rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6: Spatial Fixed Effects Estimates of the Impact of LTR on Rural Investment**

||**Change in**<br>|**Change i**<br>|**n soil conservation**<br>|**measures**|
|---|---|---|---|---|
||**improved seed**|**New or**|**New**||
||<br>**use**|**maintenance**|**construction**|**Maintenance**|
|Treatment indicator|0.064|0.099**|0.075|0.038|
||(1.008)|(2.460)|(1.544)|(1.271)|
|Treatment X Female headship|0.003|0.094**|0.026|0.050|
||(0.076)|(2.076)|(0.713)|(1.314)|
|Number of years possessed|0.001|-0.002**|-0.000|-0.002***|
||(1.187)|(1.988)|(0.194)|(2.645)|
|Parcel was purchased|-0.074**|-0.030|-0.018|-0.017|
||(2.438)|(0.901)|(0.623)|(0.940)|
|Parcel was inherited|-0.083|-0.065|-0.016|-0.052*|
||(1.373)|(1.030)|(0.334)|(1.705)|
|Acquired through other means|0.087|-0.209**|-0.133|-0.081*|
||(1.226)|(2.512)|(1.640)|(1.732)|
|Parcel size in hectares|0.032*|-0.002|-0.001|-0.004|
||(1.826)|(0.199)|(0.088)|(0.650)|
|Head's age|-0.001|-0.001|-0.001|-0.001|
||(0.927)|(0.978)|(0.720)|(0.762)|
|Female headed household|-0.052*|-0.044|-0.040|-0.003|
||(1.668)|(1.183)|(1.144)|(0.094)|
|Head has at least primary education|0.005|-0.002|0.005|-0.014|
||(0.268)|(0.118)|(0.311)|(0.805)|
|Number of male children age 14 and less|0.002|0.019**|0.011|0.018**|
||(0.176)|(2.017)|(1.160)|(2.366)|
|No. of female children age 14 and less|-0.005|-0.003|-0.004|-0.007|
||(0.430)|(0.227)|(0.519)|(0.829)|
|Number of adult male age 15 to 60|0.020*|0.020*|0.009|0.016*|
||(1.936)|(1.674)|(0.868)|(1.802)|
|Number of adult female age 15 to 60|-0.002|0.004|0.004|0.009|
||(0.146)|(0.278)|(0.335)|(0.829)|
|Male members age 60 and above|-0.041|0.001|-0.013|0.009|
||(0.910)|(0.017)|(0.301)|(0.256)|
|Female members age 60 and above|0.046|0.070*|0.060*|0.037|
||(1.095)|(1.872)|(1.832)|(1.094)|
|Constant|0.008|0.003|0.003|0.001|
||(0.619)|(0.289)|(0.308)|(0.174)|
|Number of observations|6325|6325|6325|6325|
|R2|0.014|0.020|0.010|0.016|

**Note:** The geographical neighborhood of each household is defined at a distance of 1000 meters. Absolute value of t-statistics corrected for spatial dependence (1500 meters).* significant at 10%; ** significant at 5%; *** significant at 1%.

22
# **4.2 Data Collection Wave 2: Survey**

The objective of the second wave of data collection was to explore the mechanisms underlying the results from the first wave survey as well as the findings from the ward level administrative data on presumptive taxpayers. The second wave survey questionnaire was designed to elicit recall of the tax officials visits and identify any spillover in neighboring businesses of treated wards, as well as to capture persistent changes in beliefs and attitudes that could have been attributed to the intervention.

First, we explore whether there is any persistent effect of the treatment on tax morale, which was identified in the first wave of the survey among the original sample of businesses. We indeed find that the treated disagree more strongly with the idea of refusing to pay taxes until better services are provided in the second round, confirming the persistent treatment effect from the visits. We also find that the aggregate measure of an individual’s tax morale, the own index, is positive and statistically significant. The remaining three measures of tax morale are statistically insignificant. Hence, we are able to replicate the results from the first wave along the exact same dimensions.

As we explained above, the effect of the treatment on tax morale could have one of two explanations. The first explanation is that the treatment effect was comparatively unimportant, driven by a temporary concern or fear due to the presence of the tax officials when the survey was undertaken. The second explanation is that the treatment effect reflected a change in the way the TRA is perceived by businesses because this interaction was different from a typical one, which is solely focused on audit activity and tends to be -at a minimum -burdensome to the business. As we were able to replicate the results after more than a year and a half without the presence of a tax official, we believe this provides strong evidence that the treatment effectively changed tax morale along certain (albeit limited) dimensions, and that the observed effect was not merely driven by a temporary concern or fear caused by the presence of tax officials during the survey.

Second, we explore the mechanisms for the treatment effects on tax revenue in treatment 21
all three types of analyses, we estimate Huber-White standard errors that correct for prefecturelevel clustering.<sup>10</sup>

# _2.3.1 Ordinary Least Squares (OLS)_

Our first type of analysis uses OLS regression. We conduct the OLS analysis to examine the basic relationship between the treatment (attending vocational versus academic high school) and student outcomes, while controlling for observable covariates that may confound that relationship. The basic specification for the OLS analysis is: (1) where Yij represents the outcome variable of interest (dropout, computing, or math skills) of student i in school j. Vij is a dummy variable for whether or not student _i_ attended vocational high school at the time of the baseline survey. In the absence of omitted variables bias, �� would be the treatment impact of attending vocational (versus academic) high school on Yij.

The term Xij in equation (1) represents a vector of observable baseline covariates for student _i_ in school _j._ It includes student and family covariates such as _male_ (equals 1 if the student is male and 0 if female), _age_ (in days), _urban_ (equals 1 if the student has urban residential permit status and 0 if rural), _student migrated_ (equals 1 if the student has migrated 10 Although it would be most appropriate to adjust standard errors for clustering at the school level, we conservatively adjust for clustering at the higher levels of aggregation. In particular, we adjust for clustering at the prefecture level for the OLS and matching analyses (and the county level for the IV analyses) because we add in prefecture (county) fixed effects when estimating differences across treatment and control groups. In fact, the results of the paper are substantively the same whether we adjust the standard errors for clustering at the school level (without prefecture/county fixed effects) or prefecture/county level (with prefecture/county fixed effects). Results are available from the authors on request.
privatization of the tea factories, to stimulate investment in the sector. Thus, in 1999, the government launched a phased privatization process that ensures a significant stake for tea growers and other local investors while attracting foreign investment as well.

The purpose of this paper is to provide a quantitative assessment of the likely impact of the privatization of tea estates in Rwanda, based on data from the 2004 Quantitative Baseline Survey of the Tea sector (QBST)<sup>3</sup> . The analysis is intended to serve as an input to the ongoing reform process.

The outline of the rest of the paper is as follows. Section 2 presents an overview of the tea sector and main issues. Section 3 presents our evaluation framework. Methods of impact evaluation are interpreted as ways of dealing with heterogeneity that may confound impact assessment. Such heterogeneity stems from observable and nonobservable individual characteristics. Section 4 offers a discussion of the empirical results. It starts with a description of the underlying data. Then, it focuses on comparing outcomes among tea households. Finally, we compare living standards between these households and those not directly involved in the tea sector. Concluding remarks are made in section 5.

# **2.  Overview of the Tea Sector**

Tea is one of the two main export crops in Rwanda and has tremendous potential as a source of foreign exchange as well as a means of poverty reduction. It is grown on roughly 11,500 hectares of land on hills or drained marsh areas, which accounts for roughly 1 percent of the country’s cropped area. Tea in Rwanda is mainly cultivated by small farmers, on a total surface area, per farm household that is less than 0.25 hectares. It is one of the few labor intensive crops that provide regular cash income to farmers, and employment opportunities to the general rural population. Until the onset of civil war and genocide of 1994, tea production had increased steadily.

> 3 Also known as _Enquête Quantitative de Base auprès des ménages des zones Théicoles_ (EQBT)

2
# **1 Introduction**

China’s and India’s fast economic growth during the past decade is paralleled by their increased presence in policy discussions throughout Latin America. The success of the two Asian economies is not only looked upon with admiration, but is often accompanied by concerns about the effects that growing trade integration with China (and India to a lesser extent) has on the manufacturing sector throughout the region. Textiles, apparel, shoe manufacturing and toys are amongst the sectors worst hit by international competition. In many Latin American countries requests for explicit protection are becoming more and more common. At the end of 2005 as Brazilian imports of textiles from China surged, Brazilian manufacturers officially asked their government to limit imports of Chinese silk, velvet and polyester thread by imposing import quotas and/or increasing tariffs. At the time, they also noted that an additional 70 Chinese products were being reviewed by the textile industry to determine whether similar protective measures are to be requested. A comuniqu ́e by Argentina’s Confederation of Medium Enterprises (CAME) calls for not repeating the “mistakes of the nineties, when an ‘invasion’ of Chinese products destroyed entire sectors of the manufacturing sector.”<sup>1</sup> Local politicians have not left these calls for help unanswered. After a recent meeting with its Chinese counterpart, the Brazilian Minister for Industry, Development and Commerce Luiz Furlan was quick to highlight that “I made it very clear to Minister Bo Xilai that we will take the legal steps to give Brazilian industry the right to protect itself”.<sup>2</sup> In early 2006, and following the earlier demands of Brazilian textile manufacturers, Brazil and China signed an agreement under which China was to limit its export growth of 70 textile products to Brazil. Notwithstanding their country’s privileged access to the US market, Mexican politicians show similar feelings and are growing more and more nervous about Mexico’s burgeoning trade deficit with China. It is not surprising then that after a recent meeting with Chinese leaders, president Fox was very happy to report that “Today we heard from

> 1See CAME’s comuniqu ́e of November 16th 2004 at http://procom.org.ar/comunicado.php3?id=335.

> 2As reported by Yahoo! on October 4 2005. See http://sg.biz.yahoo.com/051004/1/3veny.htm.

1
<!-- Start of picture text --> st<br>[os]<br>N<br>N<br>a4<br>)<br>3<br>fo)<br>S 2<br><!-- End of picture text --> <!-- Start of picture text --> se)<br>SI<br>2011 2012 2013 2014<br>Year<br>Note: 95% confidence interval is shown by bars<br><!-- End of picture text -->
31 whole building and then attributed to households on the basis of square meters of floor space. Although lifeline pricing of heat could be instituted by charging a lower rate per square meter for small apartments than for larger apartments, no positive conservation incentives would be generated and targeting based on house/or apartment size is not likely to be efficient nor to meet equity objectives: there is no evidence to suggest that house size is a good proxy for income level. (The reverse might even be the case for, e.g., pensioners who are typically poor but "house-rich" in the transition economies). Since there is almost no housing mobility in Poland, families cannot even move to reduce costs and some poor families occupying large houses may be forced to pay more when prices rise. Another approach would be to compensate only the certified poor for a quantity of heat that corresponds to e.g. 20 square meters of floor space. Here again, since the poor cannot adjust their consumption or limit it to the lifeline quantity, this approach would also unfairly burden them. On the other side, the inelasticity of district heat demand to a change in price, there is little efficiency loss from a lifeline heat subsidy. But as evidenced from the distribution of consumption (Tables 3 and 4) it is a poorly targeted subsidy -- the rich consume more of the subsidized good as a percentage of their income than the poor. Consumption data also implies that lifeline pricing for heat would also exclude some of the needy: farmers and worker-farmer (mixed) households make up almost 30 percent of the very poor<sup>20</sup> yet they do not consume district heat and would therefore not benefit from the subsidy (Table 5). Thus, lifeline pricing makes most sense for electricity.

# _Vouchers_

Vouchers, which could be administered by government or local social assistance offices, could be given to the eligible poor following price increases, entitling them to the purchase of a certain amount of energy at a fixed price. In principle, vouchers are more flexible than lifeline rates in that they allow some differentiation between households based on size and composition (the bloc rate would apply to the "billing address" regardless of how many consumers were present). By contrast, vouchers can be given to only the poor, assuming these can be identified. Thus the _20/ Understanding Poverty in Poland, p._ 17. The very poor are defined as families with income below the minimum pension.
Policy Research Working Paper 8903

# **Abstract**

This paper describes the methodology and findings for the extreme hazard exposure analysis of the Chinese wastewater sector to flooding and earthquakes hazards. This analysis is undertaken for changing flooding, under climate scenarios, and earthquake exposure of a spatially accurate wastewater treatment plants (WWTP) database for China, covering a total of 1,346 assets with additional attributes quantifying the treatment capacity and users dependent on each plant. For flooding, we apply and downscale a global river routing (CaMa-Flood) model that quantifies the change degree of flood exposures from the present time-period (1980–2005) to the near future time-period (2016–2035) to a far future time period (2036–2055). We find evidence that most climate models project increasing number of WWTP assets face climate-induced flood hazards in both the near and far future, potentially affecting as many as 208 million users by 2050. However, there are spatial and temporal variations from these projections which means that planning for resilient wastewater infrastructure requires more detailed understanding of the vulnerability attributes of WWTPs including their exact location, the number of users they serve as well as their flood protection standards. For earthquakes, we examine the exposure of WWTPs to earthquake hazards through both seismic shaking and induced liquefaction. Overall, we demonstrate the significant risk that earthquakes can have on the WWTP process—especially on centralized systems. By considering liquefaction susceptibility in combination with shaking, we expose a number of WWTPs that face an increased level of exposure to damages following earthquakes compared to looking at shaking in isolation.

This paper is a product of the Global Facility for Disaster Reduction and Recovery, Climate Change Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at xhu@law.harvard.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 5: Input share response to different measure of fragmentation**

||**Full Sa**|**mple**|**Households w**<br>**plots**|**ith inherited**<br>**only**|**Paddy frag**|**ments only**|**Wheat frag**|**ments only**|
|---|---|---|---|---|---|---|---|---|
||**Simpson**|**Spatial**|**Simpson**|**Spatial**|**Simpson**|**Spatial**|**Simpson**|**Spatial**|
|Seeds|-0.192***|-0.009|-0.216***|-0.009|0.001|-0.016*|0.126***|0.019***|
||(-4.876)|(-1.407)|(-4.861)|(-1.180)|(0.014)|(-1.685)|(3.203)|(2.583)|
|Fertilizer|-0.014|-0.003|-0.011|-0.002|0.000|-0.002|-0.030|-0.004|
||(-0.494)|(-0.629)|(-0.334)|(-0.390)|(0.012)|(-0.396)|(-0.845)|(-0.542)|
|Pest. & Fungicide|-0.099**|0.016**|-0.106**|0.011|-0.029|0.004|-0.013|0.016|
||(-2.063)|(2.128)|(-2.053)|(1.340)|(-0.564)|(0.397)|(-0.225)|(1.563)|
|Bullocks|0.197***|0.000|0.194***|0.000|0.034***|-0.000|0.033|-0.005|
||(4.211)|(0.064)|(3.530)|(0.017)|(4.065)|(-0.043)|(0.933)|(-0.737)|
|Machinery|-0.091***|0.001|-0.093**|-0.005|-0.154***|0.003|-0.177***|-0.005|
||(-2.704)|(0.162)|(-2.465)|(-0.861)|(-3.399)|(0.377)|(-4.071)|(-0.655)|
|Irrigation|-0.025|0.001|-0.053|-0.000|0.048|-0.017**|0.055|0.001|
||(-0.528)|(0.188)|(-1.004)|(-0.044)|(0.994)|(-2.006)|(1.162)|(0.154)|
|Hired labor|0.118***|-0.007|0.133***|0.016**|0.107**|0.009|0.150**|0.001|
||(3.08)|(-1.196)|(3.104)|(2.185)|(2.389)|(1.169)|(2.471)|(0.123)|
|Family labor|0.121***|0.007|0.137***|0.009*|0.020|0.007|0.015|-0.013|
||(4.273)|(1.484)|(4.262)|(1.747)|(0.680)|(1.236)|(0.317)|(-1.516)|
|Observations|4,790|4,790|3,814|3,814|2,571|2,571|2,216|2,216|

_Note:_ Asymptotic t-ratio reported in parentheses. Constant and other variables such as household’s characteristics, price of inputs and output and village level characteristics are included but not reported. *** p<0.01, ** p<0.05, * p<0.1 18
# **<mark>Table S10. Income loss due to the COVID-19 pandemic, by source</mark>**

||Farm income<br>reduced|Business<br>income reduced|Wage income<br>reduced|Remittances<br>reduced|Other income<br>sources<br>reduced|
|---|---|---|---|---|---|
|Ethiopia|-0.314***|0.016|-0.240***|-0.260***|-0.299***|
||(0.028)|(0.033)|(0.041)|(0.053)|(0.054)|
|Nigeria|-0.001|0.011|-0.005|-0.086*|-0.029|
||(0.024)|(0.025)|(0.042)|(0.044)|(0.055)|
|Uganda|-0.133***|0.075***|0.074*|0.028|0.116|
||(0.025)|(0.025)|(0.041)|(0.040)|(0.072)|
|Ethiopia-Nigeria|0.000|0.853|0.000|0.001|0.000|
|Ethiopia-Uganda|0.000|0.048|0.000|0.000|0.000|
|Nigeria-Uganda|0.000|0.002|0.044|0.007|0.034|
|Observations|5,624|3,849|3,447|1,687|1,574|
|R-Squared<br>_Note_: Each column r|0.080<br>eports results from|0.004<br>a single regression o|0.056<br>f a binary variable e|0.037<br>qual to 1 if the hou|0.075<br>sehold lost income|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the household lost income from that source and 0 otherwise. Reporting loss of income from a given source is conditional on receiving income from that source prior to the COVID-19 pandemic. The binary variable is then regressed on indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data are only from the first phone survey round in each country. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

37
education as a control, and within-sibling estimates. Finally, I estimate a very flexible regression model in which the log of the individual wage rate is regressed on a set of 15 schooling dummies, 10 experience dummies, and 150 interactions between experience and schooling:

- (3) _logWis5 =_ **_-qx_** _Si,_ + _Os Xi_ + **_sx (Si * Xix) '_** _+_ **_Visx,_**

where the subscripts i, s, and x stand for individuals, years of schooling, and experience cohort.'<sup>2</sup> An important appeal of this approach is that it allows the effect of education on wages to vary by experience (see Card 1999, pp. 1805-06). An obvious limitation is that this estimation strategy will produce noisy estimates in the absence of very large sample sizes.

# **_5._ Results**

I present summary statistics on the sample of wage-earning men in the APIS in Table 1, non-parametric density estimates of the logarithm of the wage distribution in Figure 1, and histograms for the years of completed schooling in Figure 2. These results are weighted by the appropriate expansion factors, and are presented for four groups: All men who report earning wages, men aged 25-65, the sub-sample of men for whom we

> 12 Years of experience have been lumped together into five-year groups, such that the first group (experience = 1) corresponds to workers with 0 to 5 years of experience, the second group (experience = 2) corresponds to workers with 6 to 10 years of experience, and so on. I do this to avoid the very small sample sizes which would have resulted had I created dummy variables for every year of experience. This model therefore estimates 160 coefficients (fifteen dummy variables have to be dropped to avoid perfect colinearity).

13