for global land areas for the period 1901-2014, with a resolution of 0.5 x 0.5 degrees (50 x 50 km). This data set is known as CRU TS and is managed by the Climate Research Unit at University of East Anglia. The data are constructed using records from more than 2,400 meteorological stations across the world’s land areas. Station anomalies are interpolated into grid cells combined with local climatology data to obtain absolute monthly values. The variables included in the data set in addition to precipitation are mean temperature, diurnal temperature range, wet-day frequency, vapor pressure and cloud cover (Harris, et al. 2014). For the empirical analysis, all rainfall and temperature variables are

> 1 The empirical models were also run without controlling for the school attainment of the individual since this indicator is endogenous to the weather shocks.

7
output in 2010. Our dataset contains information on 152 firms (727 firm-year observations) for 18 subsectors of the machine building industry.<sup>5</sup> The employment growth rate for the full sample of firms (see Table 1) indicates that the average firm in the machine building industry reduced its employment by approximately 2.5% yearly over the period considered. Interestingly, however, a small fraction of outlying firms exhibited impressive growth rates, with a maximum amount of more than 200% employment growth. At a more disaggregated level, privately organized firms tended to outperform state-owned and mixed-owned firms in terms of employment growth. Accordingly, privately owned firms reduced their number of employees by 1.8%, on average, although the maximum (annual) increase in employment in this group of firms amounted to 24.3%. The average employment growth rates in the other two groups of firms are -2.6% and -2.5%, respectively. One should be cautious, however, when interpreting these results, given the relatively small number of privately-owned firms in this sample. Similarly, comparing firms directly reporting to the Ministry of Industry with non-ministry-reporting firms, we find that the former group performed significantly worse. The average ministry-reporting firm exhibits an annual growth rate of -4.1% while non-reporting firms, on average, keep their level of employment unchanged.<sup>6</sup> Focusing on different types of firms, such as head companies of vertical chains, affiliates of such chains and independent firms, the descriptive statistics with regard to employment growth do not reveal remarkable differences across these groups. However, these types of firms may differ in other respects that affect their growth pattern. In our econometric analysis we will use this classification in order to analyse the impact of vertical integration on the growth performance in the Belarusian machine building industries.

Disaggregating firm growth by sub-industry within the machine building sector reveals that

> 5These are the automotive subsector, bearings, chemical and petroleum engineering, construction and road and municipal engineering, conveying machinery, electrical, industry interdisciplinary productions, industry metalwork, instrumentation, tools, machinery for light industry, food industry and household appliances, metallurgical engineering, mining and mining engineering, production of sanitary and gas equipment, radio industry, repair of machinery and equipment and tractor and agricultural machinery.

> 6For reasons which will be discussed in more detail below, we will focus on the information regarding the obligation to directly report* to the Ministry of Industry as an alternative measure of state-ownership.

4
<!-- Start of picture text --> 250<br>gre rr m0<br>i eVegetabmOGL Ve<br>§ 100 aFruts<br>. =Dary<br>50 , : =Proteln=Starchy:f<br>o<br>1) Mean, HIES data 2)Median.HIESdata 3)Mean,HiESdata,CPI food list = data,4)Median,HIESCPI food list 5) CPi datalist and food<br>HIES vs CPI data for CoRD<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>ge ois<br>a= ; == VegetabDGL Ver<br>Bap = Fruits<br>: =Dary= Proten f<br>20 m Starchy:<br>o<br>1)Mean, HIICS 2)Median,HIICS 3)Mean, HIICS = 4)Median, HIICS 5) CPI data<br>data data Gata,CPI food list data,CPI food list food listand<br>HINCS vs CPI data forCoRD<br><!-- End of picture text -->
Nonetheless, the bias toward import-competing activities was partly offset by export subsidies, which increased relative to the value of exports by approximately 50 percent between 1983 and 1984, and thereafter declined." The incentives to export created by export policy therefore were counter to those created by exchange rate movements.

The net effect of these changes on the number of exporting plants and the proportion of plants that exported is summarized in the last two rows of Table 1. Again, the time-series pattem largely reflects the movement in the exchange rate. Through 1984, there was net exit from the export market, and a decline in the proportion of plants exporting. After that year there was net entry and a steady increase in the participation rate. There is, however, some evidence of asymmetry in the magnitude of the response. The modest 6 percent real appreciation between 1981 and 1983 was accompanied by a decline in the export participation rate from .129 tc .113, but a much larger (45 percent) depreciation between 1984 and 1989 served only to increase the participation rate to .135. From this short time series it appears that it took both a substantial and persistent devaluation to induce entry into the export market. This is consistent with the conjecture that potential exporters faced substantial start-up costs.

Survey evidence further suppons this hypothesis.'" First, to sell in developed country markets, Colombian producers wera often required to invest in product quality upgrading. Second, there was little exporting infrastucture in the form of trading companies or distribution agents. These companies typically provide transportation, customs, and shipping services, as well as information on " This reduction in export subsidies reflected a reduction in two government programs used to promote exports. The first program rebates customs duties paid on imported materials and capital equipment for plams that export. In 1980 41 percent of the value of exports came from plants that received rebates. This rose to 62 percent in 1984 and fell to 53 percent in 1986. The second program provides direct subsidies to exporters based on the value of their exports. The average rate of subsidy increased from 7.6 percent of the value of exports in 1981 to 15.0 percent in 1985 and tben fell to 8.7 percent in 1986.

I<sup>2</sup> lThe discussion in this section is based on World Bank (1992), which summarizes and interprets interviews with the managers of several hundred Colombian plants.

9
# **Table 23: Piecemeal Sensitivity Analysis of Tanzania-Africa FTA**

||**Par**|**ameter val**|**ue**|**% Chang**|**e in Welfa**|**re (EV)**|
|---|---|---|---|---|---|---|
|Parameter|Lower|Central|Upper|Lower|Central|Upper|
|σ(qi, qj)– services sectors|1.5|3|4.5|1.00|0.10|0.06|
|σ(qi, qj)–goods sectors||See below||0.11|0.10|0.09|
|σ(va, bs)|0.625|1.25|1.875|0.08|0.10|0.12|
|σ(D,M)|2|4|6|0.10|0.10|0.10|
|σ(L,K)|0.5|1|1.5|0.10|0.10|0.10|
|σ(A1,...An)|NA|0|0.25|NA|0.10|0.10|
|σ(D,E)|2|4|6|0.09|0.10|0.10|
|εTZA|Central va|lues of all 4 s|ets of ε|0.10|0.10|0.10|
|εEU|parameters|are listed in|table 6B.|0.10|0.10|0.10|
|εAFR|Lower and|upper value|s are 0.5|0.05|0.10|0.14|
|εROW|and 1.5 t|imes central|values.|0.10|0.10|0.10|
|θr|NA|0|1|NA|0.10|0.03|
|θr - CRTS model|NA|0|1|NA|0.05|-0.02|
|θm|0.025|0.05|0.075|0.10|0.10|0.10|
|σ(qi, qj)–goods sectors|||||||
|Processedfood|4.8|9.6|14.4||||
|Beverages & tobacco products|1.5|2.3|3.1||||
|Textile &leatherproducts|2.8|3.6|4.4||||
|Manufacture ofbasic &industrialchemicals|1.7|2.5|3.3||||
|Manufacture of fertilizers & pesticides|1.6|2.4|3.2||||
|Petroleum refineries|2.8|3.6|4.4||||
|Glass & cement|2.3|4.6|6.9||||
|Ironsteel&metalproducts|3.7|7.3|11||||
|Mining & quarrying|2.8|3.6|4.4||||
|Grain milling<br>**Key:**|2.4|3.2|4||||

σ(qi, <u>qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors σ(va, bs): Elasticity of substitution between value-added and business services σ(D, M): Elasticity of substitution between domestic and imported varieties σ(L, K): Elasticity of substitution between primary factors of production in value added</u> σ(A1,...An): Elasticity of substitution in intermediate production between composite Armington aggregate goods <u>σ(D, E): Elasticity of transformation (domestic output versus exports)</u> εTZA: Elasticity of national service firm supply with respect to price of output εEU: Elasticity of EU service firm supply with respect to price of output εAFR: Elasticity of AFR service firm supply with respect to price of output εROW: Elasticity of Rest of World service firm supply with respect to price of output θr: Share of rents in services sectors captured by domestic agents θm: Shares of value added in multinational firms due to specialized primary factor imports <u>Source: Authors’ estimates.</u> 91
Figure 3. Factorial decomposition of the change in intensity of tasks performed in the workplace a) Argentina - 1998 – 2015 <!-- Start of picture text --> 0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>NRC-A NRC-I RC RM NRM<br>-0.05<br>-0.10<br>-0.15<br>Between sectors Between occupations Within occupations Interaction<br><!-- End of picture text -->

# b) Uruguay - 1995 – 2015

<!-- Start of picture text --> 0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>NRC-A NRC-I RC RM NRM<br>-0.10<br>-0.20<br>-0.30<br>Between sectors Between occupations Within occupations Interaction<br><!-- End of picture text --> Source: Our own estimation based on household surveys and O*NET The results found allow us to suggest that the intra-occupational effect , that is, the changes in the combination of type of tasks within each occupation, constitute an important channel through which change is generated in the relative intensities of tasks in the average job. In this sense, 36% of the increase in non-routine cognitive tasks in Argentina is explained by this factor, although it is not significant in the case of Uruguay. In terms of the increase of the importance of routine cognitive tasks, 76% of this increase in Argentina, and 53% in Uruguay, comes from the changes generated within occupations. Inversely, this factor explains to a

- 17 -
US$1.90-a-day poverty lines. Moderate poverty showed a smaller decline, from about 44 to 34 percentage points in 1992–2006 and has remained stagnant since then. Nonetheless, these declines are relatively small compared with those in other countries typically mentioned as examples of successful export-led growth. For example, while the share of exports in GDP in Vietnam rose from 54.7 to 70.3 percentage points in 2002–08, the share of people living on less than US$3.20-a-day declined from about 70.8 to 46.8 percentage points. Measured according to the US$1.90-a-day poverty line, the decline was also large, from about 38.0 to 14.8 percentage points.<sup>7</sup> Table 1 shows descriptive statistics on the sample used in the estimations. The focus is on urban and semiurban areas (1,982 of 2,457 municipalities), given that these areas represent more than 99 percent of total exports over 2004–14 and two-thirds of rural municipalities show zero or close to zero exports. Municipalities with a higher ratio of exports per worker exhibit a lower incidence of poverty and higher incomes (figure 4). Income inequality is similar across these two groups of municipalities, with a Gini coefficient of about 0.38 or 0.39. Moreover, a simple inequality decomposition of the Theil index shows that within-municipality inequality accounts for about three-quarters of total inequality in Mexico. Understanding the role of trade in shaping inequality at the local level in Mexico is thus relevant because trade explains most of the inequality at the national level.

# **4. Results**

# **4.1.Poverty and Income**

Table 2 shows the estimates of equation 1 using the share of the extreme poor as the dependent variable. The preferred specification is in column (3), which controls for the share of the rural population, the literacy rate, total population, and the shares of workers by sector as well as median income per capita in 2000 to account for different trends in trade and labor market outcomes along these dimensions. Illustrated in columns 1–3, exports per worker are negatively correlated with poverty at the municipal level. The results are statistically

> 7 For the export data, see “Exports of Goods and Services (% of GDP): Vietnam,” World Bank, Washington, DC, https://data.worldbank.org/indicator/NE.EXP.GNFS.ZS?end=2018&locations=VN&start=1986. For the poverty data, see Poverty and Equity Data Portal: Vietnam (database), World Bank, Washington, DC, http://povertydata.worldbank.org/poverty/country/VNM. The US$5.50-a-day poverty line is not a good reference point for comparing Mexico and Vietnam because, during this period, the vast majority of Vietnamese households were poor according to this definition.

8
For the CATI survey, there is another round of data collection – a telephone survey – after the replacement of the sample. Around 10 percent of households that agreed to participate in the

**Table 3:** **_Ex-ante_ check of balance between the F2F and CATI samples**

|Census variables|F2F|U<br>CATI|rban<br>diff|P-value|
|---|---|---|---|---|
|Household size|2.64|2.68|-0.04|0.564|
|Highest HH education|||||
|Primary or less|0.081|0.075|0.005|0.934|
|Vocational school|0.092|0.084|0.009||
|High school|0.352|0.358|-0.006||
|Higher education/university|0.475|0.483|-0.008||
|||R|ural||
|Census variables|F2F|CATI|diff|P-value|
|Household size|3.12|3.22|-0.11|0.225|
|Highest HH education|||||
|Primary or less|0.191|0.196|-0.005|0.548|
|Vocational school|0.214|0.214|0.000||
|High school|0.415|0.381|0.033||
|Higher education/university|0.181|0.209|-0.028||

Source: Authors’ calculations using the census data. Note: The t-test and Pearson's chi-squared test are used for the household size and highest education, respectively.

**Table 4: Non-response for F2F and CATI groups**

|||**Urban**|||**Rural**||
|---|---|---|---|---|---|---|
||**F2F**|**C**|**ATI**|**F2F**|**C**|**ATI**|
||Survey|Baseline|Telephone|Survey|Baseline|telephone|
|**Planned sample size**|300|3|00|300|3|00|
|**Final sample size**<br>(% response)|295<br>(98.3)|299<br>(99.7)|270<br>(90.3)|296<br>(98.7)|300<br>(100.0)|273<br>(91.0)|
|- Replacements|117|125|-|96|102|-|
|(%)|(39.7)|(41.8)||(32.4)|(34.0)||
|- Non-response (phone)<br>(%)|-|-|29<br>(9.7)|-|-|27<br>(9.0)|
|**Reasons for non-response**|||||||
|- Refusal<br>(%)|33<br>(28.2)|33<br>(26.4)|10<br>(34.5)|40<br>(40.8)|32<br>(31.4)|12<br>(44.4)|
|- No one at home<br>(%)|66<br>(56.4)|72<br>(57.6)|17<br>(58.6)|48<br>(49.0)|51<br>(50.0)|11<br>(40.7)|
|- Dwelling unoccupied<br>(%)|12<br>(10.3)|13<br>(10.4)|0|10<br>(10.2)|10<br>(9.8)|0|
|- No telephone<br>(%)|0|1<br>(0.8)|0|0|2<br>(2.0)|0|
|- Unknown|6|6|2|0|7|4|
|(%)|(5.1)|(4.8)|(6.9)||(6.9)|(14.8)|
|Total|117|125|29|96|102|27|

Source: The authors’ calculations based on the survey implementation data. Note that the parentheses show shares.

12
over periods of up to 15 years. Although the delayed effect of NAFTA’s tariff reductions are not necessarily due to the phaseout of tariffs (Besedes et al., 2020), these similarities suggest the adjustment to major trade liberalizations can take a long time.

# **3.3 The Delayed Effect of China’s NTR Liberalization**

We first document that the NNTR gap, commonly used in the TPU literature to capture the risk of a reversal of China’s NTR status, was strongly related to the applied duties on China’s exports to the United States before 1980. We then revisit the effect of the NNTR gap on China’s exports to the United States after its PNTR access, using our longer sample period. Third, we show a strong trend in the effect, and it can be traced back to the 1980 NTR liberalization.

## **3.3.1 Revisiting the Effect of the PNTR Access**

We revisit the results in Pierce and Schott (2016), changing only the sample period and the aggregation level of trade flows. Pierce and Schott (2016) focus on HS-8 product trade flows between 1992 and 2007. We use 5-digit SITC goods between 1974 and 2008 to estimate where _Xg_ is the NNTR gap for good _g_ . This equation is the same as (2), except it lacks lagged imports and includes the interaction between the China dummy and the pre-PNTR dummy. Note _β <_ 0 indicates China’s exports of high NNTR-gap goods were either relatively high in the post-PNTR period or relatively low in the pre-PNTR period. Although this specification is not able to distinguish between the two cases, the common interpretation in the TPU literature is the former.

Table 4 reports the results. Column 1 uses the same sample period as in Pierce and Schott (2016) but a coarser level of aggregation. The coefficient of interest, _β_ , measures the effect of the NNTR gap on a good’s export growth after China’s PNTR accession. We find that _β_ = _−_ 0 _._ 9, compared with _−_ 0 _._ 5 in Pierce and Schott (2016). Column 2 reports the result for our full sample period. We find that _β_ = _−_ 2 _._ 5, a 2.5 times increase from the estimate obtained using the shorter sample period. Column 3 reports our results when we 13
**Table 11. Non-recurring and recurring costs of compliance for industrial fish processors:**

|**Plant**|**Number of**<br>**Permanent/**<br>**Temporary**<br>**Employees**|**Value of Exports**<br>**2002**<br>**(US$)**|**Current**<br>**Operating Level**<br>**(%)**|**Non-Recurring**<br>**Costs**<br>**(US$)**|**Increase in Unit**<br>**Production Costs**<br>**(%)**|
|---|---|---|---|---|---|
|A|75<br>100|10.73 million|30|26,800|5|
|B|100<br>80|1.86 million|40|19,600|10|
|C|20<br>40|0.54 million|25|15,200|25|
|D|150<br>250|2.59 million|50|13,600|15|
|E|100<br>150|0.32 million|50|8,500|15|
|F|100<br>200|0.38 million|50|21,800|20|
|G|270<br>250|12.83 million|60|128,000|25|
|H|75<br>100|4.27 million|50|6,500|15|
|I|-|0|0|80,000|30|
|J|-|0|0|200,000|40|
|K|-|0|0|2,100|40|
|L|-|0|0|7,100|50|
|M|-|0|0|19,500|25|
|N|-|0|0|8,300|40|
|**TOTAL**||**33.52 million**|**-**|**557,000**|**-**|
|**Mean per plant**|||**44%***|**39,785**|**25**|

_*Operating plants only_ 61