rafmacalaba/datause-encoder-data huggingface dataset:
Bank, 2018). But there is significant variation among the countries we study, with private school teachers ranging from 5% (in Namibia) to 38% (in The Gambia) of the teaching force. Depending on the nature of a private school (e.g., elite or low-cost) and the contract type, teachers’ salary and benefits could vary substantially, which we discuss in section 3.3.

Another important group in the African teaching force is contract teachers. Due to the increasing demand for education, hiring contract teachers is common in Sub-Saharan African countries—as it is in other parts of the world (Chudgar, 2015).<sup>25</sup> Although their job responsibilities are similar to those of civil-service teachers, contract teachers’ salaries are typically substantially lower and these teachers lack job security. In the survey data we use for most countries, all employed respondents—including teachers—are asked whether they have a permanent or a fixedterm/temporary contract. The share of teachers on fixed term or temporary contracts ranges from 5% (Tanzania) to 42% (Côte d'Ivoire) (Table 2). On average, the share of teachers on a fixed term or temporary contract is slightly higher for secondary teachers (25%) than for primary teachers (23%). In Uganda, the share of teachers on a fixed term or temporary contract at the secondary level (74%) is substantially higher than that at the primary level (33%).<sup>26</sup> Table 3 presents the educational attainment of teachers. The majority of teachers have at least secondary school education in all countries in our sample. The only countries where more than 7 percent of teachers have only a primary degree are Côte d’Ivoire (13.5%) and Zambia (15.8%). Overall, 53% of teachers have post-secondary education. Relative to other wage workers, teachers are much more likely to have post-secondary education in every country except the Democratic Republic of Congo. There is a notable difference in educational attainment between primary school teachers and secondary school teachers in most of the countries. On average, 80% of secondary school teachers have post-secondary education compared to 50% among primary school teachers. The difference is less salient in Côte d’Ivoire, Nigeria, and Uganda, where at least 80% of both primary and secondary teachers have post-secondary education.

> 25 Many countries employ part of their teaching force on short-term, untenured contracts. Research in Kenya and India shows higher test scores for students taught by locally hired teachers on annual contracts (Duflo et al. 2015; Muralidharan and Sundararaman 2013), although in Kenya, those gains disappeared when the contracts were administered by government rather than by non-government organizations (Bold et al. 2018).

> 26 Beyond the share of teachers working at each level of education, we compare the characteristics of teachers on a permanent contract versus on fixed term or temporary contracts in Appendix Tables A10-A12.

14
action is coordinated. This requires an understanding of the critical role of SPGs, the distribution of status and power within the village and communities that they represent, and the distribution of control within them. The relative impact of inequality and social heterogeneity may work via the mediating influence of SPGs.

India and Indonesia are both culturally and geographically diverse countries that achieved independence within two years of each other. They have had centuries of social and economic exchange. They have important cultural similarities. Yet they have followed very different strategies of political and economic development, with Indonesia turning increasingly autocratic soon after independence – till its turn towards democracy at the end of the Suharto era in 1998. India, on the other hand, has been a stable democracy since its independence, with a vigorously independent election commission. Both countries have begun to increasingly decentralize since the 1990s, with Indonesia devolving powers to state and district governments, and India doing the same to village and district _panchayat_ s (governing councils) which are elected.

Both countries have survived ups and downs in their economies, but as of 2001 their per capita incomes were very close with India’s at PPP $2570, compared to PPP $2990 in Indonesia.<sup>8</sup> Yet Indonesia has been far more successful in providing public services to the poor. One indicator of this is that while 76 percent percent of children complete primary school in India, 91 percent complete it in Indonesia, even though India spends 7.2 percent of its GNP on primary education, while Indonesia spends only 3.2 percent. Such stark differences in human development indicators occur for health as well, with India spending 0.9 percent of GNP on health while Indonesia spends 0.6 percent, yet India’s under-5 mortality rate is 93 and Indonesia’s is 45. Indonesia has therefore not only been more effective at providing public services, it has also been far more efficient. What accounts for this stark difference in performance? I argue below that SPGs have played an important role.

# **<u>Collective Action and community development in Indonesia</u>**

Any discussion of Indonesian society has to start with the work of Geertz and, in particular, with his monumental work, _The Religion of Java_ , which laid out many of the themes that have played a central role in understandings of Javanese culture. Geertz’s work may have even shaped how Indonesia’s nationalism, with its strong Javanese flavor, has been articulated and imagined.

> 8 All data in this paragraph are from the World Development Report 2004.

11
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Despite the data limitations, international benchmarking of sectorwide performance based on partial expenditure and outcome indicators is increasingly popular in the literature as a way to increase accountability. Most of the time, such benchmarking takes the form of ranking and country-specific performance assessments at the operator level—utilities, hospitals, schools, and so forth. The approach is quite common in the education and health fields.<sup>8</sup> It is less common in the infrastructure sector, except perhaps for electricity, rail, and telecoms, where the availability of data has stimulated a wide range of publications.<sup>9</sup> The popularity of international benchmarking is, however, not universally supported, in particular when it is used to assess performance at the sectoral level. The critics are indeed strong.<sup>10</sup> Some have very valid conceptual reservations. But most are concerned that the international benchmarking of sector-specific performance leads to the compilation of country-level data that can be used and abused when policies are formed.

In this paper we try to generate some efficiency benchmarks—although at a higher level of aggregation than earlier efforts (which also sought to minimize the risks of misinterpretation identified by the critics).<sup>11</sup> Based on the partial information available, we offer a rough answer to the second of the two core questions posed at the outset—how much more could governments achieve at their current level of expenditure?<sup>12</sup> In the process, we document and discuss in some detail the data problems facing anyone seeking to make broadbrush comparisons across country groups over time in each sector. More specifically, our paper makes three main contributions:

- It provides an overview of sectoral public expenditures and outcomes by sector.

- It gives a sense of the current levels of expenditure and of their efficiency for broad income groups of countries—low, lower-middle, upper-middle—in both developing countries and developed countries.

- And it identifies areas in which government could improve its monitoring of public-sector performance in education, health, and infrastructure.

Our approach is somewhat mechanical. Indeed, our main concern was to be as systematic as possible in collecting and validating all the cross-country data on the level and efficiency of public expenditure in the three sectors over 10–15 years—a relatively long

> 8 This would call for a long bibliography. A good substitute is to go through the Asian Development Bank-ADB- (2006), which provides a very nice overview of both efficiency measures on health and education and of the harsh criticism of these measures.

> 9 See Coelli et al. (2003).

> 10 See Ravallion (2005) for a good and incisive methodological critique of this literature.

> 11 See ADB (2006) and Coelli et a,. (2003) for an overview of the previous research.

> 12 A precise answer is almost impossible to supply, as explained by Ravallion (2005) in his very tough essay on any effort to measure the efficiency of public expenditures.

3
|Pr(IQ=1,IV=1)|0.00083|[0.00024]***|-7.24E-06|[0.00014]|0.13549|[0.01622]***|-0.00082|[0.0002]***|Yes|No<br>No|No|17271|20200.75|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Pr(IQ=0,IV=1)|0.00083|[0.0018]***|-9.76E-06|[0.00019]|0.18272|[0.0241]***|-0.00110|[0.00026]***|Yes|No<br>No|No|17271|20200.75|||
|lts I<br>Pr(IV=1|IQ=1)|0.00112|[0.00031]***|-0.00002|[0.00034]|0.32791|[0.04027]***|-0.00198|[0.00047]***|Yes|No<br>No|No|17271|20200.75<br>|. One of the<br> The other is||
|stimation Resu<br><br>Pr(IV=1)|0.00202|[0.00055]***|-0.00001|[0.00033 ]|0.318221|[0.03774]***|-0.00192|[0.00046]***|Yes|No<br>No|No|17271|20200.75<br>|bi-variate probit<br>lity up-grading. <br>|oods.|
|Goods (IV) : E<br>Pr(IQ=1,IV=1)|0.00196|[0.00054]***|-0.00012|[0.00017]|0.06327|[0.05133]|0.00013|[0.00039]|Yes|No<br>No|No|17271|20200.75<br>|al efects) of a <br> frm reports qua<br>|uction of new g|
|(IQ) and New <br>Pr(IQ=1,IV=0)|0.0016|[0.00033]***|-0.00012|[0.00018 ]|0.06509|[0.05317]|0.00013|[0.0004]|Yes|No<br>No|No|17271|20200.75<br>|results (margin<br>es value 1 if the <br>|ports the introd|
|ity Upgrading <br>Pr(IQ=1|IV=1)|0.00165|[0.00032]***|-0.00026|[0.00038]|0.1394|[0.11356 ]|0.00028|[0.00086]|Yes|No<br>No|No|17271|20200.75<br>|s the estimation<br>dummy that tak<br>|e 1 if the frm re|
|ble 10: Qual<br>Pr(IQ=1)|0.00326|[0.00064]***|-0.000243|[0.00035]|0.12837|[0.10445]|0.00026|[0.00079]|Yes|No<br>No|No|17271|20200.75<br>|s table present<br> variables is a <br>|hat takes valu|
|Ta|Know.spillovers||Distance to Innovators|*Know.spillovers|International Know.spillovers||Distance to International Innovators|* International Know.spillovers|Firm ’ s characteristics|Firm s and Industry-Country’ s<br>characteristics<br>Country-Time fxed efects|Industry fxed efects|Num. of observations|Log pseudolikelihood<br>|Note: Thi<br>dependent <br>|a dummy t|

31
shock. This only holds if labor demand is constant across education and experience cohorts. As explained more fully below, this is likely not the case. In addition, the skill premium could add an upward bias in the estimation if a particular job requires skills that are becoming increasingly rare. This would shift the labor demand curve rightward, with almost no spillover of skills.

# **Table 3**

# Richer Specification and Alternative Measures of Emigration: Differences across Sectors

(Coefficient estimates; standard errors in parentheses)

||Dependen<br>|t Variable: L<br>Measure of<br>|ogof Mont<br>Emigration<br>|hlyWages<br>|
|---|---|---|---|---|
||Baseline<br>Experience<br>Categories<br>2000-06<br>(1)|Broader<br>Experience<br>Categories<br>2000-06<br>(2)|<br>Baseline<br>Experience<br>Categories<br>2004-06<br>(3)|<br>Broader<br>Experience<br>Categories<br>2004-06<br>(4)|
|Ratio of emigrants to labor force|0.515***|0.970***|0.478***|1.045**|
||(0.160)|(0.300)|(0.183)|(0.404)|
|Interactions (omitted: construction)|||||
|Agriculture * Ratio of emigrants to labor force|-0.310**|-0.349|-0.357*|-0.559|
||(0.148)|(0.304)|(0.189)|(0.504)|
|Industry * Ratio of emigrants to labor force|-0.424***|-0.426*|-0.556**|-0.793**|
||(0.160)|(0.219)|(0.214)|(0.352)|
|Service * Ratio of emigrants to labor force|-0.126|-0.150|-0.262|-0.342|
||(0.142)|(0.233)|(0.181)|(0.379)|
|Sector fixed effects|Yes|Yes|Yes|Yes|
|Occupation fixed effects|No|No|Yes|Yes|
|Number of observations|22,899|22,967|22,667|22,735|
|R-squared|0.230|0.233|0.273|0.275|

*** p<0.01, ** p<0.05, * p<0.1.

Note: The standard errors are adjusted for emigration cohort clustering. The independent variables include marital status, gender, education, experience, and experience-squared. They also include occupation and sector fixed effects, as indicated.

14
Figure 2: Labor-Force Pariticpation and Total Fertility Rate - Albania (1990 - 2012) <!-- Start of picture text --> 1990 1995 2000 2005 2010 2015<br>year<br>Male (ages 15+) Female (ages 15+) TFR<br>80<br>3<br>70<br>2.5<br>60<br>2<br>Total fertility rate (TFR)<br>50<br>Labor-force participation (%)<br>40 1.5<br><!-- End of picture text --> _Notes: Source: Albanian Institute of Statistics (INSTAT)_ 49
- Berry, J., G. Fischer, and R. Guiteras (2020): “Eliciting and Utilizing Willingness-to-Pay: Evidence from Field Trials in Northern Ghana,” _Journal of Political Economy_ , 128, 1436– 1473.

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28
# Figure 4:  Path coefficients:   Mixed causal and measurement model

<!-- Start of picture text --> .64<br>PS VA<br>p2 = .453 p1 = .445<br> Good<br>Government<br> P3 = .954 p4=.865 p5=.960 p6 =.932<br>GE RQ RL CC<br><!-- End of picture text -->

# **<u>Conclusion</u>**

We draw several conclusions from these results. First, the indicators utterly fail to distinguish between the causal, measurement, and mixed models. Variables that uphold all three models equally well probably are themselves not measuring what each claims to measure. It is hard to use these indicators to uphold a causal story when a measurement model, which implies no direct causal links (including two-way links) among the indicators, fits the data just as well. It is difficult to claim that each WGI is distinct. Some clearly represent a more general concept; it is difficult to claim that variables have meaning when they are all consistent with very different theoretical representations. Overall, the results are consistent with the proposition that the separate WGI indicators, because they are, by definition, overlapping, if not equivalent, are tautological.

Second, in the data we use, each WGI variable is represented as a “manifest,” observed indicator. Yet, in reality, none of the six indicators is a manifest variable. Each WGI indicator is a constructed index comprised of numerous other manifest (i.e., 16
# **IV. CONCLUSIONS**

Malawi is an export-led economy. Despite changes in the structure of agricultural production, and liberalization of prices, exchange and finance, a long standing relationship persists between export income and overall GDP. The driver of the economy has been real domestic revenue from exports which has created growth by stimulating non-tradeable services, and to a limited extent import substituting industrialization. Malawi has faced chronic deterioration in its terms of trade since the 1970s but has recovered largely due to improvements in the real exchange rate and more recently the tobacco price. The economy has proved highly sensitive to movements in both these variables since improvements (a) cause an immediate rise in real domestic export revenue which multiply through the rest of the economy; and (b) incentivize a production expansion by increasing tradeable returns.

However there are strong signs that despite progress the real exchange rate is still overvalued. Recent strong growth in the demand for consumer imports has failed to stimulate much of a response from domestic manufacturing. There has been extremely poor progress in diversifying out of the traditional exports of tobacco, sugar and tea. There are widespread and persistent shortages of foreign exchange, low reserves and the current account has a higher deficit than any comparator economy. Malawi is in effect running a fixed exchange rate system artificially overvalued not just from revenues from the booming tobacco sector, or aid which finances the capital account, but from government policy that incentivizes importing at the expense of exporting.

1. Growth is primarily driven by the domestic multiplier from export revenues.

2. Improvements in the real exchange rate since 1980 have been strongly beneficial to growth.

3. Growth transmission has run from agriculture to non-tradeable services and only minimally to domestic manufacturing. This is despite strong demand for consumer imports.

4. Thus the exchange rate is still overvalued leading to a strong disincentive to invest in the tradeable sector or in import-substituting industrialization.

5. A macroeconomic winter 1995-2003 suppressed investment through high inflation and interest rates, which prevented growth transmission out of agriculture.

6. Malawi’s large current account deficit (14% GDP) is financed by aid.

Aid inflows are substantial relative to the size of the external sector and have probably contributed to exchange rate appreciation. This effect has been mitigated by the increase in direct public-sector imports particularly from the fertilizer subsidy. But aid has had a limited effect on long-term productivity since it predominantly finances consumption and recurrent expenditure. Expenditure data from the Ministry of Finance indicates that less

- 42 -
developing economies. A positive shock to the world demand raises GDP in both small and large developing countries, but the effect is more pronounced in smaller economies.

Turning to domestic variables, shocks that lead to real exchange rate appreciation also increase output in small and large developing economies, with the effects being larger and more short-lived in small economies. Shocks to GDP are more persistent in larger developing economies; while shocks to government expenditures have similar degree of persistence in both groups of countries and lead to an expansion of their GDP. A positive shock to domestic real interest rate is followed by a fall in GDP in small developing economies, but has a negligible effect on GDP in larger developing countries. Lastly, unanticipated positive shocks to trade balance are followed by an expansion in GDP in small developing countries, but a small contraction in GDP in larger developing countries.

Figure **5** . Impulse Responses of GDP to Various Shocks in Developing Economies: Small vs <u>Large Countries</u> <!-- Start of picture text --> reer tby rrate us<br>.008 .006 .001<br>.006 .004 0<br>.004 .002 −.001<br>.002 0 −.002<br>0 −.002 −.003<br>0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10<br>gdp govexp rrate<br>.02 .015 .001<br>.015 .01 −.0010<br>.005 −.002<br>.01 −.003<br>0 −.004<br>.005<br>0 2 4 6 8 10 0 2 4 6 8 10<br>0 quarter quarter<br>−.005<br>0 2 4 6 8 10 small countri es large countries small countri es large countries<br>gdp us<br>.01<br>.005<br>0<br>0 2 4 6 8 10 Orthogonalized IRF<br>quarter<br>small countri es large countries<br><!-- End of picture text --> Now, contrast the impulse responses of large and small economies belonging to the developed (or high income) group. These are presented in Figure **6** . It is easy to see that the impulse responses of GDP are symmetric for the two groups of developed economies, with the exception of just the effects of US and domestic interest rate. For instance, an increase in US GDP triggers a rise in domestic GDP of both small and large developed economies and the effects are practically identical. For shocks originating in the domestic variables, positive shocks to real exchange rate, trade balance, domestic GDP and government expenditures all have expansionary effects on GDP in the small and large developed economies and the effects are quite symmetric. Shocks to the 20
**Figure 4:** Random forest importance for the top 15 most important variables for ML prediction of logs of Rice. _Notes:_ 3/4 of the crop-cut sample is used. 5 validation cross-fold splits are used.

<!-- Start of picture text --> rict of Residence<br>Border Crossing<br>g planting season<br>Welfare index<br>y growing season<br>vailability to roots<br>y growing season<br>l planting season<br>Elevation<br>nual Precipitation<br>n wettest quarter<br>ettest quarter avg<br>ousehold weights<br>nnual total rainfall<br>SR Yield<br>0 500 1000 1500<br><!-- End of picture text --> 29