rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4 Transfers to local governments, 2011–14**<sup>a</sup>

||2011|2|012|2|013|2|014||
|---|---|---|---|---|---|---|---|---|
||10<sup>9</sup>fcfa|%|10<sup>9</sup>fcfa|%|10<sup>9</sup>fcfa|%|10<sup>9</sup>fcfa|%|
|Deconcentration transfers|157.1|12.4|163.1|17.7|173.7|19.6|202.5|11.2|
|Direct transfers|122.4|9.6|48.6|5.3|83.4|9.4|141.5|7.8|
|Total transfers|279.5|22|211.7|23|257.1|29.1|344|19|
|Total State budget|1 268.5||918.6||884.3||1 806.6||

_Source:_ BOOST 2014. _Note:_ a. 2014 is based on partial execution of the budget.

In 2000 new legislation mandated 60 percent of local taxes<sup>11</sup> paid by commercial mining firms and 80 percent of license fees paid by small-scale miners to the local authorities, be paid to the communes where gold production takes place. Producing Cercles should receive 25 percent and 15 percent of these taxes, respectively, while producing regions retain 15 percent and 5 percent, respectively. Commercial mining companies are exempted from paying these taxes during the first three years of operations. Unfortunately, local authorities have little possibility of determining the actual level of the license fees that are supposed to be distributed among them. License fees from mining companies are actually not distributed as required by the above-mentioned law. The share actually allocated to communes represents 73 percent of the total amounts collected instead of the 60 percent according to the law. As a result, all the other levels of local authorities receive less than the percentages required by the law, 17 percent at the Cercle level (while the law requires 25 percent) and 1 percent at the regional level (while the law requires 15 percent).

In general, Malian local authorities have a low rate of revenue collection and therefore a low level of self-financing capacity. As a result, the transfers and subsidies from the government represent the main source of revenue to support current and especially investment expenditures. The analysis of the budgetary accounts from 5 mining communes and 24 neighboring communes reveal indeed a low level of tax collection. Mining communes have an emphasis on the resources generated by license fees paid by mining companies. These license fees represented more than 50 percent of their revenues between 2011 and 2013 compared to just 2 percent for neighboring communes (figure 14a and 14b). Although mining communes are less dependent on the transfers from the government, they still remain exposed to the risks related to the closing of mines or the drop of production in this sector because license fee payments are a function of turnover.

> 11 All other mining taxes are paid to Bamako and consolidated in the national budget.

15
Appendix Table 7: Follow-Up Summary Statistics - Short-Run Business Outcomes Mean and Standard Deviations

||Treatment|Control|(1)-(2)<br>Difference<br>(p-value)|Treatment &<br>Took-up|(4)-(2)<br>Difference<br>(p-value)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Avg. sales Dec 2008, Jan and Feb 2009|53.889|63.384|-9.495|61.075|-2.309|
|(1000s USD)|(160.545)|(163.643)|(0.626)|(103.216)|(0.919)|
|Avg. sales Dec 2008, Jan and Feb 2009|45.905|56.203|-10.298|61.075|4.872|
|(1000s USD), 1% winsorized|(99.329)|(116.830)|(0.439)|(103.216)|(0.775)|
|Feb 2009 costs (1000s USD)|42.353|43.157|-0.804|42.882|-0.275|
||(167.711)|(113.758)|(0.961)|(85.825)|(0.987)|
|Feb 2009 costs (1000s USD) , 1%|31.887|38.951|-7.064|42.882|3.931|
|winsorized|(81.698)|(83.323)|(0.485)|(85.825)|(0.765)|
|Profits (Feb 2009 sales minus costs,|10.964|11.460|-0.496|15.804|4.344|
|1000s USD)|(45.858)|(97.044)|(0.964)|(57.117)|(0.767)|
|Profits (Feb 2009 sales minus costs,|10.964|6.918|4.046|15.804|8.886|
|1000s USD), 1% winsorized|(45.858)|(47.129)|(0.506)|(57.117)|(0.271)|
|Business assets (1000s USD)|258.923|331.416|-72.493|313.187|-18.229|
||(508.865)|(1236.195)|(0.547)|(560.688)|(0.912)|
|Business assets (1000s USD), 1%|258.923|242.534|16.389|313.187|70.653|
|winsorized|(508.865)|(446.492)|(0.765)|(560.688)|(0.312)|
|Productivity residual|0.163|-0.095|0.257*|0.429|0.524***|
||(0.967)|(1.272)|(0.095)|(0.854)|(0.008)|
|Productivity residual, 1% winsorized|0.157|-0.090|0.247*|0.419|0.509***|
||(0.951)|(1.226)|(0.097)|(0.820)|(0.008)|
|Return on assets (ROA)|0.091|0.012|0.080|0.174|0.162**|
||(0.402)|(0.471)|(0.195)|(0.429)|(0.043)|
|Return on assets (ROA), 1%|0.088|0.030|0.058|0.174|0.144**|
|winsorized|(0.388)|(0.292)|(0.198)|(0.429)|(0.011)|

Note: Columns 1, 2, and 4 present means and standard deviations (in parentheses). Column 3 shows the difference in means across the treatment and control group with the corresponding p-value in parentheses. Column 5 shows the non-experimental difference between those who took-up in treatment minus those in control, and the corresponding p- value in parentheses. For the 1% winsorized variables, sales, costs, and assets are only winsorized at the top 1% since they are bounded below by zero. All other variables are winsorized at the top and bottom 1%. Significance levels: *10 percent, **5 percent, ***1 percent.

37
# **China's Trade in Auto Parts**

<!-- Start of picture text --> 11,000,000,000<br>10,000,000,000<br>9,000,000,000<br>8,000,000,000<br>7,000,000,000<br>6,000,000,000<br>5,000,000,000<br>4,000,000,000<br>3,000,000,000<br>2,000,000,000<br>1,000,000,000<br>Imports<br>0<br>Exports<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> The Chinese auto industry remained tiny, scattered and backward until the entry of VW in the mid-1980s. Growth in assembly remained modest through the mid-1990s, then suddenly accelerated from the end of the decade as production and prices fell and strong economic growth lifted household income levels, particularly in the coastal cities. Through the mid1990s, imports of motor vehicles surged occasionally, but generally remained modest, while motor vehicle exports, mostly of trucks and specialty vehicles, only began to trickle out after the turn of the century. Trade in parts, however, mirrored domestic production, tripling between 1998 and 2002. The surge in domestic demand and production induced a huge boom in inward investment, as virtually every major global assembler and first-tier parts producer scrambled to establish a presence in China. Outward investment, led by assemblers rather than parts firms, became noticeable by 2002. In 2003, China surpassed France to become the world’s fourth largest producer of motor vehicles, with just under four and half million units, of which two 10
useful. Another critical area would be to explore the linkages between the creation of entrepreneurial opportunities, their implementation through launching a new venture, and the subsequent impact on regional economic growth and development. Such analysis would help to quantify the overall performance consequences and social welfare gains of knowledge spillover through strategic entrepreneurship Finally, on the econometric methodology for these type of analyses, it would be useful to use others’ non-parametric approaches in order to evaluate how robust these findings are, since while the SFA accounts for data noise, such as data errors and omitted variables, the separation of noise and inefficiency relies on strong assumptions on the distribution of the error term.

# **References**

- Acs Z. and Audretsch D. (2003). The International handbook of entrepreneurship. Kluwer Academic Publishers, Dordrecht.

- Agarwal, R., Audretsch, D. B. and Sarkar, M. B. (2007). “The Process of Creative Construction: Knowledge Spillovers,” _Strategic Entrepreneurship Journal_ , Forthcoming.

- __________  2004). “Knowledge Transfer through Inheritance: Spin-out Generation, Development and Performance,” _Academy of Management Journal_ , 47: 501-522.

- Agarwal, R. and Bayus, B. L. (2002). “The Market Evolution and Sales Takeoff of Product Innovations,” _Management Science,_ 48(8): 1024-1041.

- Agarwal, R., Audretsch, D. and Sarkar, M. (2010). “Knowledge spillovers and strategic entrepreneurship, _” Strategic Entrepreneurship Journal_ , 4 (4): 271–283.

- Aigner, D., Lovell, C.A.K. and Schmidt, P. (1977). “Formulation and Estimation of Stochastic Frontier Production Function Models,” _Journal of Econometrics,_ 6: 21-37.

- Audretsch, D.B. (1995). <u>Innovation and Industry Evolution. MIT Press,</u> Cambridge MA.

- Audretsch, D. and Keilbach, M. (2003). “Entrepreneurship capital and Economic Performance,” _Regional Studies_ , 38 (8): 949 – 959.

- Battese, G.E. and Coelli, T.J. (1993). “A Stochastic Frontier Production Function Incorporating a Model for Technical Inefficiency Effects,” Working Papers in Econometrics and Applied Statistics, No. 69, Department of Econometrics, University of New England, Armidale.

- Birchenal, J.A. and Murcia, G.E. (1997). “Convergencia regional: una revisión del caso colombiano,” _Archivos de macroeconomía_ , 69, Bogotá, D.C., National Planning Department.

19
Table F.2: Evidentiary methods reported by validators, by behavior

||P|otentially sen|sitive beha|viors|Expen|ditures|
|---|---|---|---|---|---|---|
|Main evidence techniques|Steal<br>(1)|Marijuana<br>(2)|Gamble<br>(3)|Homeless<br>(4)|Video<br>(5)|Phone<br>(6)|
|Avg. pieces of evidence|1.1|1.3|1.1|1.7|1.0|1.2|
|Obs. (All)|240|240|239|240|239|240|
|Direct question|36%|35%|38%|5%|32%|1%|
|Indirect question|28%|46%|42%|62%|59%|92%|
|Story / scenario|36%|6%|13%|12%|2%|1%|
|Witnessed / found evidence|3%|31%|9%|62%|5%|18%|
|Third party account|3%|6%|4%|21%|0%|0%|
|Other / unclear|3%|9%|6%|13%|6%|5%|
|Obs. (Coded “did not engage” in behavior)|191|118|170|190|93|125|
|Direct question|38%|44%|39%|5%|34%|0%|
|Indirect question|26%|46%|44%|60%|58%|98%|
|Story / scenario|37%|7%|15%|12%|3%|2%|
|Witnessed / found evidence|2%|3%|1%|65%|2%|1%|
|Third party account|3%|10%|4%|24%|0%|1%|
|Other / unclear|2%|1%|1%|14%|4%|0%|
|Obs. (Coded “did engage” in behavior)|49|122|69|50|146|115|
|Direct question|29%|25%|36%|4%|30%|2%|
|Indirect question|33%|46%|38%|70%|60%|86%|
|Story / scenario|33%|5%|9%|10%|1%|0%|
|Witnessed / found evidence|10%|59%|28%|52%|7%|37%|
|Third party account|4%|2%|4%|8%|0%|0%|
|Other / unclear|8%|17%|17%|6%|8%|10%|

_Notes:_ Direct questions imply the validator asked the respondent directly about his engagement in the activity. Indirect questions imply the validator brought up the subject in general conversation (Where do you live? What do you do to make money?). Stories and scenarios are a form of indirect questioning where the respondent is invited to comment. Witnessing or found evidence implies the validator saw the respondent engaging in the activity in question or found physical evidence that the respondent recently engaged in the activity. Third party accounts imply the validator asked the family and friends of the respondent whether or not he engaged in the activity. Other or unclear methods include a handful of cases of unprompted information from the respondent, and also cases where the behavior could be inferred from other knowledge. Mainly it implies that coding was inconclusive or incomplete but is likely a form of questioning.

liv
|Mean in con-|trol group<br>(9)|0.867<br>0.906<br>0.780<br>1.323<br>0.613<br>0.494<br>0.737<br>0.775|0.168|p-values in square<br>er obtained higher|
|---|---|---|---|---|
|No.|Obs<br>(8)|4973<br>4986<br>2623<br>1850<br>4986<br>2308<br>3070<br>2990|1311|ER adjusted <br>e owner, fath|
|P-Value|(3)+(5)<br>(7)|0.048<br>0.004<br>0.007<br>0.014<br>0.640<br>0.513<br>0.054<br>0.508|0.875|tively. FW<br>e.<br> index, hom|
|FICATION<br><br>P-Value|FWER<br>(6)|[0.347]<br>[0.051]<br>[0.028]<br>[0.020]<br>[0.901]<br>[0.574]<br>[0.086]|[0.972]<br>[0.972]|ent level, respec<br>Y_t−_1 at baselin<br>old size, wealth|
|NAL SPECI<br>_Treated*Yt−_1<br>Coef|(Std.Err)<br>(5)|-0.048<br>(0.036)<br>-0.088**<br>(0.036)<br>-0.103**<br>(0.041)<br>-0.222***<br>(0.07)<br>-0.004<br>(0.030)<br>0.038<br>(0.053)<br>-0.088**<br>(0.039)<br>0.01|(0.047)<br>0.006<br>(0.077)|, 5 and 10 perc<br>ample mean of <br>e family, househ|
|CONDITIO<br>P-|Value<br>FWER<br>(4)|[0.100]<br>[0.014]<br>[0.018]<br>[0.049]<br>[0.636]<br>[0.506]<br>[0.153]|[0.759]<br>[0.874]|cance at the 1<br>e denote the s<br>t live with th|
|_Treated_<br>Coef|(Std.Err)<br>(3)|0.074**<br>(0.037)<br>0.103***<br>(0.035)<br>0.113***<br>(0.041)<br>0.215**<br>(0.086)<br>0.012<br>(0.026)<br>-0.021<br>(0.032)<br>0 .069*<br>(0.035)<br>-0.027|(0.04)<br>0.003<br>(0.019)|* denote signif<br>re by Y_t−_1m w<br>, single, does no<br>uage|
|P-Value|FWER<br>(2)|[0.013]<br>[0.021]<br>[0.232]<br>[0.232]<br>[0.347]<br>[0.844]<br>[0.495]|[0.588]<br>[0.588]|***, ** and <br>_−_1m =0, whe<br>oken at home<br> a native lang|
|CROSS-SECTION<br>_Treated_<br>Coef|(Std.Err)<br>(1)|0.036***<br>(0.012)<br>0.027***<br>(0.010)<br>0.027*<br>(0.015)<br>-0.051<br>(0.034)<br>0.014<br>(0.015)<br>-0.003<br>(0.019)<br>0.018<br>(0.014)<br>-0.014|(0.015)<br>0.012<br>(0.02)|centre level (80 clusters). <br>hesis Treated+ Treated*Y_t_<br>ars of education, english sp<br> muslim, speaks Yoruba as|
|_nt on:_|_r. (Yt):_|uld have one partner only<br> should have one partner only<br> had multiple concurrent sexual partners<br> of current sexual partners if sexually active<br>an brings condom does not mean she’s not serious<br>ndom the last time he/she had sex<br>u ever used a condom?<br> currently using condoms?|t time you will have sex, do you plan to use a condom?|ndard errors in parentheses adjusted for clustering at the screening<br>sed on 10,000 replications. ”P-Value (5)+(6)” tests the null hypot<br> each regression include: female, age, currently attending school, ye<br>dary education, mother obtained higher than secondary education,|
|_Coefcie_|_Dep. Va_|Men sho<br>Women <br>Has not <br>Number <br>If a wom<br>Used co<br>Have yo<br>Are you|The frs|Notes: Sta<br>brackets ba<br>Controls in <br>than secon|

xxiii
# **V. Labor Market Effects of Vocational Education**

This section turns from the determinants of students’ school type to their subsequent labor market experience. We examine four different outcomes: labor force participation (LFP), unemployment conditional on participation, formal sector work, and log of hourly wage.<sup>11</sup> The reduced form model estimated is: where _Yit_ is the labor market outcome of person _i_ in year _t_ . _Zi_ and _Pi_ , as in equation one, are defined as a vector predetermined individual characteristics and parental education, while _Dd_ is a set of indicators for district of junior secondary school. _Dt_ is a vector of interview year dummies, and _Ti_ is a vector of categorical dummies of the four school types, with public general excluded.<sup>12</sup> The equation is estimated using double robust regression, which rebalances the sample by reweighting observations according to the inverse estimated probability of attending the type of school that they graduated from. While this reweighting reduces precision, it makes the estimates more robust to non-linear functional forms.

A key indicator to measure the effectiveness of this reweighting procedure is the normalized difference between means of the observed control variables for different school types, compared to general public graduates (Imbens and Wooldridge, 2009). Reweighting greatly reduces the average of the normalized difference across the 42 control variables. Reweighting considerably reduces the average normalized difference with public general graduates, by 66 percent for public vocational graduates, 80 percent for private general graduates, and 95 percent for private vocational graduates.<sup>13</sup> This indicates that the reweighting was effective.

To the best of our knowledge, a plausible instrument for school choice is not available.<sup>14</sup> As a result, the OLS results reported will be biased to the extent that school

> 11 The wage of self-employed individuals is calculated using their average hourly profit. The Statistics Indonesia urban price index is used to deflate 1993 wages, while IFLS price indices are used for subsequent years. 12 We do not control for university attendance, which is partially determined by choice of school type.

> 13 After rebalancing, the normalized difference between public general and public vocational graduates is 0.006. For private general and vocational, the normalized difference is 0.005 and 0.001 respectively.

> 14 We have tried several instruments, including the share of schools of each type and the leave-out mean of enrollment in each school type in the district and year where a person graduates from junior secondary school. While the latter is a strong instrument, it is difficult to assess its validity, as variation in school attendance patterns across communities is undoubtedly correlated with local labor market conditions. The best candidate instrument would be data on historical school construction, as in Duflo (2001). However, this information is

15
Relationships between migration flows and PEFA performance are particularly relevant to any analysis of small PICs. Several small PICs, especially in Polynesia, are notable in the extent to which their populations are mobile, with extensive outwards and returning migration. For both Samoa and Tonga, the migrant population living in other countries but identifying as Samoan and Tongan respectively, exceeds the domestic population. Remittances from mobile workforces provide a large share of GDP for Tonga, Samoa, Tuvalu, and Kiribati. However, we find no evidence of a relationship between migration flows and PEFA performance within our overall sample or for small countries. For larger countries, this result may not be surprising given the very small number of migrants relevant to the population. For smaller countries, one possible explanation of the result is that the two possible effects counteract one another, with countries experiencing “brain drain” often also able to benefit from the knowledge and skill acquisition associated with labor mobility.

**Table 3: Additional Possible Determinants of PEFA Performance**

||(1)|(2)|(3)|
|---|---|---|---|
|LGDPit (log income)|0.506***|0.633***|0.681***|
||_(0.000)_|_(0.000)_|_(0.000)_|
|LPOPit (log population)|0.158***|0.234***|0.204***|
||_(0.000)_|_(0.000)_|_(0.000)_|
|Migrationit|0.012||0.010|
||_(0.200)_||_(0.317)_|
|AIDit||0.917**|1.090*|
|||_(0.048)_|_(0.065)_|
|Time trend|YES|YES|YES|
|Regional dummies|YES|YES|YES|
|Number of observations|9,671|10,259|9,249|
|Number of clusters|144|151|137|
|Adj. R-Squared|0.027|0.025|0.027|

Notes: Dependent variable is discrete PEFA score. All regressions use Tobit estimation.

p-values in parentheses are based on robust and clustered standard errors. ***p<0.01; **p<0.05; *p<0.1. Constant is included but not reported.

# **5. Capacity Constraints on PEFA Performance of Small Pacific Island Countries**

The preceding analysis suggests that the PEFA performance of small PICs is generally consistent with expectations, given their income level and population size. There are intuitive causal links between higher incomes and improved PFM performance. Richer countries are better able to pay for skilled staff, technical advisors, and up-to-date systems than poorer countries. The governance and political economy factors supporting the effective functioning of institutions for 15
Table A3. Allocation of Aggregate Origin Region by Migrant Shares over Time for Australia

|**Origin country listed**<br>**in 2001 Australian**<br>**census**|**Total immigrants to**<br>**Australia in 2001**|**Share of Soviet Union**<br>**migration to Australia**<br>**in 2001(%)**|**Number of migrants**<br>**allocated in 1966**<br>**across constituent**<br>**countries**|
|---|---|---|---|
|**Azerbaijan**|145|0.3|93|
|**Armenia**|899|2.0|576|
|**Belarus**|1,041|2.3|667|
|**Estonia**|2,386|5.2|1,529|
|**Georgia**|310|0.7|199|
|**Kazakhstan**|438|1.0|281|
|**Kyrgyzstan**|101|0.2|65|
|**Latvia**|6,690|14.6|4,287|
|**Lithuania**|3,689|8.1|2,364|
|**Moldova**|483|1.1|309|
|**Russian Federation**|15,022|32.8|9,625|
|**Tajikistan**|41|0.1|26|
|**Turkmenistan**|26|0.1|17|
|**Ukraine**|14,062|30.7|9,010|
|**Uzbekistan**|412|0.9|264|
|**Total USSR**|**45,745**|**100**|**29,311**|

_Source:_ Authors‘ calculations based on data described in text.

56
Table 3: Change in covariates after the transit shock

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Outcome:||Numb|er of kids|||Househol|d size||
|- ln distance|0.008||-0.004||0.015||0.011||
||(0.042)||(0.052)||(0.013)||(0.014)||
|_Ti_||-0.026||-0.049||0.024||0.011|
|||(0.091)||(0.104)||(0.030)||(0.029)|
|Observations|3,192|3,192|3,192|3,192|3,192|3,192|3,192|3,192|
|R-squared|0.038|0.038|0.076|0.076|0.060|0.060|0.076|0.076|
|Outcome:||Male|dummy|||Age|||
|- ln distance|0.000||-0.000||-0.008||0.031||
||(0.000)||(0.000)||(0.021)||(0.026)||
|_Ti_||-0.001||-0.002***||0.008||0.107*|
|||(0.001)||(0.001)||(0.050)||(0.057)|
|Observations|3,192|3,192|3,192|3,192|3,192|3,192|3,192|3,192|
|R-squared|0.255|0.255|0.273|0.273|0.137|0.137|0.179|0.180|
|Outcome:||High-sk|illed share|||Student|share||
|- ln distance|-0.000||-0.001||-0.002**||-0.001||
||(0.001)||(0.001)||(0.001)||(0.001)||
|_Ti_||-0.002||-0.001||-0.006***||-0.004**|
|||(0.002)||(0.002)||(0.002)||(0.002)|
|Observations|3,192|3,192|3,192|3,192|3,192|3,192|3,192|3,192|
|R-squared|0.244|0.244|0.310|0.310|0.142|0.143|0.174|0.175|
|Controls|X|X|X|X|X|X|X|X|
|State FE|X|X|||X|X|||
|Municipality FE|||X|X|||X|X|

_Notes:_ This table reports the results of a difference-in-difference specification relating changes in household composition and covariates with the transit shock. The odd columns report the results for the continuous treatment variable, and the even columns for the treatment dummy variable. The regressions are weighted by the population in 2000. Standard errors are clustered at the census tract level and reported in parentheses. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

50
6 data on the amount of short-term debt that was rescheduled or reclassified into long-term. To cover the reclassification problem, we calculate total net transfers from commercial creditors as the change in the total debt (stock of short-term plus long-term (PPG and PNG] debt) minus the interest rate (average rate of commercial long-term PPG) times the average total debt during the year. This method also has some problems, because the change of debt also reflects debt forgiveness, discounts on equity and local-currency swaps, and exchange-rate changes, none of which should count as net transfers. Exchangerate changes are important for some years, but have tended to cancel each other out over the longer periods.2 Debt swaps have been significant for only a few countries and only very recently. It seems more important to capture the negative transfers on short-term debt, as trade credit lines have shrunk. Total official transfers are t.ie reported transfers on official long-term debt plus an estimate of IMF net transfers -- disbursements minus principal repayments minus an interest rate (average official long-term) times average IMF during the year.<sup>3</sup> Official transfers refer to loans only and thus do not include official grants. Total net transfers are the sum of total commercial 2 When the dollar was rising, in 1980-84, debt increases understate net flows. When the dollar fell, since early 1985, debt increases overstated net flows. The period 1978-82 straddles a trough in the dollar values; 1983-86 straddles a hump. Of course, exchange rate changes did not fully cancel out; the yen was much higher in 1986 than in 1978.

3 The IMP has its own terminology, in which disbursement of credit is called a "purchase,' and repayment is a "repurchase."