rafmacalaba/datause-encoder-data huggingface dataset:
We find that across the four rounds of our sample users who received the Mirt stove on average used it 2.27 times per week. This result is in accord with our household survey and Gebreegziabher (2015) (for the subsample that participated in the controlled cooking test or CCT), which suggests that respondents cook _injera_ an average of about 2.5 times per week. We therefore find that on average households use the Mirt stove frequently and seemingly appropriately.

Table 3 Frequency of Mirt Stove Use Overall and by Round (Average Times per Week)

||Overall<br>Sample|Round 1<br>(June-August<br>2013)|Round 2<br>(August -<br>October 2013)|Round 3<br>(March-May<br>2014)|Round 4<br>(May – July<br>2014)|
|---|---|---|---|---|---|
|Mean|2.27|1.863|2.078|2.722|2.395|
|Minimum|0|0|0|0|0|
|Maximum|21.367|9.545|12.778|21.367|19.121|
|10<sup>th</sup> percentile|0.132|0.151|0.132|0.172|0.00|
|25<sup>th</sup> percentile|0.686|0.515|0.738|0.829|0.663|
|50<sup>th</sup> percentile|1.846|1.483|1.846|2.086|1.935|
|75<sup>th</sup> percentile|3.144|2.80|3.024|3.728|3.484|
|90<sup>th</sup> percentile|4.644|4.048|4.106|6.223|4.800|
|Observations|1209|301|287|319|302|

Table 3 presents the mean stove use per week by round and breaks the distribution down into 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> percentiles. Looking not only at the mean, but at the whole distribution is important, because we find based on Shapiro-Wilk tests for normality that frequency of stove use is not normally distributed (Z=12.078; Prob.>Z=0.000). By round and regional state, frequency is also not normally distributed and in all cases the P value is approximately 0.000.

We find that across the four rounds only 8.85% of observations had zero use, suggesting that virtually all households in all rounds actually used the stove. About 80% of observations indicate average stove use at least once every two weeks and 68% of observations averaged at least one cooking event per week. Table 3 indicates that in later rounds households generally used the stove more frequently than in early rounds, with median stove use plateauing at about 2.0 times per week. The pattern is not completely monotonic, however, with a dip in average frequency between rounds 3 and 4, which could be due to reduced cooking during the warmest season, but it seems that as cooks gain experience with the stove they generally use it more frequently.

**18 |** P a g e
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38
structural change in employment in favor of the manufacturing and services sectors, but we do not know if this structural change also entails employment growth in large (formal) enterprises. To understand whether agricultural productivity increase leads to labor reallocation in favor of large-scale (formal) firms, we take advantage of the economic census data.

# **Evidence on Large Manufacturing and Services Firms (Economic Censuses Data)**

The estimates for manufacturing employment in large firms are reported in column (5) of Table 3, and they suggest a statistically significant positive effect of rainfall in the STE sample, but no effects in the rural sample. The fact that there is no significant effect on manufacturing in the rural sample is especially striking when compared to the estimate for all firms in column (1) where the effect is both statistically significant at the 1 percent level and numerically large. Taken together, the estimates in columns (1) and (5) suggest that agricultural productivity growth in predominantly rural upazilas reallocate labor from agriculture to small-scale manufacturing firms producing locally consumed low-quality _S−_ good. Given the fact that the wage response to agricultural productivity growth is larger in magnitude in the rural sample, the results on the rural upazilas are consistent with a scenario where the demand for manufacturing _S−_ goods is much stronger than that for manufacturing _T −_ goods (i.e., _υs > υT_ ), and the weak demand effect is more than offset by a strong wage response to productivity growth in the case of _T −_ goods sector.

In contrast, in the small town sample, a higher agricultural productivity results in a very strong growth in employment in the large-scale manufacturing. A comparison of the estimated effects of rainfall shock on all manufacturing firms and the large manufacturing firms in the STE sample shows that the employment growth in large firms is more than four times of that in all firms (i.e., large and small firms combined together). This implies that the employment growth in small firms in STE sample is weak at best. The contrasting effects on large-scale manufacturing in the rural and STE samples suggest that when the wage response is high enough, it may discourage large firms in locating production in an upazila, consistent with the findings of Rosenzweig and Foster (2004) in the context of India. The negative wage effect can dominate the choices of large manufacturing firms when the _T −_ good is effectively a tradable good, but the 25
bandits, and that these regions should experience faster population recovery. To test the hypothesis, in Table 6, in addition to the conventional average Taiping effects at various years, we add two terms that allow the Taiping effects, depending on the distance to Nanjing, to have a level shift in log population, and to have a shift in the population growth rate per decade in the postwar decades. To aid interpretation, the distance to Nanjing is standardized (i.e., with a mean of zero and a standard deviation of one for the Taiping sample). For the conventional average Taiping effects, we have either the year-specific post-Taiping effects (column (1)), or the level- and growth-rate shifts specification (columns (2) and (3)). In column (3) we exclude the heavy war-casualty cities to test the sensitivity to casualty outliers. Compared with a Taiping prefecture that is one standard deviation above the mean in the distance to Nanjing, a Taiping prefecture one standard deviation below the mean would have _an initial population drop of an extra 34 percentage points_ (i.e., e<sup>-0.42</sup> -1), and a faster population growth rate per decade of 1.6 percentage points. The qualitative results are robust with all three specifications, with population recovery being much faster when the high war-casualty prefectures are excluded. These results support the distance-based stationary bandit hypothesis.

# **Effect of** **_Likin_ and disentangling alternative hypotheses**

We now examine how the Taiping Rebellion war shaped _Likin_ collection, and how _Likin_ intensity is associated with population evolution. To proxy the level of _Likin_ for the years 1880, 1910, and 1953 in our population data set, we use their annual average of years 1869 to 1879, 1880 to 1909, and 1910 to 1925.<sup>47</sup> Because _Likin_ was initiated during the Taiping Rebellion and abolished in 1931, for the years before 1880 and after 1953 (1820 to 1851, and 1982 to 2000) we assign the _Likin_ amount to be zero. Because _Likin_ was irrelevant for China after 1931, but the population in 1953 still reflect the effects of _Likin_ up to the year 1931, we exclude the periods after 1953.<sup>48</sup> We thus use the panel of 266 prefectures and the periods of 1851, 1880, 1910, and 1953.

To examine whether the rebellion led to higher _Likin_ intensity (i.e., measured as log _Likin_ revenue per thousand square kilometers) for the Taiping areas, we run the regression as follows: Likinit = dt + ηi + ∑γt tDtTPi + ei,t (3) Here⁡dt and ηi are the year and the prefecture fixed effects; TPi could be a few variants as mentioned. The standard errors are clustered at the prefecture level. The results are in Table 7.

> 47 The average _Likin_ revenues from 1910-1925 are an imperfect proxy of _Likin_ burdens for 1910 to 1953, but that is the only feasible option.

> 48 It is likely that between the abolition of _Likin_ and the Communist takeover, there was some persistence in tax burdens because local governments likely maintained similar governance structures and needed to maintain a similar level of government expenditures. Thus, _Likin_ burdens may have just changed into other forms.

31
“stock spreads” between emerging markets stock prices and the S&P500 U.S. stock market index.

Figures 2 and 3 summarize the event-study results in some detail for the case of domestic upgrades and downgrades. The four plots in each figure show the cumulative abnormal returns over that window around the time of changes in ratings. The panels on the left examine the effects of upgrades while the panels on the right report the effects of downgrades. The top panels examine rating changes of both foreign- and domesticcurrency denominated debt, the bottom panels do the same for changes in ratings of just foreign-currency denominated debt. Both figures only look at the responses in the days before and after ratings of the domestic debt. Day zero is the day of changes in ratings.

With respect to the behavior of markets in the days leading to the rating changes, the evidence seem to support the hypothesis that rating agencies may have contributed to amplify the boom-bust pattern in emerging markets. Overall upgrades occur when markets are rallying and downgrades when emerging markets are collapsing. This effect seems to be stronger in the case of downgrades. For example, bond spreads increase up to 9 percent in the 10-days prior to downgrades. Similarly, the stock market spreads decline up to 7 percent. Naturally, these fluctuations could reflect an anticipation effect. Still, we are most inclined to interpret them as evidence of procyclical behavior of rating agencies. In fact, our results are consistent with the findings in Reinhart (2001). In that paper, the author examines whether rating agencies actions anticipated the crises of the 1990s. With a large sample of countries and crises, the author concludes that rating changes far from being leading indicators of crises have turned out to be lagging indicators of financial collapses.

16
newer ―economic and human development sectors‖ (economic policy, poverty, education, health and nutrition, and social protection). The mean score for ARD, EM, TRN and URB is 5.19 (s.e.=0.24) as compared to 6.48 (0.14) for EP, EDU, HNP, POV and SP (and the difference is significant; t=4.66). The corresponding proportions of staff with a high familiarity score (over 5) are 44.58% (5.47%) and 70.68% (3.30%). In all, 41 staff in the ―hard sectors‖ (ARD, EM, TRN and URB) gave a score of 6 or more, while 279 did so in the sample as a whole. Thus the hard sectors accounted for 15% of the staff with high familiarity. By contrast, these sectors accounted for 45% of Bank lending.<sup>17</sup> Table 3 also gives my estimates of the allocation of DECRG’s staff (including managers) across sectors.<sup>18</sup> Mean familiarity amongst operational staff is positively correlated with DECRG’s staffing, with a correlation coefficient of 0.527 (t=2.85; prob.=0.016). The positive correlation is consistent with the view that staffing of DECRG matters to familiarity with research amongst the Bank’s operational staff. The marked difference in familiarity with research seen in Table 3 match the allocation of staff in DECRG. The sectors ARD, EM, TRN and URB account for 14.9% of staff in the research department, as compared to 59.1% for EP, EDU, HNP, POV and SP.

The survey also asked for a rating (also on a 10-point scale) of other sources of research besides the Bank.<sup>19</sup> The two most important sources for Bank operational staff are likely to be academia and external consultants. Tables 2 and 3 also give the mean scores by VPU and sector respectively. In both cases, the mean scores tend to be positively correlated with ratings of familiarity with the Bank’s internal research.<sup>20</sup> This is also true across staff, with a positive correlation between ratings of familiarity with WB research and ratings of both academia and

> 17 Based on aggregates for the last three years for lending on ―agriculture, fishing and forestry,‖ ―energy and mining,‖ ―transportation‖ and ―water, sanitation and flood protection‖; the sectoral breakdown of Bank lending can be found in World Bank (2011); the relevant tabulation is here.

> 18 I did this by allocating each staff member a primary and secondary sector in 2011—based on their research outputs—and giving two-thirds weight to the former. This requires a degree of judgment, but the allocation proved relatively easy. Note that the high proportion of staff in ―economic policy‖ might be somewhat deceptive, since these are often researchers who are relatively fungible, and work across many development topics.

> 19 The specific question was as follows: _“When considering all the sources of research globally, how useful is each of the following in terms of its research and knowledge have a positive impact on the quality of your AAA and/or lending operations?”_

> 20 Across sectors there is a very high correlation between familiarity with Bank research and ratings of academia (r=0.81). It is less strong for consultants but still positive (r=0.31).

9
12 Table 6: Testng for heteroskedastioity in the residuals from the Barro type regression H0: Errors are distributed homoskedastioaly H,: Errors are distnbuted beteroskedastically

|GOLDFELDT QUANDT|Test Statistic|Significance Lovel|
|---|---|---|
|w/ included variable|||
|_GMIN_|F (23,23) = 8.30|0.000|
|THMIL|F (23,23) = 8.08|0.000|
|COHFFVAR|F (23,23)=7.80|0.000|
|RTP40|F (26,26) - 4.82|0.000|
|BREUSCH PAGAN TEST|||
|w/includod variable|||
|_GIN!_|CMI SQUARED (1) = 4.54|0.033|
|TH81L|CMI SQUARED (1)=4.51|0.033|
|COBFFVAR|CHII SQUARED (1)=4.40|0.036|
|RTP40|CIM SQUARED (1) = 5.49|0.019|

Table 7: OrdinaLy Least Squares with Whito Heteroskodastic Consistent Standard Errors for 8aro Type Regression with Incquality Variables.

|Dep. Var|(1)<br>LOPC708S|(2)<br>LGPC7088|(3)<br>LaPC70s8|(4)<br>LaPC7088|_(5)_<br>LaPC7088|
|---|---|---|---|---|---|
|#of Obs|81|74|74|74|81|
|Constant|0.0154<br>(1.45)|0.0533"<br>(3.64)|0.0394"<br>(3.34)|0.05371<br>(3.98)|0.0255*<br>(2.19)|
|SaDPPC70|-0.0023<br>(-2.20)|-0.0026 *<br>(-2.01)|-0.0026<br>(-2.06)|-0.0027 *<br>(-2.10)|-0.0025*5<br>(-2.52)|
|REVC7085|-0.0017<br>(-0.17)|-0.0040<br>(-0.37)|-0.0044<br>(-0.41)|_-0.0050_<br>(-0.48)|-0.0030<br>(-0.28)|
|ASSP708S|-0.038S5<br>(-2.27)|-0.0SI I'<br>(-2.25)|-0.0527<br>(-2.31)|0.0531"<br>(-2.32)|-0.0405S<br>(-2.43)|
|PP170DVN|-0.0053<br>(-1.82)|-0.0059<br>(-1.87)|-0.0062<br>(-1.97)|-0.0063<br>(-1.99)|-0.0058*<br>(-1.90)|
|CPRIM60|0.0134<br>(1.35)|0.0116<br>(1.16)|0.0127<br>(1.30)|0.0121<br>(1.25)|0.0175*<br>(1.77)|
|CSBC60|0.0282 *<br>(1.91)|0.0193<br>(1.23)|0.0152<br>(0.97)|0.0122<br>(0.78)|0.0178<br>(1.27)|
|SOoV7088|-0.0426<br>(-1.23)|-0.0595 *<br>(-1.73)|-0.0606<br>(-1.78)|-0.0604<br>(-1.79)|-0.0479<br>(-1.40)|
|amN<br>a w_____________|___<br>________________|-0.0691<br>___(-3.04)|_||11|
|THE[L|||-0.0438"<br>( .22)|||
|C!OMFFAR<br>_______________|____ ________________|___ ________________|__|-0.0298"<br>(-3.53)|_______________353|
|RTP40<br>RTP40___________|__<br>__________________|_||__________________|-0.0022"0<br> <br>(-2.40)|
|R-Squaed|0.23|0.32|0.34|0.35|0.29|
# **School Autonomy and Accountability in Thailand: A Systems Approach for Assessing Policy Intent and Implementation**

Gustavo Arcia Kevin MacDonald Harry Anthony Patrinos<sup>1</sup> JEL No: H4,I20,I25,J24,O15 Keywords: educational policy; educational autonomy; school accountability; Thailand 1 We thank Sunee Klainin, Institute for the Promotion of Teaching Science and Technology, Thailand, for her coordination of the school survey and data coding, and Angie Rivera Olvera, for her assistance with the school-level dataset. We are indebted to Derek Pornsima, Advisor to the Secretary-General of KHURUSAPHA (Teachers’ Council of Thailand) and Kraiyos Pratawart, Quality Learning Foundation, for their support during the different phases of this study. At the World Bank Office in Bangkok we thank Vachraras Pasuksuwan for her invaluable assistance with the management and logistics of the SABER survey, and Lars Sondergaard, for his assistance and advice during the policy dialogue with the Government. We also would like to thank Benjalug Namfa, former Deputy Secretary General, Office of the Basic Education Commission, and Wongduan Suwansiri, Academic Officer, Office of the Basic Education Commission, for their participation and support during the phase of policy dialogue within the education sector in Bangkok. Gustavo Arcia and Kevin MacDonald are World Bank Consultants and Harry Anthony Patrinos is Manager, Education, World Bank.
# **(2) Related Literature**

There is a substantial and rapidly growing literature on the effects of trade costs on economic activity (for an excellent recent survey see Donaldson (2015)). The recent literature on transport costs emphasizes possible heterogeneity in the impacts of transport interventions. While transport investment is found to have significant positive impacts on many dimensions of economic development (see Donaldson (2018), Donaldson (2015),Emran and Hou (2013), Fafchamps and Shilpi (2005), Berg et al. (2016), and Emran and Shilpi (2012)), there is also evidence where these investments led to negative effects or no changes in outcomes (Faber (2014), Banerjee et al. (2012), Baum-Snow et al. (2017), Asher and Novosad (ming)).<sup>5</sup> We provide a framework that explains the spatially heterogeneous impacts in the context where a significant part of the economy may be subsistence based and not integrated with the world market.

Our analysis is closely related to the influential study of Redding and Sturm (2008) who investigate the effects of the Berlin Wall on the population growth rate of West German cities located near the border. Interpreting the Berlin Wall as a negative shock to market access and hence to trade, they find that cities, particularly the smaller cities, located near the Berlin Wall experienced a substantial decline in population growth relative to the other West German cities. Our study differs from Redding and Strum (2008) in two aspects. The trade/transport shock we consider is a shorter term shock lasting no more than 5 years as opposed to the Berlin Wall which lasted over decades. Perhaps more importantly, we not only analyze the effects on structural change in sectoral employment shares, but also provide evidence on three factors that might create path-dependence and longer-term persistence: sunk investment in built-up density, public provision of electricity infrastructure as captured by night-lights density, and skill concentration (agglomeration) as measured by the proportion of the working population with secondary and higher schooling.

The persistence of the impacts of temporary shock on spatial development has attracted

> 5Faber (2014) reports evidence of deindustrialization in China. Blankespoor et al. (2018) show that a bridge over the Jamuna river in Bangladesh that reduced transport costs by 50 percent led to deindustrialization in the newly connected hinterland, but also led to economic revival by facilitating higher agricultural productivity.

5
use these tax receipts for claims or tax refunds. The other main benefits according to firms are avoiding fines (19 percent say this), and to be obeying the law (43 percent say this). The benefit of obeying the law may also reflect less uncertainty about fines or other punishments, or reflect some psychic benefit associated with obeying the rules of society.

# **4. Modeling the choice of firms to become formal and our identification strategy**

# **4.1. The choice of formality**

Profit-maximizing firms will choose whether or not to register for taxes if the expected present discounted value of the net benefits from doing so outweighs the upfront costs. That is, a firm will choose to get a tax identification number if and only if: where πF,t denotes the firm’s profits if it is formally registered at time t, and πU,t denotes the firm’s profits if it is not formally registered at time t. θlaw-abiding denotes the utility benefit to firm owners from obeying the law and feeling they are contributing to national welfare through paying taxes. CMoney, CTime, and CInformation denote the monetary, time, and information costs from registering. The monetary costs also implicitly include the shadow value of capital for liquidity-constrained firms. For simplicity of exposition we assume here that the value of being formal at time _t_ does not depend on formality status in previous periods. If it does, the problem can be written as a dynamic optimization problem with a value function, and the same intuition as exposited here will still apply.

Equation (1) shows the key challenge of identifying the impact of formality on firm profitability. The choice to register for taxes or become formal will itself depend on the impact of formality on business profits (πF,t - πU,t). As a result, if there are no costs to registering and no non-monetary benefits from doing so, all firms for which it is profitable to be registered will have done so, and only firms for which informality does not pay will remain informal. If there are homogeneous treatment effects, everyone will

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