rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix Table A3: How does Treatment Effect Vary with Time Since Treatment?**

Dependent Variable: Real Monthly Profits

|||Males and Fe|males Pooled||Ma|les|Fem|ales|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||OLS|OLS|FE|FE|OLS|FE|OLS|FE|
|Cash Treatment at 3 months|14.27|9.12|5.89|1.13|5.05|‐2.58|11.52|3.25|
||(10.26)|(8.01)|(11.23)|(8.31)|(15.03)|(15.86)|(9.15)|(9.24)|
|Cash Treatment at 6 months|7.18|6.30|‐1.36|‐2.75|16.11|5.90|‐0.18|‐8.42|
||(9.86)|(9.16)|(17.27)|(10.34)|(17.21)|(20.45)|(10.32)|(10.91)|
|Cash Treatment at 9 months|12.97|5.99|9.60|3.74|12.64|11.01|2.37|‐0.30|
||(12.23)|(10.96)|(15.97)|(11.07)|(20.12)|(21.36)|(12.47)|(11.93)|
|Cash Treatment at 12 months|38.09***|27.98**|17.73|17.01|57.54***|30.41|10.01|8.82|
||(13.55)|(12.81)|(23.52)|(13.42)|(20.87)|(25.94)|(16.15)|(14.69)|
|In‐kind Treatment at 3 months|26.37**|26.65**|30.20**|18.86*|33.59|25.34|22.25*|14.81|
||(12.10)|(11.42)|(12.64)|(11.36)|(22.86)|(24.82)|(11.89)|(10.06)|
|In‐kind Treatment at 6 months|34.62***|32.61***|38.34***|25.49**|19.12|9.98|41.03***|35.16***|
||(11.68)|(11.19)|(12.75)|(10.93)|(15.11)|(18.99)|(15.44)|(13.10)|
|In‐kind Treatment at 9 months|48.33**|48.90**|54.91***|45.24**|39.49**|36.59*|54.76*|50.66*|
||(20.63)|(19.96)|(20.25)|(18.50)|(17.33)|(19.41)|(30.35)|(27.33)|
|In‐kind Treatment at 12 months|58.35***|46.91***|78.17***|58.00***|69.76*|75.71**|32.76*|47.10***|
||(19.42)|(17.52)|(19.23)|(17.02)|(35.62)|(36.58)|(17.47)|(15.33)|
|Constant|119.70***|102.20***|120.34***|103.05***|127.88***|128.69***|86.43***|87.33***|
||(8.85)|(4.40)|(7.38)|(3.71)|(7.52)|(6.47)|(5.40)|(4.49)|
|Baseline trimming|No|Yes|No|Yes|Yes|Yes|Yes|Yes|
|Number of Observations|4354|4203|4354|4203|1599|1599|2604|2604|
|Number of Firms|792|764|792|764|290|290|474|474|
|P‐value for testing constant effect:|||||||||
|of Cash Treatments|0.166|0.435|0.262|0.389|0.170|0.534|0.579|0.353|
|of In‐kind Treatments|0.492|0.577|0.121|0.163|0.458|0.249|0.189|0.057|

Notes: All estimation includes wave effects. Standard errors in parentheses, clustered at the firm level. *, ** and *** denote significant at the 10%, 5% and 1% levels.

Trimmed specifications trim out matched quadruplets which have at least one firm with profits above 1500 cedis per month in wave 1 or 2. OLS estimation includes dummies for the matched quadruplets.
**Table 3: Trade gap and tariff rate by country.**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|||Czech|||||||||
||Bulgaria|<br>Republic|Hungary|Latvia|Lithuania<br>Trade|Poland<br>Gap|Romania|Russia|Slovenia|Ukraine|
|Tariff|0.009|0.015|0.008|0.022|0.013|0.000|0.01|0.032|-0.004|0.044|
||(0.003)***|(0.003)***|(0.001)***|(0.004)***|(0.003)***|(0.001)|(0.003)***|(0.004)***|(0.004)|(0.005)***|
|Observations|3453|16187|22725|13122|10284|17817|9874|16575|10546|11825|
|Adj. R-squared|0.004|0.009|0.007|0.005|0.010|0.674|0.005|0.011|0.0001|0.011|

All models include year fixed effect and a constant. Standard errors, clustered on 6-digit products, are listed in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

# **4. Trade gap, tariff rates and differentiated products**

As mentioned earlier, differentiated products may lend themselves more readily to tariff evasion than homogeneous goods as their price depends on many attributes some of which may not be easily verifiable by a person unfamiliar with the product. Therefore, in the case of differentiated products it is more difficult for honest customs officers to detect an invoice stating an incorrect price and corrupt customs officers have a plausible explanation for why they failed to detect the problem with the invoice.

In our analysis, we use the classification of differentiated products developed by Rauch (1999). Rauch defined differentiated products as those not having a reference price or those whose price is not quoted on organized exchanges. Wheat and diamonds are classified as homogeneous goods, while coats and jackets are considered to be differentiated products. Rauch suggested two definitions, a conservative and a liberal one, in order to account for the ambiguities arising in the classification. The conservative definition minimizes the number of commodities that are classified as homogeneous goods, while the liberal definition maximizes this number. We employ both classifications, although the results do not differ substantially between the two. Rauch’s definitions are based on the 4-digit SITC Rev. 2 classification, and we use the concordance provided by WITS to make it compatible with the 6-digit HS 1988/92 classification used in our data set.

Table 4, which reports the average trade gap for differentiated and homogeneous goods, confirms our prior about differentiated products lending themselves more readily to tariff evasion. For all countries but Latvia and the Czech Republic, the trade gap is larger for differentiated products than for homogeneous goods. For instance in Bulgaria, there is hardly any discrepancy for homogeneous products (-2.6% in the case of the conservative and -1.6% in the case of the liberal definition), but a significant trade gap is found for differentiated products (16.6% and 17.6% for the conservative and liberal definition, respectively). In the case of Hungary, the gap increases from 3% for homogeneous goods to 11
Table 4: Spatial Disparity and Sorting of household Attributes

|||2000|||2010||
|---|---|---|---|---|---|---|
||All|Urban|Rural|All|Urban|Rural|
|**Base Case**|||Panel A (b|ase case)|||
|Neighborhood Correlation (r)|0.363***|0.385***|0.218***|0.266***|0.298***|0.199***|
||(19.929)|(13.271)|(11.820)|(20.405)|(12.998)|(14.158)|
||||Panel B ( E|ducation)|||
|Neighborhood Correlation (r*)|0.192***|0.185***|0.180***|0.160***|0.119***|0.174***|
||(13.812)|(8.201)|(10.819)|(16.060)|(8.423)|(13.316)|
|Contribution of education (r-r*)|0.171|0.200|0.037|0.106|0.179|0.025|
|% Decrease[((r-r*)/r)*100]|47.2|51.9|17.2|39.8|60.1|12.5|
|||Pa|nel C ( Skill|composition|)||
|Neighborhood Correlation (r*)|0.313***|0.349***|0.209***|0.219***|0.222***|0.199***|
||(17.881)|(12.220)|(11.560)|(18.478)|(11.111)|(14.117)|
|Contribution of skill comp. (r-r*)|0.050|0.036|0.008|0.047|0.076|0.000|
|% Decrease[((r-r*)/r)*100]|13.7|9.4|3.8|17.8|25.4|0.1|
|||Pan|el D ( Sector|al compositio|n)||
|Neighborhood Correlation (r*)|0.318***|0.384***|0.207***|0.227***|0.253***|0.191***|
||(18.082)|(13.163)|(11.508)|(18.790)|(11.838)|(13.876)|
|Cont. of sectoral comp. (r-r*)|0.045|0.001|0.010|0.040|0.045|0.008|
|% Decrease[((r-r*)/r)*100]|12.3|0.2|4.7|14.9|15.2|3.8|
||Pa|nel E ( Educ|ation, Skill a|nd Sectoral|composition|)|
|Neighborhood Correlation (r*)|0.190***|0.185***|0.176***|0.159***|0.120***|0.167***|
||(13.698)|(8.173)|(10.618)|(15.958)|(8.370)|(13.006)|
|Cont. of human capital (r-r*)|0.173|0.200|0.042|0.107|0.179|0.032|
|% Decrease[((r-r*)/r)*100]|47.6|51.9|19.2|40.3|59.8|16.1|
|||P|anel F (share|of children)|||
|Neighborhood Correlation (r*)|0.295***|0.305***|0.203***|0.246***|0.271***|0.192***|
||(17.396)|(11.135)|(11.415)|(19.581)|(12.306)|(13.912)|
|Cont. of dependency (r-r*)|0.067|0.080|0.015|0.020|0.027|0.007|
|% Decrease[((r-r*)/r)*100]|18.6|20.8|6.8|7.5|9.1|3.5|
||Panel G (|Education,|Skill and Se|ctoral compo|sition, depe|ndency)|
|Neighborhood Correlation (r*)|0.179***|0.166***|0.171***|0.159***|0.118***|0.167***|
||(13.352)|(7.682)|(10.461)|(15.937)|(8.284)|(12.992)|
|Cont. of all household sorting (r-r*)|0.183|0.219|0.047|0.107|0.181|0.032|
|% Decrease[((r-r*)/r)*100]|50.6|56.9|21.5|40.3|60.6|16.1|
|||Panel|H ( All sortin|g plus geogr|aphy)||
|Neighborhood Correlation (r*)|0.152***|0.136***|0.140***|0.128***|0.099***|0.131***|
||(12.005)|(6.510)|(9.225)|(14.284)|(7.325)|(11.398)|
|Cont. of sorting & geo -climate (r-r*)|0.211|0.249|0.078|0.138|0.200|0.068|
|% Decrease [((r-r*)/r)*100]|58.1|64.8|35.6|51.9|66.9|34.3|

Robust z statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%
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provides a simple and robust model with basin-relevant results for policy, which measures gauge station runoff with wetness values derived from satellite data (BWI) over the period 1988-2010, as proof of the concept on how sub-basin stream flow information can be expanded to the river basin where natural flow gauge information is limited and can provide the possibility of timely updates. Then, we demonstrate the use of the approach for monitoring various flow levels that provide estimates of monthly runoff in near real time in two international river basins, Zambezi and Mekong. We show how information from the model can strengthen the institutional capacity within the basin to understand the probability of various levels of extreme flow, has potential to realize the economic consequences of extreme flow events, and prepares or adjusts proper institutions to address it.

The paper is organized in five sections. Below we begin by couching the discussion of the BWI in a socio-political context by highlighting the links between climate change and water variability, conflict and cooperation, and the role of international institutions such as international water treaties. We also stress the importance of data availability given the uncertainties inherent in climate change and the consequences of water variability. Second, we describe a number of methodologies that have been used to derive flow data and likewise discuss the BWI and demonstrate its use in the Zambezi and the Mekong. Third, we discuss the accuracy and predictability of the BWI‘s estimated flow probability models. Fourth, we develop a basin economic model that incorporates the flow probability and the existing institutional frameworks that exist in these basins to gauge economic and political impact. We compare these institutions based on their relevance, structure, and design. Fifth, and in an effort to demonstrate the applicability of the BWI to a growing research agenda on conflict and cooperation over shared rivers, we review historical political events related to floods. We conclude that the satellite data we derive may reduce the uncertainty inherent in negotiating an international water agreement and help policy makers devise more efficient institutional apparatus.

# **THE SOCIO-POLITICAL CONTEXT: UNCERTAINTY, INSTITUTIONS, AND INSTITUTIONAL CAPACITY**

Although water variability and return periods for extreme events are available across river basins, climate change is predicted to increase such variability (Dai et al. 2009; Milly et al. 2008). One of the 3
**Figure 2. Credible rules, credible policy and predictable resourcing constitute the institutional environment within any perspective**

|||expecta<br>rule enf|tions of<br>orcement|expecta<br>|tions of po|licy||expecta<br>future r<br>flows|tions of<br>esource|
|---|---|---|---|---|---|---|---|---|---|
|princip|al-~~<br>moetr<br>agency~|~<br>emh-epa<br>**~4**<br>|**44e5**<br>~~~~|**o**<br>C6|siedsiedsie<br>-~~~~~<br>~~~~~~~~~~~~|~~~~~~~~~~~~C|**04'~**<br>|||
|principal-<br>agent<br>|monetary<br>incentives<br>|empha-<br>sized||||||empha-<br>sized||
|perspective|contract<br>enforcemens|empha-<br>tsized|||||||empha-<br>sized|
|principled-|claot tof<br>tyagen<br>mission<br>||||empha-<br>sized||empha-<br>sized|___|empha-<br>sized|
|agent<br>perspectiv|cultureand<br>eMUtual trUSt<br>|**empha-**<br> sized<br>|empha-<br>sized<br>|empha-<br>sized||empha-<br>sized||emnpha-<br>sized<br>|empha-<br>sized|
|patron-|long term<br>arrsm|empha-<br>sized|empha-<br>sizedpri|||||exnpha-||
|<br>client<br>perspectivec|<br>informality<br>_<br>_<br>_<br>_<br>_|<br> opposed<br>_<br>__<br>_|_<br>_<br>_<br>_|_<br>_<br>_|opposed||opposed|||

Against this context, the study has adopted a framnework for measuing the institutional environment built around measurement of these three factors that constitute the institutional environment. Accordingly, indicators are constructed to measure _how much_ rule credibility, policy credibility and resource adequacy and predictability the institutional arrangements are providing.

# _2.3 How can perfiormance be measured?_

In addition to the well-recognized difficulties in assessing organizational performance, quantifying performnance of public organizations runs into two main problems. **23** First, unlike private organizations, public organizations have no single performance indicator that can compare across different types of organization and products, such as profits earned or market share. In the public sector, only a few organizations work for profit.

**23** Acs and Gerlowski **(1996)** list the classic difficulties including measurement errors, random effects, and factors outside the control of the organization. Carter, Klein, and Day _(1995)_ lists ownership, trading/non-trading, competition, accountability, heterogeneity, complexity, and uncertainty as the organizational dimensions and argue that these dimensions show huge variation among public organizations. They also point out the difficulty in measuring the quality of service delivery.

8
given that the robustness of these results has not been tested with other model specifications and a larger sample of countries.

In sum, the evidence linking greater gender equality in rights, voice and opportunity to more rapid growth at the cross-country level is not overwhelmingly strong. There are several crosscountry growth regression studies that suggest that greater equality in access to education may pay growth dividends, but growth regressions suffer from several important weaknesses. Evidence for the impact of greater equality in labor markets is suggestive but far from conclusive. And evidence for the role of more equal rights and voice in generating more rapid economic growth simply does not exist.

Might there be indirect effects of gender equality on growth that are transmitted via the impact of gender equality on poverty alleviation? A recent World Bank report (Perry et al., 2006) enumerated the channels by which poverty impedes growth. Some of these channels with important gender elements include:

- “Poor people often have limited access to financial markets or other necessary complements to private investment (such as property rights and infrastructure) essential to the accumulation of physical and knowledge capital and participation in the growth process.

- Poor people are often in poor health, which reduces their productivity...

- Poor people attend low-quality schools and the low and late returns to education and diminished prospects for mobility deter the accumulation of human capital essential for growth...

- Poor people may face more labor market risk, or may be less able to hedge against it, and thus find returns to investing in human capital adjusted for risk to be less attractive...”

To the extent that gender inequalities reinforce the human capital disadvantages of poor women, it is likely that they also hinder growth—but this hypothesis remains to be tested empirically.

# **10. Gaps in knowledge and promising areas for future research**

This paper has attempted to distill the state of knowledge about the links between gender equality, on the one hand, and poverty reduction and economic growth on the other. The relationships are far from simple, and our knowledge is far from complete.

At the macro level, there has been significant work done exploring the links between gender equality and economic growth. The simple scatter plots presented in this paper hint at a positive relationship, as do (somewhat) more sophisticated cross-country regressions. Yet there is abundant reason to be skeptical of these results: one should never take simple correlations very seriously, and cross country regressions are plagued by a number of shortcomings that are described above in some detail. When we disaggregate gender equality into its components of rights, resources and voice, we find surprisingly little evidence—indeed, surprisingly little research at all—that specifically models or measures the impact of greater gender equality on 37
evidence, however, will not easily support quantitative claims that there was more poverty in rapidly industrializing England than in slower industrializing France (Adelman and Morris, 1978, p.254), where more of the poor were rural and out of sight. It may say more about awareness of poverty than its prevalence.

We need numbers. Ideally, we would like to have nation wide, household survey data of the kind favored by Fields (1989, p.5) in his recent survey of poverty and economic growth in the contemporary Third World. Unfortunately, no such data exists for most of the century. It is only toward the end of the 19th century that a series of detailed investigations of urban poverty were made in specific English towns. Although these investigations were still locally based, they can claim to be the precursors of the kind of household surveys on which Fields would like us to rely. (See Hennock (1987) for a useful historiography.) For earlier periods, we have to rely on data generated by the provision of poor relief by local administrators to "paupers" in Britain and America. These official pauper statistics include time series of numbers of paupers and relief expenditure by local area and year. And they sometimes tell us who were the paupers.

Pauper statistics are hardly an ideal poverty index, so what can they tell us? To answer this question, we first have to ask what determines the proportion of people in receipt of poor relief (pauperism rates) at any place and time. We can divide the determinants of pauper rates into the demand for poor relief (the extent of poverty), the supply of public relief, and the supply of private substitutes. All are of interest to us in this essay. First, consider the demand for poor relief. A rise in poverty, either generally or within a certain age and gender group, should increase the pauper 37
**Figure 5b.** Impacts of 25% increase of parking fee on urban economic activity (% change from the base case)

<!-- Start of picture text --> 14.00%<br>12.00%<br>10.00%<br>8.00%<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>-2.00%<br>Government Total rent Total property value GRP-Nominal<br>revenues<br>C2=2 C2=3.5<br><!-- End of picture text -->

# **5.1.5 Social welfare impacts of policy instruments or investment activities**

Figure 6 compares the effects of different policy instruments and investment actions on total social welfare. The impacts on social welfare calculated by the model include the effects of changes in total travel costs, total changes in rents, and total change in property values. Among the individual policies and investment activities, road expansion would increase the value of social welfare per person in the Greater Beirut Area by 200,000 to 300,000 LBP. All other policies and investment activities would reduce social welfare by varying amounts.

33
covered, only a small number of these messages by chance occurred in the week preceding an interview in the L2T survey.

Columns 1-3 in table 6 provide simple pooled regressions, as before, adding more control variables moving from left to right. The coefficient for receiving an official SMS reported in column 1 suggests those who received messages in the week preceding the interview report about .3 more reported behaviors on average, significantly higher reporting than those who do not report a message. Adding more control variables to this basic estimation (columns 2 and 3) does materially affect the coefficient of interest. The sign and significance of the control variables suggest that people living in urban areas report more risk reducing behaviors on average, as do higher income people (columns 2 and 3). However, the inclusion of controls for month of interview absorbs the differences in reporting in the basic descriptive OLS regression (column 3). This suggests that in a descriptive sense, reporting differences cannot be distinguished from a time trend.

Focusing again on changes in a fixed effects framework in columns 4 and 5 of table 6 shows that messages in the past week lead to about .47 additional behaviors. Controlling for income (column 5) does not materially affect the coefficient of interest. Thus, for both definitions of treatment, the results suggest that messages significantly increased reporting risk reducing behaviors.

**Table 6: Receiving Official SMS in Previous Week vs. Behaviors Reported**

|||y=number of<br>|behaviors repo<br>|rted (out of 9)<br>||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Gov. SMS past week|0.306***|0.458***|0.091|0.467***|0.473***|
||(0.113)|(0.131)|(0.130)|(0.119)|(0.120)|
|Urban||0.228***|0.219***|||
|||(0.070)|(0.070)|||
|Ln(income pc)||0.306***|0.307***||0.009|
|||(0.013)|(0.012)||(0.008)|
|Constant|5.904***|4.907***|3.495***|5.926***|5.898***|
||(0.030)|(0.050)|(0.177)|(0.002)|(0.023)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.000|0.063|0.112|0.002|0.002|
|Unique individuals|2,153|2,153|2,153|2,153|2,153|
|Robust standard errors in|parentheses; ***|p<0.01, ** p<0|.05, * p<0.1|||

Breaking out individual behaviors in many cases highlights the primary contributions to the overall effect, though can be challenging due to the smaller sample sizes available for changes at the individual behavior level. These estimates are reported in tables 7-15. In each table, columns 1-5 report results for relying on SMS as a primary source of information on COVID-19, and columns 6-10 report results related to having received a message in the preceding week. Although the broad patters are similar, the way messaging is measured (either relying on the source or receiving a message in the preceding week) leads to some differences in which behaviors are reported more commonly. Additionally, in some cases, only modest impacts are identified due to low variation in responses. Both mask wearing 11