rafmacalaba/datause-encoder-data huggingface dataset:
|Has children|0.064<br>|0.047<br>|0.133<br>|0.052<br>|0.058<br>|
|---|---|---|---|---|---|
||[0.046]|[0.053]|[0.097]|[0.055]|[0.053]|
|Female*Has children|-0.017|-0.072|0.096|-|-|
||[0.054]|[0.061]|[0.121]|||
|HH Size|0.001|0.001|-0.006|-0.010|0.011|
||[0.007]|[0.008]|[0.018]|[0.011]|[0.011]|
|GLOW Member|-0.142<sup>***</sup>|-0.140<sup>***</sup>|-|-0.188<sup>***</sup>|-0.094<sup>**</sup>|
||[0.035]|[0.036]||[0.053]|[0.048]|
|Risk Appetite (0-10)|-0.000|0.002|-0.006|0.001|0.001|
||[0.006]|[0.007]|[0.012]|[0.009]|[0.008]|
|Time Preference Index|-0.015|-0.004|-0.075|-0.005|-0.035|
||[0.027]|[0.030]|[0.060]|[0.040]|[0.037]|
|**Deemed as ‘Very Important’ in a job:**||||||
|Earning Good Money|0.056<sup>*</sup>|0.067<sup>*</sup>|0.041|0.015|0.105<sup>**</sup>|
||[0.032]|[0.036]|[0.071]|[0.048]|[0.044]|
|Flexibility|-0.131<sup>***</sup>|-0.145<sup>***</sup>|-0.037|-0.090<sup>*</sup>|-0.165<sup>***</sup>|
||[0.031]|[0.034]|[0.076]|[0.047]|[0.042]|
|Interaction with colleagues|0.012|0.020|-0.009|-0.036|0.061|
||[0.034]|[0.039]|[0.075]|[0.052]|[0.047]|
|Making Use of my Skills|-0.032|-0.005|-0.113|0.050|-0.109<sup>**</sup>|
||[0.036]|[0.041]|[0.084]|[0.055]|[0.050]|
|Opportunities for acquiring new skills|0.085<sup>**</sup>|0.079<sup>*</sup>|0.080|0.075|0.086<sup>*</sup>|
||[0.038]|[0.042]|[0.084]|[0.060]|[0.050]|
|Building professional profile|0.005|-0.018|0.062|-0.013|0.024|
||[0.034]|[0.039]|[0.077]|[0.054]|[0.046]|
|Working in International Teams|0.003|0.034|-0.118|0.038|-0.028|
||[0.036]|[0.041]|[0.078]|[0.056]|[0.048]|
|**Current Job Adequately Provides Opport**|**unities for ...**|||||
|Earning Good Money|-0.050|-0.073|-0.003|-0.035|-0.050|
||[0.039]|[0.047]|[0.077]|[0.058]|[0.056]|
|Flexibility|-0.077<sup>**</sup>|-0.104<sup>***</sup>|0.090|-0.061|-0.086<sup>**</sup>|
||[0.031]|[0.034]|[0.078]|[0.047]|[0.042]|
|Making Good Use of my Skills|0.025|0.032|-0.027|-0.039|0.071|
||[0.040]|[0.047]|[0.080]|[0.059]|[0.056]|
|Acquiring New Skills|0.044|0.014|0.138<sup>*</sup>|0.058|0.034|
||[0.039]|[0.044]|[0.081]|[0.059]|[0.053]|
|Building my Professional Profile|0.055|0.061|-0.000|0.104<sup>*</sup>|0.021|
||[0.041]|[0.047]|[0.084]|[0.061]|[0.058]|
|Working in international teams|0.030|0.063|-0.051|0.032|0.024|
||[0.041]|[0.046]|[0.087]|[0.063]|[0.055]|

13
likelihood that some unobservable, plot-level factors such as soil quality attenuate the nitrogen coefficient in the household FE estimates if farmers put nitrogen on plots of lower soil quality in an effort to get a higher marginal response and/or to improve longer term soil fertility on the plot. These plot-level unobservable factors are controlled for in the plot-level FE estimates, but not in the household-level FE, EA FE, District FE or pooled OLS estimates.

Yield on plots on which the rate of nitrogen application was above the government recommended rate is about 332.63 kg/ha lower than it is on plots on which the recommended rate was followed according to the garden FE estimates. It is usually recommended that basal fertilizer application in maize production be done within a week after planting in order to ensure higher yields. The results indicate that compliance with this recommendation increases yield by about 169.24kg/ha, all thing being equal. The results further indicate that the use of organic fertilizer increases maize productivity by about 126.46 kg/ha.

There is a significant, inverse relationship between plot size and maize yield. All things being equal, a hectare increase in plot size will decrease yield by 847.80 kg/ha. Larger farms are usually not farmed as intensively as smaller farms and therefore are underutilized, resulting in lower productivity. This inverse relationship between plot-size and productivity is common in the literature (Carletto et al. 2013).

Labor utilization has a positive and significant effect on agricultural productivity. All things being equal, an increase of one day in total labor (sum of family, hired and exchange labor) increases maize yield by 0.95 kg/ha. The positive effect is expected but the actual magnitude is low, indicating a low return to labor productivity. The positive effect of labor is perhaps not surprising because labor (family or hired) is needed for land preparation, weeding, mulching, fertilizer application and pest control without which yield would be very low.

19
metrics) is smaller in terms of job contraction than in gross job creation; this explains the higher _net job growth rates_ seen among SMEs. Notably, older establishments show higher contraction and gross creation rates, which result in a lower net rate: mature firms reveal higher churning than younger firms. This is a possible indication of a momentum of incumbency whereby older establishments that show employment contraction remain in the market, while young and mid-age may exit the market rather than being capable or willing to weather shocks, a so-called liability of age (for some discussion see Aga and Francis 2015).

Figure 1 further illustrates this point, particularly the volume of job churning (meaning creation and contraction combined) that is substantially higher among SMEs and among mature firms. What is more, the bias between using initial employment values to categorize establishments (as opposed to the average) is also clearly seen: both the gross creation rate and the net growth rate for SMEs are lower using the period average employment to categorize establishments than when using the initial level of employment, while both rates are higher among larger establishments when using the average level of employment for categorization. The opposite trend is shown for contraction rates (shown below the x-axis at zero). In the figure, this creates a convex U-shaped pattern for both gross job creation and job contraction (noting for bars 1-4 and 5-8 of Figure 1).

**Figure 1: Net Job Growth, Gross Job Growth, and Job Contraction**

<!-- Start of picture text --> 8.0 7.5 7.3 Gross Job Creation Rate<br>6.5<br>5.9 Job Contraction Rate<br>6.0<br>Net Job Growth Rate<br>5.1<br>4.0 4.8 4.7 3.3 3.4<br>4.0 2.7<br>2.1<br>1.7<br>2.0 1.4 2.9<br>1.1<br>0.4<br>1.3 0.1 0.5 1.1 1.2<br>0.0<br>0.0 0.0<br>-0.5 -0.6<br>-1.0 -0.8<br>-2.0 -1.5<br>-1.7 -1.7<br>-1.9<br>-2.4 -2.6 -2.2<br>-4.0<br>SME100 (in.) SME100 (avg.) Large100 (in.) Large100 (avg.) SME250 (in.) SME250 (avg.) Large250 (in.) Large250 (avg.) Young Mid-Age Mature<br><!-- End of picture text --> Source: World Bank Enterprise Surveys. Categories defined by base values defined by initial value (in.) or the average value between the two periods (avg.).

17
# **Flood Protection and Land Value Creation – Not All Resilience Investments Are Created Equal**

Paolo Avner, Vincent Viguié, Bramka Arga Jafino, Stephane Hallegatte

**Keywords** : Land Value Creation; Resilience Investments; Floods; Triple Dividend; Urban Economics; Land Use – Transport Integrated Models, Housing regulations

**JEL Codes** : R13, R14, Q54, R52, C63, H54

**Acknowledgments** : We would like to thank the World Bank Argentina Country Management Unit for their interest in this study and in particular Veronica Raffo, Maria Catalina Ramirez and Pablo Iribarren Santos for sharing with us the flood maps before and after resilience investments in Buenos Aires. We are grateful to Jun Rentschler and Marguerite Obolensky for their comments and inputs along the way. We are indebted to Maria Catalina Ramirez, Francis Fragano and Steven Rubinyi for their peerreview of and useful comments on a previous draft version. This study was supported by the Global Facility for Disaster Reduction and Recovery (GFDRR).
**-V.**

# URBAN PROPERTY TATION LESSONS FROM BRAZIL

## Table of Contents

||||Page No.|
|---|---|---|---|
|EXECU|TIVE|SUNMHARY . . . . . . . . . . . . . . . . . . . . . . . . .|. . .i|
|I.|I|RUCTI<br>. . . . . . . . . . . . . . . . . . . . . . . .|. . .<br>1|
||A.|WhyFocus<br>on Tax Administration?<br>. . . . . . . . . . .|. . .<br>1|
||B.|Municigal GovernMet in Brazil<br>. . . . . . . . . . . .|. . .<br>2|
|II.|URB|AN PROPERTY TAX ADMINISTRATION<br>. . . . . . . . . . . . .|. . .<br>6|
||A.|Identification of Tax Obiect<br>... . . . . . . . . . . .|.<br>.<br>6|
||B.|Vuatign<br>. . . . . . . . . . . . **.** . . . . . . . . . .|. .<br>6|
||C.|Procedures for Updating Assessment Rolls<br>. . . . . . .|. . .<br>7|
|||Cross-Referencing . . . . . . . . . . . . . . . . . .|. . .<br>7|
|||<br>Voluntary Compliance<br>.....<br>. . . . . . . . . . . .|.<br>.<br>9|
|||Ongoing Field Surveys . . . . . . . . . . . . . . . .|. . .<br>9|
|||<br>General Revaluations<br>. . . . . . . . . . . . . . . .|. . .<br>10|
||D.|Inflation Adjustments .10||
||E.|Tax Rates and Exemptions Polict<br>. . . . . . . . . . .|. . .<br>11|
||F.|Billing an.;Collection<br>.... . . . . . . . . . . . . .|.<br>.<br>11|
||G.|Efrceme<br>........................|.<br>12|
|I,II.|THE|RIO CASE . . . . . . . ... ...<br>....... . . .<br>. . . . .|.<br>13|
||A.|B&sure<br>.... .<br>. . . . . . . . . . . . . . . . . . .|.<br>.<br>14|
||B.|Valuation Reforms<br>.... . . . . . . . . . . . . .<br>. . .|.<br>.<br>15|
||C.|Imgact and Cost Effectiveness................|16|
represented by twins reduced enrollment levels of all children in the household. 22/ Estimates based on Malaysian data show that couples with a higher biological propensity to have births (higher fecundity or supply of births) are also characterized by lower schooling attainment for their children. 23/ At the same time, a massive literature has shown repeatedly that many characteristics of poor households contribute to high fertility -- high infant mortality, lack of education for women, too little family income to "invest" in children, and inaccessibility of family planning services. 24/ How can it be that poor parents persist in having many children if it tends to exacerbate their problems? For simplicity the answer can be divided into two categories.

Unwanted Fertility. First is the simplest explanation: that some fertility of the poor is unwanted. In many countries there are significant proportions of women who say that they would like to limit their fertility, yet do not do so. This condition of "unmet need" (as defined and measured by demographers) characterizes a quarter of married fecund women in Bangladesh, one-fifth in Nepal, one-eighth in Egypt. 25/ However, the phenornenon of "unmet need" does not imply that

- 22/ Rosenzweig and Wolpin, 1980.

- 23/ Rosenzweig and Schultz, 1987.

- 24/ Studies on the determinants of fertility are reviewed in Birdsall, 1988. See also World Bank, 1984. Birdsall and Griffin, 1988, review the evidence that reduced fertility, including among the poor, is associated with lower infant mortality, more educated mothers, better educational opportunities for children, and better access to family planning services.

- 25/ Boulier, 1985.

10
**Table 7: Availability of credit to the private sector and policy restrictiveness**

||(1)<br>|(2)<br>|(3)|
|---|---|---|---|
|Log(GDP) 2007|28.7852***|23.1826***|28.9766***|
|Log(Population)|-21.8667***|-16.3525***|-20.9216***|
|Population Density (people/sqkm)|0.0296*<br>|0.0242*<br>|0.0232|
|STRI (25, 50]|-14.7005|||
|STRI (50, 75]|-68.1747***|||
|STRI (75, 100]|-51.2875***|||
|Restr on Branches only||-16.8306||
|Operational Restr||-29.6466***||
|Major Restr Establishment||-32.8754***||
|No acquisitions (0%)|||-58.1234***|
|Minority ownership|||0.5520|
|Limit number of licenses|||-24.8711**|
|Restriction repatriation earnings|||2.2888|
|No recourse/appeal|||2.9468|
|No branch entry|||-6.0036|
|Restr on cross-border trade|||9.5379|
|Constant|-12.8094<br>|13.4692<br>|-15.7323|
|Observations|91<br>|91<br>|91|
|R-squared|0.4629<br>|0.4925<br>|0.4756|

Dependent variable is domestic credit to the private sector (% GDP). Linear regression with robust standard errors. Domestic credit as a percentage of GDP is taken from the World Development Indicators for the year 2008. For Kyrgyztan the corresponding 2007 figure and for Zimbabwe the 2006 figure is used for want of missing 2008 values in both countries.

# **7. CONCLUSION**

Our primary contribution is collecting and making available information on a key dimension of policy in an unprecedented range of countries, sectors, and modes. We then take some first steps towards measuring policy and its impact. First, we construct a services trade restrictions index (STRI) which simply and transparently captures the restrictive effect of the entire set of policies applied by a country in a given service sector and mode of supply. This measure helps us depict the broad pattern of policy across countries and sectors. Second, we 44
Table 14: Extensive margin I: Probability of export participation

|**Dependent Variable:**Probability o|f Export Participation_f pjt_||||
|---|---|---|---|---|
|Big Firm: exports > median in||HS6 product-Year|HS2 sector-Year|Year|
||(1)|(2)|(3)|(4)|
|Log (Tariff_i_,_j_,_p_,_t_)|-0.0483|-0.0487|-0.0659|-0.0302|
||(0.011)***|(0.015)***|(0.020)***|(0.020)|
|Log (Tariff_i_,_j_,_p_,_t_) x Big Firm_f _,_t_||0.0007|0.0228|-0.0223|
|||(0.017)|(0.022)|(0.022)|
|SPS_i_,_j_,_p_,_t_|-0.0280|-0.0096|-0.0240|-0.0145|
||(0.013)**|(0.022)|(0.023)|(0.025)|
|SPS_i_,_j_,_p_,_t _x Big Firm_f _,_t_||-0.0313|-0.0077|-0.0198|
|||(0.025)|(0.026)|(0.028)|
|TBT_i_,_j_,_p_,_t_|-0.0343|-0.0568|-0.0874|-0.0874|
||(0.012)***|(0.017)***|(0.022)***|(0.023)***|
|TBT_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.0351|0.0700|0.0671|
|||(0.019)*|(0.025)***|(0.026)**|
|Pre-Shipment_i_,_j_,_p_,_t_|0.0099|-0.0179|-0.0071|0.0155|
||(0.009)|(0.013)|(0.016)|(0.019)|
|Pre-Ship._i_,_j_,_p_,_t _x Big Firm_f _,_t_<br>||0.0469|0.0226|-0.0062|
|3||(0.012)***|(0.017)|(0.020)|
|QuantityControl_i_,_j_,_p_,_t_|-0.0537|-0.1089|-0.1008|-0.0686|
||(0.021)***|(0.022)***|(0.027)***|(0.028)**|
|Quantity Control_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.0925|0.0574|0.0553|
|||(0.017)***|(0.023)**|(0.025)**|
|PriceControl_i_,_j_,_p_,_t_|-0.0043|-0.0053|0.0004|-0.0427|
||(0.014)|(0.019)|(0.024)|(0.025)*|
|Price Control_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.0016|-0.0074|0.0460|
|||(0.021)|(0.027)|(0.027)*|
|Log (Import Demand_f _,_p_,_j_,_t_)|0.0110|0.0109|0.0110|0.0110|
||(0.001)***|(0.001)***|(0.001)***|(0.001)***|
|Observations|253212|253212|253212|253212|
|Adjusted_R_<sup>2</sup>|0.15|0.15|0.15|0.15|
|Fixed Effects|_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|<br>_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_<br>|_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|

This table estimates equation 4 for the extensive margin I of Colombian firm-level exports. Columns (1-4) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
appropriate siblings; a watta-bride normally must have an available brother, preferably an older one by not too many years. Thus, the likelihood of watta satta increases in the number of close older brothers a woman has and declines in the number of sisters who may be in competition with her for these brothers. Of course, the sibling sex composition of a woman’s natal family may be a reflection of the degree of her parents’ boy-preference, which may, in turn, be correlated with the woman’s own marital outcomes. Our identification strategy, discussed below, deals with this potential correlation.

After taking into account selection bias, the empirical results conform to our theoretical priors. Women in watta satta marriages have substantially and significantly lower probabilities of marital estrangement, domestic abuse, and major depressive episodes. The latter two findings in particular suggest that the peculiar institution of watta satta — a bride exchange coupled with a mutual threat of reciprocity — protects the welfare of women in rural Pakistan.

The rest of the paper is organized as follows. Section 2 describes the nature of watta satta and its relationship to marital discord in rural Pakistan. Section 3 then sketches a model illustrating the potential advantage of exchange marriage. Our empirical strategy is laid out in section 4, followed by the results in section 5. Section 6 summarizes the findings and concludes.

# 2 Marriage and its Discontents in Rural Pakistan

## 2.1 Data

The data used throughout this paper are from the second round of the Pakistan Rural Household Survey (PRHS II) undertaken in 2004-05. Our sample consists of about 3100 married woman age 15-40 in households randomly sampled from 171 villages in the two most populous provinces, Punjab and Sindh (the sample is broadly representative of these provinces). Detailed modules on marriage, domestic abuse, and mental health, among many other topics, were administered to each respondent meeting the above criteria, including those very few who were divorced or separated from their husbands.

The main objective of the mental health module was to assess major depression (MD) using an instrument adapted from the CIDI-SF survey of the World Health Organization. In the development of the questionnaire, training of the enumerators, and conducting of the field validations, we were assisted by experienced psychiatrists from the Institute of Psychiatry in Rawalpindi General Hospital. Given the sensitivity of the mental health 3
We found that climate change simulation studies for agriculture were mostly limited to India but largely excluded the Indian Himalayan states. Available literature projects reduced potential agricultural yield in India under climate change. In Nepal, however, similar agronomic crop simulations reveal an increase in rice yield with temperature up to 4 °C. Based on actual net farm output or income, however, this finding was negated by other available studies.

Based on the limited available studies, we found that economic impacts of climate change on India as a whole would be severe. For parts of Xizang province of China considered in our study, the marginal impact on net crop output would be either highly beneficial (irrigated farm) or mostly beneficial or less damaging (rain-fed). In view of the current and potential future hydropower development in our study area, there appears to be very high damage potential for GLOF. Literature shows projected risk of GLOF under climate change scenarios for Nepal would vary depending on socio-economic status of the site. Despite the importance of hydropower and agriculture in the study region, a comprehensive evaluation of economic impact was conspicuously missing. Therefore, further assessments would be needed on the economic impacts of climate change, particularly on hydropower and agriculture, the two most important economic sectors in the study area.

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27
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20