rafmacalaba/datause-encoder-data huggingface dataset:
markets and remain higher after entry. Although the figure shows some support for the selfselection hypothesis, i.e. that entrants are already more productive before entry, the estimates are not significantly different from zero. Panel B shows that TFP grows about 6% in the year of entry. Productivity growth continues to be positive and significantly different from zero up to four years after entry. Panels C and D show clear trends of increased productivity growth over longer time horizons, ranging from 3% to 4.5% between 3 and 8 years after entry.

While the results in Figure 1 are suggestive of learning-by-exporting, they should be taken with caution as they are not based on a clear-cut comparison between a treatment and a control group of plants. A better approach is based on the estimation of Equation (1) whose results are shown in Table 2 for unmatched (Panel A) and matched samples (Panel B). In both cases the treatment group consists of entrants into export markets, and the control group consists of plants that do not enter export markets during the sample period. To avoid spurious comparisons, all regressions include only one observation for each treated plant, and exclude plants that start exporting in their first year of life. However, the unmatched and matched samples differ dramatically in the number of observations in the control group. While the unmatched sample includes all the observations for all the nonexporters, the matched sample includes a single observation for each nonexporter that is matched to an entrant into export markets in the same industry and year. To obtain the matched sample, we use propensity score matching based on a probit regression explaining entry into export markets at time _t._<sup>12</sup> The probit includes as regressors

> 12 We thank Jens Arnold for sharing his STATA code for matching plants in the same year and industry. In our matching, we ensure that two technical conditions are verified: (i) plants in the matched sample belong to the common support defined by the lowest propensity score of a treated plant and the highest propensity score of a control plant, and (ii) the balancing condition is verified. See Becker and Ichino (2002) and Leuven and Sianesi (2003).

20
- **49** -

This paper estimates the output, investment, employment and wage effect of institutional credit using district-level panel data from India. The central problem of estimating the causal relationships is how to disentangle the aggregate demand for credit from its supply. What we observe here is only a total amount of credit advanced by different government lending agencies. This amount represents both the demand for and supply of institutional credit. However, identification of the effects of exogenous increases in credit supply is critical. There is an additional simultaneity problem: credit demand, output supply, and farmer investment are jointly determined in the farmer's utility maximization.

In an earlier paper (Binswanger, Khandker and Rosenzweig, 1988) we circumvented the issue of simultaneity of credit demand and supply by estimating the effect of the number of rural commercial bank branches on agricu'tural output and investment rather than the effect of the volume of credit. Because the growth of rural commercial bank branches is controlled entirely by the banks and public policy, it is exogenous to the credit demand of farmers. Our findings suggested that commercial bank expansion had significant positive effects on farmer's fixed investment, fertilizer demand and output but the input effect is stronger than the output effects. Commercial banks also increase agricultural wage and helps reduce the incidence of rural poverty. However, because banks promote private capital investment in agriculture that replaces farm labor, the positive wage effect of improved financial intermediation is due to its strong (positive) effect on rural nonfarm employment and output (Khandker, 1989).
# **6. REFERENCES**

Arze del Granado, Francisco J. (2008). ”Spatial Considerations on Decentralization and Economies of Concentration in Indonesia” in World Development Report East Asian Companion Volume, World Bank, Washington D.C.

Akita Takahiro, and Armida S. Alisjahbana (2002). “Regional Income Inequality in Indonesia and the Initial Impact of the Economic Crisis”. _Bulletin of Indonesian Economic Studies_ , 38(2), pp. 201-222.

Alesina, Alberto and Dani Rodrik (1994). “Distributive Politics and Economic Growth”. _The Quarterly Journal of Economics_ , vol. 109, no. 2, pp. 465-490.

Alisjahbana, Armida S., and Arief Anshory Yusuf (2004). Assessing Indonesia’s Sustainable Development: Long Run Trend, Impact of the Crisis, and Adjustment during the Recovery Period,” _ASEAN Economic Bulletin_ , December 2004, 21(3), pp.290-307 Balisacan, Arsenio M., Ernesto M. Pernia, Abuzar Asra (2003). Revisiting Growth and Poverty Reduction in Indonesia: What Do Sub-National Data Show?. _Bulletin of Indonesian Economic Studies_ , 39(3), pp. 329-51.

Barro, R.J (1991). Economic Growth in a Cross Section of Countries. _The Quarterly Journal of Economics_ , vol. 106, no.2, pp. 407-443.

Barro, R.J. and Xavier Sala-i-Martin (1991). Convergence Across States and Regions. Brookings Papers on Economic Activity, 1991, (1), pp.107-182.

Barro, R.J. and Xavier Sala-i-Martin (1994). Economic Growth. New York: McGraw-Hill.

Catin, Maurice, Xubei Luo and Christophe Van Huffel (2005). Openness, Industrialization and Geographic Concentration of Activities in China. World Bank Policy Research Working Paper 3706, September 2005.

Da Mata, Daniel, Uwe Deicmann, J. Vernon Henderson, Somik V. Lall and Hyoung Gun Wang (2005). Examining Growth Patterns of Brazillian Cities. World Bank Policy Research Working Papers 3724, September 2005.

- Clark, Gordon L., Maryann P. Feldman and Meric S. Gertler (2000). The Oxford Handbook of Economic Geography. Oxford University Press.

Drennan, Matthew P. and Sidney Saltzman (1998). Regional and Spatial Econometric Analysis. Methods of Interregional and Regional Analysis (Isard et al). Regional Science Studies Series. Ashgate : London.

- Easterly, William and Ross Levine (1997). Africa's Growth Tragedy: Policies and Ethnic Divisions, _The Quarterly Journal of Economics_ , 112 (4), pp 1203-1250

- Fitrani, Fitria (2005). Sub-National Growth Performance in Indonesia: Economic Crisis, Decentralization and Convergence. Unpublished Master Thesis. University of Freiburg, Freiburg, Germany.

- Fujita, Masahisa (2002). Economics of Agglomeration: Cities, Industrial Location and Regional Growth. Cambridge University Press.

27
data described above. Coefficient estimates suggest a very strong negative relationship between grammatical gender and educational attainment. After controlling for countryround fixed effects, age, and religion, we find that speaking a gender native language is associated with a 22 percentage point decline in the likelihood that a woman completed primary school and a 16 percentage point decline in the likelihood that a woman completed secondary school (Table 10, Columns 3 and 6, respectively). Both coefficients are negative and significant at the 99 percent confidence level (p-values, 3 _._ 58 _×_ 10<sup>_−_5</sup> and 5 _._ 92 _×_ 10<sup>_−_4</sup> , respectively).

Moreover, we are once again able to rule out the possibility that differences in levels of education among women are driven by cultural factors (i.e. differences across ethnolinguistic groups) that impact both men and women. We report pooled OLS specifications that include men and women in Table 11. We do find that cultural factors matter: the indicator for having a gender native language is negative and significant in all specifications. After controlling for age and religion plus interactions between these controls and the indicator for being female (along with country-round fixed effects), _men_ whose native language is a gender language are 10 percentage points less likely to finish primary school and secondary school than men whose native language is non-gender. Nonetheless, the interaction between Female and the indicator for gender native languages is also negative and significant in all specifications; moreover, after differencing out the level of education observed among men in each language group, the estimated Female _×_ Gender language interaction is largely impervious to controls. In a specification with no controls (Table 11, Column 1), the coefficient estimate indicates that women whose native language is a gender language are 12 percentage points less likely to have completed primary school (p-value 2 _._ 15 _×_ 10<sup>_−_18</sup> ). When we include controls for country-round fixed effects, age, and religion, the estimated coefficient only drops from _−_ 0 _._ 12 to _−_ 0 _._ 11 (p-value 1 _._ 07 _×_ 10<sup>_−_8</sup> ). The pattern is similar for secondary school: with or without controls, the coefficient indicates that grammatical gender is associated with a six percentage point decline in the likelihood of completing secondary school (p-value 2 _._ 48 _×_ 10<sup>_−_5</sup> without controls, 0 _._ 009 with controls). Thus, our 30
These conditions are equivalent to saying that the estimator conserves i) the total in row and column, or margins, of the trade matrix (Arvis 2011), as well as ii) the weighted average of the country factors.

However, given the invariance properties of the estimators (section 3), there are two degrees of indetermination in the problem:

- First the A and B are determined up to a scale factor irrelevant to elasticities

- In the numerical implementation we relieve the indetermination by imposing that the sums of A and B are equal.

- Then the elasticities are known up to a constant. To relieve this indetermination, in the following we choose the convention that the weighted average of elasticities is zero:

These conventions have no relevance to the predicted values of the model , but are needed for the implementation of the numerical algorithm. This algorithm recursively:

- Estimates the next A and B using the line and row conditions for a given set of elasticities ;

- Recomputes the next approximation for the elasticities using Newton’s approximation to solve the equation of conservation of average factor.

# **6 Application: Estimating the Factor Supply Elasticities for Known Endowments**

This section applies the model to actual trade data when factors of endowments are known, and determines the factor supply elasticities by product.

The Poisson pseudo maximum likelihood is implemented for one year (2010) starting with 61 sectors at the 2-digit SITC rev 3 level. The independent variables used in the model are indicators typically used to explain trade volumes or trade costs.

The following calculations have been implemented:

1. Computation of the factor supply intensity and the revealed factor intensity for each of the variables (Table 1).

2. Computation of multivariate factor supply intensity for a combination of independent variables (Tables 2-3).

The main findings are: 16
33

# - **Table 5: Brazil Characteristics of Urban Female-Headed Households Compared With All Urban Households, 1984**

|**Characteristic**|**FHH**|**All**|
|---|---|---|
|Share of households (percent)<br>|18.2<br>|100.0<br>|
|Average income per capita (minimum salaries)|1.6|1.8|
|**Household Composition**|||
|Household size|3.2|4.2|
|Number of minors|0.9|1.5|
|Number of adults|2.1|_2.5_|
|Share of household with another adult (percent)|56.0|38.0|
|Number of people with positive income|1.9|1.9|
|Share of households with children (percent)|45.0|64.0|
|Share of adults who are women (percent)|84.0|54.0|
|Dependency ratio (children per adult)|0.6|0.7|
|**Characteristics of Head**|||
|Age (years)|51.0|44.0|
|Education (years)|4.4|5.4|
|Mean income (minimum salaries)|2.2|4.4|
|Percent economically active|48.0|78.0|
|Share of household income earned by head (percent)|65.0|74.0|

Source: author's tabulations
|bility<br>3<br>0<br>1<br>m<br>Control<br>1, N Control=8935<br>in 2013<br>0<br>1<br>m<br>Control<br>4, N Control=9692<br>ail frms in sectors with high POS<br> share of frms with a POS in 2013.<br>d time trends and the DiD estimate<br>sed in Section 5.3.|
|---|
|Net VAT Lia<br>a POS in 201<br>-2<br>-1<br>Years to Refor<br>Treatment<br>g): 0.071 (0.058). N Treatment:842<br> with a POS <br>-2<br>-1<br>Years to Refor<br>Treatment<br>g): 0.059 (0.058). N Treatment:766<br> comparing ret<br>ed on a sector’s<br>s the normalize<br> fgure is discus|
|C. <br> with <br>-3<br>OLS DiD (Lo<br>Firms <br>-3<br>OLS DiD (Lo<br>ear 0), <br> is bas<br>h show<br>y. This|
|s <br>.95<br>1<br>1.05<br>1.1<br> <br>.95<br>1<br>1.05<br>1.1<br>y<br>nt<br>p<br>it|
|rm<br>Normalized Log Outcome<br> of<br>Normalized Log Outcome<br>14 (<br>me<br> gra<br>stic|
|of Fi<br>1<br> Share<br>1<br> in 20<br> assign<br>. Each <br>oskeda|
|AT<br>n Share<br>0<br>m<br>Control<br>1, N Control=8935<br> of the <br>0<br>m<br>Control<br>4, N Control=9692<br> rebates<br>reatment<br>nel titles<br> to heter|
|B. Output V<br> Above-Media<br>-2<br>-1<br>Years to Refor<br>Treatment<br>(Log): -0.024 (0.037). N Treatment:842<br>e Top Quartile<br>-2<br>-1<br>Years to Refor<br>Treatment<br>(Log): -0.039 (0.037). N Treatment:766<br>ction of the VAT<br> (control). The t<br>icated by the pa<br> errors are robust|
|with<br>-3<br>OLS DiD<br>in th<br>-3<br>OLS DiD<br>rodu<br>ation<br>s ind<br>dard|
|ctors <br>.95<br>1<br>1.05<br>1.1<br>Normalized Log Outcome<br> with<br>.95<br>1<br>1.05<br>1.1<br>Normalized Log Outcome<br>the int<br> penetr<br>roup, a<br>. Stan|
|Se<br>1<br>rs<br>1<br> <br>S <br> g<br> 2|
|A. Total Taxable Sales<br>I. Treated: Firms in <br><br>-2<br>-1<br>0<br>Years to Reform<br>Treatment<br>Control<br>LS DiD (Log): -0.005 (0.042). N Treatment:8421, N Control=8935<br>II. Treated: Firms in Secto<br><br>-2<br>-1<br>0<br>Years to Reform<br>Treatment<br>Control<br>LS DiD (Log): -0.039 (0.042). N Treatment:7664, N Control=9692<br>ement a DiD estimation around<br>tail frms in sectors with low PO<br>t cutofs to defne the treatment <br>_formt_ interaction from equation|
|.95<br>1<br>1.05<br>1.1<br>Normalized Log Outcome<br>-3<br>O<br>.95<br>1<br>1.05<br>1.1<br>Normalized Log Outcome<br>-3<br>O<br>Notes: These graphs impl<br>penetration (treated) to re<br>Panels I and II use diferen<br>_β_ on the _treatedi · PostRe_|

66
Figure 2. Volatility of Aggregate Price Indices using UNCTAD Commodity Indices <!-- Start of picture text --> 0.18<br>0.16<br>0.14<br>0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>import manufactures export manufactures<br>UNCTAD commodity 1 UNCTAD commodity 2<br>standard deviations<br><!-- End of picture text --> Note: The figure shows the evolution of the annualized standard deviations of Hodrick-Prescott filtered price series. Commodity price indices are from UNCTAD Stat (2000=100). The UNCTAD commodity 1 price index is originally in current dollars while UNCTAD Commodity 2 is in Special Drawing Rights. The aggregate price indices for import and export manufactured goods are from the Bureau of Labor Statistics (2000=100). The latter data is available using the Standard International Trade Classification from 1993 to 2005 and available using North American Industry Classification System from 2005 to 2010. We constructed an extended series throughout the period 1993 to 2010 by setting the same index value for December 2005 in those two available series.

18
Due to the specialty of the data availability, we run our regressions on two data samples. Firstly, we only work on U.S. data, with 50 states across 11 years (2006-2016). As U.S. data have the most important variables and best quality, we are able to take advantage of these estimations to better understand the determinants of property tax revenue. We use both OLS and Fixed Effects models as shown in Equation (1) - (4).<sup>8</sup> Secondly, to utilize all the data we have, we combine data from all the countries, including Australia, Canada, Chile, USA, and OECD. State and province level data from Australia, Canada, and the United States are also pooled together to construct our panel data set. Since our data availability is limited, we can only use a few control variables in these specifications, such as GDP, population, and household size (we use government transfer for both the United States and Canada). Details are shown in Equations (5) - (8)<sup>9</sup> as below. Property Tax Revenue = constant + 1 2. Population 3. Government Transfer + 4. Property Tax Rate + 5. Household Size (1) ββ . GDP + ββ + ββ Property Tax Revenue = constant + ββ ββ 1 + εε2. Population 3. Government Transfer + 4. Property Tax Rate + 5. Household Size + 6. Governance ββ . GDP + ββ + ββ Property Tax Revenue = constant + ββ ββ 1 2 ββ. Population + εε3. Government Transfer + 4. Property Tax Rate + 5. Household Size + (3) ββ . GDP + ββ + ββ Property Tax Revenue = constant + ββ ββ 1 2 Year Dummy + State Dummy + εε. Population 3. Government Transfer + 4. Property Tax Rate + 5. Household Size + 6. Governance + Year Dummy + (4)ββ . GDP + ββ + ββ ββ ββ ββ Property Tax Revenue = constant + State Dummy + εε 1 2. Population 3. Household Size (5) Property Tax Revenue = constant + ββ1. GDP + ββ2. Population + ββ3. Household Size + εε + 4. Government Transfer (6) ββ . GDP + ββ + ββ Property Tax Revenue = constant + ββ + εε β1. GDP + β2. Population + β3. Household Size + Year Dummy + State/Province/Country Dummy + ε (7) Property Tax Revenue = constant + 1 2. Population 3. Household Size + 4. Government Transfer (8) ββ . GDP + ββ + ββ ββ + Year Dummy + State/Province/Country Dummy + εε Property Tax Revenue is total property tax revenues as million dollars; GDP is GDP as million dollars; Population stands for the population as thousands of people; Government Transfer is the government transfer as million dollars;

> 8 Equations 1 and 2 are OLS, while Equations 3 and 4 are fixed effects.

> 9 Equations 5 and 6 are OLS, while Equations 7 and 8 are fixed effects.

7
|Private accessibility 2006|0.0224***|0.0120***|0.0116***|
|---|---|---|---|
||(0.00717)|(0.00290)|(0.00293)|
|Private accessibility 2007|0.0253***|0.0143***|0.0141***|
||(0.00617)<br>|(0.00289)<br>|(0.00285)<br>|
|Private accessibility 2008|0.0102<br>|0.0124***<br>|0.0120***<br>|
||(0.00891)|(0.00263)|(0.00261)|
|Private accessibility 2009|0.0169**<br>|0.0148***<br>|0.0151***<br>|
||(0.00680)<br>|(0.00304)<br>|(0.00297)<br>|
|Private accessibility 2010|0.0132**<br>|0.0160***<br>|0.0164***<br>|
||(0.00532)<br>|(0.00311)<br>|(0.00303)<br>|
|Private accessibility 2011|0.0295***|0.0188***|0.0197***|
||(0.00582)|(0.00332)|(0.00328)|
|Private accessibility 2012|0.0247***|0.0206***|0.0218***|
||(0.00678)|(0.00349)|(0.00345)|
|Private accessibility 2013|0.0288***|0.0221***|0.0242***|
||(0.00559)|(0.00378)|(0.00366)|
|Distance to CBD (in logs)|-0.00367|0.114***|0.0649*|
||(0.0303)|(0.0343)|(0.0337)|
|Distance to the closest Metro station<br>|0.0269|0.225***|0.125**|
|(in logs)||||
||(0.0648)|(0.0711)|(0.0581)|
|Distance to the closest Metro station<br>squared|<br>-0.00133|<br>-0.0167***|<br>-0.00947**|
||(0.00496)|(0.00510)|(0.00424)|
|Distance to the closest BRT station|0.0335|-0.0172|0.000931|
|(in logs)||||
||(0.0494)|(0.0263)|(0.0266)|
|Distance to the closest BRT station|<br>-0.00311|<br>-0.00153|<br>-0.00205|
|squared||||
||(0.00368)|(0.00199)|(0.00194)|
|Average number of properties sold|-0.00381***|-0.00150***|-0.00319***|
|in a month (in the same<br>||||
|development)||||
||(0.00127)|(0.000430)|(0.000531)|
|Total number of properties sold|-0.000114**|1.48e-05***|2.98e-05***|
||(5.53e-05)|(5.47e-06)|(6.70e-06)|
|Area of the property|0.000904***|0.00327***|0.00247***|
||(0.000184)|(0.000159)|(0.000152)|
|Number of bathrooms|0.0208***|0.00473***|0.00874***|
||(0.00384)|(0.00138)|(0.00208)|
|Number of bedrooms|0.137***|0.0176**|0.0666***|
||(0.0212)|(0.00895)|(0.0110)|
|Number of stories|0.00386*|0.00541***|0.00355***|
||(0.00208)|(0.00110)|(0.00124)|
|Whether the property has parking|<br>0.294***|<br>0.0698***|<br>0.128***|
|place||||
||(0.0142)|(0.0103)|(0.0163)|
|Whether the properties have|<br>0.278***|<br>0.0271***|<br>0.0933***|
|<br>bedrooms for employees||||
||(0.0196)|(0.0102)|(0.0144)|
|Type of property: duplex|0.136|-0.0399|0.0482|
||(0.146)|(0.128)|(0.0983)|
|Type of property: flat|0.0139|-0.157***|-0.120***|
||(0.116)|(0.0517)|(0.0451)|

33
rates are lower than the SC rates but this is not of great significance since more than 90 percent of the ST population lives in rural areas.

Third, with growth accelerating to above 8 percent beginning in 2003-04, poverty reduction between 2004-05 and 2009-10 also accelerated. The percentage-point reduction during this period was larger than during any other five-year period. Most importantly, the acceleration was the greatest for the ST and SC in that order so that at last the gap in poverty rates between the scheduled and non-scheduled groups declined significantly.

Finally, while the rural poverty rates were slightly higher than the urban rates for all groups in 1983, the order switched for one or more groups in several of the subsequent years. Indeed, in 2009-10, the urban rates turn out to be uniformly higher for every single group. This largely reflects progressive misalignment of the rural and urban poverty lines with the former becoming lower than the latter. It was this misalignment that led the Tendulkar Committee to revise the rural poverty line to realign it to the higher, urban line.

Table 3 reports the poverty estimates based on the Tendulkar lines. Recall that the Tendulkar line holds the urban poverty ratio at 25.7 percent in 2004-05 when measuring poverty at MRP expenditures. Our urban poverty ratio in Table 3 reproduces this estimate within 0.1 percentage point.

The decline in poverty rates between every two successive surveys for every social group in rural as well as urban areas, which we noted at the Lakdawala lines in Table 2, remains valid at the Tendulkar lines. Moreover, rural poverty ratios now turn out to be higher than their urban counterparts for each group in each year. As in Table 2, the decline is the sharpest during the high-growth period between 2004-05 and 2009-10. Finally and most importantly, the largest percentage-point decline between these years in rural and urban areas combined is for the ST followed by the SC, OBC and FC in that order. Given that the ST also had the highest poverty rates followed by SC, OBC and ST in that order in 2004-05, this pattern implies that the socially disadvantaged groups have done significant catching up with the better off groups. This is a major break with past trends<sup>13</sup> .

**Table 3** : National rural and urban poverty rates by social groups at the Tendulkar line

|Socialgroup|1993-94|2004-05|2009-10|
|---|---|---|---|
|||**Rural**||
|ST|65.7|64.5|47.4|
|SC|62.1|53.6|42.3|
|OBC||39.9|31.9|
|FC||27.1|21.0|
|NS|43.8|35.1|28.0|
|Allgroups|50.1|41.9|33.3|
|||**Urban**||
|ST|40.9|38.7|30.4|
|SC|51.4|40.6|34.1|

13 Panagariya and More (2013) find further evidence of this trend - the poverty ratio for each social group, in rural as well as urban areas in 2011-12, is lower than in 2009-10.

13