rafmacalaba/datause-encoder-data huggingface dataset:
and Vijverberg, 1989). After more than 20 years from the contribution of Van der Gaag, Stelcner and Vijverberg, econometric analysis can take advantage of the large amount of household data currently available for Peru. We compare public sector and formal private sector wages by using 2009 data from the Encuesta Nacional de Hogares (ENAHO). The ENAHO is a rich source of information. Using ENAHO data, the analysis of public-private sector wage differentials can be based on more than 16,000 observations. The large amount of information available allowed controlling for variables which might affect the wage levels, such as the level of education, the working experience, the gender, the marital status and the location (urban/rural) of the employees included in the sample.

Finally, this paper investigates how much of the difference between public and formal private sector wages is actually driven by the specific characteristics of the individuals employed in each sector. The evaluation of the remaining part, i.e. the “unexplained difference” of the wage differential will provide valuable information to determine whether public sector employees in Peru earn a wage premium which is independent from their specific characteristics and skills. To breakdown the wage differentials into an explained and an unexplained part, the OaxacaBlinder approach is used.

The topic of wage differential in Peru has been already considered in the past. In the late 1980s, Van der Gaag, Stelcner and Vijverberg (1989) used a switching regression model to show that public sector wages offered in Peru are well below those in the private sector. The rationale behind public sector penalties might be that lower wages in the public sector compensate a less competitive working environment and a level of job security which is obviously higher in the public sector. More recently, empirical contributions which based their estimations on 1990s household survey data (Panizza and Quiang, 1999; Panizza, 2001) found evidence of a public sector premium in Latin America, particularly for female workers. Empirical evidence of a public sector premium has been found also outside Latin America. Heitmueller (2006) found evidence of a public sector premium for women in Scotland by using a switching regression model. Gibson (2009) found a public sector premium in Australia, Canada, Great Britain and United States by using propensity score matching. According to the theoretical literature, public sector premia might be the result of different factors, including: (i) a higher level of unionization in the public sector (Holmlund, 1993); (ii) softer budget constraint faced by the public sector (Gregory and Borland, 1999); (iii) tighter no-shirking constraint in the public sector, given the higher firing costs (Panizza, 2001).

The structure of the note is the following: after this introduction, section 2 describes the data and the variables used; section 3 explains the methodology adopted; section 4 shows the results of the estimations; finally section 5 concludes.

3
- 16-

**Table 10** Results for Real Consumption

|Dependent Variable|Private Consum|ption|Public Con|sumption|
|---|---|---|---|---|
||Without M2|With M2|Without M|2<br>With M2|
|Constant|4211|2244|334|-23.10|
||(7.81**)|(3.47)|(2.11**)|(-0.19)|
|Real GDP|0.49|0.49|0.06|0.04|
||(24.96**)|(22.42**)|(7.68**)|(3.64**)|
|Real Interest Rate|-1870|-1321|-703|-2188|
||(-2.23**)|(-1.72*)|(-1.58)|_(-5.10**)_|
|Real M2||2.26E-09||1.08E-09|
|||(2.67**)||(3.82**)|
|Capital Account|0.13|0.13|0.004|-0.02|
||(2.96**)|(3.10**)|(0.20)||
|AR(I)|0.69|0.51|0.94)||
||(4.76**)|(3.12**)|-0.62|-0.72|
||||(-5.03 **)<br>5.92**)|(|
||0.97||0.97|0.51|
|0.65|||||
|Durbin-Watson Statistic|2.26|2.|05|2.08|
|1.80|||||
|F-Statistic<br>12.08|266|2|37|8.69|
|p-value of F-Statistic<br>0.00|0.00|0.0|0|0.00|

Note: T- Statistics in parentheses. * :significant at 10% level, **:significant at 5% level
# **5 Forced Displacement and Development in Weak States**

Table 1 presents estimates of the effect of forced displacement on economic development across three specifications and three outcomes. Panel A reports OLS estimates of equation (2), Panel B reports 2SLS estimates of equations (2) and (3), and Panel C reports reduced-form estimates obtained by regressing each outcome on Imputed Outflows _mt_ directly. The dependent variables are night-light density in columns (1) and (2), Log(night light) in columns (3) and (4), and the spatial Gini coefficient in columns (5) and (6). Odd-numbered columns exclude baseline controls and even-numbered columns include them.

The estimates consistently show that forced displacement reduced economic activity in origin municipalities. The 2SLS estimates are larger in magnitude than the OLS estimates. This aligns with the intuition that more individuals left municipalities with higher economic growth as they were the ones with greater means to leave, relative to people from other municipalities. The Kleibergen-Paap Wald F-statistic exceeds 600 in all specifications, confirming a strong first stage. Our preferred specification is the 2SLS estimate for Log(night light) with controls, reported in column (4) of Panel B. It indicates that a one-percentagepoint increase in forced displacement outflows as a share of the 1990 municipal population is associated with a 7.5 percent reduction in night-light density. Since night-light density is an established proxy for GDP, with an estimated elasticity of 0.28 (Henderson et al., 2012), this corresponds to a GDP contraction of 2.1 percent for every one-percentage-point increment in forced displacement. To gauge the aggregate magnitude, as noted above, about 7.7 million Venezuelans have left the country since 2013; approximately 2.8 million of them are registered as living in Colombia (UNHCR 2025). Relative to Venezuela’s 1990 census population of 19.83 million, the last census before Ch ́avez’s election, this represents a 14 percent reduction in population that emigrated from Venezuela to Colombia (= 2.8 / 19.83). A municipality that lost 14 percent of its 1990 population to Colombia would therefore have experienced a GDP contraction of approximately 29.4 percent (= 2.1 _×_ 14).

27
|**Table 27 Regression of the L**|**ogarithm of**|**Purchased F**|**ood****_(t_**-_stati_|_stics in paren_|_theses)_||
|---|---|---|---|---|---|---|
||_April -_|_Mav_|_June -_|_August_|_September_|_- November_|
|_Dummy variables_|_<100,000_|_2100,000_|_<100,000_|_2100,000_|_<100,000_|_>100,000_|
|_Two week/normative average_|||||||
|Abidjan|0.100<br>(0.282)|0.056<br>(-1.108)|-0.029<br>(-1.113)|-0.002<br>(-0.049)|0.030<br>(0.199)|-0.032<br>(-0.662)|
|Other Cities|-0.111<br>(-.500)|-0.024<br>(-0.394)|0.030<br>(0.199)|-0.034<br>(-0.692)|-0.046<br>(-0.298)|-0.047<br>(-0.945)|
|East Forest|-0.303<br>(-1.803)|-0.164<br>(-2.038)|0.216<br>(1.381)|0.072<br>(1.062)|-0.017<br>(-0.016)|0.235<br>(3.639)|
|WestForest|-0.088<br>(-0.314)|-0.078<br>(0.854)|0.639<br>(2.883)|0.044<br>(0.563)|0.151<br>(0.664)|0.008<br>(0.094)|
|Savannah|0.152|0.149|0.383|0.227|-0.230|-0.299|
||(0.916)|(1.890)|(2.513)|(3.518)|(-1.535)|(4.369)|
|Coffee/cocoa farner|0.142<br>(0.763)|0.088<br>(1.145)|-0.343<br>(-2.196)|-0.211<br>(-3.318)|-0.371<br>(-2.294)|-0.348<br>(-5.491)|
|Cotton farmer|-0.495<br>(-2.306)|-0.483<br>(-3.581)|-0.909<br>(4.748)|-0.295<br>(-3.080)|-0.013<br>(-0.061)|0.000<br>(0.002)|
|_Two week estimate of purchased_|_food_||||||
|Abidjan|0.245<br>(0.608)|-0.052<br>(-0.871)|.0210<br>(0.709)|0.014<br>(0.264)|-0.077<br>(-0.306)|-0.034<br>(-0.587)|
|Other Cities|0.077<br>(0.306|-0.028<br>(-0.387)|-0.011<br>(-0.007)|-0.072<br>(-1.260)|-0.064<br>(-0.359)|-0.028<br>(0.488)|
|East Forest|-0.252<br>(-1.321)|-0.133<br>(-1.404)|0.057<br>(0.319)|0.073<br>(0.916)|0.094<br>(-0.547)|0.229<br>(3.012)|
|West Forest|-0.340|-0.083|0.393|0.062|-0.036|-0.072|
||(-1.063)|(-0.772)|(1.556)|(0.679)|(-0.139)|(-0.707)|
|Savannah|0.299|-0.262|0.330|0.263|-0.214|-0.378|
||(1.530)|(2.837)|(1.893)|(3.463)|(-1.240)|(4.709)|
|Coffee/cocoa farmer|0.101|0.060|-0.249|-0.215|-0.320|-0.358|
||(0.477)|(0.672)|(-1.399)|(2.871)|(-1.717)|(4.815)|
|Cotton farmer|-0.817|-0.451|-0.919|-.0387|-0.001|-0.010|
||(3.340)|(-2.850)|(-4.208)|(-3.435)|(-0.003)|(-0.063)|

31
Substituting (8) into equation (2), the determinants of globalization can now be derived following estimation of parameters underlying the production function under different scenarios—advanced economies and low-income countries connected to the global market via exports of natural resources and primary commodities by the latter and imports of manufactured goods from the former. Another key attribute of low-income countries is rapid depreciation of the capital stock and the abysmal quality of scientific and technological infrastructures. In contrast, advanced economies which are operating near or on the global production possibility frontiers are more diversified with a growing scale of manufacturing industries employing an increasingly large share of skilled labor as part of inter-sectoral reallocation.

# **IV. Empirical Analysis and Policy Implications for Sub-Saharan African Countries**

In its current functional form, the globalization function is highly nonlinear, owing to the structure of the production function. In order to facilitate estimation of parameters, a number of assumptions underlying the theoretical model are relaxed. In particular, a reduced form of the model emphasizes high-tech exports and economic diversification in the determinants of globalization. Diversification is primarily captured by the scope of manufactured goods in countries’ output and exports, which are given larger weights in the estimation of the diversification variable.<sup>27</sup> In the absence of a reliably long and consistent series on reallocation of labor across sectors—a plausible index of sectoral diversification—this variable is not included in the empirical model.<sup>28</sup> In addition to nonlinearity, this relaxation in the empirical specification is also dictated by constraints in data availability and unbalanced panel design. Unlike advanced economies, the data on R&D is not always available in most developing countries. This framework combines simulation and instrumented variables from OECD countries to derive proxies for a number of variables underlying production in Sub-Saharan Africa. For instance, the population of  the frontier technology when (  _it_ )1 ; otherwise it produces inefficiently with the degree of inefficiency measured by the size of  _it_ . 27 The diversification index is a variation of Berthélemy and Soderling (2001) who use the composition of exports to OECD countries as a proxy for economic diversification. Although, this measure does not take into account the diversification of nontradables, it is likely that its reliability will be less affected, especially given the extremely high concentration of active labor force in the agricultural sector.

> 28 In practice, the implications of not including this variable in the model may be limited for most countries in Sub-Saharan Africa. In the absence of sectoral diversification, the active labor force continues to be overwhelmingly concentrated in the primary sector.

15
**Table A1. Hedonistic regression of house rental value**

||**Coef.**|**t-stat**|
|---|---|---|
|**Area of dwelling**|||
|Log(sq.ft of the dwelling)|0.179|(3.08)**|
|Log(sq.ft of the plot)|-0.093|(1.91)|
|Kitchen garden (yes=1)|-0.202|(2.72)**|
|**Number of rooms and room composition**|||
|Log(number of rooms)|0.553|(6.37)**|
|Share of Kitchen|-1.467|(0.69)|
|Share of toilet/bathroom|-2.619|(1.21)|
|Share of bedrooms|-2.113|(1.00)|
|Share of living/dinning room]|-1.517|(0.72)|
|Share of office|-1.185|(0.55)|
|Share of mixed use room|-2.256|(1.07)|
|Share of other rooms|-2.358|(1.11)|
|**Construction material of outside wall**|||
|Mud Bricks/stone (yes=1)|-0.197|(1.66)|
|Wood/branches (yes=1)|-0.369|(2.36)*|
|Other (yes=1)|-1.455|(7.90)**|
|**Floor material**|||
|Wood, Stone,Cement/tile or other (yes=1)|0.461|(3.66)**|
|**Roof material**|||
|Galvanized Iron (yes=1)|0.823|(6.75)**|
|Concrete, Cemnet(yes=1)|0.882|(4.90)**|
|Tiles/slate(yes=1)|0.44|(4.79)**|
|**Characteristics of windows**|||
|Shutters (yes=1)|0.379|(4.43)**|
|Screen/glass(yes=1)|0.496|(2.64)**|
|Other (yes=1)|-0.602|(2.32)*|
|**Drinking water source**|||
|Covered Well/Hand Pump|-0.25|(1.99)*|
|Open Well|-0.309|(1.80)|
|Other (yes=1)|-0.474|(3.27)**|
|**Amenities**|||
|Sanitary System (yes=1)|0.115|(0.88)|
|Garbage Disposal (yes=1)|0.121|(0.78)|
|Non-Flush/Communal Toilet (yes=1)|-0.48|(2.90)**|
|No toilet (yes=1)|-0.596|(3.47)**|
|Electric Light (yes=1)|-0.003|(0.08)|
|**District dummies**|Yes||

The dependent variable is the log of the rental value of the dwelling. Rental value is either actual or estimated in case of owner occupation. Based on NLSS 1995/96.
9 Table 1: MONETARY SECTOR BALANCE SHEET <!-- Start of picture text --> Assets Liabilities<br>Rest of the World<br>* * 1* * *<br>eLv + eBw eFh +eR<br>Government<br>- I ~~~~~~~~Bb + Bh + eBw<br>Monetary Survey<br>e3 + B + L Hh + Hf + Net Worth a/<br>Private Sector<br>Firms Hf eL + L<br>* ~ ~~~ b<br>Households Hh + h + Bh<br><!-- End of picture text --> a/ Changes in Central Bank Net Worth are assumed to absorb chanIes in the home-currency value of foreign exchange reserves given by R Ae. Thus the latter do not affect the money supply.
Policy Research Working Paper 9968

# **Abstract**

Every year, disasters account for billions of dollars in crop production losses in low- and middle-income countries and particularly threaten the lives and livelihoods of those depending on agriculture. With climate change accelerating, this burden will likely increase in the future and accurate, micro-level measurement of crop losses will be important to understand disasters’ implications for livelihoods, prevent humanitarian crises, and build future resilience. Survey data present a large, rich, highly disaggregated information source that is trialed and tested to the specifications of smallholder agriculture common in low- and middle-income countries. However, to tap into this potential, a thorough understanding of and robust methodology for measuring disaster crop production losses in survey microdata is essential. This paper exploits plot-level panel data for almost 20,000 plots on 8,000 farms in three Sub-Saharan African countries with information on harvest, input use, and different proxies of losses; household and community-level data; as well data from other sources such as crop cutting and survey experiments, to provide new insights into the reliability of survey-based crop loss estimates and their attribution to disasters. The paper concludes with concrete recommendations for methodology and survey design and identifies key avenues for further research.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at pwollburg@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
represents countries in which the variable takes the lowest (highest) values. Consistent with previous findings, foreign holdings by pension funds are larger in countries where cumulative assets represent a greater share of GDP, where yearly contributions are a higher share of GDP, and in countries with common currency regimes. For example, while foreign holdings account for 21% of total assets in countries with the lowest level of yearly contributions (quartile 1), in countries where contributions are the highest, foreign holdings are on average 37% of the pension portfolio.

**Table 1.** Average of foreign holdings sorted by variables of interest

|||**Foreign Holdi**|**ngs (Average)**||
|---|---|---|---|---|
||**Q1 **|**Q2 **|**Q3 **|**Q4 **|
|Pension Funds’ Assets as % of GDP|17%|29%|26%|31%|
|Contribution as % of GDP|21%|25%|24%|37%|
|Common Currency|15%|||60%|
|Current Account as % of GDP|21%|22%|30%|28%|
|FDI as % of GDP|25%|21%|26%|29%|
|GDP Growth as % of GDP|25%|29%|26%|20%|
|Market Capitalization as % of GDP|18%|19%|24%|24%|
|Stocks Traded (Value) as % of GDP|19%|16%|20%|25%|
|Portfolio equity, net inflows (current US)|25%|27%|22%|27%|
|Bills and Bonds as % of Total Assets|34%|34%|26%|11%|
|Short Term Assets as % of Total Assets|23%|26%|27%|30%|
|Mutual Funds Assets as % of Total Assets|8%|17%|32%|44%|

Source: OECD and WDI. Authors’ calculations.

The results have several potential explanations. On the one hand, it is possible that when pension portfolios become large, as inflows from new contributions drive asset accumulation, they grow too large relative to domestic financial markets; in effect domestic opportunities are insufficient. Large institutional investors often also face disproportional transactions costs, particularly those that operate in illiquid markets. In such cases, in order to mitigate the negative price-impact from portfolio transactions in local securities, fund managers are ‘pushed’ to accommodate inflows towards foreign assets, potentially into more liquid markets. At the same time, it is possible that the positive correlation between the size of contributions and foreign holdings is spurious. For example, if countries with more developed financial systems have a higher savings rate, a larger exposure to foreign assets may arise because of the availability of
relies on the logic underlying a measure of the pro-poorness of economic growth proposed by Essama-Nssah and Lambert (2006).

The outline of the paper is as follows. Section 2 presents the evaluative framework. It explains the computation of changes in individual welfare attributable to the tax reform, and the ranking of social states on the basis of poverty outcomes. Section 3 applies the method to data for Guinea. Concluding remarks are presented in Section 4.

# **_2.  Evaluation Framework_**

According to Sen (1999), any evaluative approach is characterized by its _informational basis_ . This entails a distinction between the information required in passing judgments within the chosen approach and that which has no direct evaluative role. Our approach is essentially welfarist to the extent that it focuses on both _individual_ and _social_ welfare. Policy evaluation therefore requires the identification of impact on individuals as well as the specification of an aggregation rule to translate individual outcomes into social impact.

# **_Individual Outcomes_**

Assessing the impact of policies on individual welfare and poverty status requires an explicit linkage between policy instruments and the determinants of individual living standard. In particular, commodity taxes affect prices at which such commodities are sold. We therefore need an expression of individual (or household) welfare as a function of relevant prices. Under the simple assumption that the consumer has an exogenous budget **x** to spend on a set of **m** commodities at fixed prices, the indirect utility is the maximum level of utility attainable given the outlay and the prevailing prices. This 5
Policy Research Working Paper 8382

# **Abstract**

This paper evaluates the impact of fiscal incentives on firm performance in the Dominican Republic. In recent years, the Dominican government has approved several new corporate tax benefits. While the literature on value-added tax incentives is extensive, the impact of corporate tax incentives is less well studied and is the subject of an ongoing debate. Using firm-level panel data from 2006 to 2015, this analysis uses fixed- and random-effects models to examine the relationship between corporate tax incentives and selected firm-level performance indicators. The results reveal that corporate income tax exemptions positively impact the performance of individual firms in the Dominican Republic, but uneven tax treatment across firms distorts competition in the industrial sector, with negative effects on overall economic productivity.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 gmele@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