rafmacalaba/datause-encoder-data huggingface dataset:
#### **Box 4. Separate set of indicators for teaching and research in the UK funding system**

<mark>In the United Kingdom the allocation of funding for higher education is based on two separate sets of indicators, one for teaching and one for research. Categorizing indicators cater for high specificity in the choice of indicators and allow unique follow-up procedures to be established for each set of indicators.</mark>

### _<mark>Teaching grant</mark>_

<mark>Funding for teaching is based on several indicators such as number of students, length of courses, size of the institution, location, level of specialization and the number of disadvantaged students. The indicators have built in flexibility in order to give recognition to differing institutional circumstances.</mark>

### _<mark>Research grant</mark>_

<mark>The calculation of funding for research is based on indicators such as the number of qualified researchers, published research documents, the number of research students and external research income. The assessment process and the allocation of funding is not solely based on the quantitative scoring on each indicator. In the so-called research assessment exercise (RAE), committees of peers evaluate the overall quality of research activities in each institution.</mark> _Source: HEFCE 2003_

# **3. DESIGN OPTIONS – INTERNATIONAL CASES**

Significant variety exists between countries in the methods and processes applied to select the criteria by which tertiary institutions are evaluated. Although all contain idiosyncratic details, systems can be classified into two categories: _System-wide fixed set of indicators_ where little change is introduced over time on the criteria used to evaluate institutions and _fixed-term performance contracts_ with explicit evaluation criteria, periodically negotiated between the supervising agency and each tertiary institution.

## **Fixed set of indicators**

For performance systems with a fixed set of indicators, a distinction can be made between _simple criteria systems_ where institutions are evaluated based on one or two indicators and _multiple criteria systems_ with several indicators. France, Denmark and the Netherlands are examples of countries basing a significant part of institutional funding on a simple criteria system. Countries operating a funding system with multiple performance criteria include the United States and South Africa. In the Latin American region, Argentina is an example of a country that has – on a small scale – experimented with multiple criteria funding of higher education.

## _Simple criteria systems_

The **French** tertiary education system receives about 50 percent of its funding based on a simple input formula. Funding is tied to the number of students enrolled while adjusting for variance in expenses per students in different careers. Calculated funding is allocated to institutions as a lump-sum, which provides tertiary institutions with significant budgetary autonomy.

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# **4 Data and statistics summary**

In order to estimate equations (1), (2) and (3), we use data from the Orbis database of Bureau Van Dijk. The Orbis dataset provides retrospective information on company ownership and reports the country of origin of each foreign shareholder, although the share value is often unknown. It also provides information on employment and turnover. We extracted data on foreign ownership on all manufacturing firms (an annual average of about 63,000 companies) for the period 1997 to 2012. The data covers about 58% of Polish firms as reported by Eurostat and coverage varies significantly across regions (Farole et al., 2017).<sup>5</sup> Figure A.4 shows the evolution of the percentage of companies with at least one foreign shareholder from either Germany, French, the United States or the United Kingdom. In the case of German shareholders, sectors are divided into those with the highest pre-determined export share (above median, red line) and those with lower level of specialization (below median, blue line). The figure confirms the sharp increase in German acquisitions of Polish firms (anticipated in Figure 1) right after EU accession, and at a remarkable greater extent for sectors with higher export shares. The percentage of German owned companies peaked in 2007 and 2009 at about 1.8% overall, corresponding to about 18% of total employment (according to our sample). While foreign acquisitions began in 2001 in concomitance with the conclusions of the accession negotiations, it was only after joining the EU that FDI experienced a sharp rise. Foreign acquisitions increased also from other EU countries, such as France and the United Kingdom, and non-EU major trade partners, such as the US, but to a much lower extent. This suggests that German foreign investment played an important role. This pattern is confirmed also when considering an alternative measure of foreign ownership based on the country of origin of the Global Ultimate Owner (GUO) as shown in Figure A.5. Data on firm performance in the Orbis database, however, are limited to information on employees and revenues.<sup>6</sup> Due to missing data on employment, the final sample is reduced to about 61,000 firms.

The rest of the empirical analysis draws on a survey of firms with more than 10 employees collected by the Central Statistical Office of Poland. These data are best suited to examine overall effects on firm performance, given the limited coverage for performance variables, especially revenues, in the Orbis database. Survey data are available for the period 1995-2013 and contain information on a set of firm

> 5On the other hand, when comparing the distribution of firms across sectors in Orbis and in the survey of firms provided by the Central Statistical Office of Poland (Figure A.3), the two distributions are broadly aligned suggesting that coverage is likely to be similar across sectors.

> 6In our empirical analysis of firm performance, we exclude the year 1999 due to anomalies in the data.

9
couple, or become her husband’s property? And if legally such assets remain the property of the wife, to what extent can she manage these independently during the marriage? The IFPRI survey in the Philippines collected data on individual land owned by each spouse at the time of marriage. It also collected useful information on the total land owned by the parents at this time, allowing analysis of whether sons or daughters were favored by early inheritance of land.

The ICRW surveys in West Bengal and Kerala also ask about assets brought to the marriage by wives. For each type of asset, the survey asks whether that asset was acquired before marriage, immediately after marriage, or a few years after marriage. The survey asks a number of specific questions about dowry, including whether the in-laws demanded it, whether it was considered sufficient, and whether the assets/cash brought to the marriage were purchased or earned by the wife herself.

As noted above, several LSMS questionnaires solicit data on how land was acquired, thus providing data on inheritance of land. Others only provide very fragmented information on inheritance, collecting data only on whether the head or spouse inherited land during the year prior to the survey. The IFPRI questionnaires greatly improve on them. The survey for Indonesia collected data on inheritance by individuals for land, housing and livestock. The IFPRI questionnaire for Ghana was innovative in that it collected data on land inheritance by individuals and asked about the rules of inheritance. The KIDS questionnaire for South Africa also collected data on all assets that individuals inherited at any point in time.

Of the other surveys reviewed in Table A6, it is worth noting that those undertaken by the University of Wisconsin and partners in Nicaragua and Honduras collected data (for titled land) on whether land was inherited and from whom at the individual level, as well as on whom has inherited land from them.

The IFPRI questionnaire for Ghana was also innovative in asking which assets individuals retained after the dissolution of a marriage. Several of these IFPRI surveys also delved into the purchase and sale of assets in a dynamic context (Table A4). The questionnaire for the Philippines included questions about who made the decision to dispose of an asset.

Worth mentioning as well is the ICRW questionnaire for Uganda, which explored the significance of property rights for women in terms of their ownership of land, crops, animals and other property. The ICRW questionnaires for Kerala and South Africa also explored decision-making.

# 4.4. Summary

Of the LSMS questionnaires reviewed here, none provide individual data on all of the six broad asset categories; the Nicaraguan LSMS for 2001 was the most complete. The most progress has been made in the collection of data on individual ownership of housing and land, although the manner of collecting this information remains deficient. Individual 28
policies in rural areas. However, while there have been impressive reductions, the incidence of rural poverty is still more than double the urban poverty. The focus of current policies may need to integrate the needs of urban households whose progress in reductions in deprivation incidence is slower.

The overlap analysis shows a clear exit of households experiencing multiple deprivations over the last decade. The movement of various combinations of multiple deprivation indicators illustrated using Venn diagrams in previous sections shows that more people are moving out of multidimensional poverty. While the choice of the number and mix of the indicators depends on the current priorities, the various illustrations show that for any set of three indicators considered, households who previously were experiencing three deprivations simultaneously are now experiencing two, one, or none of these deprivations. Those who were money poor had fewer deprivations in other dimensions in the later years.

# **References**

- Alkire, S., A. Conconi, and S. Seth (2014), “Multidimensional Poverty Index 2014: Brief Methodological Note and Results”, The Oxford Poverty and Human Development Initiative (OPHI), Oxford Department of International Development, University of Oxford.

- Alkire, S., Conconi, A. and J. M. Roche  (2013), “Multidimensional Poverty Index 2013:  Brief Methodological Note and Results.” The Oxford Poverty & Human Development Initiative, University of Oxford.

Alkire, S. and J. Foster (2007), “Counting and Multidimensional Poverty Measurements”, OPHI Working Paper No. 7, University of Oxford.

26
Because the MSCI weights are partially affected by domestic shocks that change a country’s relative market value, we also employ as alternative instruments the sum of other countries’ total equity value and other countries’ volume of equity issuances. Those two instruments depend exclusively on foreign shocks and are therefore plausibly exogenous to demand shocks of the subject country. To deal with the possibility that equity inflows to emerging markets can be correlated across countries, our specification includes year fixed effects, which control for common shocks across them. To remove any further common shocks, we also employ orthogonalized versions of those same instruments, where we orthogonalize the sum of equity values (the sum of issuances) with respect to the market value (the issuances volume) in the subject country. In all cases, we find that instrumented inflows, contemporaneous and lagged, lead large firms to raise significantly more equity.

Our data on capital inflows are measured annually and the precise timing lag between the stimulus of instrumented capital inflows and the response of issuances is not known a priori. Therefore, when modeling the timing of the connection between instrumented capital inflows and equity issuances, we consider two alternative dynamic formulations. In one formulation, we regress issuances on contemporaneous inflows (where it is possible that issuances could occur at the same time as the capital inflows, or slightly before or after the inflows but within the same year). In the second formulation, we regress issuances on lagged inflows. Our results are similar under either of the two formulations.<sup>5</sup> In the last section of the paper, we analyze the real effects of equity capital inflows. First, we estimate the effect of instrumented equity capital inflows on a variety of potential

> 5 Because we employ instrumented inflows, we do not rely on lagging inflows for identification. However, by using lagged values of instrumented inflows, we reduce the likelihood that the relation between inflows and issuances reflects the response of inflows to increased demand for capital by issuing firms.

5
The decision to close the old system was also taken because the Government wanted to make clear that the affiliates are the owners of the resources in the system, something that is essentially undefined in a "pay as you go" system. In addition, keeping both systems open either for a long time or permanently has several potential drawbacks: (i) total administrative costs may be higher, (ii) total contribution rates may need to be higher to give both systems a critical mass of funds to manage, (iii) the contribution to the "pay as you go" system continues to be perceived as a tax on labor making more difficult enforcement of payment of all contributions, (iv) the issue of the level playing field between the two systems will continually resurface, and (v) outstanding Government guarantees may be higher.

The only reason to keep both systems in place was to reduce the financing requirements of the transition at the expense of higher public sector liabilities. Closing the old system in Bolivia at the same time that the new system started, although certainly worth doing it, requires a larger financing effort, particularly in the short run. The requirement of having to invest most of the workers contributions of the next few years in treasury bonds to help finance the transition is questionable. This requirement could lower the impact of the pension reform through lower returns on worker's pension accounts<sup>33</sup> and could discourage affiliation.

Financing Requirements of the Pension Reform

|Year|1997|1998|1999|2000|2007|2017|2027|2037|
|---|---|---|---|---|---|---|---|---|
|%**of GDP**|2.24|2.72|2.57|2.23|1.33|0.86|0.57|0.18|
|US$**mn.(1996)**|152|192|188|170|134|141|146|72|

> **33** Efforts are ongoing to develop an efficient market for treasury bonds, so that their return would approach market rates.

20
Again, we find evidence of two clubs – rich nations and poor nations – with little mobility between them.<sup>21</sup> GDP per capita is four times higher in AA countries than in BB countries, and _t_ -statistics reject the equality of means of GDP per capita between the two groups. There was also increasing divergence in income levels between the two groups over time (figure 12). Income per capita on average remained largely stagnant for AA countries between 1975 and 1995, but it increased substantially thereafter. The average per capita income of the BB countries declined until around 1995 after which there was a slight recovery, but the average real income of the BB group was the same in 2005 as in 1975. The CV of GDP per capita of AA countries increased from 0.77 in 1975-94 to 0.87 in 1995-2005. The CV of the BB group increased from 0.26 to 0.30. Thus, part of the increasing income inequality identified in section 3 is driven by the large and rising income dispersion among countries in the richer group.

**Figure 12: GDP per capita by country-group**

<!-- Start of picture text --> 5000<br>4500<br>4000<br>3500<br>AA<br>3000<br>2500<br>AB BA<br>2000<br>Median<br>1500<br>1000<br>BB<br>500<br>0<br>1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> 21 To test the robustness of the country group classification we estimated pooled and fixed-effect regression models. Country group coefficients for income levels are statistically significant at the 5 percent level; they are sizable and have the expected signs. These results suggest that the country group classifications are relevant and highly stable in predicting income levels (Arbache and Page, 2008).

20
**Figure 8(c): Indonesia: Return to skill by sector over time (Tertiary and above)**

<!-- Start of picture text -->
1.6<br>1.4<br>1.2<br>1<br>Agriculture/Minin<br>0.8<br>g<br>Manufacturing<br>0.6<br>0.4 Trade/Services<br>0.2<br>0<br>1994 1999 2003 2007<br>Year<br>Return<br><!-- End of picture text -->

**Figure 8(d): Indonesia: Proportion of skilled workers by sector over time (Tertiary and above)**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>Agriculture/Minin<br>15 g<br>Manufacturing<br>10<br>Trade/Services<br>5<br>0<br>1994 1999 2003 2007<br>Year<br>% skilled<br><!-- End of picture text --> 46
**Table I.7: Dominican Republic Total GDP of which Agriculture, Crops, Livestock 1970-1999 (in constant 1970 RD$ prices)**

|Year|Total Agriculture|Crops|Livestock|
|---|---|---|---|
||% Share<br>GDP|% Share<br>GDP|% Share GDP|
|1970|23%|16%|7%|
|1975|17%|11%|6%|
|1980|16%|10%|6%|
|1985|16%|10%|6%|
|1990|13%|7%|6%|
|1999|11%|5.5%|5.5%|

Note: Percentages are rounded off. Total Agriculture also includes fisheries and forestry. Source: ECLAC (2001).

In addition to the domestic policy biases against traditional export commodities, these commodities have faced unfavorable international prices in recent years. Downward price trends have exacerbated the underlying structural deficiencies that had prevailed, but had often been concealed by periodic price spikes and periodic Government bailouts in response to emergency situations. According to ECLAC (2001, p.86): “In the sugar industry, financial difficulties [have] continued to be a problem, as did worn-out machinery and equipment, and _in the coffee industry, the assistance could not make up for the low yields resulting from the failure to maintain the plantation and the shortage of labor, especially foreign labor_ .” Increasing market and trade liberalization and the phasing out of the National Institute for Price Stabilization has worsened the balance of agricultural trade for Dominican Republic (table I.8).<sup>14</sup> This change is largely a function of declining international prices and quantities exported for traditional export commodities and increases in imports of grains and oilseeds (much of it for livestock and poultry feed).

|**Table I.8: Trade Balance of Agricultural Products 1980-1999 (in US$ millions)**|
|---|

|1980|1985|1990|1995|1996|1997|1998|1999|
|---|---|---|---|---|---|---|---|
|311|307|50|-147|-108|-129|-188|-379|

Source: ECLAC (2001)

# **D.2 Recent Agricultural Policies**

Like many Latin American and Caribbean countries, agricultural policy in the Dominican Republic was fundamentally restructured during the 1980s and 1990s (Raynolds, 2002). Traditional policy biases against exports have been lowered or removed, direct subsidies have become more infrequent, and the public role in agriculture has been altered. These changes represent significant policy shifts.

Historically, the GoDR heavily taxed its export agriculture sectors as a means of raising revenues. Beginning in the 1990s, export tariffs began to fall, but biases in favor of import-substituting

> 14 Also, as a result of this liberalization, exposure to international price and market prices risks has increased for farmers.  Increased price variability and declining international prices for agricultural commodities is having a negative impact on some of the rural poor (World Bank, 2001).  As a result, it might be worthwhile considering direct income support programs to mitigate negative impacts of trade and agricultural reforms on small farmers, like the widely acclaimed PROGRESA program in Mexico.

19
**_Table 6. Relationship between Task Team Leader (TTL) turnover and IEG outcome rating_**

||**De**|**pendent variable: I**|**EG 1-6 outcome rat**|**ing**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Dummy for preparation TTL change|-0.053|||-0.018|
|in the 1<sup>st</sup>or 2<sup>nd</sup>ISR|(0.044)|||(0.045)|
|TTL turnover during first half (Number of||-0.412***||-0.309*|
|TTLs/Number of ISRs)||(0.156)||(0.166)|
|TTL turnover during second half (Number of|||-0.579***|-0.575***|
|TTLs/Number of ISRs)|||(0.171)|(0.166)|
||2.066***|2.240***|2.471***|2.603***|
|Constant|(0.330)|(0.331)|(0.365)|(0.354)|
|Observations|2,763|2,762|2,757|2,752|
|Adjusted R-squared|0.105|0.107|0.108|0.110|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects.

**_Table 7. Relationship between staff experience, staff predicted performance, and IEG outcome rating_**

||**Dep**<br>**Staff pr**|**endent varia**<br>**edicted perfo**|**ble:**<br>**rmance**|**Dep**<br>**IEG 1**|**endent varia**<br>**-6 outcome r**|**ble:**<br>**ating**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Number of projects prepared before|0.068***|||-0.012|||
|by the preparation TTL|(0.014)|||(0.010)|||
|(Log) Number of ISRs signed before||0.062***|||0.010||
|by the mid-point supervision TTL||(0.019)|||(0.021)||
|(Log) Number of ISRs signed before|||0.058***|||0.042*|
|by the final supervision TTL|||(0.019)|||(0.023)|
||-0.105***|-0.184***|-0.230***|2.051***|2.030***|1.978***|
|C|||||||
|onstant|(0.037)|(0.056)|(0.059)|(0.344)|(0.342)|(0.324)|
|Observations|2,038|2,560|2,513|2,767|2,762|2,757|
|Adjusted R-squared|0.011|0.004|0.003|0.105|0.105|0.104|

Note: Robust standard errors clustered at the country level are reported in parentheses. (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS. Columns (1) (2) (3) do not include any controls. Columns (4) (5) (6) include controls for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects.

28