rafmacalaba/datause-encoder-data huggingface dataset:
the control and the _t_ -statistic of this difference across 22 observables that summarize household characteristics, behaviors, health outcomes and knowledge of pollution. Of the 66 comparisons we make, five are significant at conventional levels. Households in treatments one and two live one floor lower than those in the control or treatment 3. Of households in treatments two and three, 100% cook with natural gas, compared to 97% in the control and 98% in treatment one. The most meaningful difference is that households in treatment three are slightly wealthier on average than those in the control or other treatment groups. Controlling for wealth in our subsequent analysis does not change any of our results.

# **3 Empirical strategy**

## **3.1 Information treatment**

Our main specification compares outcomes across treatment groups using OLS regressions of the form: where _i_ denotes households and ∆ _Y_ is the change from survey round one to survey round two in the outcome of interest for that household. _Treatment_ is an indicator for assignment to one of the three treatment groups ( _Leaflet_ , _Outdoor_ , _Outdoor and Indoor_ ). Our outcomes of interest are three indices of household knowledge, actions, and health. These indices capture a household’s performance in the related category across all questions related to this outcome. For example, we ask households whether they experience a variety of health symptoms in the month prior to the survey. Our health index is then the total number of symptoms the household experienced. For more details on these calculations see Appendix 3. Because the health symptoms of pollution exposure and the common cold are similar, to alleviate concerns that our results are driven by seasonal respiratory disease (or COVID-19) we exclude households that have been diagnosed with COVID-19 during our sample period and those that visit the doctor explicitly for a respiratory disease from our analysis.<sup>9</sup> In all analysis we cluster standard errors at the census block level.

> 9Including these households does not meaningfully change our results.

9
Financial Regulation and Development Table E1: Compliance in Regulation: Within-Year Variation

||(1)||(2)|
|---|---|---|---|
|Variables||NBE Bills (million birr)||
|Private Lending _×_|0.265***|||
|Policy|(0.0395)|||
|Private Lending _×_|||0.206***|
|Policy|||(0.0848)|
|Private Lending _×_|||0.0621|
|Policy _×_|||(0.0959)|
|Big Bank||||
|Bank FE|Yes||Yes|
|Month FE|Yes||Yes|
|Obs.|168||168|
|Number of Banks|14||14|
|_R_<sup>2</sup>|0.875||0.878|
|Adjusted _R_<sup>2</sup>|0.864||0.866|
|Mean Dep. Var.|625.3||625.3|
|SD Dep. Var.|781.8||781.8|

_Note:_ This table reports OLS estimates. The unit of observation is bank level, and bank and month fixed effects are included. Standard errors are bootstrapped at bank-level cluster. NBE bills is the amount of bills issued by the NBE in million birr, and is a continuous variable. Its mean and standard deviation are reported in the final two rows. In column (1), NBE bills is regressed on the interaction between Private Lending, in million birr and a continuous variable, and the Policy dummy, taking unit value from April 2011 onward. In column (2), we also add an interaction of this variable with the Big Bank dummy, presented in Figure 4, to verify whether big banks comply differently. The null hypothesis is that in column (1) the coefficient is 0.27, as prescribed by the NBE directive, and we cannot reject this. In column (2), the null hypothesis is whether the big bank interaction is statistically different from zero, and * we cannot reject this either. The _R_<sup>2</sup> and adjusted _R_<sup>2</sup> of these regressions are also reported. ***, **, and indicate significance at the 1%, 5%, and 10% levels, respectively.

interaction and verify whether there is any differential implementation of the policy across big and small banks. Although the point estimate of _b_ drops to 0 _._ 20 (with standard errors doubling), we can also verify that _c_ is not statistically different from zero. This supports the hypothesis that all banks were equally affected by the policy and complied.

49
Table 10: Do Student Test Score Trends Predict Being Taught by a Contract Teacher?

||(1)<br>Mean Test Scores|(2)<br>Mean Test Scores|(3)<br>Had a Contract Teacher|
|---|---|---|---|
|_Year_|0.134***<br>(0.013)|0.145***<br>(0.013)||
|_I_(_ReceivedContract Teacher_)|0.048<br>(0.078)|0.069<br>(0.083)||
|_Year ×I_(_ReceivedContract Teacher_)|-0.015|-0.011||
||(0.023)|(0.024)||
|_MeanTest ScoreGain_|||-0.014<br>(0.016)|
|District FE|Y|Y|Y|
|School FE|N|Y|N|
|Grade by Lagged Test Score Interactions|Y|Y|N|
|Number of Observations|25,296|25,296|15,956|
|Adjusted R Squared|0.637|0.677|0.037|
|Clusters|478|478|497|

This table tests whether better students are allocated to contract teachers. The first column compares trends in student test scores before the receipt of a contract teacher in schools that did and did not receive contract teachers. The next column compares the test score gains of students within schools who did or did not receive contract teachers before the receipt of the contract teacher. The final regression regresses an indicator for whether a student ever had a contract teacher on their mean test score gains (residualized by testing round and grade) in the years prior to receiving a contract teacher. In this sample, each student is observed once. Standard errors are clustered at the school level.

Table 11: The Effect of Teacher Contract Status on TVA

||(1)<br>Mean TVA|(2)<br>SE|(3)<br>One-Sided T-test|(4)<br>N|(5)<br>Within School Mean TVA|(6)<br>SE|(7)<br>One-Sided T-test|(8)<br>N|
|---|---|---|---|---|---|---|---|---|
|OLS (Full Sample)|-0.004|0.042|0.541|1,337|0.024|0.026|0.181|1,278|
|RD (Full Sample)|-0.004|0.052|0.533|1,337|0.056|0.041|0.088|1,278|
|RD (2 Year)|0.840|0.550|0.068|227|0.360|0.322|0.137|201|
|RD (3 Year)|0.219|0.241|0.184|376|0.254**|0.123|0.022|336|
|RD (4 Year)|0.350|0.234|0.070|393|0.193*|0.097|0.026|350|
|RD (5 Year)|-0.074|0.120|0.732|661|0.035|0.057|0.268|604|
|RD (6 Year)|-0.026|0.106|0.598|690|0.040|0.053|0.225|631|
|RD (7 Year)|-0.036|0.106|0.632|692|0.035|0.052|0.250|632|

This table regresses mean TVAs on whether a teacher has a temporary contract in all public schools using the ordinary least squares and 6 fuzzy regression discontinuity specifications. In the RD specifications, contract status is instrumented for with an indicator variable for whether a teacher was hired after 1998. All regressions contain linear time trends which are allowed to differ before and after the budgetary shock and district fixed effects. The table presents instrumental variables (RD) specifications for the full sample and for bandwidths of 2-7 years before and after the budgetary shock. Observations are at the teacher level, and standard errors are clustered at the school level in the OLS specification and the month hired level in the regression discontinuity specifications. Columns 3 and 7 report p-values for a one-sided t-test of whether the temporary contract effect is negative.

42
24 study, indicates that urban employment and unemployment both increased between 1960 and 1980, but that the level of unemployment increased much faster thIan did the level of employment. The study also suggests that formal sector employment increased faster than did employment in the informal sector. This finding challenges the traditional beliefs of most outside economic observers, but it is not clear what Mingat's source for informal sector data is. Bloch (1985) also figures and attributes them to the Statistical Directorate of the Ministry of Finance. He presumes that employment in the informal sector is calculated as a residual because no methodology could be identified. Our attempts to gather basic data on the informal sector lead us to conclude that the informal sector is one of the least understood activities in Senegal's economy and that data on this sector must be treated with extreme caution. It is for instance likely that the unemployed are effectively part of the informal sector, although statistical information on this aspect of unemployment is scant.

# 4.3 Industrial Employment in the Modern Sector

As Table 4.2 indicates, the number of modem secto'r industrial firms appears to have increased sharply from 1963 to 1977, but has remained about the ssme since then. However, it is not clear whether the definitioii of the modern sector remained the same from 1963 to 1977; if not, the major increase in the number of firms during that period may be somewhat spurious. The definition during the past ten years has, in contrast, been uniform. Under this definition, a firm is considered to be part of the modem sector if it can be reached by the tax authorities. The important point is that even relatively small firms (e.g., with a dozen workers) are included. The data from 1977 to 1984 are hence fairly reliable. Further, they indicate that the muchdiscussed exodus of French entrepreneurs has not resulted in a significant decrease in the number of industrial enterprises. Our inquiries in this area suggest that most departing expatriates have been selling their firms to other expatriates or to Senegalese nationals. By the same token, it should be noted that there has not been any growth in the total number of modern sector firris.
Work to improve the investment climate is recognized as a key pillar of the World Bank Group to promote economic growth and poverty alleviation in developing countries. The World Development Report 2005 defines the investment climate as “the factors in a particular location that shape the opportunities and incentives for firms to invest productively, create jobs, and expand.... Firms are the starting point of the framework.”<sup>2</sup> In this article we focus on productivity, sales and employment as indicators of the goals of the firm. These goals are shaped by the regulatory and institutional factors defined above as the “investment climate”.

This paper examines the widely assumed causal relationship between three variables: (i) productivity and other performance measures, (ii) investment climate constraints as perceived by individual firms, and (iii) the ownership of firms, distinguishing between privatized ones and new entrants. We ask whether one can show causal effects of the various investment climate constraints and of ownership on productivity or performance. These questions have important policy implications because the relative effects of various investment climate constraints and ownership on productivity serve as a guide for such prioritization.

Serbia provides a good example of how these lessons can be applied. Four years have passed since the October 2000 “velvet revolution”. During these four years, the investment climate was heavily affected by political instability: uncertainty related to the future status of Kosovo and the Hague criminal tribunal. Yet, several major policy steps conducive to a better investment climate were implemented: fiscal stability and trade liberalization were achieved; tax code reforms were carried out; a new privatization law was adopted and a program is being implemented; leasing, collateral and concessions laws were introduced. A critical factor in Serbia (as in all the other parts of former Yugoslavia) is the challenge presented by the legacy of “social ownership”, quite different from state ownership, it is an ownership-management system in which insiders had legal claims on the assets. The heritage of social ownership still poses major obstacles to the development of the private sector and negatively affects the investment climate **.** The analysis in this paper focuses on the case of Serbia in 2000-2004 and provides an overview of Serbia’s investment climate in an international context, drawing on several datasets (Box 1). _First_ , it examines how Serbia compares to other countries, in particular to other transition economies, in terms of its output and productivity. _Second_ , it reviews which characteristics of firms worldwide are associated with high productivity and strong performance, show how investment climate variables affect productivity, and show how the characteristics of firms worldwide affect complaints about the investment climate. _Third_ , it shows which Serbian firms have high productivity and are growing quickly, and present the investment climate constraints that they face.

3
**Figure 3:  Destination of manufacturing output**

<!-- Start of picture text --> Share of output sold domestically Share of output exported<br>0.80 0.08<br>0.70 0.07<br>0.60 0.06<br>0.50 0.05<br>0.40 0.04<br>0.30 0.03<br>0.20 0.02<br>0.10 0.01<br>0.00 0.00<br>1995 2005 2015 1995 2005 2015<br>Intermediates Final demand Intermediates Final demand<br>SSA average SSA average SSA average SSA average<br><!-- End of picture text --> Notes: Own calculations based on the EORA data.

In sum, the economy of Rwanda is even less well integrated with international markets than most sub-Saharan African countries. On the output side, manufacturing firms export only 2% of output, compared to 11% for the rest of the region. More of its agricultural output is exported, but at 6.8% that fraction is still much lower than the 25.4% that is the average for the region. On the input side, the difference is smaller: 13.3% of inputs in manufacturing are imported, which is similar to the 14.5% average for the region. Inputs to agriculture are almost all sourced domestically, but the same pattern prevails in the rest of the region.

The objective in the remainder of the paper is to investiage what this aggregate exposure implies at the firm level. How many firms are internationally engaged, which firms, and to what extent? Of the firms selling or sourcing internationally, what regions are they mostly exposed to and what products are they producing? To answer these questions, we work with an exhaustive dataset of all formally registered firms in Rwanda that is introduced in the next section.

# **3. Data**

This project uses administrative, micro-level data for Rwanda that includes information on the universe of registered firms from three different sources of administrative data:

- Trade-transactions data from custom’s: Imports & exports by firm-year in both value and quantity terms, broken down by product (HS 8-digit) and partner country.  While this is recorded at the transaction level, it is aggregated to yearly totals.

- Payroll data: Number of employees and total wage bill.  These are the reports provided to the payroll authority for the purpose of reporting wages and paying payroll tax. This dataset should be seen as representative of the universe of registered firms that have payroll, i.e. formally employ workers beyond the owner.

7
Policy Research Working Paper 7086

# **Abstract**

This paper uses evidence from three Indian states, one of which amended inheritance legislation in 1994, to assess first- and second-generation effects of inheritance reform using a triple-difference strategy. Second-generation effects on education, time use, and health are larger and more significant than first-generation effects even controlling for mothers’ endowments. Improved access to bank accounts and sanitation as well as lower fertility in the parent generation suggest that inheritance reform empowered females in a sustainable way, a notion supported by significantly higher female survival rates.

This paper is a product of the Agriculture and Rural Development Team, Development Research 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://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org.

_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
**C STATA Codes**

**62**

# **Mathematical Notations**

## **Welfare measures**

|_CV_|_Compensating variation_|
|---|---|
|_EV_|_Equivalent variation_|
|_CS_|_Consumer surplus variation_|
|_LV_|_Laspeyers variation_|
|_PV_|_Paasche variation_|
|_S_|_Substitution effect_|
|_I_|_Income effect_|
|**Functions**||
|_u_(_._)|_Direct utility_|
|_ν_(_._)|_Indirect utility_|
|_e_(_._)|_Expenditure_|
|_Dk_(_p_)|_Demand_|
|_xk_(_P, m_)|_Marshallian demand_|
|_hk_(_P, m_)|_Hicksian demand_|
|∆(_._)|_Absolute variation_|
|_d_(_._)|_Proportional variation_|
|_λ_(_._)|_Proportion of the error term_|
|**Vectors**||
|_p_|_Price_|
|_x_|_Quantity_|
|_m_|_Income_|
|_e_|_Expenditure_|
|_sk_|_Expenditure share of good k_|
|**Scalars**||
|_θ_|_Income elasticity_|
|_η_|_Non compensated price elasticity_|
|**Indexes**||
|_k_ = 1_,_2_, ..., n_|_Products (subscript)_|
|_t_=_a, b_|_State/Time (superscript)_|
|_o_= 1_,_2_, ..., n_|_Taylor degree of approximation (superscript)_|

3
An entrepreneur can open a shop or have a craft business if he meets the following requirements:

1. To be of age and capable for business,

# **2. To be citizen of Bosnia and Herzegovina,**

3. That a competent body did not prohibit him to perform this activity, 4. That he is not a full time employee, 5. That he meet general health requirements and special health requirements if defined so by the law for certain business, 6. That he has the equipment i.e. means for work, staff and adequate premises, unless the nature of business i.e. work does not require that.

Competent minister defines for which activities business premises are not required.

**If the law defines adequate level of education for certain business, apart from requirements in the previous paragraph, applicant is to submit proof of adequate level of education or to hire a person with adequate education level.**

# **<mark>RS AMENDMENT</mark>**

# **LAW**

# **ON AMENDMENTS TO THE LAW ON CRAFTS**

# **Article 1**

In the Law on crafts (RS Official Gazette No. 16/02) in the article 14, paragraph 1, item 2 is to be amended and read ‘‘to be citizen of Bosnia and Herzegovina. ’’

# **Article 2**

This law becomes effective on the eighth (8) day from the day of publishing in the RS Official Gazette.

No: Chair

# **<mark>FEDERATION OF BOSNIA AND HERZEGOVINA</mark>**

_LAW ON TRADE_ **(FBiH Official Gazette, No. 2/95,19/96)**

# **<u>Existing text</u>** <u>:</u>

# **Article 8b**

Person who meets the following requirements will get permit for opening of a trade shop:

- 1- That he is citizen of the Federation and citizen of Bosnia and Herzegovina,

- 2- That he is capable to work,

- 3- That he meets health requirements if health is defined as a special requirement for retail trading with certain products in trade shops,

- 4- That he has business premises that meet general and special requirements,

- 5- That he does not work or already have a trade shop founded,

- 6- That he was not prohibited to work in this business by a legally effective decision on introducing a protective measure,

Page 44 of 51
**Table 6: First stage estimation - Impact of changes in unanticipated mining revenues on** **<u>changes in public expenditure for key categories, by area</u>**

|_Dependent variable:_|Public S|pending|
|---|---|---|
||Rural IV|Urban IV|
|Unanticipated mining revenues|11.424|6.397|
||(0.434)***|(0.196)***|
|Other Controls|YES|YES|
|Number of observations|688|1,214|
|Partial R<sup>2</sup>of excluded instrument|0.513|0.473|
|F-test for weak identification|692|1063|

Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006, and Ministry of Economic and Finance Budgetary Expenditure for 2000–2006.

Note: The independent variable is the amount of unanticipated mining revenues. The estimations include regionalfixed effects, time-fixed effects, and all controls included in the 2<sup>nd</sup> stage estimation. Spending measured in 100 million soles. Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. They include regional-fixed effects and time-fixed effects.

# **S5 ALTERNATIVE INTERPRETATIONS OF THE RESULTS**

# **S5.1 Differences in public spending quality between urban and rural areas**

When public expenditure expands in rural areas this could lead to the displacement of more effective private services (e.g. pediatricians) as demand switches to the cheapest public service (public health center). Given that rural markets are very small, it is quite possible that an enlarged public sector could leave no space for the private sector to make a profit. This could have a harmful effect on child well-being if public health centers (the only access measure increasing in rural areas) are less effective than other health facilities. This is probably the case since neither doctors nor nurses work at public health centers. These centers are equipped with health technicians who provide care following manuals which guide and limit their functions. Furthermore the high rates of absenteeism and irregular working hours that usually characterize poorly monitored public health facilities in rural areas (Devarajan and Reinikka 2004; Chaudhury et al. 2006; Banerjee and Duflo 2011) will reduce the effectiveness of the services rendered. Chaudhury et al (2006) conducted a survey focused on the presence of teachers and 22