rafmacalaba/datause-encoder-data huggingface dataset:
# **2.2 Student**

The phenomenon of primary and high school children taking extra classes to supplement in-school instruction in Vietnam is well known, see [Ha and Harpham, 2005] and [Dang, 2007]. Table 2 indicates that while Dev7 students spent roughly 4.7 hours in such classes (total of OUTMATH, OUTLANG and OUTSCIE), the Vietnamese student spends nearly 2 hours more for a total of 6.6 hours per week in such classes, with the difference being highest for OUTMATH. Vietnamese students also spent about 1 additional hour per week doing homework (total of ST57Q01 and ST57Q02) compared to Dev7 students. The highest difference in this set of variables concerns the variable ST57Q04, which relates to extra classes taught by a commercial company. While most of the schools in Vietnam are public or government schools, it is interesting to note that students report nearly 5 hours of commercially provided extra lessons, while the total for Dev7 countries is only about 2 hours per week. Collectively, these variables indicate that Vietnamese students spent about 16 hours per week studying outside of school, compared to 13 hours per week for Dev7 students.

**Table 2: Student studying time out of school**

|||Dev7 co|untries|Viet|nam|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
|**Weekly out-of-s**|**chool hours per subject**|||||
|OUTMATH _(r)_|weekly out-of-school<br>lessons in math|1.828<br>(2.1539)|23603|3.1305<br>(2.3133)|3227|
|OUTREAD _(r)_|weekly out-of-school<br>lessons in ’test language’|1.2882<br>(1.9623)|23531|1.4483<br>(1.8837)|3223|
|OUTSCIE _(r)_|weekly out-of-school<br>lessons in science|1.5609<br>(2.0456)|23298|2.0927<br>(2.1776)|3205|
|**Weekly out-of-s**|**chool hours approach**|||||
|ST57Q01 _(r)_|Out-of-school time<br>homework|5.0953<br>(5.0319)|23696|5.8145<br>(5.7196)|3164|
|ST57Q02 _(r)_|Out-of-school time<br>guided homework|2.551<br>(2.9296)|19355|2.8814<br>(3.2384)|2285|
|ST57Q03 _(r)_|Out-of-school time<br>personal tutor|1.7276<br>(2.7884)|20367|1.5749<br>(2.938)|3049|
|ST57Q04 _(r)_|Out-of-school time<br>classes by company|1.892<br>(3.3487)|19517|4.878<br>(4.8058)|3091|
|ST57Q05 _(r)_|Out-of-school time<br>parent/family member|2.1354<br>(3.055)|21542|1.7646<br>(3.2442)|3092|
|ST57Q06 _(r)_|Out-of-school time|2.588|21338|1.8029|3079|
||learn on computer|(3.5519)||(3.0496)||

Notes: The variables relate to the questionnaires administered to students in the rotated booklet, marked with _(r)_ . For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix. The variable means of Dev7 and Vietnam are statistically different at the 95% significance level. Figures in parenthesis represent standard deviations.

8
**Table 6: Probit estimates of SMK choice in 1997 (IFLS2, n= 823, 418 & 405 )**

||_SMK_<br>||||||
|---|---|---|---|---|---|---|
||All||Female||Male||
|Independent|Probit|Marginal|Probit|Marginal|Probit|Marginal|
|variables|Coefficient|effect|Coefficient|effect|Coefficient|effect|
|RURAL|-0.403***|-0.149***|-0.331**|-0.122**|-0.562***|-0.204***|
||(0.107)|(0.038)|(0.147)|(0.052)|(0.162)|(0.055)|
|FEMALE|-0.042|-0.016|||||
||(0.093)|(0.036)|||||
|AGE|0.010|0.004|0.017|0.006|0.018|0.007|
||(0.036)|(0.014)|(0.052)|(0.020)|(0.052)|(0.020)|
|EDU_HH|-0.062***|-0.024***|-0.023|-0.009|-0.105***|-0.040***|
||(0.013)|(0.005)|(0.019)|(0.007)|(0.019)|(0.007)|
|HH INCOME|-0.030***|-0.013***|-0.041***|-0.016***|-0.023|-0.009|
|(million IDR)|(0.010)|(0.004)|(0.015)|(0.006)|(0.014)|(0.005)|
|EBTANAS1|-0.035***|-0.013***|-0.029**|-0.011**|-0.040***|-0.015***|
||(0.008)|(0.003)|(0.011)|(0.004)|(0.012)|(0.005)|
|AGE 0 T0 5|-0.004|-0.001|-0.016|-0.006|0.025|0.009|
||(0.078)|(0.030)|(0.114)|(0.043)|(0.109)|(0.042)|
|AGE 6 TO 14|-0.032|-0.012|0.013|0.005|-0.097|-0.037|
||(0.046)|(0.017)|(0.062)|(0.024)|(0.069)|(0.026)|
|AGE ≥ 15|0.005|0.002|0.012|0.004|-0.023|-0.009|
||(0.024)|(0.009)|(0.031)|(0.012)|(0.038)|(0.015)|
|P_SMK|0.764***|0.291***|0.926***|0.350***|0.645***|0.247***|
||(0.138)|(0.052)|(0.197)|(0.074)|(0.197)|(0.075)|
|P_PUBLIC|-0.083|-0.032|0.135|0.051|-0.327*|-0.125*|
||(0.129)|(0.049)|(0.184)|(0.070)|(0.187)|(0.071)|
|Intercept|1.274||0.387||2.023*||
||(0.831)||(1.188)||(1.183)||
|R<sup>2</sup>|0.116|0.116|0.094|0.094|0.168|0.168|

Using the predicted value of SMK probability and with controls for college entry censoring, similar result can be seen that attendance at SMK does not improve the probability of finding employment or earnings (Table 7).

17
Eastern households. Ultimately, increasing the resilience of these households to protect consumption from the downside of risk is essential to secure the gains in welfare for these regions.

**Table 12: Welfare changes: a regional story**

||Centre and<br>West|North and<br>East|
|---|---|---|
||Log real con<br>cap|sumption per<br>ita|
|Log of rainfall (percent of needs measured by||0.488**|
|WRSI)|0.444**||
||(0.825)|(0.219)|
|Log of beans price|0.245*|0.674***|
||(0.130)|(0.122)|
|Log of number of fatalities|0.057|0.004|
||(0.053)|(0.016)|
|Constant|7.239|4.050**|
||(1.603)|(1.701)|
|Observations|1,585|1618|
|Number of HHID|504|1022|

Source: Staff calculations using UNPS 2006-2010. Note: Dependent variables is real per capita consumption. Month of interview dummies included but not shown. Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

# VII. Conclusion

Over the last 15 years Uganda has been an African success story, reducing extreme poverty faster than most other countries in the continent. However, Uganda remains a very poor country. In 2013, a third of its citizens still lived below the international extreme poverty line of US$1.90 (2011 PPP) a day. Understanding the drivers of past success provides important lessons for those interested in seeing further poverty reduction both in Uganda and elsewhere. This paper contributes to this endeavor by focusing on the drivers of an important source poverty reduction for households in Uganda— agricultural income growth—and assessing how these drivers impacted welfare dynamics during the period 2006-2012.

The results show that Uganda was able to get many of the fundamentals right. The government secured stability in the north and enabled the development of private agricultural markets across the country, resulting in real relative prices increases for agricultural commodities that poor farmers grow and sell. However, the results also highlight areas where less progress was made. Extension services and input use were associated with crop income growth, but overall production practices did not change much in agriculture. As a result, modernization of agricultural practices contributed very little to crop income growth.

Our findings also underscore that luck was on Uganda’s side: good weather benefited many households and the positive price trends in international food and commodity markets during this period contributed to price increases for maize, beans and other commodities that benefited farmers. As a result, a favorable external environment (some of it facilitated by policy and some of it not), 26
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or are highly correlated with variables that also affect the growth of income. Rodrik, Subramanian, and Trebbi (2004) found that openness has no significant effect on growth once institution-related variables are added to the regression analysis. Other studies show that the relationship between trade policy and growth depends on a country’s level of development. For example, Yanikkaya (2003) and DeJong and Ripoll (2006) found a negative relationship for developing countries between trade openness and growth.<sup>11</sup>

**Table 6. Stochastic Frontier Analysis with Conditional Inefficiency Effects, Baseline Model**

Dependent variable: log real GDP

||**(1)**<br>**1996-2013**|**(2)**<br>**1996-2013**|**(3)**<br>**1996-2013**|**(4)**<br>**1996-2013**|**(6)**<br>**1996-2013**|**(7)**<br>**1996-2013**|
|---|---|---|---|---|---|---|
|_Frontier_|||||||
|Ln (E/WAP)<br>Ln (K/Y)|0.568***<br>(20.44)<br>0.409***<br>(15.99)|0.425***<br>(29.29)<br>0.581***<br>(38.66)|0.476***<br>(25.03)<br>0.542***<br>(29.52)|0.466***<br>(24.18)<br>0.546***<br>(27.01)|0.501***<br>(34.48)<br>0.493***<br>(33.75)|0.483***<br>(28.65)<br>0.483***<br>(25.61)|
|Time trend|Yes|Yes|Yes|Yes|Yes|Yes|
|_Inefficiency model_|||||||
|**Pillar 1**|||||-0.78***<br>(-3.32)|-0.306***<br>(-3.66)|
|**Pillar 2**||||||-0.0002***<br>(-8.56)|
|**Pillar 4**<sup>**11**</sup>|||||-2.75***<br>(-5.08)|<br>-0.392*<br>(-1.37)|
|**Pillar 5,6**||-0.73***<br>(-2.70)|-1.336***<br>(-2.44)|-0.387**<br>(-2.00)|0.227***<br>(4.55)|0.227***<br>(3.04)|
|**Pillar 7**|||||||
|Tax rate||||1.988***<br>(3.16)|0.467<br>(0.97)|0.975***<br>(4.32)|
|Regulatory quality||-1.82***|-1.142***|<br>-0.268***|<br>0.36*|<br>-0.013|
|||(-6.80)|(-2.36)|(-1.52)|(1.56)|(-0.14)|
|Tariffs|||-0.045*|-0.0547***|||
||||(-1.28)|(-3.88)|||
|Openness|||||0.002*<br>(1.59)|0.00131***<br>(3.02)|
|**Pillar 8**|||||||
|Women (%)|||||0.227***<br>(4.55)||
|CBR|||||0.185|0.969***|
||||||(0.57)|(6.34)|
|**Pillar 9**|||||-0.048|0.200***|
||||||(-0.24)|(3.06)|
|EU|||||-0.259*|-0.214|
||||||(-1.31)|(-0.26)|
|_Variance of inefficiency_|||||||
|<br>**Pillar 4**<sup>**a**</sup>|||-18.88*<br>(-5.88)|-5.016***<br>(-2.35)|-8.26***<br>(-5.60)|-7.951***<br>(-4.91)|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1300|573|316|308|444|409|
|Wald test|8153.7|21346.8|18740.9|15878.0|17114.7|15135.8|
|P-value|0|0|0|0|0|0|
|LogLik|787.5|55.29|101.6|96.55|112.3|197.0|

_Note: t_ statistics in parentheses; * p<0.2, ** p<0.1, *** p<0.05 aPillar 4: change in terms of trade (log).

> 11 As already discussed, results may be affected by factors, including collinearity of variables and the choice of time period.

17
**Table 14:** _Net_ **_Income Changes With Compensation (Tax Exemption_** _for_ **_Weavers)_**

|||Case 3|Case 4|Case 5|
|---|---|---|---|---|
||ExportPolicy||No export quotas||
|Policy Changes|Hank Yam<br>Obligation|50%|None|None|
||Compensatory<br>Policies|No tax (weaver)|No tax (weaver)|No tax (weaver)<br>and cone yam<br>consumer subsidy<br>(handloom)|
||Cotton Farmer|_+15_%|_+15_%|_+15 %_|
|Net Income|Spinning Mill|+1 %|+15 %|+15_%_|
|Changes|Textile Weaver<br>Powerloom|-2 %<br>+1 %|-3 %<br>-0 %|-1%<br>-0 %|
||Handloom|-7 %|-11 %|-3_%_|
||Total|+4%|_+5_%|+6 %|

**Table 15:** **_Net Income Changes With Compensation (Subsidies to Weacers)_**

|||Case 6|Case 7|Case 8|
|---|---|---|---|---|
||ExportPolicy||No export quotas||
|Policy Changes|Hank Yam<br>Obligation|_50%_|None<br>I|None|
||Compensatory<br>Policies|Producer subsidy<br>(weaver)|Producer subsidy<br>(weaver)|Producer subsidy<br>(weaver) and yarn<br>consumer subsidy<br>(handloom)|
||Cotton Farmer|_+15_%|+15 %|+15 %|
|Net Income|Spinning Mill|+0 %|+15 %|+15 %<br>|
|Changes|Textile Weaver<br>Powerloom|+5 %<br>+6 %|+3 %<br>+6 %|+4%<br>+6 %|
||Handloom|+1 %|-3 %|+2%|
||Total|+7%|+8 %|+9 %|

# **_Changes in Export Values_**

The simulation analysis has so far focused on the income changes of various groups, paying special attention to handloom weavers. When we analyze the impacts of the policy reform, however, there are two other important factors to be considered - changes in exports and government revenue. This section analyzes how the reform will affect the exports of cotton, yarn, and textiles.

First, the export liberalization will undoubtedly increase the volume of cotton export, as the cotton farmers respond to the increase in domestic cotton price. Not only in terms of volume but also in terms of value cotton exports will increase by 37 to 44 per cent in value (Table 16). This is because India is not a major exporter of cotton (even after the liberalization) in the world market, and hence the export increase does not have major impacts on the world price. The hank yarn liberalization decreases cotton exports, as the spinning mills consume more cotton (Cases 2 and 8).

Second, although cotton yarn exports will also increase, it will not be as much as cotton exports 39
direct democracy helps spend taxes according to citizen preferences, and the motivation to pay taxes may increase. Such results are similar to some previous findings. Two studies have explored Switzerland due the fact that the level of direct democracy varies among the 26 cantons. Pommerehne and Weck-Hannemann (1996) use cross-section/time series regressions with Swiss data and find that tax evasion is lower in cantons with a higher degree of direct political control. Torgler (2005b) also finds with Swiss survey data that a higher direct democracy leads to a higher tax morale. Looking at papers on voting and tax compliance, Alm, McClelland, and Schulze (1999), Feld and Tyran (2002) and Torgler and Schaltegger (2005) use experimental methods, and show that voting on tax issues has a positive effect on tax compliance. Moreover, more recently the link between local autonomy and tax morale and tax compliance has been analyzed (Torgler, Schneider and Schaltegger, 2010). The advantage of smaller structures in tax policy is that citizens‘ preferences are able to be better served than in a framework where a uniform tax system is designed for a population with heterogeneous preferences. Moreover, there is an intensive everyday interaction between taxpayers and local politicians and bureaucrats. This closeness between taxpayers, the tax administration and the local government may induce trust and thus enhance tax morale. Politicians and members of the administration are better informed about the preferences of the local population. Furthermore, there is a politico-institutional aspect: if politicians are elected at the local level, they have an incentive to take the preferences of their constituency into account and thus to spend the local tax revenues according to local preferences (see Frey and Eichenberger, 1999). Decentralization brings the government closer to the people. Many economists point out the relevance of giving sub-national governments the taxing power (see, e.g., Bahl, 1999). One of the strengths of a decentralized system is greater transparency between the tax price and the public services received. Taxes are comparable to prices in some sense, especially at the local level (Blankart, 2002). Even the (progressive) income tax is a good instrument for a local structure. It is always under individuals‘ test, providing citizens the opportunity to use the instruments of exit and voice (see Hirschman, 1970). The mechanism of fiscally induced migration in federal states provides a strong incentive to provide public services in accordance to taxpayers‘ preferences. Moreover federalism and local autonomy is connected to innovation.

24
<!-- Start of picture text --> Estimated GDP Actual GDP<br>28.2<br>28<br>27.8<br>27.6<br>27.4<br>27.2<br>27<br>26.8<br>199219931994199519961997199819992000200120022003200420052006200720082009201020112012<br>ln(GDP)<br><!-- End of picture text --> Source: WDI (2014) and NGDC (2014). Authors’ calculations.

Estimating county-level GDP growth based on growth in county night lights since 2000, we find that the lakeside county of Siaya has grown fastest (estimated GDP growth of 10 percent per annum between 2000 and 2011), followed by Elgeyo Marakwet (8.8 percent per annum) and Tharaka Nithi (8.6 percent – see Figure 6). While these counties are still poor compared to the national average, the intensity of their night lights as picked up from space grew strongly since the turn of the century: The sum of lights seven-folded in Siaya and almost five-folded in Tharaka Nithi and Elgeyo Marakwet. At the other end, Bomet grew slowest of all counties (1.2 percent per annum), followed by Kericho (2.4 percent) and Marsabit (2.9 percent). Nairobi county grew at an estimated 4.1 percent and Mombasa at 3.8 percent. Based on night lights, Nakuru and Kisumu grew at 5.1 percent and 4.4 percent, respectively.<sup>5</sup> <u>Figure 6: Average annual county-level GDP growth in Kenya, 2000-2012 (%)</u>

> 5 Standards errors of the estimates are small and not shown in Figure 6.

13
<!-- Start of picture text --> 90<br>———————<br>70<br>60<br>50<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>30 — Work<br>— Student<br>20 —— Refugees<br>40 ——Family<br>——EU/Nordic Nationals Years since arrival<br>oO + . |<br>12042246 6 7 Bf BH Rm ss<br><!-- End of picture text -->
is to prosper and create employment, funding for growth will be critical. A growing literature on financing for innovation finds that mature and new innovators rely on different sources of finance, and government can support entrepreneurship by providing innovation finance. Lerner (1999) examines the effect of federal funding to small businesses in the high tech sector in the United States and finds that even in the United States, which has the highest degree of VC funding, government funding matters. Firms funded by government tended to do well if they were in areas where there was already significant venture capital funding of firms. In fact, government funding was an effective complement to innovation but did not provide additional encouragement when private VC activity was not substantial.<sup>44</sup> Howell (2014), examining similar subsidies to energy firms, finds that a government award increases patenting activity and also doubles the probability of subsequent VC funding. She concludes that the financing is important, but that the receipt of an award reveals relevant and otherwise unattainable information about the firm, and thus encourages private funding.<sup>45</sup> _Macroeconomic and structural policies_ to support output growth are discussed in Piacentini and Pini in Vivarelli and Pianta (2000). They point to demand growth as an important factor in influencing the employment effect of innovation. If output growth falls short of the rate of increase in labor productivity, aggregate unemployment can increase. They point out that innovation driven supply shocks resulting in lower labor demand affect both average wages and their distribution. Both of these changes affect individual demand and may result in lower private aggregate demand, at least in the short run. The macroeconomic consequences of lower consumption (or investment) in the “transition”

> 44 The author contends that government funding may help reduce the risk of funding and may help fill gaps for entrepreneurs. VC activity also helps monitor firms.

45 Tsu, Tian and Xu (2014), examining data from 32 developed and emerging economies, find that there is more innovation in high‐tech industries in countries that have more developed equity markets. The development of credit markets does not have similar effects, as high‐tech firms tend to have intangible assets and banks are inherently more risk averse and seek tangible collateral.

32
inequality that we are unable to capture with our relatively narrow event window.

We present additional evidence supporting our identification strategy with fully-specified event study models in Figure 2. We extend our triple DiD model with country-RTA fixed effects and event time fixed effects (Columns (2) and (4) in Tables 2–4) by interacting our treatment variable _Childig_ with a full set of event-time dummy variables rather than a simple dichotomous post treatment variable as in Equation (5). We omit the dummy variable for the year before an RTA enters into force as a reference year. As in our triple DiD models with event time fixed effects, we cannot separately identify trade liberalization dynamics with this event study specification. Instead, our estimated event study coefficients will reflect the dynamics of outcomes in RTAs with child labor provisions relative to RTAs without, normalized to the year before the RTA with provisions enters into force. Violation of the parallel trends identifying assumption should be evident as pre-trends in these event studies. We present event study coefficient estimates and 95% confidence intervals in Figure 2.<sup>16</sup> We find no evidence that our results are spuriously driven by differential trends.<sup>17</sup> We also consider the role that enforceability plays in child labor outcomes which we report in the Appendix. To do so we add an additional differencing dimension (i.e. a quadruple difference model) capturing whether provisions are legally binding and enforceable under the RTA. Coefficient estimates of the added interaction effect are generally imprecise as the majority of the RTAs with provisions in our sample have enforceable provisions (21/29). For some outcomes, it seems the dimension of enforceability may attenuate the impacts from the provisions themselves (child employment, primary education) however, for secondary school enrollment, the larger effect is driven by the agreements with enforceable child labor provisions. While we cannot reject the hypothesis that there is no difference between the two types of provisions, our estimates of the differential effect lack precision. We present additional results on child wage premia, the skill premium in the Appendix.

> 16Confidence intervals are derived from two-way cluster robust standard errors clustered by country-RTA and country-year.

> 17In addition to this event study evidence that supports our identifying assumptions, our synthetic DiD approach is uniquely designed to address failures in parallel trends by “parallelizing” pre-trends.

25