rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 4. Balance T**<br>**Variable**|**Mean for Male Own**<br>**est - Female vs Male Owned Firms**|**ed**<br>**Difference in Mean for**<br>**Female Owned**|
|---|---|---|
||Mean/sd<br>|Mean/se<br>|
|% owners with high education|57.46<br>(0.50)|0.07<br>(0.05)|
|Age of firm|10.54<br>(11.91)|-0.04<br>(1.15)|
|Total employees|9.46<br>(10.61)|-2.81***<br>(0.93)|
|Trade|24.62<br>(0.43)|0.036<br>(0.045)|
|Manufacturing|27.61<br>(0.45)|-0.031<br>(0.044)|
|Tourism|23.13<br>(0.42)|0.006<br>(0.043)|
|Non-farming agriculture|24.63<br>(0.43)|-0.011<br>(0.043)|
|% female employees|41.49<br>(0.33)|15,28***<br>(3,85)|
|% employees with high education|28.88<br>(0.36)|-2,20<br>(3.79)|
|% of firms operating in a conflict area|18.66<br>(0.39)|0.067<br>(0.04)|
|% of firms with license or permit to operate|94.03<br>(0.24)|0.00<br>(0.02)|
|Gender equality policy<br>% firms providing:|69.17<br>(0.46)|0.06<br>(0.05)|
|Subsidized childcare|21.09<br>(0.41)|0.01<br>(0.04)|
|Paid annual leave|37.59<br>(0.49)|-0.06<br>(0.05)|
|Sick leave|81.34<br>(0.39)|0.05<br>(0.04)|
|Maternity leave|46.46<br>(0.50)|-0.03<br>(0.05)|
|Paternity leave|45.24<br>(0.50)|-0.06<br>(0.05)|
|Subzidized housing|63.43<br>(0.48)|0.07<br>(0.05)|
|Subsidized food|67.91<br>(0.47)|0.11**<br>(0.04)|
|Subsidized transport|15.67<br>(0.37)|-0.08***<br>(0.03)|
|Firm has a bank account|55.91<br>(0.50)|-0.19***<br>0.05|
|Firm has documents to request a loan|61.29<br>(0.49)|0.00<br>(0,05)|
|Firm has a loan|8.07<br>(0.27)|0.01<br>(0.03)|

18
foreign lobbying activities which is sent from the US Attorney General to the US Congress. The FARA annual reports contain the name and address of foreign agents, the name of the principals, the purpose of the agency and the amount of money in return for the agent’s activities.<sup>8</sup> First we separated the foreign lobbying expenditure data related to trade on agricultural and industrial goods from those involving trade in services and other types of foreign lobbying<sup>9</sup> . Then, each lobbying expenditure related to trade on goods was mapped into 3-digit ISIC industries. This mapping process was repeated for each entry found on the FARA report for 33 countries in Latin America. We used the FARA reports for 1997, 1998, 1999 and 2000. Finally, we calculated the average lobbying expenditure over this four year period by 3-digit ISIC industry and by country. Trade related FARA contributions by Latin American exporters reach 60 million dollars during this period.

Some of the trade related contributions could not be mapped into particular sectors when they were undertaken by government agencies or private chambers of commerce that encompass several industries. Most of the paper excludes these contributions, except for the section on robustness tests, where these contributions are included in the econometric analysis.

One characteristic of the foreign lobbying expenditure data is the high concentration by sector and country. The ISIC 3-digit industries with the largest expenditures are agriculture and livestock production (ISIC 111), crude petroleum and natural gas production (ISIC 220) and manufacture

> 8For a detailed description of how foreign agents operate, see Jobst (2003).

> 9See the appendix for more information on foreign lobbying data.

5
_Low-Income Countries’ Access to Private Debt Markets_ More recent research by Zoli (2004) and Pescatori and Sy (2004) has taken a different approach by modeling the supply of funds using a nonlinear specification that is bounded by a ceiling. Credit rationing occurs when default is imminent because no risk premium can compensate creditors. Zoli’s empirical analysis of emerging market sovereign bond spreads indicates that the ceiling varies over a fairly wide range (between 570 to 850 basis points depending on the specification). In contrast, estimates reported by Pescatori and Sy indicate that the ceiling is around 1000 basis points, which they argue is a critical psychological threshold for market participants.

The analysis by Zoli and Pescatori and Sy suggests that countries are unable to access the international bond market unless bond spreads are below a threshold level. While there are few examples of bonds issued with spreads above 1000 basis points,<sup>19</sup> it is unclear that this constitutes credit rationing, in the sense that there is a binding constraint on the supply of funds. Demand-side considerations are also likely to play an important role as corporations and governments assess the high cost of borrowing from abroad. Moreover, spreads on first-time bond issues vary over a wide range (figure 15). Of the 94 governments and corporations in developing countries that issued bonds for the first time over the period 1990-2007, spreads exceeded 500 basis points in 20 cases. There were also 8 cases where spreads on firsttime bond issues were below 50 basis points. Governments and corporations typically do not wait until spreads are below a threshold level before accessing the international bond market for the first time.

# **Figure 15 Spreads on first-time bond issues, 1990-2007**

<!-- Start of picture text --> 1000<br>Sub-sovereign<br>750<br>Public Private<br>Sovereign corporate corporate<br>500<br>250<br>0<br>Bond spreads<br><!-- End of picture text --> Source: Dealogic DCM Analytics and Loan Analytics.

> 19 One example was in November 2002 when Kyivstar GSM ZAT, Ukraine’s largest telecom company, issued a 3-year US-dollar denominated Eurobond with a yield to maturity of 13 percent, marking the first external bond issue by a private corporation located in the country.

21
# **Exhibit 3.8 – Trading activity including all Egyptian stocks**

_Cumulative distribution of % of non-trading days for selected countries, 2008 (upper left graph) Cumulative distribution of % of non-trading days in Egypt, 2004-08 (upper right graph) Fraction of illiquid stocks in Bloomberg sample and all listed firms, 2004-07_ <!-- Start of picture text --> Number of Number of<br>stocks, % stocks, %<br>100% 100<br>90%80% Egypt 9080 2008<br>70% Abu DhabiAmman 7060 20072006<br>60%50% CasablancaKuwait 5040 2005<br>40% Manama 30 2004<br>30% Muscat 20<br>20% Qatar 10<br>10% 0<br>0% Non-trading<br>Non-trading days, %<br>days, %<br>Illiquid stocks as % of sample<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2004 2005 2006 2007<br>Illiquid in Bloomberg sample Illiquid in sample of all listed companies<br>0% 3% 5% 8% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0.0% 2.5% 5.0% 7.5% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%<br><!-- End of picture text --> _Source: EGX, Bloomberg; World Bank staff analysis_ _Notes: To construct the lower graph we assume that the stocks not covered by Bloomberg, are illiquid. This number is added to the number of illiquid stocks in Bloomberg and is taken as a fraction of the total number of listed firms reported by CASE. Note for upper graphs: 2008 information is up to and including June 20_<sup>_th_</sup> _._

# _3.2.4 Transaction costs: Transaction costs have come down and compare favorably to other countries; commissions seem to represent the largest cost component_

Market activity and liquidity crucially depend on the cost to execute a trade. The total transaction cost is typically thought to consist of three components: a broker commission, stamp or duty fees, and the market impact of the transaction, induced by the trade itself. If transaction costs are relatively high, prices will not fully reflect all available information since a trade will only take place if the value of the information outweighs the transaction cost.

In general, Egypt’s transaction costs have come down and compare favorably to other countries (Exhibit 3.9). EGX-specific transaction costs have been reduced in 2007 from 0.125 per thousand of each side of the transaction’s value to 0.12 per thousand (up to a maximum of LE5,000). However, during the second quarter of 2007, with almost 70 basis points, Egypt’s transaction costs seem high relative to other countries. Indeed, according to EGX, the brokerage market has been relatively concentrated: the top five firms―most notably Commercial International Brokerage Company, Financial Brokerage Group, and Hermes Securities Brokerage―capture 40 percent of the market where currently more than 140 licensed companies are active. And competition seem to have deteriorated further: in the first quarter of 2008, the two largest brokers (Beltone Securities and Financial Brokerage Group) account for 49 percent of total value traded.

29
# 1. **Introduction**

There is a widening circle of development thinkers, policymakers, and practitioners who believe that the participation of local communities in public services is instrumental in achieving better development outcomes. This has sparked the creation of new (or revival of existing) local agencies around the developing world. Yet, more often than not, these new institutions are constrained—they have no real authority to hire and fire public providers, no real resources to use at their discretion, and no real responsibility for service delivery outcomes. Nevertheless, there is a sense among development practitioners that what these institutions might do is provide a “voice” to the people, a forum for “collective action,” and facilitate “bottom-up” or “demand-driven” initiatives that make a difference. The hope is that local agencies will strengthen people’s participation in improving the functioning of basic services, and thereby lead to better development outcomes.

In the context of India, and in particular the primary education sector, decentralized participation has been given central importance in the roll-out of a massive government push for universalization of elementary education, Sarva Shiksha Abhiyan (SSA). Habitation-level planning and community participation has been envisaged as an essential element for ensuring universal enrollment, retention, and achievement of a satisfactory level of learning. Village education committees in particular, consisting of village government leaders, teachers, and parents, are visualized as the mechanism through which public funds for education services will flow to the village, and planning and implementation will be coordinated.<sup>1</sup> Creation of local agency People’s Improvement Improvement participation in service in human (Eg. A Village in service delivery development Education Committee) delivery (Eg. children’s learning)

> 1 Appendix 1 and 2 provide details from UP state government documents on the SSA program and the Village Education Committees.

3
Table 6: The growth elasticity of poverty based on HFCE per capita

|VARIABLES|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|gr_change|-2.139***|-2.379***|-3.724**|-1.771**|
||(0.364)|(0.455)|(1.482)|(0.833)|
|initial poverty rate|0.0165|-0.0242|0.0155|0.0439|
||(0.0482)|(0.0446)|(0.0483)|(0.0408)|
|initial inequality|0.0779|-0.0480|-0.0436|0.00392|
||(0.109)|(0.125)|(0.134)|(0.110)|
|intial HFCE|1.49e-05|1.20e-05|1.53e-05|1.88e-05|
||(1.12e-05)|(1.14e-05)|(1.12e-05)|(1.15e-05)|
|interval|0.00544|0.00221|0.005|0.00511|
||(0.005)|(0.005)|(0.005)|(0.0045)|
|Baseline: other regions|||||
|SSA||0.00223|||
|||(0.0270)|||
|gr_change x|||||
|SSA||1.846***|||
|||(0.690)|||
|initial headcount gini|||3.878||
||||(3.061)||
|Baseline: <2000|||||
|2000-2010||||-0.0427|
|||||(0.0434)|
|2011 and above||||-0.0604|
|||||(0.0410)|
|gr_change x|||||
|2000-2010||||-0.587|
|||||(0.918)|
|2011 and above||||0.0531|
|||||(0.891)|
|Baseline: Consumption|||||
|Income|-0.0169|-0.00369|-0.0171|-0.0257|
||(0.0411)|(0.0467)|(0.0410)|(0.0395)|
|Constant|-0.0827*|-0.0105|-0.0294|-0.0194|
||(0.0474)|(0.0616)|(0.0683)|(0.0692)|
|country fixed effect|No|No|No|No|
|Observations|505|505|505|505|
|R-squared|0.219|0.233|0.223|0.237|

Notes: Table shows results of a regression of the annualized log change in poverty headcount on the annualized log change in HFCE per capita. Sample consists of all consecutive spells between 1981 and 2021, dropping spells with baseline and endline poverty rates lower than 2 percent. Robust standard errors in brackets. ***: Statistically significant at 1%; **: Statistically significant at 5%; *: Statistically significant at 10%.

23
# **3 Poverty effects of trade policy reforms**

This section first presents the results of the general equilibrium model and then the poverty estimations obtained by linking changes in the macro variables to the household survey data.

# **_3.1 Policy scenarios_**

The Dominican Republic-Central America Free Trade Agreement (DR-CAFTA) is a recently negotiated free trade agreement (FTA) between Central American countries (Costa Rica, El Salvador, Guatemala, Honduras and Nicaragua), the Dominican Republic and the U.S.<sup>12</sup> Central American countries have already been enjoying the preferential market access to US markets through the Caribbean Basin Initiative (CBI) program. DRCAFTA will not only consolidate and formalize these existing benefits, but also provide greater market access by eliminating remaining tariffs on goods that have been exempted from the CBI preferences. The agreement also incorporates a gradual opening of Central American markets for US agricultural commodities.<sup>13</sup> Other main achievements of DRCAFTA include the greater flexibility of rules of origin for textiles and apparel, commitments to help producers meet sanitary and phytosanitary standards required for the entry into the U.S., reciprocal commitments on access to service markets, and the institutional and legal framework to ease foreign direct investment (FDI) flows to the region.<sup>14</sup> Besides providing almost full free access to one of their major markets, the DRCAFTA agreement should assist the implementation of additional domestic market reforms, and produce significant efficiency gains due to resource reallocations toward more competitive sectors by requiring reciprocal opening. However, as brilliantly

> 12 DR-CAFTA was approved by the US Senate and House of Representatives in June-July 2005 and it has also been approved by the legislatures in El Salvador, Guatemala and Honduras, while its approval is pending in Costa Rica, the Dominican Republic and Nicaragua as of writing this paper.

> 13 DR-CAFTA, however, exempts from further liberalization some sensitive agricultural products including sugar imports to the U.S., white maize imports to four Central American countries (El Salvador, Guatemala, Honduras and Nicaragua), and potatoes and onions to Costa Rica.

> 14 For more details see: (Francois _et al_ ., 2005; World Bank, 2005).

16
In all cases we assume that there is zero covariance between the error terms across countries. Non-zero error covariances usually arise from omitted common factors that influence all countries. In this paper, the common factors are modeled explicitly given thatthe measures of return, risk, and wealth of a given country are all relative to the rest of the world. Furthermore, to eliminate any remaining common factors, we also estimate the model allowing for time-specific effects. Given these considerations, the assumption of zero error covariances across countries is not overly restrictive.

# **3. Data**

## **3.1 NFA and Wealth**

The cornerstone of our data is **a** set of foreign asset and liability stocks for **a large group** of industrial and developing countries spanning the period from the 1960s to the present. The data set excludes 'small island' economies (specifically, those with population under **I** million in **1995)7** as well as former socialist economies, for which data availability is too limited. In addition, we also drop from the basic data set a handful of developing countries that have experienced prolonged war episodes over the sample years. For our empirical experiments in this paper, we further restrict the country sample to those economies possessing a number of annual observations sufficient to allow country-specific econometric estimation; we set such minimum at 20 (consecutive) years. This results in an unbalanced panel of 48 countries with time coverage ranging from 20 to **33** years.

Construction of the data is thoroughly documented in Kraay _et al._ **(1999),** and for brevity we will limit our remarks here to the main issues.<sup>8</sup> The starting point is the definition of the net foreign asset position **(NFA)** of countryj in year t:

### **_NFA 6,t) = NFQAO,t) + NFLA 6,t)_**

## **_= [FDIA(j,t) - FDIL(j,t) +EQYA(j,t) -_** _EQYL(j,t)]+[RAO,t)_ **_+LAO,t)_** _-LLY,t)_ **_]_**

> Small economies are excluded because they tend to display higher volatility than larger economies (Easterly and Kraay **1999),** and this would add too much noise to our empirical experiments below. In addition, they also include a number of tax havens attracting disproportionately large financial flows, which would distort the cross-country dimension of the data.

> **8** In the construction of the data set we have benefited from Lane and Milesi-Ferreti **(1999),** which documents a

> similar effort of estimation of foreign asset and liability positions. In this regard, several conversations with Gian Maria Milesi-Ferretti were particularly valuable.

12
**Figure 1: Aggregate royalty payments/aggregate sales** .  The clear bars indicate the average annual aggregate royalty payment to sales ratio for various samples of affiliates in reforming countries before reforms occur. Averages are calculated as the mean of annual ratios in each country over all years in the pre-reform period.  The shaded bars depict average ratios for the year of and years following reform. The first two bars are for the full sample, the next two for affiliates of parents that cannot be matched to the NBER patent database, the third pair of bars are for the sample of affiliates of parents that do not make extensive use of patents in the U.S., and the final pair are for affiliates in the high patent use sample.

<!-- Start of picture text --> 0.018<br>0.016<br>0.014<br>0.012<br>0.01 Before Reform<br>0.008<br>After Reform<br>0.006<br>0.004<br>0.002<br>0<br>Full Unmatched Low Patent High Patent<br>Sample<br><!-- End of picture text -->

**Figure 2: Resident and non-resident patent filings around the time of reforms** .  The Non-Resident Patent Filings line traces out an index of foreign patenting around the time of reforms.  This index is calculated by first computing the median ratio of the number of patent filings by foreigners in a particular year relative to reform to the number of patent filinings by foreigners in the year prior to a reform and then selecting the median ratio across reforming countries.  The Resident Patent Filings line display the index for resident patent filings.

<!-- Start of picture text --> 2.5<br>Non-Resident Patent Filings<br>2<br>1.5<br>1<br>Resident Patent Filings<br>0.5<br>0<br>-3 -2 -1 0 1 2 3<br>Year Relative to Reform<br><!-- End of picture text --> 43
61 Mall I . PFP Projections and Actuals <!-- Start of picture text -->
-44<br>4o<br>41 .14 - *  -f  ,<br>.11  ~  ~  Os  Is$11<br>06 0?  00  00  U  t  0;tY2<br>Yewr<br>-<br>bng +AOSNIS<br><!-- End of picture text -->

Mall 3 - PFP Projections and Actuals <!-- Start of picture text --> Ge... .avUuaVGDP<br>20 t @0 @0 9.2<br>S 1<br>Y"r<br>- Prejdton +A1umls<br><!-- End of picture text --> Mall 5 - PFP Projec¢tons and Actuals <!-- Start of picture text --> IS N<br>C.s I<br>20 ~~~ew<br>FV.I n Doeo<br>~Pv*lseee +AshrnOt<br>Souoem Predlon . PFPIO 12190,1 . 07/<br>Adualo. *NOAOVCeu* ddfs<br>P! J.-. Dee;l<br><!-- End of picture text --> Mall 2 - PFP ProjectIons and Actuals <!-- Start of picture text -->
*10<br>-'  n<br>e<br>-1Mjete  +Aotw*e  1 - 4 A  TOT .4usd<br>TOT * Trms  Trade<br><!-- End of picture text -->

Mall 4 - PFP ProjectIons and Actuals <!-- Start of picture text --> Dem*ak &vIq.VODP<br>E . 1 §<br>C~I' Ss 0<br>Yew<br>Pjectlee. +4Atuue<br>-<br>Mall 6 PFP Projections and Actuals<br>'-X<br>.<br>U 0? 00 02 S 02<br>Yea<br>+8|<br><!-- End of picture text -->
Table 4: Key results, impact of conservancy by region

||_Kunene_|_Caprivi_|
|---|---|---|
|**1. Simple Comparison Without Control, Mean Differences in:**|||
|a) Mean Household Income|1299.83*|1702.36**|
|(Changes in proportion)|(0.20)|(0.24)|
|b) Mean Per Capita Income|589.50**|45.53|
|(Changes in proportion)|(0.44)|0.03|
|c) Mean Household Expenditure|-15.77|757.41**|
|(Changes in proportion)|(-0.02)|(1.32)|
|d) Mean Per Capita Expenditure|17.01|143.13**|
|(Changes in proportion)|(0.12)|(1.05)|
|**2. Multivariate Analysis, (changes in proportion):**|||
|**Overall Effects of Conservancy Program**|||
|a) Household Income|0.18|-0.12|
|b) Per Capita Income|0.28**|-0.18|
|c) Household Expenditure|-0.05|0.63**|
|d) Per Capita Expenditure|0.04|0.58**|
|**Interaction Effects Between Conservancy Program and:**|||
|**A. High Education on:**|||
|a) Household Income|-0.41**|-0.21|
|b) Per Capita Income|-0.51**|-0.24|
|c) Household Expenditure|0.19|-0.35*|
|d) Per Capita Expenditure|0.07|-0.37*|
|**B. Female headed households:**|||
|a) Household Income|-0.30|0.21|
|b) Per Capita Income|-0.23|0.14|
|c) Household Expenditure|-0.04|0.32*|
|d) Per Capita Expenditure|0.00|0.23|
|**C. Asset-Rich households:**|||
|a) Household Income|0.03|0.19|
|b) Per Capita Income|-0.07|0.14|
|c) Household Expenditure|0.13|-0.44*|
|d) Per Capita Expenditure|0.03|-0.48*|
|**D. Livestock-Rich households:**|||
|a) Household Income|-0.03|-0.15|
|b) Per Capita Income|-0.22|-0.20|
|c) Household Expenditure|0.44**|-0.37*|
|d)Per Capita Expenditure|0.24|-0.41|

Note:

1.        Extreme values of income and expenditure were excluded.  Households with annual income above 50,000 Namibian dollars and monthly expenditure above 10,000 Namibian dollars were excluded as extreme values. Households reporting zero income or expenditure were also excluded.

2. Statistical significance of the coefficients are indicated by * for 5 percent and ** for 1 percent.

23
controls for firm size, location, and age; and the excluded category includes formal and legal firms; see Levy and López-Calva (2016) for further details.

With one exception, in all years, formal and legal firms have higher revenue productivity than all other firms implying that, on average, resources are more valuable in these firms. Informal legal firms, which table 1 shows are the majority, are always the least productive of all, and productivity differences between these firms and illegal informal ones are significant.

The fact that informal firms, legal or illegal, have systematically lower revenue productivity than formal ones indicates that the effect of distortions in Mexico is to allocate too many resources to informal firms. And the fact that this result is observed in all periods considered shows that distortions operate systematically in the same direction.

**Table 3. Productivity Differences by Firm Type, 1998–2013**

_<u>(percent, relative to formal legal firms)</u>_

||**_1998_**<br>**_TFPR_**|**_2003_**<br>**_TFPR_**|**_2008_**<br>**_TFPR_**|**_2013_**<br>**_TFPR_**|
|---|---|---|---|---|
|**Mixed**|0.001|−0.029|−0.032|−0.177|
||(0.002)|(0.002)|(0.002)|(0.002)|
|**Legal and informal**|−0.414|−0.360|−0.401|−0.633|
||(0.002)|(0.002)|(0.002)|(0.001)|
|**Illegal and informal**|−0.120|0.023|−0.162|−0.184|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Observations|2,368,471|2,537,348|2,655,551|3,371,272|
|R-squared|0.091|0.056|0.072|0.072|

_Source:_ Author’s computations based on Economic Census data _. Note:_ Standard errors are in brackets. All coefficients are significant at the 99 percent level.

# **2.4. Formal and Informal Firms Co-exist in the Same Sectors**

At times the reference to formal and informal sectors suggests that firms in each sector produce very different goods. This is not the case in Mexico. Formal and informal firms co-exist in the same six-digit sector, and in fact the overlap between firms in the same sector has been increasing, in parallel to the increase in misallocation. In 1998 informal firms (legal and illegal) were the 11