rafmacalaba/datause-encoder-data huggingface dataset:
those models we are then able to compare short- and long-run poverty impacts of higher food prices, both in the case when the prices of individual crops rise independently and when all food prices rise together.

When considering the impacts of changes in food prices, it is very important to consider the source of the change. A change in food prices resulting from a shock such as a rise in demand for rich-country biofuels that is more or less exogenous to the agricultural sectors of developing countries is likely to be associated with quite different impacts on poverty than one that arises from a change in rates of productivity growth in developing countries—whether from investments in research and development, from weather shocks or from sustained changes in climate. For simplicity of interpretation we focus in this paper primarily on changes in poverty that result from price shocks modeled as border price changes not accompanied by changes in domestic agricultural productivity or other domestic policies.

The effects of price changes on household incomes and on poverty rates are nonlinear for two reasons: (i) because the effects of price changes on real household incomes are nonlinear once we take into account output adjustments, and (ii) the effects of changes in prices on poverty are nonlinear because of differences in the income and expenditure shares of different groups and changes in the number of people near the poverty line. Because of these nonlinearities, and because of uncertainty about the size of future price shocks, we consider the impact of price increases over a large range; in particular we consider shocks of 10, 50 and 100 percent. To investigate differences between the effects of changes in all agricultural prices and in prices for particular goods, we compare the impacts of increases in all agricultural products with the results obtained when some key prices are increased individually.

We believe that the approach that we use, involving both economy wide simulation models and household models, is an important complement to studies such as Jacoby (2013) and Headey (2014). It avoids the very real concerns about causality that plague any econometric analysis and allows exploration of the impacts of different sizes and scopes of price changes, and of the impacts through different channels on different types of households. If both methodologies provide broadly similar results on key impacts, then we can gradually begin to reduce the uncertainty we face about the important but challenging linkages between food prices and poverty. If they do not, then we need to look deeper for the causes of the discrepancies between the results emerging from these two different approaches.

4
**Table 23: The effect of baseline school level test scores**

||Math|English|
|---|---|---|
|WSD|-0.04|-0.03|
||(0.08)|(0.09)|
|Grant|-0.08|-0.11|
||(0.07)|(0.08)|
|Baseline Test Score|0.14**|0.30***|
||(0.07)|(0.09)|
|WSD × Baseline Test Score|0.02|-0.10|
||(0.10)|(0.11)|
|Grant × Baseline Test Score|0.00|-0.13|
||(0.10)|(0.11)|
|6<sup>th</sup>Grade Dummy|0.70***|0.77***|
||(0.04)|(0.04)|
|Constant|-0.26***|-0.29***|
||(0.17)|(0.06)|
|Observations|2313|2313|
|R Square|0.04|0.07|

Notes: Robust Standard errors in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. District 2004 SES is the district level composite measure of the socio-economic background as measured in 2004 – Prior to the interventions. The variables included in the factor analysis are the quality of the housing (floor, roof, walls, electricity), the assets (phone, motorcycle, fridge, car, TV, fan, generator, livestock), and the expenditure on educator the past 12 months – Higher values of the factor associate with higher economic status of the district.

39
- [14] Diewert, W.E. and T.J. Wales. “Flexible Functional Forms and Global Curvature Conditions.” Econometrica 55 (1987), 43-68.

- [15] Feng, G. and A. Serletis. “Productivity Trends in U.S. Manufacturing: Evidence from the NQ and AIM Cost Functions.” Journal of Econometrics 142 (2008), 281-311.

- [16] Fuss, M.A. “The Demand for Energy in Canadian Manufacturing: An Example of the Estimation of Production Structures with Many Inputs.” Journal of Econometrics 5 (1977), 89-116.

- [17] Gallant, A.R. “On the Bias in Flexible Functcional Forms and an Essentially Unbiased Form: The Fourier Flexible Form.” Journal of Econometrics 15 (1981), 211-245.

- [18] Guilkey, D., Lovell, C., and Sickles, R. “A Comparison of the Performance of Three Flexible Functional Forms.” International Economic Review 24 (1983), 591—616.

- [19] Hall, V.B. “Major OECD Country Industrial Sector Interfuel Substitution Estimates, 1960-1979.” Energy Economics 8 (1986), 74-89.

- [20] Hudson, E.A. and D.W. Jorgenson. “U.S. Energy Policy and Economic Growth, 19752000.” Bell Journal of Economics and Management Science 5 (1974), 461-514.

- [21] Jones, C.T. “A Dynamic Analysis of Interfuel Substitution in U.S. Industrial Energy Demand.” Journal of Business and Economic Statistics 13 (1995), 459-465.

- [22] Magnus, J.R. “Substitution between Energy and Non-Energy Inputs in the Netherlands: 1950-1976.” International Economic Review 20 (1979), 465-484.

- [23] Moschini, G. “Imposing Local Curvature in Flexible Demand Systems.” Journal of Business and Economic Statistics 17 (1999), 487-490.

- [24] Pindyck, R.S. “Interfuel Substitution and the Industrial Demand for Energy: An International Comparison.” Review of Economics and Statistics 61 (1979), 169-179.

- [25] Ryan, D.L. and T.J. Wales. “A Simple Method for Imposing Local Curvature in Some Flexible Consumer Demand Systems.” Journal of Business and Economic Statistics 16 (1998), 331-338.

- [26] Ryan, D.L. and T.J. Wales. “Imposing Local Concavity in the Translog and Generalized Leontief Cost Functions.” Economics Letters 67 (2000), 253-260.

- [27] Serletis, A. and A. Shahmoradi. “Flexible Functional Forms, Curvature Conditions, and the Demand for Assets.” Macroeconomic Dynamics 11 (2007), 455-486.

20
Table 6. Heckman Selection Model

|||**First (exte**|**nsive) stage**||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||FAI_>_0|FAI_>_0|FAI_>_0|FAI_>_0|
|Rate Diferential|1.792***|2.107***|4.427***|5.621***|
||(0.526)|(0.648)|(1.594)|(1.418)|
|Host KF-METR|0.006|0.005|-0.022**|-0.025**|
||(0.007)|(0.006)|(0.010)|(0.010)|
|Weighted Non-Host|-0.001|-0.001|||
|KF-METR|(0.001)|(0.001)|||
|(using capital)|||||
|Weighted Non-Host|||-0.001|-0.001*|
|KF-METR|||(0.001)|(0.001)|
|(using sales)|||||
|||**Second (inte**|**nsive) stage**||
||(1)|(2)|(3)|(4)|
||LnFAI|LnFAI|LnFAI|LnFAI|
|Rate Diferential|1.490***|1.457***|1.839***|1.977***|
||(0.405)|(0.420)|(0.394)|(0.420)|
|Host METR|0.013***|0.013***|0.009**|0.009**|
||(0.004)|(0.003)|(0.004)|(0.004)|
|Inverse Mills|1.831***|1.955***|0.676***|0.742***|
||(0.161)|(0.203)|(0.113)|(0.130)|
|Observations|4,666|4,006|4,322|3,731|
|Exclusion<br>restrictions:|Weighted METR,<br>by capital|Weighted METR,<br>by capital<br>VAT and PIT Rates|Weighted METR,<br>by sales|Weighted METR,<br>by sales<br>VAT and PIT Rates|
|||Tax Revenue / GDP||Tax Revenue / GDP|
|FEs at both stages:|Parent, Year|Parent, Year|Parent, Year|Parent, Year|
|Control variables|Host, Parent|Host, Parent|Host, Parent|Host, Parent|
|at both stages:|Bilateral|Bilateral|Bilateral|Bilateral|

_Notes:_ These are Heckman selection results using an unbalanced panel of all available host-parentyear observations. By the argument set out in the text, the weighted average of KF-METRs outside the host country is included at the first stage but excluded at the second. This non-host country KF-METR term, is constructed in two ways: in columns (1) and (2) using capital-based weights, and in columns (3) and (4) using sales-based weights. Columns (2) and (4) include additional exclusion restrictions as indicated. Second stage results are from OLS specification using the log of the FAI ratio as a dependent variable on the restricted sample of positive FAI only, with an inverse Mills ratio. Standard errors are clustered at the host country level. In both stages we include the following controls for each of host and parent country: GDP (in log), capital account openness index, trade openness, inflation rate, government expenditure share, and exchange rates. Gravity variables are also controlled for. Parent and year fixed effects are included. ***, ** and * indicate significance at the 1, 5, and 10 percent levels respectively.

54
When  = 0, the highest poverty is once again in the rural sector. But the comparison between _P_  _,s_ and _P_  _,c_ essentially comes down to which location has the higher unemployment rate (or fraction of population in informality) since given the poverty line this is in fact the poverty rate.

The comparison of unemployment rates in this case is ambiguous. An examination of (14) and (16) shows that there are three opposing forces that determine unemployment: (i) higher migration costs (transport, search and settlement) lower unemployment; (ii) a higher gap between the formal wage and the rural wage increases unemployment; and (iii) a higher gap between the formal and informal sector lowers unemployment. While higher migration costs and lower informal wages unambiguously lower unemployment in the model, the role of formal income appears ambiguous. On the one hand, a high formal wage raises the gap with the rural wage (increasing unemployment), and on the other hand, it raises the gap with the informal wage (reducing unemployment). Taking the derivative, which is positive given (19), it can however be seen that, in the model, higher formal wages come along with larger unemployment rates (and lower formal wages with lower unemployment rates).

Thus, a falling _P0_ poverty gradient for rural to town to city then becomes more likely when cities have higher migration costs, lower formal sector wage gaps with the rural wage and higher formal-informal wage gaps (or when cities have higher migration costs and lower informal and formal wages, which all discourage city migration, in turn leading to lower city unemployment and poverty). And if the gradient holds for _P0_ it will also hold for _P1_ and for all  greater than 1, given that ( _z – wos_ ) > ( _z – woc_ ). Hence, under certain conditions there will again be a general poverty gradient from rural, through secondary town, to big city.

# **4. Job Creation and Poverty Reduction**

Consider now the motivating policy question of whether the government should invest in small towns or big cities. We specify this in the context of our model as a policy of increasing formal sector jobs in either of the two locations. If the objective is to reduce national poverty, will the impact be greater with an increase of _Es_ or an increase of _Ec_ ? Of course, the final answer to the policy question will depend on the costs of creating a job in the town versus the city. That is not the focus of this paper, but the answers we provide; can be used to benchmark the cost differential which will flip the targeting of public investment in one direction or the other.

9
1998). This created the environment in which a demand-driven approach to land reform would be feasible.

Indeed, there was a significant increase in the number of land sales transactions - more than 6% of all agricultural land changed hands in both 1997 and 1998. Land prices declined by more than 15% between 1994 and 1999 in real terms, and more than 10% of all agricultural land in the commercial farming areas are currently up for sale. Due to increased supply, market values of agricultural land are close to, and in some cases below, the net present value of agricultural profits. Skilled entrepreneurs are able to buy land on the market, financing the purchase price from the proceeds from farming it. Land rental markets, which were small prior to 1994 mushroomed as well; nearly 10% of the total land area were, in 1998, farmed under rental arrangements. Before assessing how this has affected the opportunities for productive development by South Africa's most disadvantaged, we briefly describe general characteristics of rural poverty in the country.

# **2.2 Rural poverty and livelihoods in South Africa**

The apartheid system relied on the existence of a rural population in traditional _bantustans_ unable to be self-sufficient and therefore bound to a migrant labor system, but still tied to the land. In this way, rural areas provided residence, services and some income for urban and mine workers' families, to supplement the low wages paid to them. Similarly, outside the bantustans, rural areas are characterized by the existence of an over-capitalized, over-mechanized, job-shedding commercial agriculture sector, a vulnerable farm worker population, and rural dormitory towns that offer few economic opportunities. For _bantustans_ bordering commercial farming areas, large numbers of farm workers are threatened with losing both job and housing. Thus, inside and outside the _bantustans,_ rural areas entered South Africa's transition with a legacy of squandered assets and inappropriate production and investment strategies. The contraction of the economy and the erosion of the rural economic base through population growth in the absence of productive infrastructure implied that households previously dependent upon a cash income were left with neither the income nor the assets from which they could generate an adequate income. The resulting combination of lack of assets, absence of economic opportunities, and high levels of poverty threatens to persist long after South Africa's political transition.

The extent of rural poverty is confirmed by data from the 1993 SALDRU survey. As Table I illustrates, approximately half of South Africa's total population are poor but the poverty headcount in rural areas is 71%, compared to 29% in urban areas (SALDRU 1994). Thus, most of South Africa's poor, about 13.7 million people, live in rural areas. More than three quarters (76%) of the total poverty gap is accounted for by rural households, although they make up only half of the total population. Table 2 shows the poverty gap and the percentage of the population categorized as non-urban by the 1996 census for each 6
Table D.15. Quality of School Teachers

||Total Enrollment|Total Teachers|Certifed Teachers|Certifed Teachers with<br>High-School|Teachers with<br>High-School|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Guerrilla control|9.764|0.519|0.320|0.350|0.452|
||(35.31)|(1.155)|(1.123)|(0.969)|(0.991)|
|Observations|1,522|1,522|1,522|1,522|1,522|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|2.266|
|Dependent mean|386.7|13.42|12.78|11.51|11.88|

_Notes:_ This table shows the effects of guerrilla control on school size (Columns 1 and 2) and quality of school teachers (Columns 3–5). Data was obtained from the 2013 teacher census provided by the Ministry of Education. “Total enrollment” and “Total teachers” refer to the total number of students and teachers at the school level, respectively. “Certified teachers” refers to teachers who have received a formal accreditation in pedagogy from the Ministry of Education.

Table D.16. Simpson’s Index

||All plots|Commercial plots|Subsistence plots|
|---|---|---|---|
||(1)|(2)|(3)|
|Guerrilla control|0.0402*|0_._0399<sup>_†_</sup>|1.28e-05|
||(0.0217)|(0.0267)|(0.0267)|
|Observations|2,266|1,913|1,963|
|Bandwidth (Km)|2.266|2.266|2.266|
|Dependent mean|0.530|0.420|0.460|

_Note:_ The table presents the results of equation 1 for the Simpson’s Index calculated for all plots, commercial plots, and subsistence plots in the Agrarian Census of 2007. The calculation of the Simpson’s Index is _S_ = 1 _−_ (<sup><u>�</u></sup> <u>�</u><sup>_N_</sup> _iNi aai_<sup>2</sup> _<u>i</u>_ )<sup>2where</sup><sup>_ai_</sup> refers to the size of each plot. The unit of observation is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, _†_ p _<_ 0.15.

88
WPS4185 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 1</mark>

# **Insurgency and credible commitment in autocracies and democracies**

Philip Keefer Development Research Group The World Bank

**Abstract** :  This paper suggests a new factor that makes civil war more likely:  the inability of political actors to make credible promises to broad segments of society.  Lacking this ability, both elected and unelected governments pursue public policies that leave citizens less welloff and more prone to revolt.  At the same time, these actors have a reduced ability to build an anti-insurgency capacity in the first place, since they are less able to prevent antiinsurgents from themselves mounting coups. However, while reducing the risk of conflict overall, increasing credibility can, over some range, _worsen_ the effects of natural resources and ethnic fragmentation on civil war.  Empirical tests using various measures of political credibility support these conclusions.

**Acknowledgement** :  The assistance of Cristina Abodea was essential to the writing of this paper.

World Bank Policy Research Working Paper 4185, April 2007

> _The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post-Conflict Transitions Project can be found at_ _<u>http://econ.worldbank.org/programs/conflict</u>_ ). _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 these  papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ _<u>http://econ.worldbank.org</u> ._
where _Xi_ is a vector of baseline characteristics of the owner and the firm, _PROFITSi,March 2005_<sup>and</sup><sup>_PROFITS_</sup> _i,November 2004_<sup>are the firm’s profits in March 2005 and November 2004</sup> respectively, the δt are wave effects, and μi is a firm random effect. We estimate this for the unbalanced sample of untreated firms experiencing business damage. This gives 702 observations on 80 firms. Since real profits are expressed in logs, the vector of coefficients β multiplied by 100 can be interpreted as the percentage growth in profits associated with a one unit change in _Xi_ . It is likely that the association with _Xi_ will vary from quarter to quarter, in which case β is giving the average effect of _Xi_ over the three years post-tsunami studied.

The choice of variables to include in _Xi_ in equation (3) is guided by several competing hypotheses about the factors that might be most strongly linked to the speed of enterprise recovery. Schooling and gender were discussed above. We also include variables indicating the age of the owner and enterprise and the marital status of the owner. We have shown that access to capital may be important, at least for retailers. Since the tsunami destroyed physical capital of the business, access to credit is likely to be one determinant of how quickly firms can replace this capital and recover. Our baseline survey asks whether firms have ever had a loan from a private bank, government bank, microfinance program, or government program. This category includes 30 percent of untreated firms with business damage. However, 26 percent of firms still had outstanding loans dating from before the tsunami, for which payments generally continued to be paid. Previous use of credit may therefore not be a very good indicator of the ability to raise new capital after the tsunami.

Another characteristic that might affect a firm’s recovery is its formality status. A firm’s registration (in our case with the Pradeshiya Saba (municipal organization) or District Secretariat) arguably offers a potential advantage by establishing a record of the firm’s existence prior to the disaster, and providing some basic information on its sales 27
Table 17 Composition of New Supply of Industrial Equipment Fund by Industry (billion yen and percentages)

||Total|Stocks|<br>Bonds|Loans|PFIs|GFIs|Special<br>Account|
|---|---|---|---|---|---|---|---|
|Manufactu|ring|||||||
|1956/60|2532|530|<br>113|1889|1607|279|4|
|||21.0|<br>4.5|74.6|63.5|11.0|0.2|
|1961/66|9322|1555|<br>576|7191|6258|921|12|
|||16.7|<br>6.2|77.1|67.1|9.9|0.1|
|Mining||||||||
|1954/60|199|32|<br>3|163|109|47|7|
|||16.1|<br>1.5|81.9|54.8|23.6|3.5|
|1961/66|265|22|<br>9|234|122|84|29|
|||8.7|<br>3.4|88.7|<br>46.0|<br>31.7|<br>10.9|
|Agricultu<br>|re, Fores<br>|try&<br>|Fishery<br> <br>|||||
|1954/60|397|13|<br>1|384|173|178|31|
|||3.3|<br>0.3|96.4|43.6|44.8|7.8|
|1961/66|890|21|<br>7|862|427|421|13|
|||2.4|<br>0.8|96.8|48.0|47.3|1.5|
|Ulectric|Power|||||||
|1954/60|1397|134|<br>62|1201|773|167|261|
|||9.6|<br>4.4|86.0|55.3|12.0|18.7|
|1961/66|1539|192|<br>300|1046|733|120|194|
|||12.5|<br>19.5|68.0|47.6|7.8|12.6|
|Land Tran|sportatio|n||||||
|1956/60|262|28|<br>24|211|182|14|16|
|||10.7|<br>9.2|80.9|69.5|5.3|6.1|
|1961/66|1147|81|<br>111|854|710|49|194|
|||7.1|<br>9.7|83.1|61.9|4.3|16.9|
|Marine Tr|ansportat|ion||||||
|<br>1954/60|<br>440|<br>46|<br>1|393|262|131|--|
|||10.5|<br>0.2|89.6|59.3|29.6|0.0|
|1961/66|561|13|<br>--|548|237|282|28|
|||2.3|<br>0.0|97.7|42.3|50.3|5.0|

Note: Figures for 1964 are excluded for electric power, land and marine transportation.

Source: Bank of Japan, Economic Statistics Annual, various issues.

48