rafmacalaba/datause-encoder-data huggingface dataset:
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(2018), we separate trade flows into two components: (i) the trade flow of products that are considered as “sticky” and hence are associated with rigidity in the buyer-seller relationship, and (ii) the trade flow in products that are “not sticky”. We categorize as “sticky” all products that belong to the top 25% percentile in terms of stickiness as estimated by Martin et al. (2018).<sup>24</sup> We first show graphically in figure 6 the relationship between the long term change in GDP co-movement to the long term change in trade flows in _sticky products_ and _non-sticky products_ over 20 years from 1995 to 2014. From this, a significant positive slopes emerge for sticky trade and GDP fluctuations, while there is no positive relationship between non sticky trade and GDP correlations. We then estimate the role of sticky trade controlling for time windows and country-pairs fixed effects, with: Table 11 presents the results of our analysis and shows that the “relational” aspect of trade flows (defined as the degree of stickiness in trade flows) is an important element driving the positive association between trade and GDP correlation for country-pairs including at least a high income country. Indeed, for those country-pairs, trade in sticky products is significantly associated with more GDP correlation (for HP filter and First Difference), while non-sticky products are not.

**Table. 11.** Trade stickiness and GDP comovement

||corr G|DP<sup>_HP_</sup>|corr G|DP<sup>_BK_</sup>|corr ∆|GDP|
|---|---|---|---|---|---|---|
|Include X inc.|All|High|All|High|All|High|
|ln(_TP_<sup>_non sticky_</sup>)|_−_**0.009**<sup>_∗_</sup><br>(0.005)|0.002<br>(0.007)|_−_**0.012**<sup>_∗∗_</sup><br>(0.005)|_−_0.0003<br>(0.008)|_−_**0.012**<sup>_∗∗_</sup><br>(0.005)|_−_0.005<br>(0.007)|
|ln(_TP_<sup>_sticky_</sup>)|0.001|**0.011**<sup>_∗_</sup>|_−_0.001|0.010|0.003|**0.019**<sup>_∗∗∗_</sup>|
||(0.004)|(0.006)|(0.004)|(0.006)|(0.004)|(0.006)|
|Country-Pair FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Time Window FE|Yes|Yes|Yes|Yes|Yes|Yes|
|_N_<br>|15,445|9,004|15,445|9,004|15,445|9,004|
|R<sup>2</sup>|0.099|0.148|0.118|0.177|0.166|0.261|
|_Notes:_|_∗_p_<_0.1; _∗_|_∗_p_<_0.05;|_∗∗∗_p_<_0.01.|In parenth|esis: std. de|viation.|

The great financial crisis, which was associated with both large disruptions in buyer-seller relationships and a synchronized drop in GDP in many countries, could influence the results

> 24Notice that this specification also mirror the distinction between trade in intermediate inputs and trade in final goods. Indeed, as mentioned in Martin et al. (2018), products with a low degree of “measured stickiness” tend to belong to BEC classifications associated to final goods (like cars and consumer goods).

24
PROVISION OF WATER TO THE POOR IN AFRICA

**Table 1.5 Maputo water source by type of peri-urban area, 2006** Percent; N = 600

|Water sources used|Inner peri-<br>urban|Middle<br>peri-urban|Outer peri-<br>urban|
|---|---|---|---|
|Formal household connection|30|30|2|
|SSIP connection|1|8|31|
|Neighbors tap/other neighborhood|36|30|6|
|Public standpipe|22|13|7|
|SSIP standpipe|6|8|25|
|Private well/borehole (neighbors)|4|5|14|
|Own well/borehole|1|7|10|
|Public handpump (on well/borehole)||0|7|
|Total|100|100|100|

Source: Thompson, SAL Consultants (2007).

15
- 36 -

f, **-** di + Edt In Pj j (19) This in a linear logit (or logistic) model of cost **shares.** It should be noted that it is not related to logit modele of discrete choice. The **share** elasticities are: M - d1k - Z Sjdk (20) and the own and cross price (partial) price elasticities are _Be_ H + _S_ (21) No - Hu + Si -1 (22) Certain ccaditions have to be imposed in order for these equations to be consistent with theory as is shown by Considine and Mount (1984). **Homogeneity** of the cost function with respect to prices can be imposed by the restrictions: Z du - d all i (23) j where d is an unknown scalar that can be set equal to zero. Symmetry can be iAposed by the constraint du* - dj* a lliLj (24) where du* - du/SI* (25) and _Sj*_ is the mean cost share for fuel i. It can be shown that for two inputs the logit cost share model collapses to a constant elasticity (CES) model. The estimating equations for the three fuel case can be written: ln(S,/8 3<sup>)-</sup> (di - _d4)_ - [S2*d,2 * + **_(8,*_ +** 8*)d,,*J ln(P 1 /Pb) **+** (d12* - dD*IS2* ln (PJP) (26) ln(SdS 3<sup>)</sup><sup>**-**</sup> (cl - d3) - 1S,*d4a* + (8a* + Ss*)dJ*]ln(P2/P$) **+** (d1 2* - d,,*)S* ln (PI/Ps) (27) These equations can be estimated by full information maximum lkellhood using price ratios and expenditure shares. Parameters can be recovered using mean shares. ConsidLne shows that, for a set of fuel shares for U.S. industry
from the linkage of the top-down model with the bottom-up model. The carbon tax required to meet the NDC under the linkage case is found to be less than one-third of that in the absence of the linkage. This explains the reason why the economic impacts under the former case are lower as compared to that of the latter. The conclusion here is that top-down CGE models tend to overestimate the impacts of climate change policies because they are unable to reflect detailed technology specific energy standards and policies already in place or planned for the near future.

Although the linked modeling approach tends to correct the likely overestimates of the economic impacts of a climate change policy, the results should be interpreted with care, as the initial projection of the energy-supply mix produced by the bottom-up model might have influenced by modeler’s optimistic assumptions on energy efficiency improvements. If so, the baseline GHG emissions get underestimated, thereby resulting in a smaller gap between the target and baseline emissions, and the required carbon tax to achieve the mitigation target. It is, therefore, the development of the baseline (implicit assumptions on the driving variables of the baselines) that is critical in both models. The linkage of the two models, however, dampens the variations in the baselines from the two models, thereby helping to estimate more reliable economic and environmental impacts of climate change mitigation policy as compared to that estimated by the stand-alone models.

# **References**

Böhringer, C., and T. Rutherford (2008). “Combining bottom-up and top-down.” Energy Economics, 30 (2), pp. 574–596.

Böhringer, C., and T. Rutherford (2009). “Integrated Assessment of Energy Policies:

- Decomposing Top-down and Bottom-up.”  Journal of Economic Dynamics and Control, 33, pp. 1648–1661.

Bukowski, M. and Kowal, P. (2010). “Large scale, multi-sector DSGE model as a climate policy assessment tool.” Working paper. Institute for Structural Research, Poland.

- Chen, Wenying, Xiang Yin, and Ding Ma. 2014. “A Bottom-up Analysis of China’s Iron and Steel Industrial Energy Consumption and CO2 Emissions.” _Applied Energy_ 136: 1174– 83.

- Chen, Wenying. 2005. “The costs of mitigating carbon emissions in China: findings from China MARKAL-MACRO modeling” Energy Policy, 33:885-896.

22
25 short lived.

Similar indications are obtained from estimations of the demand for money.<sup>13</sup> The results are shown in table 4,14 Using dummies for the various stabilization attempts we found a clear trend in the shifts in the demand for money. In Brazil, for example, there was an upward shift in money demand between the beginning of the Cruzado plan till the implementation of the Bresser plan. This shift was reversed in the period from the Bresser plan onwards. Likewise, the dummies for Argentina indicate a positive shift in the demand for money during the period of the Austral plan (including the mild cycles), followed by a negative shift during the period of the strong cycles. Finally, as can be seen from figures 10.a and 10.b which show the residuals from the money demand equations, our equations tend to 13 The estimations were done using the Cagan demand for money function using monthly data for Argentina and Brazil. The estimation period for Argentina goes from 1982.12 to 1989.04 and for Brazil from 1980.01 to 1989.06. Separate estimations were done for the whole sample and for the period before and after the HSs. We also used dummy variables to find out whether there were shifts in the demand for money in the period.

14 The estimation was done using OLS. The results reported in this regression use current inflation as a proxy for the expected rate of inflation in the period. The results of these regressions were basically similar when we used a weighted average of past inflations and even future inflation as a proxy for the expected return on domestic money.
|**Table 5: Heterogeneity in Emp**|**loyment Ou**<br>|**tcomes by Ag**<br>|**e*Gender**<br>|**Stratifyin**<br>|**g Variables**<br>Transformed<br>||||
|---|---|---|---|---|---|---|---|---|
||Working<br>at all|Employed<br>20 hours+|Weekly<br>Hours|Monthly<br>Income|Monthly<br>Income|Occupational<br>Status|Formal<br>Work|Formal<br>Income|
|ITT for males under 25|-0.021|-0.034|-0.952|-12.112|-0.241|-0.495|0.003|20.734|
||(0.030)<br>[-0.04, -0.01]|(0.030)<br>[-0.06*, -0.02]|(1.626)<br>[-2.5, -0.3]|(31.384)<br>[-58**, 1]|(0.223)<br>[-0.40*, -0.15]|(1.299)<br>[-1.4, +0.1]|(0.028)<br>[-0.02, 0.02]|(30.160)<br> [-24.9, 33.0]|
|ITT for males over 25<br>|0.069** (a)<br>(0.029)<br>[0.06*, 0.10***]|0.047<br>(0.031)<br> [0.03, 0.07**]|2.917*<br>(1.772)<br>[1.0, 4.2**]|35.304<br>(37.501)<br>[-17, 62*]|0.383<br>(0.244)<br>[0.23, 0.58**]|2.997**(a)<br>(1.332)<br>[1.4, 4.3***]|0.047<br>(0.032)<br>[0.03, 0.07**]|32.019<br>(36.733)<br> [-28, +55]|
|ITT for females under 25|0.011|0.016|0.952|14.938|0.140|0.567|0.017|15.571|
||(0.025)|(0.025)|(1.355)|(20.999)|(0.181)|(1.161)|(0.024)|(20.519)|
||[0.01, 0.01]|[0.02, 0.02]|[0.8, 1.0]|[12, 15]|[0.13, 0.14]|[0.5, 0.6]|[0.02, 0.02]|[13, 16]|
|ITT for females over 25|0.023|0.018|0.761|26.052|0.174|1.034|0.019|23.058|
||(0.020)|(0.019)|(1.001)|(17.321)|(0.141)|(0.917)|(0.018)|(16.788)|
||[0.02, 0.03]|[0.02, 0.02]|[0.7, 0.8]|[22, 27]|[0.16, 0.18]|[1.0, 1.1]|[0.02, 0.02]|[19, 24]|
|p-value for testing equality:|0.188|0.276|0.457|0.712|0.268|0.294|0.768|0.982|
|<br>Control Mean: young men|0.489|0.407|20.1|347|2.90|19.2|0.307|275|
|Control Mean: older men|0.621|0.548|27.3|518|3.94|24.3|0.466|458|
|Control Mean: young women|0.403|0.356|18.1|269|2.50|17.3|0.295|238|
|Control Mean: older women|0.295|0.247|12.0|187|1.69|12.4|0.196|161|
|Sample size<br>Notes: Robust standard errors<br>(a) denotes significant after a<br>Square brackets show Lee upp<br>All regressions include contro<br>Test of equality tests the null<br>Appendix A defines the outco|5497<br>in parenthe<br>pplying the B<br>er and lowe<br>ls for course<br>of equality o<br>me variables|5529<br>ses. *, **, an<br>enjamini-Ho<br>r bounds, alo<br>*gender*age<br>f treatment i<br>.|5439<br>d *** indic<br>chberg pro<br>ng with in<br>group lott<br>mpact acro|5396<br>ate signific<br>cedure to<br>dicator of t<br>eries appli<br>ss the fou|5396<br>ance at the<br>maintain the<br>heir signific<br>ed for.<br>r age*gende|5418<br>10, 5 and 1 per<br>false discove<br>ance.<br>r strata.|5508<br>cent levels<br>ry rate acro|5464<br>respective<br>ss the fou|

Notes: Robust standard errors in parentheses. *, **, and *** indicate significance at the 10, 5 and 1 percent levels respective (a) denotes significant after applying the Benjamini-Hochberg procedure to maintain the false discovery rate across the four Square brackets show Lee upper and lower bounds, along with indicator of their significance. All regressions include controls for course*gender*age group lotteries applied for. Test of equality tests the null of equality of treatment impact across the four age*gender strata. Appendix A defines the outcome variables.

38
and adverse distributional effects (Bromley 2009). Distinguishing policy or regulatory reform from titling and showing that these have very different land market activation effects allows us to reconcile these views. We document robust land market effects that can be unambiguously attributed to regulatory reform which, by reducing transaction costs, increased the number of registered transfers and mortgages for parcels that had already been registered or were registered sporadically in response to this policy change. This makes our paper the first to document robust credit market effects from regulatory and institutional reform. Our finding that (subsidized) systematic titling had a negative net impact on land market activity suggests that land market activation is best achieved via policy reform.

Second, while a large literature suggests that women having formal land rights enhances their well-being by increasing investment (Ali _et al._ 2014), labor force participation (Newman _et al._ 2015) and bargaining power (Menon _et al._ 2014), less is known on what is needed for women to have their land rights documented and participate in markets in the first place. Results suggest that legal change can help increase female land ownership, its full potential will be realized if combined with other measures. While in our case this was performed by systematic registration, financial incentives or nudges to include women on title documents (Ali _et al._ 2016) may be equally or more effective once regulatory reforms have been enacted.

Third, compared to an upsurge in the use of administrative data including for evaluating large programs (Muralidharan _et al._ 2016) or assessing tax compliance (Pomeranz 2015), the advantages of registry data in terms of frequency, granularity, reliability and zero marginal cost to evaluate land programs have not been fully realized with studies mainly remaining at a descriptive level (Ali _et al._ 2017; Holden and Tilahun 2020). We show that econometric analysis of spatially aggregated registry data can help identify impacts of policy reform that elude a traditional DID approach, in addition to remedying some of the shortcomings of household survey data. Establishing systems to provide such data routinely in land programs could thus enhance accountability; improve tracking of outcomes and understanding of the channels for impacts to materialize; and provide insights long after interventions have wound down.

The rest of the paper is structured as follows. Section 2 provides conceptual framing of the general challenges of evaluating land registration projects as well as the specific characteristics of the project to be considered here. Section 3 discusses descriptives based on administrative data and describes in detail the empirical methodology for evaluating the impact of this intervention using both household survey and administrative data. Section 4 discusses econometric evidence from analyzing household survey and administrative data, compares results from these, and draws out methodological and conceptual conclusions. Section 5 concludes by highlighting some implications for future research.

3
the weather characteristics did not play a significant role in determining the concentration of PM2.5 in the kitchen air.

|**Table 5: Regression results for log C**|**O concentratio **|**n(ppm) in ki**|**tchens**|
|---|---|---|---|
|**Explanatory variables**|**(1)**|**(2)**|**(3)**|
|_Fuel and Stove use_||||
|Household uses wood in traditional stoves|0.116|0.056|0.034|
||(0.63)|(0.31)|(0.19)|
|Household uses wood in improved stoves|−1.132**|−1.146**|−1.176**|
||(−3.65)|(−4.03)|(−4.11)|
|Household uses charcoal in improved stoves|0.102|0.128|0.129|
||(0.70)|(0.91)|(0.91)|
|Household uses ethanol|−2.443**|−2.288**|−2.306**|
||(−9.74)|(−9.47)|(−9.72)|
|_Kitchen characteristics_||||
|Open Kitchen|-|−0.053|−0.050|
|||(−0.63)|(−0.58)|
|Airy Kitchen|-|−0.021|−0.026|
|||(−0.15)|(−0.18)|
|Kitchen with permeable roof|-|0.222|0.241|
|||(1.43)|(1.55)|
|Kitchen with permeable wall|-|0.443|0.439|
|||(1.26)|(1.24)|
|Size of kitchen (Sq. ft.)|-|−0.027**|−0.027**|
|||(−5.07)|(−5.06)|
|Size of kitchen vent (Sq. ft.)|-|−0.058|−0.065|
|||(−0.78)|(−0.86)|
|_Weather characteristics_||||
|Strong Wind|-|-|−0.339*|
||||(−1.70)|
|Dry day|-|-|−0.044|
||||(−0.28)|
|Rain-free day|-|-|−0.054|
||||(−0.32)|
|Warm Temperature|-|-|0.003|
||||(0.03)|
|R<sup>2</sup>for the regression model|0.359|0.400|0.403|
|Number of HHs (groups)|334|334|334|
|Number of observations|848|841|841|

Note: Figures outside the parentheses are regression coefficients and in the parentheses are t-statistics based on robust standard errors. *=statistically significant at 10% level, **= statistically significant at 5% level or better. Regressions additionally control for survey rounds and locations. Source: World Bank household survey (2009-2010) Results presented in Table 5 highlight the significant role of improved wood stoves as well as ethanol in reduction of CO in the kitchen air. Column 2 indicates that the effects of kitchen characteristics on concentration of CO are similar to those on PM2.5 concentration, with only the size of the kitchen showing a significant inverse effect. Estimates presented in column 3 15
**7 |** P a g e

continued to increase. In terms of goods exports in current prices, the situation was slightly different: BiH recorded stronger export growth not only during the post‐war phase but also during the current expansionary phase. The differences in LSG rates in favor of BiH were quite significant: 18.2 percentage points and 7.4 percentage points, respectively (see Table 3).

**Table 3: BiH export performance in goods and non‐factor services in constant prices and in goods in current prices in comparative perspective in 1996‐2007**

||LS<br>|Growth Rate(%<br>|)<br>|Change 2007<br>|
|---|---|---|---|---|
|Country|1996‐2000|2000‐2003|2003‐2007|over 2006(%)|
|**Exports of goods & non‐factor services in co**|**nstant 2000 prices**||||
|Bosnia and Herzegovina|16.0|5.8|11.7|12.5|
|SEE‐4(excl. BiH)|na|5.5|7.0|8.4|
|Memo: EU‐8|10.6|6.6|12.5|12.2|
|**Exports of goods in current prices**|||||
|Bosnia and Herzegovina|25.8|6.5|28.9|25.4|
|SEE‐4(excl. BiH)|na|na|27.6|27.2|
|Memo: EU‐8|7.6|16.2|21.5|27.8|

Note: EU‐8 includes Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovak Republic and Slovenia: SEE‐4 includes Albania, Croatia, Macedonia and S&M.

Sources: World Bank WDI database for goods and non‐factor services and UN COMTRADE Statistics for goods.

BiH has also recorded better performance than the average for other SEE‐4 economies across all three phases. It had a larger edge in the increase of exports in terms of volume than in value indicating that other SEE‐economies faced more favorable change in their terms of trade.

Relatively impressive record of the BiH export performance raises several questions addressed in the following sections: Has the expansion consisted simply of exporting more of the same goods? What do the changes in import demand tell us about the pace of industrial restructuring? What do the changes in exports depending on the degree of processing embodied in exported products tell us about BiH’s industrial capacities? What characteristics do new star performers embody in terms of technology and the degree of processing?

**2. Drivers of export expansion: Endowments in factors of production and changes in competitiveness**

Levers underlying the current phase of export expansion are complex ranging from more of the same to those heralding a significant change in competitiveness in the future. On the one hand, traditional sectors continue to generate most of BiH foreign currency earnings and account for the bulk of BiH exports. They have been growing and have demonstrated their competitiveness in world markets.
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