rafmacalaba/datause-encoder-data huggingface dataset:
overview of climate-sensitive ODA investments, their exposure to current climate impacts and recent recovery costs are provided in Table 4.

**Figure 1:** Spatial distribution of climate-sensitive ODA investments supported by the five main donors in 2006. (Data source: ODAmoz, 2007; DbIS, 2007).

The European Commission, the main donor in Zambézia, committed major investments in road infrastructure in this province. The Caia bridge and Namacurra road constructions are taking place in districts highly exposed to cyclone impacts with some of them additionally exposed to floods. The World Bank Railways and Ports project is implemented in Maputo, Nampula and Sofala where the majority of districts are similarly highly exposed to cyclones or floods. Even though ODA projects in some sectors, _e.g.,_ agriculture and infrastructure, are sometimes designed taking current climate risks into account, the tremendous climate damages in the early 2000s demonstrate the insufficient adaptation to such extreme events. In total, 61% of the recovery costs following recent climate impacts were recorded in Maputo, Nampula, Inhambane and Sofala 9
|Students’ and peers' informal comments are very important in|-0.320||
|---|---|---|
|faculty evaluation|(0.705)<br>||
|Peer evaluation is very important in the faculty evaluation|-1.463*<br>(0.788)||
|Practical experience is very important when hiring faculty||-3.417<br>(2.234)|
|Almost all or all faculty participated in professional training last year||2.663*<br>(1.467)<br>|
|Faculty are evaluated more than once per year<br>||-4.393*<br>(2.305)|
|_Other Practices_|||
|Update or review admin data more than once per year|-0.274<br>(0.725)||
|Student body, program, and HEI characteristics (PCA scores)|✓|✓|
|Noise controls, country-and field-fixed effects|✓|✓|
|Observations|1,525|1,692|
|Mean of dependent variable|14.03|18.58|
|R-squared|0.085|0.107|
|Adj. R squared|0.0613|0.0890|

# _Source_ : Own estimations using WBSCPS data.

_Notes_ : This table shows coefficients from the (second stage) OLS regressions of dropout rate and extra time to graduation (ETG) on the determinants selected by LASSO in the first stage. The unit of observation is a program. Regressions are weighted using WBSCPS sampling weights. See definitions of outcomes and PCA scores in Appendix 1. Specifications control for PCA scores of characteristics of the student body, program, and HEI as well as for survey noise controls, country fixed effects, and field fixed effects. Standard errors clustered at HEI level are in parenthesis. Significance levels: * p<0.1, ** p< 0.05, *** p<0.01.

84
manufacturing industries have not been explicitly studied, but some insights from the **NEG** literature can be borrowed to conjecture about those effects. FDI liberalization in an industry is expected to bring the entry of new foreign firms, with a direct effect on the concentration or dispersion of manufacturing across regions, but can also affect the location decisions of (new or incumbent) domestic firms, with an indirect effect on the concentration of manufacturing across regions.

Focusing first on the entry of new foreign firms, **NEG** models suggest that if this new FDI enters the country to supply the domestic market, it would tend to locate close to large markets to economize on inter-regional transport costs. **If** those transport costs are low, however, then foreign firms would be indifferent about their location since they could serve domestic customers from anywhere, possibly contributing to the geographic dispersion of the industry. The theoretical model and the evidence in Amiti and Javorcik (2008) show that market access was crucial in determining the location of new FDI inflows across **(Ili1. %..** provinces. For new FDI aimed at exploiting production advantages such as cheap unskilled labor and targeting export markets, the optimal location choice is not clear. On the one hand, a new foreign firm may have an incentive to locate away from main industrial centers where wages are higher **-** leading to industrial dispersion **-** but on the other hand it could prefer to locate near major centers to use a pool of skilled labor or to be near ports or major borders **-** increasing industrial.-1iin i:f1i. Amiti and Javorcik (2008) show that lower production costs also played an important role in attracting new FDI inflows to certain Cilio %..provinces. For new FDI of both types, the degree of usage of domestic versus imported inputs could also affect location choices. **A** new foreign firm relying heavily on domestic **10** linkages would have an interest in locating close to suppliers which may or may not be already located in highly concentrated industrial areas. Amiti and Javorcik (2008) show that supplier access was critical in determining the location choices of new FDI inflows in China.'<sup>9</sup> Focusing next on the location decisions of domestic firms in response to the entry of foreign firms, on the one hand domestic firms would have an incentive to locate away from foreign firms if those are competitors in serving the domestic market as in **NEG** models. But on the other hand, the possibility of **10** linkages with (and expected knowledge spillovers **"See** Ottaviano and Thisse **(2008)** for a survey of the rationales for location of new FDL

**15**
17

**Figure 11:** Investments, Depreciation, Disposals, and Net Investments in the Three Periods

<!-- Start of picture text --> 40,000<br>2000 2005 2010<br>35,000<br>30,000<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>Total I Dep Disp Net I<br>-5,000<br>RM thousand<br><!-- End of picture text --> <!-- Start of picture text --> RM thousand<br><!-- End of picture text --> Source: Department of Statistics Malaysia (DOSM) Note: Investments take into account all firms in the three census periods, including ones that have reported an inconsistent year of starting operations. Total I = total investment during a particular census period; dep = depreciation of assets during the particular census period; disp = assets disposed during a particular census period; net I = total investment – depreciation of assets – disposed assets.

Therefore, initial investment Io is: where I̅ is the average investment of firm i during the three census years; and pyi is the average value-add of firm i at the initial year.

Initial capital stock K0 is calculated as: where d is the depreciation rate, which is assumed to be at 5%. The growth rate of investment, g is calculated to be zero: that is, within the firm, the investment rate is constant across the census periods.

The initial capital stock K0 will be at the period in which a non-zero investment value is first observed for a firm, and the capital stock for the other periods will either be iterated forward or backward. Specifically, the capital stock iterated forward is constructed by depreciating the capital stock in the previous period and adding the flows of extrapolated average investments: Kt+5 = Kt+4(1 −δ) + It+5 . (21) Equation (20) is iterated forward through repeated substitution of Kt+5 in equation (21): Capital stock observed in the later census period is iterated backward: The advantage of building the capital stock using this approach is that it allows us to use the investment values that may better reflect the current market values. However, we also consider
in middle school at the time of the survey (2007-08). The first outcome indicator measures the probability that girls have given birth. This analysis reveals no differential effects on the probability of girls 17-19 having given birth. A second exercise looks at the number of births given for the sub-sample of girls who became mothers during the period under study. The empirical analysis based on this sample indicates that, relative to the control group, girls exposed to the FSSP have on average 0.3 fewer children. However, this result is marginally significant around the 10 percent level.<sup>30</sup> Even though both are partial measures of fertility given the young age of the girls and do not reflect fertility targets, the findings may still signal some changes in fertility that may be sustained over the long-term. In the event that the total fertility rates of both treatment and control groups converge over time, the reduction in the number of children by early adulthood still implies that girls in stipend districts tend to delay their subsequent births (Table 9).

# **_Heterogeneity of Program Impacts_**

This section presents evidence on the heterogeneity of the FSSP effects across some socioeconomic characteristics of program beneficiaries such as socioeconomic status, parental education, location (rural or urban), age, and length of exposure of the beneficiaries.<sup>31</sup> We first find that girls who live in rural villages and have parents with no education benefit much less from the program in terms of the probability of completing middle school, but their labor participation also decreases more. The evidence indicates that most educational benefits (as measured by middle school completion) attributed to the program accrue to girls who belong to households in urban centers and have parents with at least primary education. On the other hand, these girls also reduce their participation in the labor market less than girls living in rural areas and with parents that have no education. Unfortunately, with the data available, it is not possible to tease out the underlying reasons that may help reconcile the lower school attainment and lower labor force participation identified for girls in these rural, less-educated households (Table 10).

> 30 A caveat of this analysis is that the follow-up survey has a significant number of missing values for the number of births. Further calculations show that this issue is equally prevalent in both stipend and non-stipend districts. Moreover, the problem of missing values does not appear to be systematically correlated with some determinants of fertility.

> 31 Estimates from the DD and RDD models shown in tables 4 to 8 were obtained from a DD framework for the whole sample with a control function of district literacy rates that determine program eligibility. The fact that the results are very similar may suggest that there is low impact heterogeneity across districts. Yet, this does not imply that there is not impact heterogeneity for different types of subgroups defined based on some socioeconomic characteristics.

22
- 27 -

The summary statistics are shown in Table 2. Firms look different in size as well as factor intensity. The operating cost ranges from 10,000 to 423 million U.S. dollars with a mean of about 3.9 million U.S. dollars. The average wage is estimated at around US$ 5,400 per annum. In the sample the labor cost amounts to 22 percent of total costs on average. However, the degree of labor intensity varies considerably from nearly zero to 81 percent. The energy cost share also differs between nil to 66 percent with an average of about 8 percent. The number of days without sufficient electricity supply reaches 28 days per year on average. The average duration needed for power restoration is about 2 hours. But these levels of public electricity services are markedly different from country to country and across regions within each country. Table 3 shows simple correlations between these variables.

<u>Table 2. Summary Statistics</u>

|Varia|ble|Obs|Mean S|td.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|_C_|Operating cost|990|3,875|17,891|10|423,860|
|_Y_|Output (total sales)|990|4,468|20,407|15|469,362|
|_WL_|Wage (average per full time employee)|990|5.39|4.43|0.33|81.13|
|_WE_|Energy and fuel price|990|0.73|2.74|0.00|67.00|
|_WK_|Capital price|990|11.21|63.61|0.02|1841.00|
|_S L_|Cost share of labor expenses (0 to 1)|990|0.22|0.14|0.01|0.81|
|_S E_|Cost share of energy and fuel expenses (0 to|990|0.08|0.08|0.00|0.66|
|_Z P1_|Days without electricity supply a year|990|28.44|82.73|0.00|365.00|
|_Z P2_|Duration of electricity suspension in hours|990|1.99|3.38|0.00|24.00|
|Sect|or dummy||||||
||Mining|990|0.02|0.13|0|1|
||Construction|990|0.09|0.29|0|1|
||Manufacturing|990|0.41|0.49|0|1|
||Transport|990|0.07|0.26|0|1|
||Trade|990|0.23|0.42|0|1|
||Real estate|990|0.07|0.25|0|1|
||Hotels and restaurant|990|0.07|0.25|0|1|
||Other services|990|0.05|0.21|0|1|

Note: All monetary variables are in thousands of U.S. dollars; unless otherwise, indicated.

the hypothesis cannot be rejected in all the countries but Bulgaria and Romania. For further data sampling issues, see World Bank (2007a).
**Figure 4**

<!-- Start of picture text --> Argentina versus Chile<br>Panel A. Real GDP per Capita and Social Indicators<br>Panel A.1. Argentina Panel A.2. Chile<br>120 80 200 40<br>100 100 60<br>160<br>80 120 80<br>120<br>60 140 100<br>80<br>40 160 120<br>40<br>20 180 140<br>0 200 0 160<br>Unemployment Rate Monetary Poverty Unemployment Rate Monetary Poverty<br>1/HDI Real GDP per capita (right axis) 1/HDI Real GDP per capita (right axis)<br>Panel B. Cyclical Component of the Real GDP per Capita and Social Indicators<br>Panel B.1. Argentina Panel B.2. Chile<br>120 -140 200 -300<br>100 -100<br>160 -220<br>80 -60<br>120 -140<br>60 -20<br>80 -60<br>40 20<br>40 20<br>20 60<br>0 100 0 100<br>Unemployment Rate Unemployment Rate<br>Monetary Poverty Monetary Poverty<br>1/HDI 1/HDI<br>Real GDP per capita - Cyclical Component (right axis) Real GDP per capita - Cyclical Component (right axis)<br>Notes: Poverty line of 5.50 dollars per person a day (2011 PPP). The cyclical component of the Real GDP per capita is defined as<br>the cycle over trend.<br>Sources: Authors’ estimations based on SEDLAC (CEDLAS and World Bank), Human Development Report Office (United<br>Nations), and WEO (October 2018).<br>Index (2002=100) Index (2002=100)<br>Index (2002=100, inverted axis) Index (2002=100, inverted axis)<br>Index (2002=100) Index (2002=100)<br>Index (2002=-100, inverted axis) Index (2002=-100, inverted axis)<br><!-- End of picture text --> 20
the International Food Policy Research Institute (IFPRI), the National Aeronautics and Space Administration (NASA), the National Oceanographic and Atmospheric Administration (NOAA), and the World Bank. Table 2 summarizes the data sources for assessments of inundation zones and impacts.

# **Table 2 Summary of Data Sources**

|**Dimension**|**Dataset**<br>**Name**|**Unit**|**Resolution**|**Source(s)**|
|---|---|---|---|---|
|Coastline and<br>country<br>boundary|WVS||1:250,000|NOAA/NASA|
|Elevation|SRTM 90m<br>DEM V2|km<sup>2</sup>|90m|CIAT|
|Population|GPW-3|Population<br>counts|1km|CIESIN|
|Economic<br>activity|GDP2000|million US<br>dollars|5km|World Bank, based on<br>Sachs_et al._(2001)|
|Urban extent|GRUMP V1|km<sup>2</sup>|1km|CIESIN|
|Agricultural<br>extent|GAE-2|km<sup>2</sup>|1km|IFPRI|
|Wetlands|GLWD-3|km<sup>2</sup>|1km|CESR, Lehner, B. and<br>Döll, P.(2004)|

# **II.2 Methodology**

The country indicator database was developed by following the six-step procedure described below.

# **II.2.1** Preparing country boundaries and coastlines

Country coastlines were built by sub-setting polygons from the World Vector Shoreline (polygon), a standard National Geospatial Intelligence Agency (formerly Defense Mapping Agency) product at a nominal scale of 1:250,000. It contains worldwide coverage of shorelines and international boundaries. The subset country coastlines were also used as a mask for calculating country totals for the selected exposure indicators.

7
# **<mark>5. DISCUSSION</mark>**

The analysis of firm-level data across 28 countries showed a significant correlation between frequency of tax-related interactions between government agents and a particular firm and this firm’s perception of tax administration as corrupt. This measure gets beyond self-reports of tax morale (e.g., attitudes towards tax evasion, see Torgler & Schneider, 2009) to more discrete and non-cognitive operationalization. Though our measure is still self-reported, the likelihood of social desirability bias is lower than is the case with affective measures like tax morale.

Based on the correlations observed in the data, the following storyline seems to emerge. Where businesses view the tax system as corrupt, perception of overall corruption as an obstacle to firms’ operation is high. At the same time, the mere fact of undergoing tax inspection has no apparent impact on perceptions of bribery, but with frequency of inspection increasing, the positive correlation with corruption is observed. The relative size of the bribe does not influence perceptions of bribery in dealing with taxes, while bribe requests in the context of tax inspection do. This suggests that the theory that corruption may be an efficient substitute for the formal systems may not hold: distortions from bribery regardless of its size are viewed by the private sector as an obstacle.

The analysis indicates that frequency of interaction is associated with higher corruption, but it cannot account for the notion that frequency can be interpreted differently depending on context. Governments could regularize and/or reduce the number of tax-related (and other) interactions they have with private enterprises, but the mode of interaction and institutional strength of tax administration in a country may play a role. The recent rise of e-services in tax administration may or may not help to improve these interactions. Looking at different ways of providing such e-services could shed light on their effectiveness. It is not possible to examine these based on the available data.

At a minimum, pursuing tax collection strategies involving minimum friction (e.g., easy to use and monitor electronic tax collection mechanisms) in addition to appearing desirable on its own terms, may also result in the added benefit of driving down overall perceptions of corruption in a country. To be clear, while the correlational analyses above are suggestive that frequency means something different in the latter than the former, our data do not speak specifically to this sort of interpretation. This calls for future research on interconnectedness between corruption in tax administration and modes of intervention as well as interpretation of frequency of tax inspections in different country and governance contexts.

13 | P a g e
(environmental management) certification. An important factor in the decision to obtain quality licenses was the pressure from foreign partners. Several managers pursued such policy independently since they wanted to establish their own trademark products on foreign markets.

# **VI.** Performance Measures

To measure the extent of enterprise restructuring, we focus on labor productivity, average operating profitability and total factor productivity (TFP) growth. All three measures are important indicators of enterprise restructuring, but to different degrees depending on the stages of reform. Taken together, they present a fairly complete picture of the restructuring process. The three measures rely on basic data (revenues and expenses) and should not be greatly affected by the still-evolving accounting practices in Slovakia.

**Labor productivity** (defined as value added per employee in constant 1996 prices) is a useful measure of restructuring in the early stages of enterprise adjustment. Labor productivity is regarded as a _leading_ indicator of restructuring (Wolff, 1996) since wage and labor adjustment measures can be taken more rapidly than modernizing the capital stock, entering new markets, etc.

We next measure the extent of restructuring by examining firms' **average operating profitability** over time. Changes in operating profitability (defined here as [total revenues - wages - material inputs] \ total revenues) reflect a large number of restructuring measures: labor and wage rationalization, adjustment of input use to reflect new relative prices, better output quality and higher sales revenues, and the movement of resources toward higher-productivity firms and sectors. In measuring these changes, we use operating profitability rather than net profitability. The difference between operating and net profitability is in (not) accounting for interest and other financial charges; and depreciation. Given the often arbitrary allocation of liabilities under central-planning, the inclusion of these variables could introduce unnecessary noise in measuring enterprise restructuring.

21
# **Contents**

|**1**|**Introduction**|**3**|
|---|---|---|
|**2**|**Direct estimation**|**5**|
|**3**|**Basic indirect estimators**|**10**|
|**4**|**Area level models**|**15**|
|**5**|**Unit level models: Linear indicators**|**26**|
|**6**|**Unit level models: General indicators**|**36**|
|**7**|**Semi-parametric and machine-learning meth-**<br>**ods**|**55**|
|**8**|**Challenges and potential research topics**|**62**|
|**R**|**eferences**|**68**|

2
<!-- Start of picture text --> & 240<br>o<br>= 220<br>oO<br>x=<br>® 200<br>on<br>xo}<br>a= 180<br>= 160<br>&<br>te 140<br>2<br>& 120<br>Ss<br>100<br>SFND SF$ SH SHgo 2 SHND SSgs 2<br>PP 2 & r@ sO Ss C & SP<br>Ce SES SE FP PF SS SS<br>Se SX FEF MX SK SS<br>& & QP Q oF oF & o Ww LQ WS<br>14%<br>12% ><br>10%<br>a a<br>8%; | e ry<br>6% +*<br>4% a LL ala<br>2% | a | | a<br>0%<br>2001 | 2009 | 2000 | 2009 | 2000 | 2006 | 2001 | 2009 | 2001 | 2009 | 2001 | 2009<br>CostaRica | Panama | Guatemala | El Salvador | Honduras | Nicaragua<br>@Total m5 years of education or more<br><!-- End of picture text -->
restriction, we take advantage of the Conley et al. (2012) bounds approach (see section 6.3 below).

The next question we address is that of relevance of the historical railroad as an instrument. Recall that our measure of market access is based on travel time through roads and highways. A natural question then arises: why should we expect colonial railroad to have a signi�cant correlation with the gravity measure of market access based on roads and highways? If colonial railroad is only tangentially related to the market access through roads and highways in 1996 (or 1988, 2004) then the IV estimates will be biased and unstable, and can yield implausible magnitudes (Stock and Yogo (2005)).

To check whether the historical railroad locations have systematically higher market access, we plot the kernel density function of market access for two samples, with and without a colonial rail station. Figure 1 shows clearly that the presence of a colonial railroad in the 1880s shifts substantially the density function of market access in 1996 to the right. The districts with colonial railroad have a higher mean and lower variance.<sup>14</sup> The �rst stage F statistics later in the IV regressions con�rm that the 1880 railroad length in a district has substantial power in explaining the variation in market access of districts in 1996.

There are a number of plausible reasons behind a signi�cant positive correlation between colonial railroads and current market access via roads and highways in Figure 1. To the extent transport infrastructure placement is determined by topography, we would expect a positive correlation between the placement of rail line and roads. Two topographical features are especially important for the placement of railroads: slope and curvature. The optimal rail track location tries to minimize the slope (for steep slope, going up hill requires more powerful locomotive, and braking is di�cult going down hill), and curves (a sharp curve reduces the maximum speed) (AREMA, 2003, Chapter 6). These two factors are also important for the choice of cost-minimizing road and highway routes.<sup>15</sup> Because of the topographical constraints, it is common to have rail tracks and highways placed close to each other. In fact, the old

> 14Districts with colonial railroad: mean=15.641 and variance=0.348. Districts without railroad: mean=14.911 and variance=0.372.

> 15The most widely used HDM highway planning model of the World Bank highlights the importance of slope (rise and fall) in choosing an �optimal' path for highways. See the discussion by Robinson and Thagensen (2004).

13