rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2:** Determinants of the World Bank’s Influence at the Design Stage of the Policymaking Process

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|lnaid<br>~~d~~pl<br>~~d~~isb<br>~~p~~c<br>~~0~~<br>~~m~~<br>~~1~~p|-0.02<br>(0.03)|||||0.01<br>(0.02)|-0.03<br>(0.02)|-0.04<br>(0.03)|||
|lnaid<br>~~i~~nv<br>~~d~~isb<br>~~p~~c<br>~~0~~<br>~~m~~<br>~~1~~p||-0.01||||0.01|-0.05|0.04|-0.03|-0.02|
|||(0.04)||||(0.01)|(0.04)|(0.05)|(0.04)|(0.04)|
|n<br>~~e~~sw<br>~~m~~<br>~~1~~p|||0.17*|||0.09**|0.12**|0.18**|0.18**|0.20**|
||||(0.09)|||(0.05)|(0.05)|(0.09)|(0.09)|(0.09)|
|dpl<br>~~p~~roj<br>~~n~~<br>~~m~~<br>~~1~~p||||0.37*|||||0.39**||
|||||(0.19)|||||(0.19)||
|pa<br>~~m~~<br>~~1~~p|||||0.03|||||0.04|
||||||(0.03)|||||(0.03)|
|lngdp<br>~~p~~c<br>~~1~~st|-22.79***|-22.63***|-19.09***|-19.27***|-19.58***|1.16|-5.79|-28.17***|-22.77***|-22.98***|
||(7.34)|(7.53)|(6.98)|(7.08)|(7.02)|(0.84)|(4.66)|(7.83)|(7.61)|(7.56)|
|lngdp<br>~~p~~c<br>~~1~~st<br>~~2~~|1.52***|1.48***|1.27***|1.28***|1.30***|-0.10*|0.35|1.87***|1.50***|1.51***|
||(0.48)|(0.49)|(0.46)|(0.46)|(0.46)|(0.06)|(0.31)|(0.51)|(0.50)|(0.49)|
|lnpop<br>~~1~~st|-0.06|0.13|-0.31|-0.86|-0.54|-0.16***|-0.76|1.18|0.22|0.59|
||(1.92)|(2.03)|(1.91)|(1.90)|(1.88)|(0.04)|(1.11)|(2.07)|(2.09)|(2.08)|
|aid<br>~~g~~ni<br>~~1~~st|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|
|polity2<br>~~1~~st|-0.07*|-0.06|-0.07*|-0.07*|-0.06|-0.00|-0.04|-0.08**|-0.08*|-0.08*|
||(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|(0.01)|(0.03)|(0.04)|(0.04)|(0.04)|
|left<br>~~1~~st|-0.09|-0.06|-0.17|-0.03|-0.10|-0.02|-0.07|-0.11|0.08|0.04|
||(0.35)|(0.37)|(0.35)|(0.34)|(0.35)|(0.10)|(0.22)|(0.38)|(0.37)|(0.38)|
|Respondent-level controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Policy Cluster Pair FEs|Yes|Yes|Yes|Yes|Yes|No|No|Yes|Yes|Yes|
|Country FEs|No|No|No|No|No|No|Yes|No|No|No|
|Policy Cluster FEs|No|No|No|No|No|Yes|No|No|No|No|
|Observations|670|675|702|702|702|656|769|656|675|675|
|BIC|3322|3365|3500|3507|3485|2617|3341|3249|3345|3336|
|AIC|2209|2245|2334|2337|2333|2352|2690|2155|2230|2230|

Notes: All regressions use inverse-probability weights to account for variation in unit non-response rate. * _p <_ 0 _._ 1; ** _p <_ 0 _._ 05; *** _p <_ 0 _._ 01.

22
**Figure 4: Distribution of activities in 2000 by 1997 high school students**

<!-- Start of picture text --> 100%<br>16%<br>80% 37%<br>43%<br>60% In college<br>Not working<br>38%<br>40% Working<br>20% 41%<br>26%<br>0%<br>SMA SMK<br><!-- End of picture text -->

# **IV. Employment and earnings**

# **Comparing estimates with and without data censoring correction for college entry**

In this section, the effect of participation in vocational education on labor market outcomes in terms of levels of employment and labor earnings is estimated. The primary focus is the coefficient on the SMK dummy variable, holding constant the other explanatory variables relating to various background and personal characteristics. A positive and significant coefficient on the SMK dummy variable would indicate that participants in the vocational track at the senior secondary level are on average more likely to find employment and achieve greater earnings than the SMA attendants.

The full set of variables used in the estimation is as follows: 10
# 3.1 Data

The dataset contains both cross-sectional and time-series variation in MTEF presence. The sample consists of 181 countries over the period 1990-2008. The country sample re‡ects data availablility on MTEF status. The period sample re‡ects data availability on public ...nances. Here we brie‡y discuss the key variables. Section A.3 of the Appendix contains the complete list of variables together with their data sources.

The construction of the MTEF indicators relied upon an extensive data collection e¤ort as no single type of document su¢ ciently describes the existing institutional arrangements for all countries or even individual countries. Thus, the data were compiled from a large number of sources, including IMF Article IV country reports, IMF Reports on the Observance of Standards and Codes (ROSC), ...scal transparency modules, World Bank Public Expenditure Reviews (PERs), World Bank Country Financial Accountability Assessments (CFAAs), OECD documents, donor case studies, and country websites. Additionally, World Bank and IMF public sector specialists supplemented the above information with technical details.<sup>19</sup> We measure ...scal discipline, an indicator of macro ...scal performance, by the central government’s overall balance. Although the literature suggests alternative indicators, e.g., primary balance and debt, data availability limited the choice to the overall balance. Moreover, by including government borrowing, the overall balance is a good indicator of the state of public ...nances.<sup>20</sup> Allocative e¢ ciency does not have a universally accepted de...nition. Potential proxies for allocative e¢ ciency are budget composition volatility and volatility of core spending (health and education). Since volatility in these sectors jeopardizes long-term objectives, health care and public education spending should be largely una¤ected by short-term ‡uctuations in GDP. In other words, allocative e¢ ciency implies that spending in core sectors where needs are fairly constant does not behave in a volatile manner. Given data constraints and the requirement that the public good category should be reasonably comparable across countries, we choose to work with the volatility of per-capita health spending, in PPP$. We de...ne volatility of a time series yi;t for country i as the absolute yearly growth rate of the

> 19For the purposes of this paper, we refrain from making judgments to distinguish between an MTEF present in the law (de jure) and a well-functioning MTEF (de facto). Such a distinction would introduce a signi...cant amount of subjectivity into the analysis.

> 20It could be argued that the overall balance does not account for the e¤ect of in‡ation on interest payments and that interest payments are a function of the accumulated debt and not the present ...scal stance.

11
income either through deposit mobilization or directly through fees or through cross-selling of other services. Fortunately, that study also shows that the cost of handling transactions via agents is about 25 percent lower than through branches.<sup>11</sup> Deposit mobilization has been influential in decisions about the design and implementation of agent networks as it can be a way for banks to make agent networks viable by lowering their cost of funds.<sup>12</sup> Agent networks can also enable microfinance institutions to benefit from savings mobilization because regular savers are more predictable, which should reduce the volatility of their portfolios. And maintaining deposits in the system brings opportunities to cross-sell products such as insurance and loans.

Regulators often allow agents to set their own fees or do not provide the necessary oversight to prevent agents from doing so. While there is limited evidence for banking agents, in the early days of mobile money, agents in the Philippines and Tanzania were allowed to set their own fees to foster competition. More recently, even mobile money agents who are formally given a tariff structure often informally set their own prices for commissions, as fees are paid in cash and not recorded in the system (Davidson and Leishman 2012). In South Africa and Uganda, network operators set different tariffs for different types of outlets by design (Davidson and Leishman, 2012) while in Nigeria, an estimated 45 percent of mobile money agents decide and set customer prices, ignoring those set by FSPs.

There is ample evidence from a variety of countries that mobile money agents charge extra fees (Annan, 2019; Breza et. al, 2020). In the near-term, extra agent fees may warrant intervention given that they are disproportionately paid by less empowered customers, including women, low-literacy citizens, and individuals living in remote areas (Brown et al. 2019). Over the long term, however, markets may simply mature out of this problem as consumers learn and agents self-regulate in response to more sophisticated consumers. An experiment with garment workers in urban Bangladesh demonstrated that workers who received digitized wage payments were more likely to personally execute transfers through their own phones, avoiding the use of agents altogether, which led agents to change behavior (Breza, Kanz, and Klapper 2020). Notably, this learning by factory workers had spillover effects on non-factory workers as agents

> 11 <u>https://www.cgap.org/blog/6-ways-microfinance-institutions-can-adapt-digital-age</u>

> 12 IFC, 2018: Aligning Expectations: The Business Case for Digital Financial Services.

13
To make it simple, the research created two categories: “private firms” that were privately owned/controlled; and “government firms” that were government owned/controlled, based on who were the majority owners. Further, “collectively owned enterprises”<sup>9</sup> and “Chinese-foreign joint ventures”<sup>10</sup> were lumped into “private firms;” and all SOEs at both central and provincial levels, as well as projects sponsored by all “public institutions,”<sup>11</sup> were grouped together as “government firms.” The following findings emerged from the data thus compiled:

- **_Private OFDI in Africa has hiked in the last decade, both proportionally and in absolute number. By the end of 2011, the private sector registered 923 projects in Africa, representing 55% of all Chinese OFDI projects in the continent._**

No private OFDI project in Africa was recorded in the MOC system prior to 2000. In 2002, only 2 of the 18 MOC registered OFDI projects in Africa were “private.” Private investors became aggressive in Africa in 2005, in which the number of projects jumped to 52, increasing their total share of China’s OFDI in Africa to a notable 35%. Since then, the number has continued to climb, reaching a total of 923 by the end of 2011. This represents a hefty 55 percent of the total Chinese investment projects in Africa versus 45% owned or controlled by state-owned Chinese firms.

<!-- Start of picture text --> Figure 1. The role of private Chinese investment in Africa, 2000-<br>2000 2011<br>1500<br>1000<br>500<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Total number of Chinese firms Total number of Chinese private firms<br>Source: Calculated based on the total registry data from MOC<br> of Projects<br>Number<br><!-- End of picture text -->

> 9 In China, the ownership structure of “collectively owned enterprises” can be complex, and include those owned by rural villages, urban communities, and sometimes special civic groups.  Although sometimes involving grassroots government interference, most of the “collectively owned businesses” function like private shareholding companies, responsible for their own risk-taking and profit-making, with the ultimate property rights stayed with the members of collectives. “Collectively owned firms” involve only a very small number in the database.

> 10 Chinese-foreign joint ventures” also involve insignificant numbers.

> 11 “Public institutions” (“事业单位” shi-ye-dan-wei) functions similarly with SOEs.  They are of a small number too.

6
|**Table**<br>H0|**9.**Test of validity and w<br>Statistic|eakness of instrument<br>MENA|s<sup>+</sup><br>OECD|
|---|---|---|---|
|Instruments are valid|OIDR<br>p-value|2.82<br>0.42|0.54<br>0.46|
|Instruments are weak|CDME<br>Critical value|9.60<br>4.32|27.27<br>5.44|

+ OIDR: Overidentifying restrictions test statistic (Anderson and Rubin, 1950); CDME: Cragg and Donald (1993) minimum eigenvalue statistic (Stock and Yogo, 2005); Critical value is the highest critical value relating to the LIML size of nominal 5% Wald test (Stock and Yogo, 2005).

# **_PSM robustness check_**

Let us now go one step further in the investigation of the effects of Provision and Protection on Participation by performing a propensity score matching (PSM) analysis (Rosenbaum and Rubin, 1983; Abadie and Imbens, 2006, 2011, 2016; Dehejia and Wahba, 2002, Imbens and Wooldridge, 2009), which is known to be an alternative approach to IV for handling endogeneity. PSM is also known as efficient in solving self-selection problems.<sup>46</sup> PSM allows one to analyze the impact of a treatment on an outcome by reconstructing the original dataset within the framework of a quasi-experiment. Indeed, two groups of individuals are defined, namely, a treated group and a control group. Then, a matched sample is constructed based on "propensity scores," that is, the probabilities that the individuals receive the treatment given a set of observed covariates. As stressed by Rosenbaum and Rubin (1983), propensity scores are balancing scores in the sense that they allow both treated and control groups to exhibit on average no significant differences in their characteristics. These characteristics are materialized by a set of explanatory variables used in the estimation of the propensity scores. In our case, the outcome variable is Participation ( ). We define two treatment variables, that is, one for Provision and another for Protection. The Provision ( ) treatment PP3 variable named is equal to 1 if and 0 otherwise. Similarly, for PP2 Protection ( ) the treatment variable, namely , takes value 1 if and PPrrPPpp_ddiim dd_1 PP2 > 0.5 0 otherwise. The treatments are and , respectively. We are interested in the PP1 PPrrPPffppmm_ddiim dd PP1 > 0.5 impact of each of these treatments on Participation. For Provision, the treated group includes PP2 > 0.5 PP1 > 0.5 the countries for which and the control group contains countries that exhibit PP2 > 0.5 PP2 ≤

> 46 For a discussion on the advantages of PSM over regression, see Grier and Grier (2021) and Ichino et al. (2008).

27
**Table 2**

**Maize Prices during Export Bans**

||**--------------- Exp**<br>|**ort ban’s duration --**<br>|**-------------**<br>|**------- Price (Ts**<br>|**h/kg, CPI-deflat**<br>|**ed, 2010) -------**<br>|
|---|---|---|---|---|---|---|
||**First**<br>**month**|**Last**<br>**month**|**Duration**<br>**(months)**|**First**<br>**month**|**Last**<br>**month**|**Percent**<br>**change **|
|_First_|January 2004|December 2005|24|457|302|-34%|
|_Second_|March 2006|December 2006|10|423|200|-53%|
|_Third_|January 2008|April 2008|4|445|442|-1%|
|_Fourth_|January 2009|September 2010|21|457|296|-35%|
|_Fifth_|July 2011|December 2011|6|447|373|-17%|
|_Average _|||13|446|323|-28%|

**Notes:** Prices refer to the Dar es Salaam market. Export bans were in effect for 65 months (out of 144 months in the sample).

25
because data on the innovation module is available only for this category. Besides, measuring firm innovation in the services sector is intrinsically different from the innovation process in manufacturing (Ettlie and Rosenthal 2011). The sample does not include small jurisdictions that may specialize in a limited number of industries. Our final sample includes 104 countries, of which 12 are high-income countries, 33 are upper-middle countries, 36 are middle-income countries, and the remaining 23 are lowincome countries. For 52 economies we have data for 1 survey round; two survey rounds are available for 40 additional countries; and 3 survey rounds for the remaining 12 countries (see Appendix A1 for the list of countries and number of survey waves).

# _2.2 Measuring firm innovation_

Given the available data, we adopt a broad definition of innovation as the accumulation of knowledge and implementation of new ideas. Specifically, innovation is defined as the implementation of a new or significantly improved product (good or service) or process, a new marketing method, or a new organizational method in business practices, workplace organization, or external relations (OECD 2015). We consider innovation defined both as “invention” or “discovery”, i.e., those developments to push the technological frontier, and as “diffusion” or “adoption” of existing technologies and practices that lead firms to novel ways of producing or acting (in short adoption). Invention is proxied through expenditures in research and development.

We compute 5 variables to measure the extent of firm innovation and different typologies. as being innovative. Two variables capture the overall degree of a firm’s innovativeness (i.e., the intensive margin). This first variable ( _innovation score_ ) measures the intensity of innovation without considering what type of innovation is pursued. It is constructed by summing up 4 questions from the World Bank Enterprise Surveys (WB ES), weighted by the number of questions answered by each firm.<sup>3</sup> It ranges between 0 and 1, with the highest values indicating higher innovation. The second variable ( _innovation dummy_ ) measures whether a firm innovates or not. This innovation dummy takes a value of 1 if a firm innovates in any of the 4 elements composing the innovation score, 0 otherwise. We employ the innovation score in OLS regressions to estimate the association between a firm financial structure and the intensity of firm innovation (see equation 2 below). The innovation dummy is instead used to obtain estimates of a firm

> 3 In detail, if a question is left unanswered, the answer is not considered for the computation of the innovation score. In robustness tests we drop firms for which answers are not available for all the questions composing the innovation score. The results reported in section 3 remain qualitatively the same.

6
**Figure 2.  Average annual direct losses from natural disasters as a share of GDP**

<!-- Start of picture text -->
1.8%<br>1.6%<br>Low Income Country<br>1.4%<br>Middle Income Country<br>1.2%<br>High Income Country<br>1.0%<br>0.8%<br>0.6%<br>0.4%<br>0.2%<br>0.0%<br>1980-85 1986-90 1990-95 1995-2000 2001-2006<br><!-- End of picture text -->

Source: Cummins and Mahul (2009).

Finally, disasters have a particularly disruptive effect on the fiscal balances of developing countries.<sup>3</sup> The US$83 billion budget appropriation voted by Congress after Hurricane Katrina hardly registered on the scale of the United States budget. Most developing country governments, particularly in smaller and less advanced economies, cannot afford such largesse. Further compounding the recurrent lack of resources, developing countries’ governments often lack the appropriate mechanisms to effectively mobilize resources in response to an emergency.

# Financial Protection and Disaster Risk Management

It is often argued that financial protection strategies treat the symptoms but not the cause of disasters. Good strategies can help governments cope with the financial impact of calamities, but do little to shelter populations and assets from the destruction of cyclones and earthquakes. As such, it is important to underscore that financial protection is only one component of a comprehensive disaster risk management strategy. Financial protection will help governments mobilize resources in the immediate aftermath of a disaster, while buffering the long‐term fiscal impact of disasters. However, a comprehensive risk management strategy should cover many other dimensions, including programs to better identify risks, reduce the impact of adverse events and strengthen emergency services (see Figure 3).

> 3 Very little literature exists on the fiscal impact of natural disasters, that is, the effects of natural disasters on the (national and/or local) government budget.  The main difficulty in analyzing these fiscal impacts results from the fact that budget processes in most countries do not record emergency expenditures in specific budget lines. Adding to this complexity, emergency relief and early recovery activities are generally financed out of the recurrent budget in the year of the disaster, while reconstruction activities are financed out of the capital expenditures of subsequent fiscal years.

5
Table 1: Summary statistics

||1966|1970|1980|1985|1990|2000|2006|
|---|---|---|---|---|---|---|---|
|Overall mean|37,095|39,871|38,455|38,536|40,682|49,330|49,582|
|Top 5%: mean|147,774|153,513|151,149|156,166|200,505|297,669|310,457|
|Top 5%: threshold|88,910|96,650|98,188|100,965|108,510|135,043|136,985|
|Top 1%: mean|288,018|286,433|278,975|293,308|453,733|755,730|799,571|
|Top 1%: threshold|170,001|174,502|166,212|164,912|205,199|300,179|314,160|
|Top 0.5%: mean|379,552|373,430|368,796|402,933|666,268|1,152,718|1,217,897|
|Top 0.5%: threshold|234,381|233,496|218,958|211,241|294,606|444,401|479,634|
|Number observations|73,606|73,949|122,446|68,743|67,727|114,160|114,662|

Note: All mean and threshold incomes refer to sum of income components, and are expressed in 2006 USD. Excluding capital gains and negative observations. CPI data are taken from Saez (2013).

similar (Larrimore et al., 2016). Third, we report results for two alternative ways of splitting income from self-employment and closely-held businesses: (a) We show that our results are robust to including 54% of S-corporation prots with labor income instead of allocating them entirely to capital income.<sup>9</sup> As Figure 1 shows, this pass-through income has become increasingly important at the top (also see Cooper et al., 2016).<sup>10</sup> Using evidence from rm owner deaths, Smith et al. (2017) estimate that 54% of S-corporation prots at the top represent labor income. (b) Our conclusions are also robust to moving in the opposite direction and allocating a greater share of self-employment income to capital. Following Saez and Zucman (2016), we include all of self-employment income (as well as S-corporation prots) with capital, instead of only one-third in the baseline.

# 2 Decomposition by factor incomes

We begin the analysis with a decomposition of top income shares by factor incomes. This is a formal derivation and the rst empirical application of the decomposition by Atkinson (2007), who builds on Meade (1964). It is closely related to factor income decompositions of

> 9Wages that the owner-manager of the S-corporation pays herself would have already been included in wage income.

> 10S-corporation ling status became more attractive following TRA86, which reduced the top personal tax rate below the corporate tax rate (Slemrod, 1996; Auerbach and Slemrod, 1997). Since then, the incentives to le as an S-corporation or a C-corporation have remained similar (Smith et al., 2017).

7
Guatemala and El Salvador) due to their dependence of manufacturing exports that compete with Chinese exports in the U.S. market. Together, these countries accounted for 86 percent of Latin Americas FCS in 2003.

# **3. Relative stocks in 2003**

In this section we analyze the FCS levels in Latin American countries relative to those in China, China and Hong Kong (China) together, and India. Table 1 reports these ratios for the world total, OECD, U.S. and U.S. manufacturing FCS.

The first column shows how important China has become as a destination for FDI: by 2003 total FCS in China were bigger than in any Latin American country. Brazil and Mexico, the countries with the largest FCS of the region, had only 58 and 73 percent of China’s stock respectively. At the same time, Argentina, Chile, and Venezuela reported less than a quarter of China’s FCS.

Nonetheless, OECD FCS in China were significantly smaller than in the major Latin American countries. In particular, Mexico had almost twice as much capital from the OECD than China. This disparity is more noticeable in disaggregated data from the U.S. The third column of table 1 shows that the relative stocks from the U.S. were bigger than both the aggregate and the OECD relative stocks. This reflects the fact that the U.S. is a relatively more important source of FDI for Latin American countries than for China. In 2003, U.S. stocks in China were only $11.5 billion, quite small compared to the $59 billion in Mexico or the $ 37 billion in Brazil. These data show that when it comes to OECD and U.S. stocks, China is still far from being a major host of FCS.

7