rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 1: Household level descriptive statistics</u>**

||**Total**||**By tercile of**|**parcel di**|**stribution**||
|---|---|---|---|---|---|---|
|**Variables**||**1**<sup>**st**</sup>|**2**<sup>**nd**</sup>|**t-stat**|**3**<sup>**rd**</sup>|**t-stat**|
|**Holding characteristics**|||||||
|Number of parcels|4.786|2.302|4.416|***|8.316|***|
|Simpson land fragmentation index|0.523|0.337|0.584|***|0.711|***|
|Shannon crop diversity index|1.136|0.983|1.159|***|1.315|***|
|<br>Distance to homestead in hours (self-reported)|0.167|0.126|0.180|***|0.208|***|
|Minimum cost path to homestead in hours|0.106|0.075|0.112|***|0.140|***|
|Total cost distance in hours|0.815|0.376|0.834|***|1.367|***|
|Soil type diversity index|0.194|0.100|0.207|***|0.303|***|
|<br>Parcel topography diversity index|0.172|0.094|0.183|***|0.265|***|
|Range of altitude covered in meters|986-2839|986-2799|1158-2833||1150-2839||
|Total holding in hectares|0.805|0.588|0.756|***|1.129|***|
|Total cropped area hectares|0.614|0.531|0.570||0.761|***|
|<br>Household has parcel outside the cell|0.403|0.235|0.450|***|0.580|***|
|<br>Share of land located outside the cell|0.074|0.054|0.088|***|0.087||
|Area share of rented-in parcels|0.150|0.150|0.173|**|0.129|***|
|**Household-level land use**|||||||
|Number of crops grown|4.506|3.602|4.534|***|5.653|***|
|<br>Value of crop output per hectare (US$/ha)|558.7|593.7|536.8||532.12||
|Share of land cultivated with extension input|0.103|0.108|0.101||0.098||
|<br>Share of land with crop shock|0.275|0.327|0.281|***|0.204|***|
|<br>Share of total cultivated area with grains|0.501|0.521|0.496|***|0.479|*|
|Share of cultivated area with tubers|0.262|0.246|0.276|***|0.271||
|Share of cultivated area with tree crops|0.225|0.222|0.217||0.237|**|
|Share of cultivated area with vegetables|0.012|0.011|0.011||0.013||
|**Parcel characteristics**|||||||
|Parcel area in hectares|0.194|0.270|0.153|***|0.145||
|Average number of years parcel possessed|17.908|19.101|16.564|***|17.516|**|
|Share of land located in wet land|0.082|0.068|0.080|*|0.101|***|
|Share of land with access to irrigation|0.055|0.054|0.058||0.055||
|<br>Share of high quality land|0.362|0.369|0.353||0.359||
|Share of medium quality land|0.155|0.135|0.165|***|0.171||
|Share of poor quality land|0.484|0.495|0.483||0.469||
|**Household characteristics**|||||||
|Female headed household|0.290|0.368|0.264|***|0.211|***|
|Head completed only primary school|0.576|0.523|0.584|***|0.637|***|
|<br>Head completed secondary education|0.066|0.045|0.064|***|0.096|***|
|<br>Age of household head|46.793|48.176|45.473|***|46.134||
|<br>Members aged 14 and less|2.136|1.985|2.140|***|2.328|***|
|<br>Adult members between 15 and 35|1.605|1.485|1.634|***|1.735|***|
|Adult members between 35 and 60|0.854|0.785|0.807||0.984|***|
|Members aged 60 and above|0.244|0.272|0.231|***|0.218||
|Number of observations|6626|2721|1801||2104||

_Source:_ Own computation from 2010/11 Rwanda LTR Household Survey _Note:_ Stars indicated significance levels for t-tests of the equality of means for each of the variables between subsequent terciles (*significant at 10%; **significant at 5%; *** significant at 1%).

15
# **1 Introduction**

Corporate tax regulation generates heterogeneity in the effective tax rates faced by firms due to exemptions, deductions, and deferrals. At the same time, there is a large amount of dispersion in firm-level revenue productivity even within narrowly defined industries. This suggests that effective corporate tax rates can potentially generate an inefficient allocation of resources across firms by directly impacting revenue productivity. An inefficient allocation of resources will have a direct negative effect on total factor productivity (TFP).

This paper quantifies the effect of effective corporate tax rates on aggregate TFP through allocative efficiency. First, we use Chilean manufacturing census data for the years 1998 to 2007 and document several characteristics of the effective tax rate distribution. Two important findings are a large dispersion in the effective tax rate faced by firms and a mass of firms with a 0 percent tax rate. Next, we incorporate these features into a standard monopolistic competition model with capital and output wedges, where firms endogenously choose the tax rate they face. We then calibrate the model and find that TFP would increase between 4 and 11 percent if the economy implied by the Chilean tax code of the 1998 – 2007 period changed to a hypothetical economy without effective corporate tax rate inefficiencies. Afterward, we study the effects of imposing the same tax rate on all firms, which we call a flat tax rate policy. We find a monotonically decreasing relationship between the level of the flat tax rate and TFP.

To carry out our analysis, we use the ENIA (Encuesta Nacional Industrial Anual), a plant-level manufacturing census from Chile that covers all establishments with more than 10 employees, for the time period 1998-2007.<sup>1</sup> The data set is an unbalanced panel that contains detailed balance sheet and production information. Importantly, it specifies net after-tax firm income and corporate taxes paid by firms. We use these two variables to construct the average effective tax rates faced by firms, which is essential for our analysis.

1. Different years of this data set have been used in several well-known studies, such as Liu (1993), Levinsohn and Petrin (2003), Oberfield (2013), Petrin and Sivadasan (2013), and Asker, Collard-Wexler, and De Loecker (2014).

2
case by comparing condom use among different groups, including married men and women during extramarital sex.

Many studies have explored condom use in Africa but most have not been comprehensive analyses of condom use in both a variety of contexts and by many subgroups of the population. This study uses nationally representative data from 13 SubSaharan African countries to describe and compare condom use by different sectors of the population. By analyzing and contrasting condom use by men, women, married and unmarried individuals with different types of partners (any type, spouse, someone other than a spouse), patterns emerge that both substantiate findings from earlier studies and provide new insights into high risk behavior.

Specifically, we confirm across all countries included that condom use overall is generally low, that men report using condoms more than women, and that unmarried individuals report using condoms more than married individuals with their spouses. We also describe condom use in extramarital intercourse for males and females. Reported condom use by males in extramarital situations is fairly similar to condom use by unmarried males. However, an alarming result that has not been shown across so many contexts in the current literature is that married females report using condoms in extramarital sex significantly less than unmarried females.

3
their firms. Panel D of Figure 1 and column 5 show a small and insignificant impact on the number of employees, which has stayed constant over time. Table A8 shows this remains the case if we examine employees in all firms (only measured in the long-run survey), as well as if we restrict attention to paid employees. This lack of labor impact is consistent with the meta-analysis of shortterm training impacts in McKenzie et al. (2023), who find a relatively precise zero impact of business training on employment in small firms. This may arise because the existing workforce (including the owner) has some slack and scope to work harder without the firm needing to hire more workers (Egger et al, 2024), as well as firms being able to use more capital in place of additional labor.

Column 6 shows that personal initiative training instead caused large increases in the capital stock of firms operated by men, with a 2-year impact of $1,298, and 7-year impact of $3,627. This 7- year impact represents an 81% increase on the control mean of $4,461, and the long-run effects are significantly larger than the short-run. Figure A1 shows that firms run by women had much lower capital stock to begin with, and that women increase capital stock much less over time. The treatment effect at 7-years of $1,166 is less than one-third of that of men, is not statistically significant, and we can reject equality with the impact for men (p=0.100). As with profits, the quantile treatment effects for capital stock are much larger for men across the distribution (Figure A1). Table A9 examines impacts on the different types of capital stock, and shows that the largest increases, and biggest gender differences, are in the amount of machinery and equipment, and vehicles.<sup>3</sup> In addition to building up more capital, there is also a positive treatment effect for men on the amount they think could borrow on short notice (Table A8).

The remaining columns of Table 2 consider impacts on different aspects of A, which are then combined into an overall index measure in the last column. Column 7 shows that there is a significant long-term impact of personal initiative training on entrepreneurial self-efficacy for men of 0.15 standard deviations, compared to a negative and insignificant effect for women. This impact is larger than in the short-run, and suggests a possible self-reinforcing psychological mindset, whereby success increases self-efficacy, giving men confidence to further invest and grow their businesses. Column 8 shows that while the short-run impacts on personal initiative were

> 3 The value of land and buildings also increases, but we exclude this from our measure of capital stock in Table 2 given that it is an asset that can be intertwined with the household, and that has a very skewed distribution with only 16 percent of firms listing a positive value.

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INFRASTRUCTURE AND GROWTH IN AFRICA

# **Figure 10. Potential growth per capita benefits in Africa due to higher infrastructure development**

Scenario 4: Reaching the infrastructure level of the median in Western Europe, using the [IK1] index of aggregate infrastructure and 2001–5 averages

## a. Africa

<!-- Start of picture text --> 9.0<br>8.0<br>7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>North Africa West Africa East Africa Southern Africa Central Africa AFRICA<br>Main Telephone Lines and mobiles Electricity Generating Capacity Length of Road Network<br>Quality of Telecoms Quality of Electricity Road Quality<br><!-- End of picture text -->

## b. North Africa

<!-- Start of picture text --> 5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>Algeria Egypt Libya Morocco Tunisia<br>Main Telephone Lines and mobiles Electricity Generating Capacity Length of Road Network<br>Quality of Telecoms Quality of Electricity Road Quality<br><!-- End of picture text -->

## c. West Africa (ECOWAS)

<!-- Start of picture text --> 11.0<br>9.0<br>7.0<br>5.0<br>3.0<br>1.0<br>-1.0 Benin Burkina Faso Cote d'Ivoire Ghana Guinea Gambia, The Guinea Mauritania Niger Nigeria Senegal Sierra Leone Togo<br>Bissau<br>Main Telephone Lines and mobiles Electricity Generating Capacity Length of Road Network<br>Quality of Telecoms Quality of Electricity Road Quality<br><!-- End of picture text --> 42
# 1. Introduction

Europe is struggling to become a competitive and dynamic knowledge economy in the modern world. One key ingredient in its strategy is likely to be the investment in human capital. Although individuals invest in human capital over the whole life-cycle, more than one half of lifetime human capital is accumulated through post-school investments on the firm (Heckman, Lochner and Taber, 1998). This happens either through learning by doing or through formal on-the-job training. In spite of its importance, economists know surprisingly less about the incentives and returns to firms of investing in training compared with what they know about the individual’s returns of investing in schooling.<sup>1</sup> Similarly, the study of firm investments in physical capital is much more developed than the study of firm investments in human capital, even though the latter may well be at least as important as the former in modern economies. In this paper we estimate the internal rate of return of firm investments in human capital. We use a census of large manufacturing firms in Portugal between 1995 and 1999 with detailed information on investments in training, its costs, and several firm characteristics.<sup>2</sup> Most of the empirical work to date has focused on obtaining estimates of the return to training for workers using data on wages (e.g., Bartel, 1995, Arulampalam, Booth and Elias, 1997, Mincer, 1989, Frazis and Loewenstein, 2005). Even though this exercise is very useful, it has important drawbacks (e.g., Pischke, 2005). For example, with imperfect labor markets

> 1An important part of the lifelong learning strategies are the public training programs. There is much more evidence about the effectiveness (or lack of it) of such programs compared with the available evidence on the effectiveness of the private on-the-job training.

> 2We will consider only formal training programs and abstract from the fact that formal and informal training could be very correlated. This is a weakness of most of the literature, since informal training is very hard to measure.

2
# **TABLE OF CONTENTS**

|**I. Introduction**.................................................................................................................. 3|
|---|
|**II. Methodology**................................................................................................................. 6|
|**Domestic Water Pricing**............................................................................................... 6|
|**Assessment of Acceptability and Willingness to Pay**................................................. 8|
|**III. Chongqing Survey**.................................................................................................... 10|
|**Water Pricing in China**.............................................................................................. 10|
|**The Chongqing Survey**............................................................................................... 11|
|**Major Statistics of the Survey**.................................................................................... 12|
|**IV. Empirical Analysis**.................................................................................................... 13|
|**Acceptability**................................................................................................................ 13|
|**WTP Estimation**.......................................................................................................... 13|
|**Burden and Affordability**........................................................................................... 14|
|**Determinants of WTP**................................................................................................. 15|
|**V. Summary and Conclusion**......................................................................................... 16|
|**Reference:**........................................................................................................................ 18|
administrative decisions, and results obtained. Since it mainly concerns the accountability of government officials, it should be viewed as mainly a property rights institution. Another key measure used in the literature is the Government Expropriation index from ICRG (Knack and Keefer 1995). This variable was discontinued after 1997, however, and in the absence of recent values we opted not to use it.

We also have three measures of property rights institutions pertaining specifically to the financial sector. “Financial Freedom” (from the Heritage Foundation) measures the degree to which banking and financial regulations contribute to an accessible and well-functioning formal financial system that ensures the availability of diversified savings, credit, payment, and investment services to individuals. Larger values imply regulations for banks and stock markets that encourage integrity and transparency, and promote disclosure of assets, liabilities and risks, without limiting competition or interfering with the pricing of capital.<sup>9</sup> The rationale behind this “Financial Freedom” indicator is consistent with findings by Cull et al. (2006), who document how a variety of local financial intermediaries emerged endogeneously to meet demand from small and medium-sized enterprises in late 18<sup>th</sup> to early 20<sup>th</sup> century Western Europe and North America. They show that governments “played little role” in the creation of these financing mechanisms “beyond providing a secure propertyrights environment and establishing national financial institutions, such as central banks; more crucially, “governments did little to inhibit their formation.” Similarly, we have two measures that capture the protection of property rights for investors in the context of equity markets. The first, coming from Doing Business, is “Investor Protection.” It measures “legal protection of minority shareholders against expropriation by corporate insiders” (Djankov et al. 2008). Unlike the previous measures of property rights institutions that mainly concern government expropriation, Investor Protection mainly concerns expropriation of elites towards nonelites. Large shareholders are elites of the society, and many of them have various types of connections with the government (Fisman, 2000; Faccio, 2006). Without strong protection of minority shareholders, large shareholders can tunnel funds to their subsidiary companies and convert them into private consumption. With strong protection of minority shareholders’ rights, owners and top managers of firms will focus more on value creation rather than rent extraction, and thus improve the

> 9 (See  http://www.heritage.org/index/book/chapter-5.)

12
Table 1: The Causal Impact of Covid-19 Lockdown on Traffic Congestion

||Bogotá|NewDelhi|NewYork|Paris|
|---|---|---|---|---|
|**A. Result 1**|||||
|Actual|0.593|0.767|0.721|0.486|
|Prediction|3.118<br>(0.120)|3.337<br>(0.117)|2.235<br>(0.206)|2.367<br>(0.399)|
|Relative effect|-80.99%|-77.00%|-67.76%|-79.48%|
|95% CI|[-89%, -74%]|[-84%, -70%]|[-85%, -50%]|[-114%, -47%]|
|Tail-areaprobability|0.001|0.001|0.003|0.001|
|**B. Result 2**|||||
|Actual|0.593|0.767|0.721|0.486|
|Prediction|2.787|3.344|2.208|2.158|
||(0.116)|(0.085)|(0.103)|(0.405)|
|Relative effect|-78.74%|-77.05%|-67.35%|-77.49%|
|95% CI|[-87%, -71%]|[-82%, -72%]|[-76%, -58%]|[-116%, -41%]|
|Tail-areaprobability|0.001|0.001|0.001|0.001|

_Notes:_ The table shows the effect of the Covid-19 lockdowns on traffic congestion in Bogotá, New Delhi, New York City, and Paris. For each city, we report the BSTS model using: (A) traffic conditions in Hong Kong and weather in treated cities as the synthetic control series; and (B) traffic condition in Hong Kong, Cairo, Sofia, Stockholm, and Tokyo as the synthetic control series.

social distancing, mandatory face mask-wearing measures, or state of emergency. Using a sample of cities for which we have the required data, we augment our initial list of synthetic control series—i.e. weather conditions in treated cities and congestion in Hong Kong SAR—with traffic congestion data from Cairo, Sofia, Stockholm, and Tokyo, all of which enforced some form of relatively flexible and less constraining lockdown policies. Like in the case of Hong Kong SAR, we do not expect traffic restrictions in the treated cities to directly influence traffic conditions in these cities. However, unlike Hong Kong SAR, people in these cities might have responded to the softer lockdown measures by changing their travel behavior in the midst of the Covid-19 outbreak. But this is unlikely to occur exactly the day when the strict lockdown measures where enforced in the treated cities.

We show in Figure 4 the posterior inclusion probability of the synthetic control series in the regression sub-model of the Bayesian Structural Time Series model. For Bogotá, the covariates with a probability of inclusion exceeding 0.05 are traffic conditions in Stockholm (with an inclusion probability of 1), Cairo (0.79), Hong Kong SAR (0.46), 23
# **IV. Living arrangements**

# _Levels and changes in the living arrangement of non-orphans_

Before addressing the living arrangements of orphans, the living arrangements of nonorphans are discussed. To some extent, this serves as a benchmark for “customary” living arrangements in a country and how these might be changing over time—including ways that relate to changes in the orphan rate.<sup>18</sup> Table 3 shows the percentage of non-orphans who reside with one or both of their parents. The remaining children live with grandparents, other relatives, or non-relatives. The vast majority—on the order of 85 to 90 percent—of non-orphans live with one or both parents.<sup>19</sup> The majority—on the order of 60 to 70 percent—of these children live with both their parents. There is considerably more heterogeneity across countries in the percentage of non-orphans who live with their mother only, or their father only—although in most countries about 10 to 20 percent live with only their mother and around 5 percent live with their father only.

For non-orphans in countries where the orphan rate rose substantially (Group C), two distinct features of living arrangement trends are noted. Non-orphans in these countries are more likely to live with their mother only, and less likely to live with either parent, than non-orphans in other countries. Part of this arises because of the anomaly of Namibia where almost 30 percent of non-orphans live with their mother only and only 65 percent of non-orphans live with either one or both their parents. Nevertheless, when Namibia is excluded from the average for Group C countries, non-orphans still tend to have a higher likelihood of living either with their mother only, or away from both their parents, than in other countries.

Figure 3 shows these changes graphically in the first row of figures. The lower row shows the changes in the three remaining categories of living arrangements: living with grandparents, other relatives, and non-relatives. In general, the living arrangements of nonorphans have remained fairly stable: changes are typically limited to no more than 5 percentage

> 18 Nyambedha, Wandibba and Aagaard-Hansen (2003) note that customary living arrangements for orphans may define some caregivers for orphans as “...culturally `inappropriate’...”, such as matrilineal kin and strangers in the case of their study in western Kenya. Whether children are cared for by matrilineal or patrilineal kin cannot be studied here, since the status of non-parent caregivers is not collected in detail.

> 19 There is little evidence to support Monasch and Boerma (2004) who emphasize that fostering is higher in southern Africa than eastern Africa. They specifically note Botswana, Namibia, and South Africa for southern Africa and Burundi for eastern Africa, for which there are no DHS data. In the set of countries in this study, Mozambique, Malawi and Madagascar do not have higher rates compared to Tanzania and Uganda.

13